Causes and Consequences of Recombination Rate Variation in Drosophila by Laurie Sherie Stevison Department of Biology Duke University Date:_______________________ Approved: ___________________________ Mohamed Noor, Supervisor ___________________________ Corbin D. Jones ___________________________ Thomas Mitchell-Olds ___________________________ Gregory Wray ___________________________ David MacAlpine Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biology in the Graduate School of Duke University 2011 ABSTRACT Causes and Consequences of Recombination Rate Variation in Drosophila by Laurie Sherie Stevison Department of Biology Duke University Date:_______________________ Approved: ___________________________ Mohamed Noor, Supervisor ___________________________ Corbin D. Jones ___________________________ Thomas Mitchell-Olds ___________________________ Gregory Wray ___________________________ David MacAlpine An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biology in the Graduate School of Duke University 2011 Copyright by Laurie Sherie Stevison 2011 Abstract Recombination occurs during meiosis to produce new allelic combinations in natural populations, and thus strongly affects evolutionary processes. The model system Drosophila has been crucial for understanding the mechanics underlying recombination and assessing the association between recombination rate and several evolutionary parameters. Drosophila was the first system in which genetic maps were developed using recombination frequencies between genes. Further, Drosophila has been used to determine genetic and environmental conditions that cause variation in recombination rate. Finally, Drosophila has been instrumental in elucidating associations between local recombination rate and nucleotide diversity, divergence and codon bias, as well as helping determine the causes of these associations. Here I present a fine-scale map of recombination rates across two major chromosomes in Drosophila persimilis using 181 SNP markers spanning two of five major chromosome arms. Using this map, I report significant fine-scale heterogeneity of local recombination rates. However, I also observed ‘‘recombinational neighborhoods,’’ where adjacent intervals had similar recombination rates after excluding regions near the centromere and telomere. I further found significant positive associations of finescale recombination rate with repetitive element abundance and a 13-bp sequence motif known to associate with human recombination rates. I noted strong crossover interference extending 5–7 Mb from the initial crossover event. Further, I observed that fine-scale recombination rates in D. persimilis are strongly correlated with those obtained from a comparable study of its sister species, D. pseudoobscura. I documented a significant relationship between recombination rates and intron nucleotide sequence iv diversity within species, but no relationship between recombination rate and intron divergence between species. These results are consistent with selection models (hitchhiking and background selection) rather than mutagenic recombination models for explaining the relationship of recombination with nucleotide diversity within species. Finally, I found significant correlations between recombination rate and GC content, supporting both GC-biased gene conversion (BGC) models and selection-driven codon bias models. Next, I looked at the role of chromosomal inversions in species maintenance by examining the impact of inversions distinguishing species to disrupt recombination rates within inverted regions, at inversion boundaries and throughout the remainder of the genome. By screening nearly 10,000 offspring from females heterozygous for 3 major inversions, I observed recombination rates within an inverted region in hybrids between Drosophila pseudoobscura and D. persimilis to be ~10-4 (similar to rates of exchange for inversion heterozygotes within species). However, despite the apparent potential for exchange, I do not find empirical evidence of ongoing gene exchange within the largest of 3 major inversions in DNA sequence analyses of strains isolated from natural populations. Finally, I observe a strong ‘interchromosomal effect’ with up to 9-fold higher (>800% different) recombination rates along collinear segments of chromosome 2 in hybrids, revealing a significantly negative association between interchromosomal effect and recombination rate in homokaryotypes, and I show that interspecies nucleotide divergence is lower in regions with larger changes in recombination rates in hybrids, potentially resulting from greater interspecies exchange. This last result suggests an effect of chromosomal inversions on interspecies gene exchange not considered previously. v Finally, I experimentally tested for a novel male-mediated effect on female recombination rates by crossing males that differed by either induced treatment variation or standing genetic variation to genetically identical females. After assaying recombination frequency in the offspring of these genetic crosses, I fitted these data to a statistical model where I showed no effect of male temperature treatment or male genetic background on offspring recombination rate. However, I did observe a difference of recombination rates of offspring laid 5-8 days post-mating between males treated with Juvenile Hormone relative to control males. Environmental variation in male ability to affect recombination rate in their mates suggests the potential for sexual conflict on optimal proportion of recombinant offspring, perhaps leading to changes in populationlevel recombination rates with varying levels of sexual selection. Overall, my map of fine-scale recombination rates allowed me to confirm findings of broader-scale studies and identify multiple novel features that merit further investigation. Furthermore, I have identified several similarities and differences between inversions segregating within vs. between species in their effects on recombination and divergence, and I have identified possible effects of inversions on interspecies gene exchange that had not been considered previously. Finally, I have provided some evidence that males may impact female recombination rates, although future work should attempt to explore the range of male differences that impact this trait and the mechanism through which males impact the outcome of female meiosis. vi Dedication I dedicate the work presented here to the loving memory of Jesse Brit Lovvorn (May 3, 1934 – October 13, 2010) and Sarah Purcello Seifert (October 8, 1920 – August 26, 2010). vii Contents Abstract .............................................................................................................................. iv Dedication ......................................................................................................................... vii List of Tables ..................................................................................................................... xii List of Figures .................................................................................................................. xiii Acknowledgements ............................................................................................................ xv 1. Introduction: Recombination Rates in Drosophila ...................................................... 1 1.1 Using Recombination Frequencies to Map the Genome ...................................... 2 1.2 Mechanics of Crossing Over ................................................................................. 4 1.2.1 Figuring out the steps ...................................................................................... 4 1.2.2 Additional modern recombination models – SDSA ....................................... 5 1.2.3 Unique features of recombination in Drosophila ............................................7 1.3 Variation in Crossing Over ................................................................................... 8 1.4 Evolutionary Effects of Recombination on Structure of the Genome ................. 9 1.4.1 Nucleotide diversity and divergence ..............................................................10 1.4.2 Causes .............................................................................................................10 1.4.3 Distinguishing between different scenarios ................................................... 11 1.4.4 Codon bias ...................................................................................................... 13 1.4.5 Other patterns ................................................................................................ 14 2. Genetic and Evolutionary Correlates of Fine-Scale Recombination Rate Variation in Drosophila persimilis ........................................................................................................ 16 2.1 Introduction ........................................................................................................ 16 2.2 Methods ............................................................................................................. 20 2.2.1 Design of Recombination Markers ............................................................... 20 viii 2.2.2 Genotyping..................................................................................................... 21 2.2.3 Construction of the Recombination Map ..................................................... 22 2.2.4 Analyzing Correlates with Recombination Rate ........................................... 24 2.3 Results................................................................................................................ 28 2.3.1 Crossover Rates Along the 2nd and XR Chromosomes ................................ 28 2.3.2 Recombinational Neighborhoods ................................................................. 29 2.3.3 Motif Analysis ............................................................................................... 30 2.3.4 Crossover Interference.................................................................................. 33 2.3.5 Comparison of Crossover Rate Between Species .......................................... 33 2.3.6 Nucleotide Diversity Within Species and Divergence Between Species....... 34 2.3.7 Codon Bias and Biased Gene Conversion ..................................................... 36 2.4 Discussion .......................................................................................................... 38 2.4.1 Broad-Scale Patterning of Recombination Rates.......................................... 39 2.4.2 DNA Sequence Motifs Associated with Crossover Rate in Drosophila ........ 40 2.4.3 Recombination Rates Strongly Correlate Between Species........................... 41 2.4.4 Correlation to Diversity and Divergence at Varying Scales .......................... 42 2.4.5 Codon Bias Versus GC-Biased Gene Conversion (BGC) ............................... 43 2.4.6 Conclusion .................................................................................................... 45 3. The effect of chromosomal inversions on recombination in hybrids between Drosophila pseudoobscura and D. persimilis .................................................................. 46 3.1 Introduction ....................................................................................................... 46 3.1.1 Expected rates of exchange within inverted regions based on within species inversion polymorphisms ......................................................................................... 48 3.1.2 Recombination suppression of inversions extends beyond breakpoints ...... 50 3.1.3 Inversions as global recombination modifiers ............................................... 51 ix 3.2 Methods ............................................................................................................. 53 3.2.1 Single generation estimate of inversion crossover rate in hybrids ............... 53 3.2.2 Population genetics analysis of interspecies migration rates within inversion 53 3.2.3 High-throughput genotyping to analyze recombination rate changes throughout the genome outside inversions .............................................................. 55 3.3 Results.................................................................................................................57 3.3.1 Single generation estimate of inversion crossover rate in hybrids ................57 3.3.2 Population genetics analysis of interspecies migration rates within inversion 58 3.3.3 Analysis of recombination rate reduction in single-generation hybrids at inversion boundaries ................................................................................................. 61 3.3.4 3.4 Analysis of interchromosomal effect using high-throughput genotyping .... 62 Discussion .......................................................................................................... 64 3.4.1 Exchange across XR inversion detected in one generation, however prolonged evidence of exchange not observed ......................................................... 64 3.4.2 Recombination suppression extends 2.5-3 Mb at inversion boundaries ..... 65 3.4.3 Interchromosomal effect highest in regions of low recombination along chromosome 2........................................................................................................... 66 4. Male-mediated effects on female recombination ...................................................... 69 4.1 Introduction ....................................................................................................... 69 4.2 Methods ..............................................................................................................75 4.2.1 Experiment 1 – Effect of male treatment on female recombination rate ...... 77 4.2.2 rate Experiment 2 – Effect of male genetic background on female recombination 78 4.3 Results................................................................................................................ 79 4.4 Discussion .......................................................................................................... 86 4.4.1 Experiment 1 – Male hormone treatment alters female recombination rate 87 x 4.4.2 Experiment 2 – Variation in male genotype does not contribute to variation in female recombination rate .................................................................................... 88 5. Major Conclusions ..................................................................................................... 90 Appendix A........................................................................................................................ 97 Appendix B...................................................................................................................... 100 References ....................................................................................................................... 104 Biography ......................................................................................................................... 122 xi List of Tables Table 2.1: Corrected order of potential misassemblies in the D. pseudoobscura genome based on D. persimilis recombination map...................................................................... 28 Table 2.2: Results of regression of recombination rate variation with various measures on chromosome 2 and XR in D. persimilis....................................................................... 35 Table 2.3: Results of diversity within D. persimilis and divergence between D. persimilis and D. miranda on chromosome 2 calculated at increasing scale (300 kb to 2 Mb). ..... 35 Table 3.1. Summary of diversity and divergence calculations for each of the 11 markers along the XR chromosome arm. ....................................................................................... 60 Table 3.2. Summary of recombination suppression at inversion boundaries as compared to Kulathinal et al. 2009. The refined range combines the results of both studies. ......... 61 Table B.1. Proteins Identified as differentially expressed between Zim6 and Zim30 using Mass Spectrometry. …………………………………………………………………………………………… 102 xii List of Figures Figure 1.1: A genetic linkage map of the four chromosomes of Drosophila ....................... 3 Figure 1.2: Two major models of genetic recombination (a) Szostak (1983) DSBR model (b) Allers and Lichten (2001) SDSA model. Modified from Haber et al. 2004 .................. 6 Figure 2.1: Total recombination map of chromosome 2 (left), and chromosome X, right arm (XR) (right), each with 95% confidence intervals shown in grey. Centromere is shown on the right in panel a and on the left in panel b. Map consists of 130 markers on chromosome 2 and 51 markers on XR assayed in 1330 individuals................................. 24 Figure 2.2: Plot of association between crossover rate in D. persimilis and a 13-bp motif abundant in human recombinational hotspots (a), and repetitive elements (b). ............ 32 Figure 2.3: Interference results on chromosome 2 in D. persimilis (A), in D. pseudoobscura (B), and on the XR in D. persimilis (C). .................................................. 33 Figure 2.4: Comparison of chromosome 2 crossover rates between D. persimilis (generated in this study) and D. pseudoobscura (data from Kulathinal et al. 2008). Centromeric end is shown on the left. .............................................................................. 34 Figure 2.5: Small intron %GC versus cM/Mb which represents the effects of GC-biased gene conversion (BGC) only (a), fourfold degenerate 3rd position %GC versus cM/Mb which represents the combined effects of......................................................................... 37 Figure 3.1. Inter-chromosomal Effect. A plot of the log (base 10) fold change difference in recombination rates along the 2nd chromosome between the published D. persimilis map and the map of recombination rate in between species hybrids. ..................................... 63 Figure 4.1 Results of Redfield experiment showing changes in post-mating recombination rates between markers Stubble (Sb–3R:11.9Mb) and scarlet (st– 3L:16.5Mb) on the 3rd chromosome (Modified from Redfield 1966). ............................... 71 Figure 4.2. Experimental Design. (A) Experiment 1: Females with homozygous lethal dominant mutants were crossed to wild type males to produce heterozygous females. Genetic replicate heterozygous females were then crossed to either control or treated males from two treatment groups: temperature (25°) or Juvenile Hormone (JHa). (B) Experiment 2: Inbred lines were crossed to produce heterozygous females. Genetic replicate heterozygous females were then crossed to 1 of 5 males from different genetic backgrounds. For both experiments, proportion of recombinant offspring were compared based on the identity of the male parent. ......................................................... 77 Figure 4.3. Results for experiment 1. Results of (A) male temperature treatment and (B) male hormone treatment on female recombination rate between markers Stubble and Glued on the 3rd chromosome (~17cM apart). The numbers next to each data point xiii indicate the sample size for each male treatment group per two-day collection period and the bars represent standard error values. ..........................................................................81 Figure 4.4. Results from Experiment 2 prior to accounting for female variation. Recombination rate variation as a function of male parent for sex-linked markers (A) and autosomal markers (B) and a summary (C) showing the total results of the experiment. The numbers next to each data point refers to the number of offspring genotyped to obtain the mean and standard error values plotted. .................................. 83 Figure 4.5. Results for Experiment 2 accounting for variation in female genotype plotting recombination rate of offspring based on the genotypes of both the male and female parents. Numbers indicate the sample size of offspring genotyped at each category on both the 3rd and X chromosomes. Female genotype was split based on the 4 possible allelic combinations at the marker CAD (heterozygous in both parental stocks) on chromosome 2L (20.76Mb). ............................................................................................. 85 Figure A.1. Preliminary results for experiment 2. Contingency tables of the number of recombinant (R) and non-recombinant (NR) offspring of males from three different Zimbabwe stocks split into offspring from (A) first 1-4 days post-mating and (B) next 5-8 days post-mating. These were analyzed separately because maternal age affects recombination rate. .......................................................................................................... 98 Figure B.1. Individual protein results of top three candidates (from Table B.1) showing variability between each of three replicates in total protein intensity for both zim6 and zim30 samples. ................................................................................................................103 xiv Acknowledgements “If I have seen further than others, it is by standing upon the shoulders of giants” –Isaac Newton All scientists should begin by acknowledging the vast body of knowledge collected by researchers who have preceded their own work – it is this body of knowledge to which I now have the honor of contributing. I would like to give immense thanks to my advisor Mohamed Noor. He may never fully understand how much his willingness to believe in me so many years ago has led me to where I am now, and has directed where I want to go. May he forever be the research scientist in my head, and my dear friend! Much of the research done during my dissertation would not have been possible without the guidance, personal insights, mental support, and technical assistance of several of my lab-mates throughout my time in the Noor lab – including, but not limited to, Daniel Ortiz-Barrientos, Amanda Moehring, Sarah Bennett, Shannon McDermott, Annie Boehling, Suzanne McGaugh, Caiti Smukowski, and Brenda Winkler. My fellow grad students and friends – especially Irene Liu, Carrie OlsonManning, David Garfield, and Matt Johnson – have helped me along the way, have come to my talks, and have provided me with much needed sanity over the past couple of years! I would like to thank my family, especially my mom who always told me I could do anything I wanted if I put my mind to it! I would like to thank my graduate committee members who have taken the time to guide my progress by challenging me and providing feedback not only on research, but also on fellowship/grant applications, and on my career as a research scientist. Funding for the projects presented in this dissertation were provided by the following grants: NSF-0909824 to myself, and NSF-0715484, NSF0509780, NIH-GM076051, and NIH-GM086445 to Mohamed Noor. xv 1. Introduction: Recombination Rates in Drosophila Population genetics is concerned with the inheritance of traits from generation to generation within populations. Offspring generally resemble their parents, but the inheritance of phenotypic combinations is not always in the same combinations as the parents, which leads to ‘recombinant’ offspring. There are two distinct genetic mechanisms that lead to recombinant offspring: independent assortment and crossing over. Mendel’s second law of independent assortment explains how each parental chromosome is equally likely to be segregated to the gametes, such that new combinations of traits on different chromosomes arise each generation, based simply on the principles of probability. The focus of this article is on the second mechanism of producing recombinant offspring, known as crossing over. Although technically the term ‘recombination’ could refer to either independent assortment or crossing over, typical usage focuses on the latter, and I use it in that context here. The underlying molecular pathway involves physical exchange between chromosomes and leads to alleles on the same chromosome being shuffled in the offspring. This process occurs in sexual species during meiosis and is hypothesized to serve two main categories of functions: to stabilize chromosomes during meiosis and to increase adaptability of sexual organisms. The model system Drosophila has been crucial in the scientific exploitation of this process to determine the order of genes on chromosomes, but some fungal systems have proven more useful in the discovery of key enzymes in the molecular pathway. Further, surveys of Drosophila have been instrumental in the discovery of factors that cause recombination rate variation, as well as how recombination rate variation shapes the genome. 1 1.1 Using Recombination Frequencies to Map the Genome Pairs of genes that assort in a 1:1 ratio as expected due to Mendel’s law of independent assortment are said to be in linkage equilibrium. However, when they deviate from this expectation, scientists refer to the pair of genes as being in linkage disequilibrium. One of the most influential genetic observations in the early 1900s was the deviation of recombination fraction between certain genes from the expected ratio of 1:1. This observation led scientist Alfred Sturtevant to conclude that these genes were physically linked on a chromosome (Sturtevant 1913). By measuring the recombination fraction between series of genes in Drosophila, he constructed the first genetic linkage maps, which are maps of the linear order of genes along a chromosome (Figure 1.1). This analysis was possible before the development of molecular tools because of the many different phenotypic mutant markers discovered in the Thomas Hunt Morgan fly lab. Linkage maps are now generated using a variety of phenotypic or molecular markers. By using a combination of markers with known relative genomic locations, scientists can pinpoint where in the genome an unknown mutation resides based simply on the recombination fraction between markers in a genetic cross and the phenotype of interest. For species such as humans, genetic crosses between individuals are more difficult. As a result, scientists either use pedigree information to reconstruct the genotypes of individuals at various markers or, more recently, survey populations for a million or more genetic markers at known locations throughout the genome. This information is then used to infer the order of those genotypes in individuals, referred to as haplotypes, or to map traits of interest. 2 Figure 1.1: A genetic linkage map of the four chromosomes of Drosophila 3 Although Drosophila played a central role in using recombination fractions to construct the first genetic maps, fungal systems have been particularly useful in elucidating the molecular pathway of recombination. One reason for this is the ease of tetrad analysis with fungal systems such as yeast and Neurospora. Unlike the egg and sperm products of meiosis in fruit flies and other animals, fungal tetrads constitute four haploid gametic products of a single meiosis that are fused, and deviations from the expected 1:1 ratio can be easily observed by dissecting and analyzing these tetrads directly. 1.2 Mechanics of Crossing Over 1.2.1 Figuring out the steps It has taken scientists decades to decipher the recombination pathway, and new critical enzymes are still being discovered. Organisms that lack crucial enzymes offer insight into these models and suggest additional pathways that can lead to the same outcome. In 1964, Robin Holliday presented a model for recombination that is still influential, at least in a modified form, today (Figure 1.2a) (Haber et al. 2004; Stahl 1994). This early model described a single-stranded nick in the deoxyribonucleic acid (DNA) backbone; however, we learned through the experiments in 1981 by Orr-Weaver and colleagues that recombination is initiated by a double-stranded break (DSB) in the DNA (Orr-Weaver et al. 1981; Szostak et al. 1983). In 1997, Scott Keeney and colleagues discovered the cause of this double-stranded break was an endonuclease protein called Spo11 in yeast that breaks DNA and causes a series of subsequent recombination events to occur (Keeney et al. 1997). These experiments were carried out in yeast, but homologous proteins have been found in other systems including Drosophila. Early models predicted many of the steps needed for recombination to take place, however, the actual enzymes that perform these steps were discovered much later. For 4 example, scientists knew meiotic exchange between chromosomes required some type of DNA strand invasion, but the double-stranded DNA ends needed to become singlestranded first. Then, scientists discovered the Mre11 exonuclease enzyme complex that is involved in chopping DNA after DSB initiation to create single-stranded DNA (D'amours and Jackson 2002). This DNA then invades the nearby homologous chromosome and causes a displacement known as the D-loop formation (see Figure 1.2). The scissorshaped DNA complex that results is known as a double Holliday Junction (dHJ) since this type of formation was predicted in 1964 in Dr. Holliday’s original model for recombination. It is the cleavage of these Holliday junctions that decides whether the recombination pathway results in crossover or non-crossover products. In a crossover pathway, a horizontal and a vertical cleavage result in rearrangement of the flanking sequence, whereas two horizontal cleavages result in a non-crossover. Non-crossovers do not result in downstream genetic exchange, but they can allow some very localized genetic exchange through mismatch repair. During recombination, the formation of mismatched DNA resulting from exchange of heterozygous DNA sequence yields heteroduplexed DNA, where non-complementary bases are paired in the double helix. This mismatching targets mismatch repair mechanisms in the cell to fix the strain in the double helix. This can lead either to no exchange or unequal exchange where one chromosome is modified to have the same genetic material as the homologous chromosome and the original information is lost. This process is known as gene conversion, and unlike crossovers which can cause exchange over megabases of DNA, gene conversion events are generally under a kilobase. 1.2.2 Additional modern recombination models – SDSA The small scale of gene conversion events makes them difficult to detect without very dense genetic mapping; however, crossovers are much easier to detect. Despite this, 5 experimental work shows that the majority of recombination products result in noncrossovers rather than crossovers. This observation has led to modification of the simple Holliday junction resolution models which predicted either outcome to be equally likely. One such advance is the arrival of synthesis dependent strand annealing (SDSA) models, which rather than predicting a 50% crossover rate, predicts a majority gene conversion rate, fitting empirical results better than previous models. Figure 1.2: Two major models of genetic recombination (a) Szostak (1983) DSBR model (b) Allers and Lichten (2001) SDSA model. Modified from Haber et al. 2004 As shown in Figure 1.2, SDSA models differ from the classic double-strand break repair (DSBR) models in a few critical ways (1) strand invasion and D-loop formation, (2) location of dHJ relative to DSB and (3) outcome of crossovers versus non-crossovers (Allers and Lichten 2001; Haber et al. 2004). First, strand invasion is seen as ratelimiting, such that initially only one strand participates until the second strand finally captures the invading loop to result in a D-loop formation. Unlike DSBR models where D-loop formation always occurs, D-loop formation is not always the result of SDSA models. Once D-loop formation has occurred, the resulting dHJ is depicted as on one 6 side of the DSB. In DSBR models, the dHJ is always depicted as flanking the DSB. Finally, this process results in a majority of non-crossover events as discussed earlier, which contrasts the traditional DSBR models. This also means that recombination proceeds in a fashion more reminiscent of transcription rather than replication since the strand where the DSB formation occurs is the only strand that has newly synthesized DNA whereas in DSBR, both strands receive some new genetic material. 1.2.3 Unique features of recombination in Drosophila As discussed earlier, the ability to perform genetic crosses in Drosophila make them very useful for determining the genomic location of unknown markers in the reconstruction of genetic maps. The current availability of very dense genetic maps in multiple species with a variety of markers and marker types make it a great model for looking at fine scale recombination events. Further, haplotype reconstruction is often not necessary in fruit flies since the haploid genotypes of individuals are known due to the widespread use of inbred lines which are homozygous at most genetic loci. When genetic crosses are performed, an extra layer of experimental control is available to fruit fly geneticists due to a unique aspect of male Drosophila biology. Drosophila males do not undergo crossing over, allowing for greater experimental control over recombination events from generation to generation. The biological phenomenon where the heterogametic sex, the male in Drosophila, does not undergo crossing over is referred to as the Haldane–Huxley rule. This is not the case for all species of Drosophila as Drosophila ananassae males do exhibit some limited crossing over. Further, many Drosophila researchers take advantage of ‘balancer’ chromosomes that have large chromosomal inversions preventing recombination to maintain a known haplotype within the stock (see Chapter 3: “The effect of chromosomal inversions on 7 recombination in hybrids between Drosophila pseudoobscura and D. persimilis”). Such balancers are used for making segments of the genome homozygous or for maintaining haplotypes that are homozygous sterile or lethal. Although the meiotic products of crossovers within the chromosomal inversion are not viable, the viability of the stock is not reduced as these meiotic products are shunted to the polar bodies during oogenesis. This means that genetic crosses using these stocks do not suffer from reduced numbers of offspring, another useful experimental feature of fruit flies. Genetic manipulation of fruit flies is also possible to make them even more useful as a model in studying recombination. As discussed earlier, fungal systems are unique in their ability to do tetrad analysis; however, a few systems including Drosophila have found ways to artificially create fused gametes. In Drosophila, irradiation can generate fused X-chromosomes which are inherited as a single unit in female offspring. These fused chromosomes, or half tetrads, can be used for tetrad analysis to determine the location of actual genetic exchange similar to analysis in fungal systems (Hilliker et al. 1994). By using a combination of markers on the X-chromosome, genetic exchange over very small genetic distances can be observed in females. This technique has been useful for determining gene conversion rates in Drosophila. 1.3 Variation in Crossing Over Recombination rates can vary dramatically between species, ranging from no recombination in asexual systems to very high levels of allelic exchange across the genomes of some sexual systems. Within any particular genome, meiotic recombination rates are also variable, such that some regions have higher rates of crossing over than others. Drosophila have been particularly useful in determining several factors which cause recombination rate variation. For example, early work in the Morgan lab found that there is variation with maternal age, where eggs developing later in life exhibit 8 higher numbers of crossovers than eggs generated earlier in life (Bridges 1927; Bridges 1929). Further attempts to examine plasticity in recombination rates have demonstrated that factors such as temperature (Plough 1917; Plough 1921), nutrition (Neel 1941), age of mating (Redfield 1966) and number of matings (Priest et al. 2007) also affect recombination rates in Drosophila (see Chapter 4: “Male-mediated effects on female recombination”). These factors seem to affect recombination rates in other systems as well. The pattern that has emerged from the synthesis of these data is that stressful conditions tend to trigger an increase in recombination rates (Agrawal et al. 2005; Hadany and Beker 2003; Parsons 1988). In addition to condition-dependent changes in recombination rates, recombination within the genome can vary based on genotype. For instance, some alleles can induce more double-strand breaks leading to higher rates of recombination in particular regions of the genome (de Massy 2003; Symington 2002). Recent interest in fine scale variation in recombination rates has led to the discovery of recombination hotspots which are regions of high recombination limited to very small parts of the genome. These findings are particularly surprising when summed over the whole genome. For example, in humans, hotspots cover only 6% of the sequenced genome, but approximately 60% of recombination in the genome occurs in these locations (Frazer et al. 2007). Such hotspots seem to be associated with particular DNA sequence motifs in some cases (Myers et al. 2008). 1.4 Evolutionary Effects of Recombination on Structure of the Genome Recombination is well-known for its evolutionary advantages of combining advantageous alleles onto single haplotypes and removing deleterious alleles from haplotypes that persist within a species. However, recombination rate variation is also 9 associated with various aspects of the structure of eukaryotic genomes, and the nature of these associations is sometimes less clear. Three associations that have been studied extensively in Drosophila species are those of local recombination rate with nucleotide diversity within species, nucleotide divergence between species and codon usage bias. 1.4.1 Nucleotide diversity and divergence The seminal compilation of data from 20 loci by Begun and Aquadro (1992) elegantly demonstrated an overall strong positive association of recombination rate and nucleotide diversity in this species. Subsequent studies have repeatedly confirmed this observation in this and other Drosophila species, including whole-genome sequencing efforts of its sister species Drosophila simulans (Begun et al. 2007), examinations of fine scale crossover variation and sequence diversity along one chromosome in distantly related species Drosophila pseudoobscura (Kulathinal et al. 2008) and studies of a few loci in D. ananassae (Baines et al. 2004). 1.4.2 Causes Such a positive association between recombination rate and nucleotide diversity could be driven by a variety of mechanistic or evolutionary causes. Mechanistically, the simplest explanation would be that the process of crossing over, or its DSB precursor, is mutagenic. Consistent with this possibility, a few studies have found evidence for a mutational effect of recombination in primates (Lercher and Hurst 2002), and functional studies of yeast recombination support possible mutagenicity of DSBR (Strathern et al. 1995). Mutation rate and recombination rate may also be associated indirectly via common causes, such as base composition or rates of biased gene conversion. For example, areas that are targets for DSBs may incidentally be regions that experience higher mutation rates. Also, heteroduplexed DNA resulting from 10 recombination may be preferentially repaired to keep GC nucleotide bases over AT nucleotides, creating a GC bias in the gene conversion process. Multiple evolutionary processes can explain such an association of recombination rate and nucleotide diversity as well. In regions of low recombination, the spread of a new, advantageous allele will be associated with a reduction in nucleotide diversity across a large window of the genome, a process called hitchhiking. In regions of high recombination; however, the advantageous allele will dissociate from most of its neighbors during its spread. Negative selection against deleterious mutations can also generate a similar pattern through a process called background selection. As new detrimental mutations arise within a population, other alleles at loci near these new mutations are destined for eventual loss in regions of very low recombination (because they cannot dissociate from them), whereas new mutations can be eliminated without loss of much nearby variation in regions of high recombination. Other evolutionary processes may also contribute to this pattern, such as interference among weakly selected alleles in close proximity to one another. 1.4.3 Distinguishing between different scenarios Simple mechanistic causes for an association of recombination rate and sequence diversity predict that recombination rate should also be associated with sequence divergence between species. However, the opposite has typically been observed: Drosophila species often exhibit similar levels of sequence divergence in regions of high and low recombination (e.g. Noor and Kliman 2003), demonstrating the lack of an association of recombination rate with divergence (but see Chapter 2: “Genetic and Evolutionary Correlates of Fine-Scale Recombination Rate Variation in Drosophila persimilis”). Any association between recombination rate and divergence between species is consistently weaker than the association of recombination rate and sequence 11 diversity within species. These observations argue against neutral explanations for the associations in Drosophila. However, complicating factors may obscure the association of recombination rate to sequence divergence between species. For example, recombinational ‘hotspots’ arise and disappear quickly over generations within some species, and such turnover of recombination rates over time may obscure associations with interspecies divergence (Spencer et al. 2006). Additionally, studies in Drosophila have assumed that mutations would be associated with crossovers in particular, but mutations may instead be associated with DSBs more generally, including ones that result in gene conversion without crossing over. In regions of severely reduced recombination, such as adjacent to centromeres, DSBs occur but do not resolve into crossovers. To date, no one has tested directly for an association between interspecies divergence and DSB rate in Drosophila, though one study failed to identify such an association among Saccharomyces species (Noor 2008b). Hence, overall, although selective forces clearly contribute to the association of recombination rate and sequence diversity within species, it is unclear if there may also be a (lesser) contribution from possible mutagenicity of recombination. Perhaps an even greater challenge is distinguishing the relative contributions of hitchhiking versus background selection as selective forces driving the association of recombination rate and nucleotide diversity within species. Looking across loci, the process of hitchhiking should produce an excess of abundant new alleles, and this frequency distortion of alleles is not predicted by background selection. This prediction has been developed and applied using test statistics, and strong signatures of hitchhiking have been identified in genome-wide comparisons. However, detecting hitchhiking from the spread of adaptive alleles does not disprove the operation of background selection and its effects on overall patterns of nucleotide diversity. As with mutational 12 contributions, we are left with support for one explanation but an ambiguous picture regarding the relative contribution of others. 1.4.4 Codon bias Although the genetic code permits multiple synonymous codons to produce the same amino acid, many taxa exhibit strong biases for particular codons across their genome. This ‘codon usage bias’ is thought to be driven largely by selection favoring efficient translation of codons into proteins, though perhaps the pattern may also be influenced by mutational biases (see later). Because natural selection is more effective in regions of high recombination than low recombination as described earlier, selective explanations for codon usage bias predict that it should be the most apparent in regions of high recombination. This expectation has been validated, particularly in studies of Drosophila (e.g. Hey and Kliman 2002 and see Chapter 2). Many studies have suggested that codon bias is weak in regions of low recombination because of the so-called Hill-Robertson (1966) effects. Essentially, selection would be less efficient at particular sites in such regions because of interference from selection acting on many other nearby sites. For example, a favored codon may be near an unfavored one, and thus its spread is hampered, particularly in regions of low recombination. If such interference is common, then looking across a gene, those codons near the centre should exhibit the most interference (since they have many other codons near them) whereas codons at the ends of genes should exhibit less interference. As a result, one would predict that the center codons of long amino-acid coding sequences should exhibit less codon bias than the codons near the edges, and this prediction has been supported (Comeron and Kreitman 2002). With high recombination, interference is reduced because the codons evolve more independently, and the association of codon 13 bias and recombination seems to disappear in Drosophila melanogaster when the recombination rate exceeds 1.5 cM/Mb (Hey and Kliman 2002). However, a mutational process may also explain some of the association of recombination rate and codon bias. In Drosophila, preferred codons typically end in a C or G. As mentioned earlier, in some recombination hotspots, the repair of mismatches that arise during recombination is biased towards G+C richness, such that gene conversion preferentially results in increased numbers of C or G nucleotides (Marais 2003; Marais et al. 2001). If such biased gene conversion solely contributes to the relationship of recombination to codon bias, one predicts that recombination should be equally strongly associated with GC content in third codon positions as in nearby noncoding regions. This predicted relationship was not observed in D. melanogaster (Hey and Kliman 2002; Kliman and Hey 2003), suggesting that natural selection for Cor G-ending codons is a driving force for causing codon bias. 1.4.5 Other patterns Many other patterns in the genome associate with recombination rate. From an evolutionary perspective, the reduced effectiveness of natural selection (through interference or otherwise) in regions of low recombination should leave multiple other signatures in the genome. Recombination increases the effectiveness of protein adaptation throughout the D. melanogaster genome because genes residing in low recombination regions are more often recombinationally linked with detrimental mutations for multiple reasons. First, low recombination makes it more difficult to remove the deleterious alleles and they accumulate in regions of low recombination, and second, beneficial mutations are less likely to spread in regions of low recombination than in regions with higher recombination rates and are thus lost due to random chance more often (Presgraves 2005). 14 Additionally, high transposable element densities have been identified in low recombination regions of multiple Drosophila species, but the exact form of selection causing this pattern is not understood (Bartolome et al. 2002; Dolgin and Charlesworth 2008). Various DNA sequence motifs also associate with recombination rate in many species (Myers et al. 2008), but whether these motifs are actual causes of recombination rate variation or both parameters covary with another causative factor is not yet known (see Chapter 2: “Genetic and Evolutionary Correlates of Fine-Scale Recombination Rate Variation in Drosophila persimilis”). Clearly, we have only begun to scratch the surface of understanding effects caused by or related to recombination rate variation within and among genomes. 15 2. Genetic and Evolutionary Correlates of Fine-Scale Recombination Rate Variation in Drosophila persimilis 2.1 Introduction Meiotic recombination rates vary both at coarse and fine scales across almost all genomes assayed (Coop and Przeworski 2007; Copenhaver et al. 2002; Gerton et al. 2000; Kulathinal et al. 2008; Rockman and Kruglyak 2009; Stephan and Langley 1998; Sturtevant 1913). Recombination rate variation correlates with various genomic features such as particular sequence motifs, transposable elements, nucleotide diversity, nucleotide divergence, GC content, and gene density (Marais 2003; Myers et al. 2005; Nachman 2002; Petes 2001; Sella et al. 2009). Because these features are not consistently correlated with recombination rate variation across every biological system, researchers struggle to identify what causes recombination rates to be drastically higher in regions known as hotspots of recombination and what the consequences of this variation are within and across genomes. The various features that correlate with recombination frequency may have cause/effect associations or may be incidentally related. For example, sequence motifs associated with higher recombination could contribute to higher recombination directly by serving as recruitment sites for proteins involved in double-strand break repair (Baudat et al. 2010; Petes 2001). In contrast, codon bias has been hypothesized to accumulate in regions of higher recombination due to more efficient selection (Gordo and Charlesworth 2001; Marais 2003; but see Marsolier-Kergoat and Yeramian 2009). Several sequence motifs have been found to be associated with recombination rate variation, in Drosophila, humans, and yeast (Cirulli et al. 2007; Myers et al. 2005; Steiner et al. 2009). Recombination proteins bind to ‘open’ chromosomes to induce 16 double-stranded breaks (DSBs), therefore particular sequence motifs can serve as binding sites for recombination proteins directly or indirectly bind transcription factors that induce histone modifications and unwind chromatin (Petes 2001). In humans, a 13bp sequence motif associates with 40% of known hotspots of fine-scale recombination using HapMap data (Myers et al. 2005; Myers et al. 2008). In addition, transposable and repetitive elements have been highlighted as correlating with recombination rate variation (Myers et al. 2005). Over evolutionary time-scales average genomic recombination rates diverge between species (Dumont and Payseur 2008). Fine-scale recombination rates, such as the location of particular recombination hotspots, appear to diverge rapidly, as documented among human populations (Baudat et al. 2010) and in human–chimpanzee divergence (Ptak et al. 2005; Winckler et al. 2005), whereas broader-scale recombination rates (~1 Mb) are highly correlated between these species (Duret and Arndt 2008). Different genetic processes may influence broad-scale and fine-scale variation in recombination rates. For example, crossover rate is often reduced in broad regions adjacent to centromeres (Gaut et al. 2007; Kliman and Hey 1993; Kulathinal et al. 2008). In addition, crossovers in one location of the genome prevent crossovers nearby, a process known as crossover interference. The level of crossover interference has been shown to decrease with distance from the initial crossover, with the size of total interference varying across taxa (102–104 kb) (Copenhaver 2005; Copenhaver et al. 2002; Fitzpatrick et al. 2009; Foss et al. 1993; Hillers 2004; Housworth and Stahl 2003; Stahl et al. 2004). The most consistent predictor of recombination rate variation is nucleotide variation across a genomic region, which has been observed in humans, mice, tomatoes, 17 fruit flies, etc. (Begun and Aquadro 1992; Hellmann et al. 2003; Nachman 1997; for review see Nachman 2002; Stephan and Langley 1998). Further, this correlation has been shown to vary with the scale at which recombination is assayed, with stronger associations at finer scales (Kulathinal et al. 2008; Spencer et al. 2006). Several hypotheses have been proposed to explain this association (see section “Evolutionary Effects of Recombination on Structure of the Genome” of Chapter 1), one of which suggests that the mechanism of crossing over increases local genetic diversity via mutagenicity of the process itself (Lercher and Hurst 2002). A more widely accepted hypothesis proposes that recombination rates play a role in how selection shapes the genome. When non-neutral mutations occur, hitchhiking and background selection reduce local genetic diversity in linked regions. Recombination mitigates these processes by reducing the size of the region effectively linked to the mutation (Charlesworth et al. 1993; Maynard Smith and Haigh 1974). One can distinguish these neutral and selective hypotheses by evaluating the correlation between recombination rate and nucleotide divergence between species. If mutations occur more readily in or near the sites of recombination events (e.g., Strathern et al. 1995), then nucleotide diversity within species and divergence between species will be elevated at neutral sites in regions of high recombination. In contrast, selective processes will decrease diversity, but not affect mean divergence at linked neutral sites (Birky and Walsh 1988; Sella et al. 2009). To test for such hypotheses, for example, synonymous coding or non-coding sites have been used under the assumption that they are evolving neutrally or are under only very weak selection (de Procé et al. 2009). Results of this test vary across systems, with recombination rates correlating with nucleotide diversity but only sometimes with nucleotide divergence (Begun and Aquadro 18 1992; Begun et al. 2007; Bussell et al. 2006; Coop and Przeworski 2007; Kulathinal et al. 2008; Noor 2008b; Roselius et al. 2005; Tenaillon et al. 2004). Natural selection or mutational processes may cause associations of recombination rate with various other genomic parameters, including codon bias, GC content, and gene density (Birdsell 2002; but see Drouaud et al. 2006; Gerton et al. 2000; Hey and Kliman 2002; Kliman and Hey 2003; Kong et al. 2002) and see section “Evolutionary Effects of Recombination on Structure of the Genome” of Chapter 1. Because selection is expected to be more efficient in regions of higher recombination rate, codon bias should positively correlate with recombination rates (Hey and Kliman 2002; Marais et al. 2001). In Drosophila, more frequently used (‘‘preferred’’) codons typically end in GC, possibly due to tRNA abundance, and therefore GC-content of third positions in codons (hereafter GC3) is often used as a measure of codon bias (Akashi and Schaeffer 1997; Bachtrog 2007). However, such an association of GC3 with recombination rate may also be driven by biased mutational repair during gene conversion events. GC-biased gene conversion (BGC) occurs when heteroduplexed recombination intermediates with A:C and G:T mismatches are repaired preferentially into the G or C bases, resulting in increased GC-content (presumably also at GC3) across regions of high recombination (Marais 2003). Distinguishing neutral from selective explanations for the recombination-GC3 correlation has proven challenging (Marais et al. 2001). To examine the various correlates of recombination rates described above, I have generated a fine-scale recombination map for the second chromosome and right arm of the X chromosome (hereafter, XR) of Drosophila persimilis, a North American fruit fly in the D. pseudoobscura species group. A broader-scale linkage map of the second chromosome was previously generated for D. pseudoobscura (Kulathinal et al. 2008), 19 which diverged from D. persimilis approximately 0.5–1 million years ago. I examined how recombination rates correlated with GC content, codon bias, repetitive elements, and specific sequence motifs in D. persimilis. I compared the fine-scale recombination maps between D. persimilis and D. pseudoobscura, as well as the extent of crossover interference within these species. Kulathinal et al. (2008) found a strong correlation with nucleotide sequence diversity within D. pseudoobscura and divergence to D. persimilis (Kulathinal et al. 2008). However, Noor (2008a) suggested that due to the recent species divergence, ancestral polymorphism could have complicated the divergence correlation (see “Discussion” section). Here, I analyzed how fine-scale recombination rate varies with nucleotide diversity within D. persimilis and divergence to a more distant relative, D. miranda, to reduce the impact of segregating ancestral polymorphism (but see Charlesworth et al. 2005). 2.2 Methods 2.2.1 Design of Recombination Markers Two inbred lines of Drosophila persimilis, Mount Saint Helena (MSH) 1993 and MSH 3, were crossed to generate heterozygous F1 females. These females were backcrossed to MSH 1993 males, and 1,440 progeny were genotyped to assay regional recombination rates. The genome sequence of the MSH 3 strain was published previously (Clark et al. 2007), and I obtained partial genomic sequence (0.5x) of MSH 1993 using Roche/454 technology (NCBI SRA accession SRA010365) (Margulies et al. 2005). The new 454 sequences were aligned to the MSH3 strain assembly, the D. pseudoobscura genome sequence assembly 2.0 (with gene annotations from FlyBase Release 2.3) (Richards et al. 2005), and available 454 sequence for D. miranda (NCBI SRA accession SRA008268) following the methods outlined in Kulathinal et al. (2009), simultaneously with the sequence alignments described therein. 20 Design of single nucleotide polymorphism (SNP) based genotyping markers consisted of selecting markers to correspond to the SNP locations from Kulathinal et al. (2008) and then defining the remainder of the allotted 384 markers to be evenly spaced across the two target chromosomes based on the D. pseudoobscura sequence assembly. Next, the genomic sequence of each region (~10–40 kilobases [kb]) was extracted from the MSH 3 genome, and the heuristic alignment FASTA (Pearson and Lipman 1988) was used to search for raw MSH 1993 Roche 454 reads that matched to portions of the target sequence. I then used a series of custom Perl scripts (available from the Dryad repository at doi:10.5061/dryad.1877) to parse the FASTA outputs and find SNPs which distinguished the two inbred lines and contained 100% matching sequence 50 bp upstream and downstream of the SNP. I used reciprocal BLAST (Altschul et al. 1997) to determine that the marker was unique in both genomes. When more than one marker was available for a given target region, I used quality scores and/or the coverage of a given SNP in the MSH 1993 genome. For a subset of markers, I confirmed that the SNPs were diagnostic via Sanger sequencing in both lines of D. persimilis to confirm the sequence. Eight of nine markers confirmed that the putative SNPs differentiated the lines, and the single error was in the published MSH 3 genome sequence assembly. I then used the NCBI trace archives to confirm the quality scores of a subset of markers in the MSH 3 genome. For these markers, the base call was confirmed to be of high quality and coverage, thus I assumed resequencing errors to be rare. 2.2.2 Genotyping DNA was isolated from 1,440 backcross offspring using the Gentra PureGene protocol (Qiagen, Hilden, Germany). SNP markers were screened in all offspring using the Illumina BeadXpress platform (Fan et al. 2003) (Illumina, Inc. San Diego, CA) at the Genomic Analysis Facility within the Duke University Center for Human Genome 21 Variation. The output consisted of raw genotypes at all markers for all individuals. A subset of individuals (106 on chromosome 2 and 110 on X chromosome) did not provide genotypes at all markers and were not used. A subset of markers did not work on any individuals (58 markers). Another subset of markers were homozygous in all individuals and thus represented sequencing errors in one of the two genomes. These sequencing errors were represented equally between the two genome sequences (64 in MSH 3; 47 in MSH 1993) suggesting that there was no bias in the quality of the genomes. Finally, 34 markers were excluded which either produced no genotype in 20% or more of the individuals surveyed or were not heterozygous in the 6 F1 individuals included in the dataset. The final recombination map on the right arm of the metacentric X chromosome (XR) consisted of 51 markers, with 672 crossovers in 1,330 individuals over the last 10 Mb of the 30 Mb chromosome arm. The final map of the second chromosome consisted of 1,334 individuals assayed at 130 markers over the whole 31 Mb of the acrocentric chromosome with 1,692 crossovers observed. The X chromosome coverage is much less due to the lack of continuity in the assembly on the centromeric end. I chose to focus on the 2nd chromosome because it is the only chromosome that both is in a single assembled scaffold (unlike the X-chromosome arms or 4th chromosome) and does not bear within-species inversions (unlike chromosome 3). Recombination data on XR was of interest due to other ongoing related projects (see Chapter 3: “The effect of chromosomal inversions on recombination in hybrids between Drosophila pseudoobscura and D. persimilis”). 2.2.3 Construction of the Recombination Map The centiMorgans per interval were calculated using the number of crossovers and the total sample size (recombination fraction) and applying a correction for multiple crossovers and interference of closely linked crossover events (Kosambi 1944). The 22 physical distance between markers was based on the D. persimilis assembly reference positions, except where intervals spanned multiple scaffolds when the D. pseudoobscura distance was used as a substitute. Physical distances calculated using the D. pseudoobscura and D. persimilis reference positions were consistently within 50 kb except at 2 of 179 intervals (interval 69 and 96 on Chr 2). These intervals were removed from the scale analyses described below. Distances were used to calculate centiMorgans per megabase (cM/Mb) for the total map of recombination for each chromosome (Figure 2.1). 95% Confidence intervals were calculated as in Cirulli et al. (2007); I tested whether the distribution of crossovers across regions deviated from a random distribution of crossovers via computational resampling by randomly placing the observed number of crossovers across all intervals, accounting for the sizes of the intervals. To be conservative, two intervals of anomalously high recombination were excluded from further analyses. The interval on the second chromosome with recombination rate 30.1 cM/Mb may be anomalously inflated, as the interval size used in the denominator could be underestimated since the interval spanned multiple scaffolds in D. persimilis. On the XR chromosome, the interval at the telomeric end had an unusually high recombination rate (16.2 cM/Mb) that could not be confirmed by distal markers, since it was at the end of the map. 23 Figure 2.1: Total recombination map of chromosome 2 (left), and chromosome X, right arm (XR) (right), each with 95% confidence intervals shown in grey. Centromere is shown on the right in panel a and on the left in panel b. Map consists of 130 markers on chromosome 2 and 51 markers on XR assayed in 1330 individuals. 2.2.4 Analyzing Correlates with Recombination Rate Centromeric and telomeric regions were included in generating the recombination map. These regions, however, consistently undergo very little crossing over even though they experience double-strand breaks and repair with gene conversion. Thus, they can potentially disrupt correlations of recombination rates to other genomic features and were excluded from all subsequent analyses (Andolfatto and Wall 2003; Kulathinal et al. 2008). For this exclusion, 5 Mb upstream and downstream from the centromere and telomere on both chromosomes were removed from the dataset for analyses described below. To compare whole chromosome recombination rates, the total number of crossovers across the whole chromosome were summed and divided by the total physical distance. To analyze auto-correlation of recombination rate across adjacent intervals along my map, I adjusted for size by using the 1 Mb scale recombination map (see 24 below). The recombination rates were regressed against the recombination rate of the adjacent intervals to determine if recombination rates were dependent on neighboring regions and how far the auto-correlation extended. To determine particular sequence motifs that correlate with regions of high recombination rates, I extracted the genomic sequence of three of the highest and three of the lowest recombination rates (excluding intervals with no crossovers and intervals with greater than 0.5% N nucleotides). These were then parsed for frequency of all possible 5-bp motif combinations and summed over all three high recombination regions and all three low recombination regions (adjusting for differences in size). I chose 5-bp motifs so as to be consistent with similar motif searches in D. pseudoobscura (Cirulli et al. 2007), to avoid too many comparisons reducing statistical power, and because smaller sized fragments have in the past been useful in later discovery of larger motifs (Myers et al. 2005; Myers et al. 2008). To determine which motifs were most predictive of recombination rate, motifs were sorted based on the difference between the frequencies of occurrence of each motif in the high versus low recombination sequences. The ten motifs with the greatest differences were chosen for subsequent analyses. A custom Perl script was then used to compute the frequency of these and other described motifs in each interval in order to regress it against recombination rate of the interval (see Cirulli et al. 2007). Repetitive element abundance was calculated using the Tandem Repeats Finder (Benson 1999) and the percent abundance for each interval was regressed to cM/Mb for each chromosome. Interference was assayed at the 1 Mb scale, with all recombination intervals condensed to this scale. If there was a gap in the recombination map >1.5 Mb, the interference analysis of the region was split. Chromosome 2 had a large gap in the map (approximately 4 Mb) because of a series of adjacent markers that did not amplify and was therefore analyzed as two separate regions of approximately 15 Mb each. Coefficient 25 of coincidence was calculated using two different methods. First, a partial measure of coincidence was calculated as in Weinstein (1918) using the equation: xn/az where a is the number of crossovers in the 1st crossover interval being assayed, z is the number of crossovers in the 2nd crossover interval being assayed, n is the total number of individuals genotyped, and x is the number of individuals with a crossover in both a and z. Second, the Schweitzer measure which uses the fraction realized as the measure of coincidence was calculated (Schweitzer 1934; Schweitzer 1935). For this measure, I used the equation: xn/(a+x)(z+x) where all values are defined the same as above except n, which is the sum of a, z, x and the total number of individuals with no crossovers in the entire dataset. The conditional probability of a crossover in one interval based on the presence of a crossover in another interval, known as interference, was calculated as 1—coincidence for each measure. This value was then plotted as a function of the interval size. Because a crossover in an interval can occur in any portion of the region, the distance between the midpoint of the two regions was used as the interval size. This was calculated using the following equation: a/2 + b + c ... + x + y + z/2 where a represents the size of region 1 and z represent the size of region 2 and b through y represent the size of all the intervals between a and z. Because the previous study on recombination in D. pseudoobscura did not include interference (Kulathinal et al. 2008), interference was calculated for this dataset (only chromosome 2 was assayed in D. pseudoobscura) as a comparison between species and between studies. 26 To compare the recombination rates between D. persimilis and D. pseudoobscura, I used the recombination map of D. pseudoobscura chromosome 2 from Kulathinal et al. (2008). For comparison, I reduced my map for D. persimilis to intervals covering the same genomic region. Because my marker design aimed to produce a map similar to that from Kulathinal et al. (2008), the borders of the recombination intervals were very close in genomic location. The recombination rate in D. pseudoobscura was regressed against the recombination rate in D. persimilis in JMP 8.0 (SAS Institute, Cary, NC). For the analysis of diversity and divergence, intron and intergene diversity was calculated by counting matches and mismatches between the D. persimilis reference genome and the D. persimilis 454 genome and then dividing the number of mismatches by the total number of bases. Intron and intergene divergence was calculated similarly, except the D. persimilis reference genome and the available D. miranda 454 genome sequences were used. All analyses of introns excluded 10 bp adjacent to each splice site. More conservative analyses wherein particular bases of small introns were surveyed (approaches of de Procé et al. 2009; Parsch et al. 2010) were also conducted yielding nearly identical results, thus I only report the results of the first analysis. For the analysis of codon bias, a custom Perl script was used to calculate the following for each interval: total GC content, percent N bases, intergene GC content and GC content for introns above and below 100 bp (separately). I then summed over each recombination interval: the %GC3 for all codons, %GC3 for fourfold degenerate codons, average GC3 based on the number of genes in the interval, and the total number of genes in the interval. Each calculated parameter was regressed against recombination rate (cM/Mb) for the total recombination map in StatView (SAS Institute, Cary, NC). To see how these measurements were affected by averaging over larger recombination intervals, the 27 chromosome 2 dataset was condensed to larger scales (300 kb, 500 kb, 1 Mb, and 2 Mb), all measures were recalculated, and the regression of each calculation was repeated. The XR chromosome data were not suitable for scaled analysis because they only spanned 10 Mb (5 Mb once the telomeric end was removed). 2.3 Results Surveying 1,440 backcross progeny, I observed on average 1.27 crossovers per individual on chromosome 2 (range 0–5) and 0.51 crossovers per individual on chromosome XR (range 0–5). Local crossover rates varied along chromosomes from 0.0 cM/Mb to 16.25 cM/Mb. Overall, there was significant heterogeneity of recombination rates along both chromosome 2 and XR (Figure 2.1, P=0.0001). I also identified a potential mis-assembly in the D. pseudoobscura genome for Chromosome 2 in the first 3 Mb and on the XR group 6 assembly (see Table 2.1). Comprehensive data associated with this work can be downloaded from the Dryad repository at doi: 10.5061/dryad.1877. Table 2.1: Corrected order of potential misassemblies in the D. pseudoobscura genome based on D. persimilis recombination map. First 3 Mb of chromosome 2 3' end position 5' start position 3' end position D. pseudoobscura D. pseudoobscura D. persimilis position D. persimilis position position position Scaffold 7: 4,240,444 Scaffold 7: 2,957,975 Chr 2: 1,440,366* Chr 2: 172,530 Scaffold 7: 2,734,786 Scaffold 7: 1,745,167 Chr 2: 2,563,917 Chr 2: 1,594,426 Scaffold 7: 1,608,171 Scaffold 7: 58,048 Chr 2: 2,743,727 Chr 2: 4,293,923 Within inversion on XR group 6 that distinguishes D. pseudoobscura & D. persimilis 5' start position 3' end position 5' start position 3' end position D. pseudoobscura D. pseudoobscura D. persimilis position D. persimilis position position position Scaffold 20: 292,476 Scaffold 35: 799,311 XR group 6: 10,340,780 XR group 6: 8,946,664 Scaffold 27: 941,106 Scaffold 27: 198,713 XR group 6: 7,518,002 XR group 6: 8,475,355 Scaffold 41: 703,394 Scaffold 41: 226,091 XR group 6: 7,347,047 XR group 6: 6,867,474 5' start position *telomeric end of chromosome arm assembly 2.3.1 Crossover Rates Along the 2nd and XR Chromosomes The total analysis for XR covered 50 cM with a chromosome average recombination rate of 5 cM/Mb. The total analysis for chromosome 2 covered 126 cM 28 with a chromosome average recombination rate of 4.1 cM/Mb (frequency of all crossovers divided by total physical distance assayed). However, without the first and last 5 Mb of each chromosome’s assembled sequence, the XR covers only 25.6 cM with a recombination rate of 5.26 cM/Mb, and the 2nd chromosome extends 105 cM with a chromosome recombination rate of 4.98 cM/Mb. The confidence intervals of these chromosomal sums overlap extensively, which does not support a difference in the distribution of recombination rates between the two chromosomes. All analyses described below were done without these first and last 5 Mb of chromosomes to exclude possible chromosome- end effects (see “Methods” section) (Gaut et al. 2007; Kliman and Hey 1993; Kulathinal et al. 2008). 2.3.2 Recombinational Neighborhoods To test whether recombination in a given interval was independent of neighboring recombination rates, I regressed recombination rates against the recombination rate of the adjacent intervals and observed a significant spatial autocorrelation on chromosome 2 (P = 0.0002; r = 0.415; N = 76) and on the XR (P = 0.0362; r = 0.471; N = 20). The intervals used in the above analysis varied in size from 55 to 547 kb (with one outlier at 620 kb). Because each interval was used twice in the initial analysis above, once as the focal interval and once as the adjacent interval, I also removed every other interval to correct for non-independence, and the result remained significant (P = 0.0002; r = 0.577; N = 46). I further observed a significant Durbin– Watson autocorrelation of recombination rate (P=0.0001). I attempted to precisely estimate the size of the ‘‘recombinational neighborhoods’’ by standardizing the intervals to a size of 1 Mb (using the interference data). At the 1 Mb size-scale, the correlation of cM/Mb to adjacent cM/Mb was not significant, indicating that the size of the ‘‘recombinational neighborhoods’’ is smaller than 1 Mb (data not shown). Because of 29 variance in initial interval sizes surveyed, I was unable to standardize the dataset to sizescales smaller than 1 Mb (while maintaining comparison of adjacent intervals) to refine the size of the neighborhoods further. Since autocorrelation of GC-content has been observed in mammalian genomes due to isochore structure (Duret et al. 2002), I attempted to rule out any GC-content structure affected by my recombination autocorrelation. To do this, I took the residuals of GC-content at a given interval versus recombination rate and regressed these residuals against the recombination rate in the adjacent window. The results (P = 0.0001; r = 0.389; N = 94) remained highly significant after correcting for GC-content, suggesting the recombinational neighborhoods are not solely due to GC mediated structuring of recombination rates. 2.3.3 Motif Analysis Among all possible 5-bp motifs, AT-rich motifs differed most dramatically in the three highest recombination regions from the three lowest recombination regions in abundance. The frequencies of the ten motifs that differentiated the highest and lowest recombination regions most were counted across all intervals in the total dataset. A regression with recombination rate was then computed excluding the six regions above. I found two motifs that correlated significantly, albeit weakly, with recombination rates on chromosome 2—the 5-bp motifs ATAAA (P = 0.0282; r = 0.262; N = 70) and AATAA (P = 0.0179; r = 0.282; N = 70). These two motifs were not significantly correlated with recombination rates on the XR (data not shown). In addition, I analyzed the entire dataset for the 5-bp motif CACAC identified as significant on the left arm of the X chromosome in D. pseudoobscura (Cirulli et al. 2007) and found no significant association on chromosome 2 (P = 0.4293; r = 0.092; N = 76) or XR (P = 0.696; r = 0.084; N = 24). 30 I also examined the 13-bp motif noted as highly correlated to recombination rates in humans (CCNCCNTNNCCNC: Myers et al. 2008) and found a highly significant correlation with recombination rates in D. persimilis on the second chromosome (P<0.0001; r = 0.464; N = 76) and on the XR (P = 0.0231; r = 0.462; N = 24). The combined results of the 13-bp motif and recombination rates for these two chromosomes are shown in Figure 2.2a. The association of the 13-bp motif with recombination rates on both chromosomes was significant via a non-parametric Spearman rank correlation (P<0.0001; ρ = 0.3814). I corrected for any possible GC-bias due to the 13-bp motif by computing the residuals of the regression of the percent 13-mer per interval against percent GC-content per interval. These residuals were regressed against recombination rate per interval and still shown to be significant (P = 0.0014; r = 0.332; N = 95). I further examined whether this motif explained recombination rates from D. pseudoobscura using available chromosome 2 local recombination rates (Kulathinal et al. 2008) and genome sequence (Richards et al. 2005), and a very strong correlation to the 13-bp motif was also detected using this dataset (P<0.0001; r = 0.712; N = 40). I tested for a correlation of repetitive elements with recombination rates using Tandem Repeats Finder (Benson 1999), and found a highly significant correlation on chromosome 2 (P = 0.0035; r = 0.331; N = 76) and XR (P = 0.0031; r = 0.578; N = 24). The combined results of repetitive elements with recombination rates for these two chromosomes are shown in Figure 2.2b. Because the D. persimilis genome sequence bears the lowest coverage and quality of the 12 Drosophila genomes, there are many noncontiguous segments, each containing large numbers of undefined bases. Repetitive sequences likely contributed greatly to the assembly failures at this low sequence coverage. Therefore, the percent of undefined bases in an interval is most likely related to repetitive element abundance in the same interval. Similar to my results for repetitive 31 elements, I found a strong statistical significance between recombination rate and percent of each interval containing N bases in the sequence on both chromosome 2 (P = 0.018; r = 0.272; N = 76) and XR (P = 0.029; r = 0.456; N = 23). Finally, I conducted a multiple regression to determine which elements were most predictive of recombination rate variation—motifs, GC content, repetitive elements, or %N. The only feature that remained significant in the multiple regression was the 13bp motif (P = 0.0122). Figure 2.2: Plot of association between crossover rate in D. persimilis and a 13-bp motif abundant in human recombinational hotspots (a), and repetitive elements (b). 32 2.3.4 Crossover Interference Crossover interference was very strong in both D. pseudoobscura and D. persimilis as far as 5–7 Mb, at which point it appeared to vary stochastically around zero (Figure 2.3a), likely because fewer intervals were surveyed with increasing interval size. This was true for chromosome 2 in D. pseudoobscura (Figure 2.3b) and D. persimilis as well as XR in D. persimilis (Figure 2.3c). Figure 2.3: Interference results on chromosome 2 in D. persimilis (A), in D. pseudoobscura (B), and on the XR in D. persimilis (C). 2.3.5 Comparison of Crossover Rate Between Species The recombination dataset for D. persimilis was condensed to intervals comparable with the dataset available for D. pseudoobscura across chromosome 2. The recombination rates between these closely related species were highly correlated (Figure 2.4; P<0.0001; r = 0.591; N = 38) across intervals. A non-parametric Spearman rank correlation also indicated significant covariance of local recombination rates between species (P = 0.0011; ρ = 0.535; Z = 3.255). In addition, I tested for intervals bearing recombination rates differing between the species through an interval-by-interval χ2 test of crossovers versus non- crossovers, using 1 degree of freedom. This analysis revealed that the recombination rate was significantly different in only 5 of 38 intervals, after 33 Bonferroni correction. One of these 5 intervals (apx. 6 Mb in Figure 2.4) had recombination markers located on non-contiguous scaffolds, and thus the D. pseudoobscura physical distance was used (see ‘‘Methods’’ section). This interval may therefore have a similar measure of recombination if a more precise measure of physical distance was available. The other notable peaks in D. persimilis recombination rate (apx. 17 and 24 Mb in Figure 2.4) relative to D. pseudoobscura in Figure 2.4 are not significantly different from one another, suggesting hotspots between these species are shared at the scale analyzed here. Figure 2.4: Comparison of chromosome 2 crossover rates between D. persimilis (generated in this study) and D. pseudoobscura (data from Kulathinal et al. 2008). Centromeric end is shown on the left. 2.3.6 Nucleotide Diversity Within Species and Divergence Between Species Table 2.2 presents regression analyses of recombination rates along the second chromosome on various measures, including nucleotide diversity within D. persimilis and divergence between D. persimilis and D. miranda. Along the 20 megabases surveyed on chromosome 2 (hence, excluding the five megabases at each end), 34 nucleotide diversity of introns was significantly correlated with recombination rate in D. persimilis, while intergenic sequence diversity was not. Intron and intergene divergence between species were both not significantly correlated to recombination rates on chromosome 2. Table 2.2: Results of regression of recombination rate variation with various measures on chromosome 2 and XR in D. persimilis. 2nd chromosome P value r 0.258 0.133 0.004* 0.333 0.559 0.069 0.128 0.180 0.109 0.191 0.002 0.353 0.002 0.360 0.005 0.329 0.006 0.323 0.011 0.295 Intergene diversity Intron diversity Intergene divergence Intron divergence Intergene %GC Big Intron %GC Small Intron %GC %GC3 Average GC3 per gene Total %GC N 74 73 74 73 72 72 72 72 72 74 XR chromosome P value r 0.2329 0.272 0.239 0.269 0.3494 0.215 0.254 0.260 0.052 0.430 0.183 0.300 0.152 0.324 0.670 0.099 0.775 0.067 0.131 0.340 N 21 21 21 21 21 21 21 21 21 21 *Values in bold indicate statistical significance To determine how variation in size of intervals surveyed influenced recombination’s correlation to diversity and divergence, I adjusted the dataset to have standardized interval sizes at varying scales. Intron diversity remained strongly or significantly correlated to recombination rates across all scales studied (Table 2.3). No measures were significantly correlated with recombination rate along the XR chromosome arm (Table 2.2), but only 5 Mb of data were available for analysis after excluding failed markers and chromosome ends. Table 2.3: Results of diversity within D. persimilis and divergence between D. persimilis and D. miranda on chromosome 2 calculated at increasing scale (300 kb to 2 Mb). 300 kb 500 kb 1 Mb P value r N P value r N 0.034* 0.417 26 0.13 0.305 26 P value 0.003 *Values in bold indicate statistical significance 35 2 Mb r N 0.781 12 P value 0.161 r N 0.731 5 2.3.7 Codon Bias and Biased Gene Conversion GC content was strongly correlated with recombination rates on chromosome 2 and not on XR (Table 2.2). Because small introns (>100 bp) appear to evolve under less evolutionary constraints than larger introns (de Procé et al. 2009; Haddrill et al. 2005; Marais et al. 2005; Parsch 2003), I separated the intron GC content analysis into large (>100 bp) and small (<100 bp) introns (Figure 2.5a). Except for intergene GC, all GC content calculations were strongly correlated with recombination rates on chromosome 2 (Table 2.2), suggesting the action of BGC. To examine how codon bias might be influenced by recombination rates, I computed GC3 across entire intervals and average GC3 per gene within intervals (Akashi and Schaeffer 1997; Bachtrog 2007). Both measures correlated strongly with recombination rates (and were nearly identical to each other). Because not all codons can mutate to an A/T and still create a synonymous change with respect to the amino acid encoded, I limited my 3rd position codon analysis further to those positions that are four-fold degenerate and still observed a significant correlation to recombination rate (Figure 2.5b; P = 0.0146; r = 0.283; N = 74). 36 Figure 2.5: Small intron %GC versus cM/Mb which represents the effects of GC-biased gene conversion (BGC) only (a), fourfold degenerate 3rd position %GC versus cM/Mb which represents the combined effects of codon bias and BGC (b), and the subtracted effects of the two which should reflect the effects of only codon bias (c). Since a significant association of GC3 at fourfold degenerate sites could be due to BGC, I sought to separate the effects of BGC and selection-driven codon bias. To do this, I subtracted small intron %GC content (representing the effects of BGC only) from fourfold degenerate 3rd codon position %GC (representing the combined effects of BGC and selection-driven codon bias) within each interval, and I observed a significant relationship of the independent effect of selection-driven codon bias (Figure 2.5c; P = 0.0183; r = 0.279; N = 71). In addition, following Marais et al. (2001), I computed the 37 residuals from the x versus y regression of small intron %GC content plotted against fourfold degenerate 3rd codon position %GC and regressed them against recombination rate. This analysis generated a marginally significant result (P = 0.072; r = 0.215; N = 71), explaining a similar proportion of the variance in recombination rate as the subtraction analysis above. Another method used by Marais et al. (2001) was to examine AT preferred codons which should not experience BGC, however, no preferred codons in D. pseudoobscura have been identified as ending in AT (Akashi and Schaeffer 1997; Bachtrog 2007). Because neither GC content nor GC3 content was correlated with recombination rates on the surveyed region of XR, I did not extend analysis of codon bias or BGC on this chromosome. 2.4 Discussion Fine-scale linkage maps allow researchers to address a host of genetic and evolutionary questions (Gerton et al. 2000; Groenen et al. 2009; Ihara et al. 2004; Maddox et al. 2001; Mancera et al. 2008; Menotti-Raymond et al. 1999; Niehuis et al. 2010; Paigen et al. 2008; Samollow et al. 2004; Shifman et al. 2006; Singer et al. 2006; van Os et al. 2006; Wong et al. 2010; Zenger et al. 2002). However, because of the cumbersome nature of their development, direct, empirical estimates of fine-scale recombination rate are not broadly available. Instead, methods have been developed for inferring recombination ‘‘hotspots’’ via patterns of nucleotide variation and linkage disequilibrium (Buard and de Massy 2007; Coop et al. 2008). Some of these methods rely on particular assumptions such as no population structure, and common evolutionary forces such as selection can lead to both false positives and false negatives of hotspots (but see Khil and Camerini-Otero 2010; McVean 2007; Reed and Tishkoff 2006). 38 In this study, I generated a fine-scale map of recombination across two major chromosomes in Drosophila persimilis using low coverage next-generation Roche-454 resequencing followed by high-throughput genotyping with Illumina BeadXpress analyzer. Although studies of some other organisms have used far more markers to develop linkage maps, I focused instead on a large panel of progeny to test, ensuring high precision of crossover rates estimated. I observed on average 1.27 crossovers per individual along the 2nd chromosome. I detected both fine-scale and broad-scale structuring of recombination rate across both chromosomes, crossover rate autocorrelation in neighboring windows, and crossover interference extending 5–7 megabases from crossover events, consistent with results in D. melanogaster (Foss et al. 1993) and in D. pseudoobscura (Fitzpatrick et al. 2009). Average crossover rate did not differ between the X-chromosome arm and the second chromosome, but regional recombination rates were strongly correlated between D. pseudoobscura and D. persimilis. My primary focus was to evaluate several genetic and evolutionary correlates with local crossover rate, and I discuss their implications below. 2.4.1 Broad-Scale Patterning of Recombination Rates I noted a significant correlation of recombination rates among adjacent intervals, creating what I term ‘‘recombinational neighborhoods.’’ Recombination rate maps of some other systems appear to also show such spatial autocorrelation, but these studies fit polynomial curves on observed crossover rates, artificially producing these relationships (Gaut et al. 2007; Niehuis et al. 2010). I observe these recombinational neighborhoods without any manipulation of the observed crossover rates, and the autocorrelation appears to decay in under a megabase. Broad effects on recombination rates are well known in centromeric and telomeric regions, but such broad structuring of recombination rates elsewhere in the genome has not been reported previously in 39 Drosophila or other systems to the best of my knowledge, aside from within mutant constructs (Petes 2001). This observation opens several additional questions for future study. For example, do the same mechanisms that specify either fine-scale recombination rate variation or centromeric/telomeric variation also define the intermediate scale of recombination rate structuring observed in this study? I observe spatial autocorrelation of overall G–C content, so this nucleotide compositional patterning may contribute to, or be a consequence of, the autocorrelation of local crossover rates. Nucleasehypersensitive chromatin may also contribute to the observed broad-scale patterning of recombination rates (Petes 2001). 2.4.2 DNA Sequence Motifs Associated with Crossover Rate in Drosophila I surveyed all possible 5-bp sequence motifs and identified two AT-rich ones (AATAA and ATAAA) that correlate negatively with recombination rate. This observation is consistent with my observed positive correlation between recombination rate and GCcontent. Because my motif search was limited to 5-bp motifs, it is possible that these motifs are part of a larger AT-rich motif that future studies can examine for correlation with recombination rate variation. I extended my motif analysis to those identified in previous studies of Drosophila or humans as significantly correlated to recombination rates. Cirulli et al. (2007) observed significant associations of D. pseudoobscura finescale crossover rate with both simple repeats and the motif CACAC. I confirmed similar associations in D. persimilis with simple repeats but not CACAC. More strikingly, I observed a strong positive correlation of both D. pseudoobscura and D. persimilis crossover rate to a 13-bp motif associated with crossover hotspots in humans (Myers et al. 2008). The recombinational effect of this 40 motif is suggested to be mediated through the zinc finger DNA binding array of PRDM9 in humans and mice (Baudat et al. 2010). While the association of D. persimilis crossover rate with this motif suggests similar mechanisms controlling hotspot usage, the finding of a strong correlation with the exact motif found in humans is surprising considering the motif is not associated with chimpanzee crossover hotspots (Myers et al. 2010), and there is no known ortholog of PRDM9 in Drosophila. Further investigation of the variation in recombination rates at different scales may help to tease apart the mechanistic bases of these associations and the directionality of cause and effect. 2.4.3 Recombination Rates Strongly Correlate Between Species I observed a strongly significant correlation between D. pseudoobscura and D. persimilis in fine-scale recombination rates. The conservation of recombination rates in crosses performed using strains from different species both implies that the crossover rates I measured are not highly genetically variable within these species and supports the repeatability of my methods (Kulathinal et al. 2008). Recombination rate differences between humans and chimps have been found at very fine scales using different approaches (Ptak et al. 2005; Winckler et al. 2005). However, the correlation of recombination rates between these species is stronger at broader scales. These results suggest that correspondence between species for recombination rates may increase when analyzing larger interval sizes. My study extends the latter observation in Drosophila by showing a very strong correlation between sister species for recombination rates at ~500 kb scale. Future studies comparing interspecies recombination rates should analyze their results at varying scales (see “Correlation to Diversity and Divergence at Varying Scales” section) to determine if crossover rates are consistently more strongly correlated between species at broad rather than fine scales. 41 2.4.4 Correlation to Diversity and Divergence at Varying Scales I note that nucleotide variation within species at introns was significantly correlated with recombination rates, whereas divergence between species was not correlated with recombination rate. Because diversity varies significantly across the genome for reasons other than recombination rate variation (such as functional constraints), I analyzed intergenic diversity and intron diversity separately (de Procé et al. 2009; Hellmann et al. 2005). Curiously, I did not observe the same predicted correlation between diversity and recombination rate on the D. persimilis XR chromosome arm, but my coverage of this chromosome was low suggesting there may not have been enough variation in recombination rate or in nucleotide diversity to detect a correlation. I also did not detect a significant relationship between intergenic diversity and recombination rate on either chromosome; however, this failure could result from functional constraints (such as UTRs or ncRNAs) in intergenic regions that limit nucleotide diversity (de Proce´ et al. 2009). Finally, my measure of divergence based on a newly obtained D. miranda sequence, is less likely to be confounded with ancestral polymorphisms than estimates of divergence used previously comparing D. pseudoobscura and D. persimilis (Kulathinal et al. 2008), which are known to hybridize at low levels in the wild (Dobzhansky 1973), with detectable gene flow on the nucleotide level (Machado and Hey 2003; Machado et al. 2002, 2007). Therefore, my measure of nucleotide divergence using this recently available outgroup sequence is potentially better than the measure used in our group’s previous publication (Kulathinal et al. 2008). The lack of correlation of D. persimilis recombination rates and divergence between D. miranda and D. persimilis is consistent with the selective hypothesis for explaining the correlation between diversity and recombination rates (Begun and 42 Aquadro 1992; Nachman 2002). However, the lack of a correlation of fine-scale recombination rate with divergence may also reflect evolutionary changes in the recombinational landscape (Spencer et al. 2006); although I found that D. pseudoobscura and D. persimilis share local recombination rates, we do not know that D. miranda and the common ancestor also share these local rates. This hypothesis is being tested with a fine-scale linkage map being developed now in D. miranda. I also detected a continuing association of recombination rate with nucleotide diversity with interval sizes up to 1 Mb. I tested for scale-dependence of the association of recombination rate with diversity because nonselective models should predict the association to be restricted to the finest spatial scale (e.g., Spencer et al. 2006). My observation that the relationship holds true at much larger scales seems to contradict this hypothesis, but the observation of recombinational neighborhoods extending out to similar scales makes strong inference from this pattern uncertain. This increasing association at broader scales is also consistent with human recombination rate variation and various correlates (Duret and Arndt 2008). 2.4.5 Codon Bias Versus GC-Biased Gene Conversion (BGC) I saw a correlation between GC3 content, and intronic GC content to recombination rates (Table 1). This implies the action of selection-driven codon bias or BGC (Marais 2003; Marais et al. 2001). These results could be an artifact of higher gene density in regions of high recombination rates; however, the correlation of gene density to recombination rate was non-significant (results not shown). Although calculations of codon bias correlate with recombination rates, in Drosophila calculations are based on codons which end in G or C. Therefore, the measure is confounded with non-selective forces casting doubt on the overall conclusion of these studies (Marais et al. 2001). To further distinguish between selective processes 43 and mutation bias, I examined GC content in small introns (<100 bp) to see if the mutational bias affected nearby introns which have been shown to be less constrained by selection (de Procé et al. 2009). I also examined GC content at fourfold degenerate codons, which have also been shown to be less constrained than non-degenerate codons, to relate codon bias to recombination rate variation (de Procé et al. 2009). I predicted that if both BGC and natural selection contribute to stronger codon bias in regions of high recombination and are acting in concert, then the effects of the two could be divided by factoring out the effects observed in introns (presumably non-adaptive) from those in codon 3rd positions (influenced by selection). I found that the difference was still correlated with fine scale crossover rate (Figure 2.5), and I conclude that both BGC and codon bias are influencing or influenced by recombination rates. Although, I observe a signature of selection-driven codon bias, the signature reflects historical processes (see Zeng and Charlesworth 2010), and selection may not have operated in modern D. persimilis. By inferring independently, the selective and non-selective forces contributing to codon third-position GC content, I suggest that both neutral processes (BGC) and selection processes (codon bias) contribute to the correlation between recombination rate and GC content in coding regions. Although BGC is not an adaptive process, the accumulation of codon bias in regions of high recombination rates is, at least in part, a selective process. Because recombination acts to increase the local effective population size of the genome, it reduces the effects of Hill-Robertson interference (HRi). HRi reduces the overall efficiency of selection because positively selected loci reduce each other’s probability of fixation (Felsenstein 1974; Hill and Robertson 1966). In regions of high recombination, HRi is very weak because positively selected loci are more easily combined onto the same 44 haplotype increasing their collective probability of fixation (Felsenstein 1974; Gordo and Charlesworth 2001; Haddrill et al. 2005). 2.4.6 Conclusion As high-throughput methods for genome sequencing and assembly as well as high throughput genotyping technologies increase in availability, recombination maps are becoming cheaper and less challenging to generate. These types of studies also offer more empirical evidence as to how recombination rates shape the genome and provide long-awaited answers to many evolutionary questions regarding recombination rates, such as what factors shape fine-scale recombination rate variation across genomes and why there is such a strong relationship between recombination rates and genetic polymorphism. These studies also generate novel questions such as which factors predict shifts in recombination landscapes between species and what factors shape broad-scale recombination patterns across the genome? In the last 30 years, we have come from the assumption that recombination rates are uniformly distributed across genomes to many new understandings, but retaining the realization that there is much left to learn about recombination rates, what makes them so variable, and how they impact patterns of evolution in natural populations. 45 3. The effect of chromosomal inversions on recombination in hybrids between Drosophila pseudoobscura and D. persimilis 3.1 Introduction In the study of speciation, a crucial question is how species are maintained despite gene flow. One potential solution is that chromosomal inversions partition the genome into regions protected from gene flow by reducing recombination over long stretches. Although reduced recombination in inversion heterozygotes has been well documented within species (Ishii and Charlesworth 1977; Roberts 1976), there are few empirical estimates of recombination rates in interspecies hybrids heterozygous for inversions. Further, by focusing on the importance of inversions in the reduction of recombination within inverted regions, recent work has largely overlooked the major global increases in recombination rate in inversion heterozygotes (but see Portin and Rantanen 1990), known as ‘interchromosomal effect’ (Schultz and Redfield 1951). Rather than determining the effects of the interchromosomal effect on global nucleotide variability, this field of research has instead shifted focus towards identifying the mechanism of this process (Joyce and McKim 2010). Here, I focus on hybrids between Drosophila pseudoobscura and D. persimilis, a classical model system for studying chromosomal inversions, hybridization and speciation (Dobzhansky 1937). D. pseudoobscura is found across Western North America extending as far north as Canada, as far east as Central Texas and as far south as parts of Central America. D. persimilis maintains a smaller range restricted mainly to Western United States, and is contained within the range of D. pseudoobscura (Dobzhansky and Epling 1944). These species are morphologically identical, however male hybrids are sterile. They diverged approximately 0.5-0.85 million years ago 46 (Aquadro et al. 1991; Hey and Nielsen 2004; Leman et al. 2005), however parts of the genome carry a signature of more recent hybridization (Hey and Nielsen 2004; Kulathinal et al. 2009; Machado et al. 2007; Machado et al. 2002). Despite this ongoing gene flow, these taxa were diagnosed as different species due to three major paracentric inversions distinguishing them – two on the X chromosome and one on the 2nd chromosome (Dobzhansky and Epling 1944). The D. pseudoobscura arrangement of the right arm of the X (hereafter XR) is also present in populations of D. persimilis exhibiting meiotic drive with skewed sex ratios, referred to as Sex Ratio D. persimilis (Sturtevant and Dobzhansky 1936). Additionally, both species segregate multiple arrangements among individuals within species on chromosome 3 (Powell 1992). I present here an empirical study examining the extent to which inversions differentiating these species affect recombination rates (1) within inverted regions, (2) at inversion boundaries, and (3) throughout the remainder of the genome. First, I focus on the largest inversion, the XR (not-fixed) inversion, because I expect higher potential for double-crossover events and gene flow within this inversion (Navarro et al. 1997). I evaluate the double crossover rate in female hybrids between D. pseudoobscura and D. persimilis across this ~12.5 Mb inversion through a direct assay. I also indirectly estimate gene exchange in natural populations along the same inverted region using a series of markers sequenced in several inbred lines of each species. I also examined the effects of heterozygosity for inversions on hybrid recombination rates elsewhere in the genome. To this end, I quantified restricted recombination at the boundaries of inversions by estimating the amount of recombination between markers outside, but near, the breakpoints of each inversion. These regions have higher rates of divergence, similar to markers inside inverted regions (Kulathinal et al. 2009; Machado et al. 2007; Noor et al. 2007), suggesting they 47 experience less recombination than neighboring regions. Finally, I calculate the effect inversions have on enhancing recombination rates throughout the rest of the genome by comparing recombination rates in hybrids to a published recombination map of D. persimilis (Stevison and Noor 2010). 3.1.1 Expected rates of exchange within inverted regions based on within species inversion polymorphisms The major prerequisite of modern chromosomal speciation models (see Faria and Navarro 2010 for recent review) is that crossover products are rarely recovered between inverted segments. The rarity of this occurrence is the reason that commonly used balancer stocks are so effective in their maintenance of homozygous lethal markers (see section “Unique features of recombination in Drosophila” in Chapter 1). In Drosophila, single crossovers between heterokaryotypes of a paracentric inversion result in nonviable gametic products, which are subsequently shunted to the polar bodies prior to oviposition (Carson 1946; Coyne et al. 1993; Navarro et al. 1997; Sturtevant and Beadle 1936). Therefore, exchange between heterokaryotype regions is likely due to either gene conversion or double-crossover events. The rate of gene conversion is often assumed in models to be uniform across an inversion, whereas the rate of double-crossover is expected to be highest near the center, due to the role of crossover interference (Andolfatto et al. 2001; Navarro et al. 1997). Crossover interference is the impact one crossover event has on the probability that another crossover event will occur nearby, determining the optimal distance between crossover events. Therefore, if two crossover events occur within the inversion loop created at meiosis, they are more likely to span the center of the inversion than any segments closer to the inversion breakpoints (Navarro et al. 1997). Due to the strong effect of interference acting over distances as long as 8-10 Mb in Drosophila (Fitzpatrick et al. 2009; Foss et al. 1993; Stevison and 48 Noor 2010; Weinstein 1918), only inversions with large recombinational distances are expected to achieve an observable rate of double-crossover (>20 cM, Navarro et al. 1997). Empirical analysis on exchange in inversion heterozygotes has followed two approaches: controlled crosses to measure the rate of double-crossover and sequence analysis of markers within an inversion to infer historical exchange. Empirical estimates between segregating inversions in Drosophila have observed between 10-3-10-5 double crossovers in a single generation with an average of 10-4 for a simple inversion (Ishii and Charlesworth 1977; Levine 1956). These studies used phenotypic mutants located near the center and ends of inversions spanning 30-80 cM (Ishii and Charlesworth 1977). In comparison, the double-crossover rate across similar regions in homokaryotypes is greater than 10% (Gruneberg 1935; Novitski and Braver 1954; Robbins 1974; Spurway and Philip 1952), showing that double-recombination products are not produced as frequently in inversion heterozygotes. Further, these observed rates of double-crossover in inversion heterokaryotypes approach the expected genomic rate of gene conversion (~10-5-10-6), estimated using the rosy locus in Drosophila (Chovnick 1973; Smith et al. 1970). Sequencing approaches have enhanced our understanding of differentiation within inverted regions by being able to detect historical gene flux (Hasson and Eanes 1996; Laayouni et al. 2003; Nobrega et al. 2008; Pegueroles et al. 2010; Schaeffer and Anderson 2005; Wesley and Eanes 1994; White et al. 2009) due to conversion or doublecrossover events within an inverted region. These studies confirm predictions by theoretical models for flux rates to be highest near the center of inverted regions (Feder and Nosil 2009; Kirkpatrick and Barton 2006; Navarro et al. 1997). Despite knowing the rates of double-crossovers in inversions differentiating populations of Drosophila, there are few, if any studies examining how inversions that 49 differentiate species might differ in rates of exchange from inversions segregating within species. There are three reasons to expect exchange rates might differ in interspecies hybrids: (1) inversion heterozygotes segregating within populations are more abundant than those between species, which are dependent on the rate of migration between species and premating isolation, (2) mechanistically, levels of sequence divergence influence rates of crossover, predicting lower levels of exchange when sequence divergence is higher between homologous chromosomes (Modrich and Lahue 1996), and (3) inversion heterozygotes between species will likely suffer some fitness consequences because of alleles conferring differential adaptation or incompatibilities within them, allowing selection to influence the detectable rate of historical exchange (Kirkpatrick and Barton 2006; Noor et al. 2001). Although this latter difference may not influence the rate observed in a single generation cross, it may impact the observed exchange rate in a sequence-based analysis. For this reason, I used both direct cross analysis and sequencebased approaches to detect exchange within the inverted region of XR. 3.1.2 Recombination suppression of inversions extends beyond breakpoints Markers outside inverted regions, but near the breakpoints, tend to have heightened divergence similar to markers found within inversions (Kulathinal et al. 2009; Machado et al. 2007; Noor et al. 2007). This effect is consistent with the observation of persistent recombination suppression in inversion heterozygotes outside the inversion, near boundary regions (hereafter inversion boundaries) (Dobzhansky and Epling 1948; Kulathinal et al. 2009; Maynard Smith and Maynard Smith 1954; OrtizBarrientos et al. 2006; Pegueroles et al. 2010; Spurway and Philip 1952). This pattern is not unique to Drosophila (Roberts 1976; Strasburg et al. 2009; Stump et al. 2007; White 50 and Morley 1955), and similar effects of extended divergence at centromeric boundaries suggest this pattern is not unique to inversion boundaries (Carneiro et al. 2010). Using hybrids between D. pseudoobscura and D. persimilis, recombination suppression at inversion boundaries was empirically determined to extend between 2-4 Mb (Kulathinal et al. 2009). However, the pattern of heightened divergence between D. pseudoobscura and D. persimilis was not consistent at all inversion boundaries, suggesting recombination suppression may be variable. In the D. pseudoobscura – D. persimilis system, some inversion breakpoints are near chromosome ends where interchromosomal effect should be strong (see “Inversions as global recombination modifiers” section below), perhaps reducing the extent of recombination suppression at these inversion boundaries. My systematic analysis of recombination rate at inversion boundaries aims to refine the previous estimate from Kulathinal et al. (2009) for the extent of recombination suppression at inversion boundaries. 3.1.3 Inversions as global recombination modifiers In addition to their unequivocal effects on species maintenance, chromosomal inversions have been shown to increase recombination rates significantly throughout the rest of the genome (Schultz and Redfield 1951; Sturtevant 1919). Inversions have been shown to alter crossover rates on the same chromosome (intrachromosomal effect) and/or on other chromosomes (interchromosomal effect). Early studies made two important observations with regard to inter/intrachromosomal effect (collectively hereafter ICE): (1) ICE has a stronger impact on proximal (centromeric) and distal (telomeric) portions of chromosomes, and (2) due to its impact on interference, ICE seems to impact the preconditions necessary for exchange, rather than influencing whether the process of recombination will result in a crossover or not (Lucchesi and Suzuki 1968). 51 The first observation comes as the result of several studies on inversions segregating within Drosophila melanogaster (Krimbas and Powell 1992; Lucchesi and Suzuki 1968; Portin and Rantanen 1990; Schultz and Redfield 1951), and thus is surprising because these regions (chromosome ends and centromeres) tend to have more restricted recombination within homokaryotypes. The observation of heightened effect on chromosome ends/centromeric regions has also been observed in maize and grasshoppers (Lucchesi and Suzuki 1968); however, restricted recombination in these regions is not universal. For example, while in Drosophila both centromeric and telomeric regions tend to have reduced recombination (Begun et al. 2007), in mammals, telomeric regions tend to have higher than average recombination (Kong et al. 2002). The second observation is based on data showing coupled changes between ICE and levels of interference, however recent studies have identified pch2 (a pachytene checkpoint protein) as responsible for ICE by stalling the pachytene checkpoint in inversion heterozygotes likely allowing more double-strand breaks to be resolved as crossovers instead of non-crossovers (Joyce and McKim 2010). Despite renewed interest in inversions and their importance in speciation (see recent reviews: Brown and O'Neill 2010; Faria and Navarro 2010; Hoffmann and Rieseberg 2008), advances in fine-scale recombination mapping technology, and the observation of extensive variation in recombination rate within Drosophila (Cirulli et al. 2007; Kulathinal et al. 2008; Stevison and Noor 2010), there has been relatively little research done on the role of ICE in contributing to nucleotide variation. Here, I map recombination rates on the 2nd chromosome in single-generation hybrids (heterozygous for 3 inversions) and compare these measures to the fine-scale homokaryotype recombination map on the same chromosome recently published in D. persimilis (Stevison and Noor 2010, see Chapter 2). 52 3.2 Methods 3.2.1 Single generation estimate of inversion crossover rate in hybrids Genome lines of Drosophila pseudoobscura, Mesa-Verde, CO 2-25 (MV2-25, San Diego stock number #14011-0121.94, Richards et al. 2005) and D. persimilis, Mount Saint Helena 3 (MSH3, San Diego stock number #14011-0111.49, Clark et al. 2007), were crossed to generate heterozygous F1 hybrid females. These females were backcrossed to MV2-25 males to generate approximately 10,000 progeny to screen for crossovers along the XR inversion. Using microsatellite or indel markers at the breakpoints and center of the XR inversion (spanning 12.5 Mb), I tested for double crossovers in F1 hybrids between D. pseudoobscura and D. persimilis. Chromosome arm XR differs by a single inversion between D. pseudoobscura and D. persimilis, and the breakpoints of this inversion have been mapped (Bhutkar et al. 2008; Noor et al. 2007). The following markers were used to assay interspecies double crossover rate inside the inversion –DPS X063 (XR_group6: 12,588,339, center of inversion), Centromeric breakpoint, CBP (XR_group6: 6,219,093) and Telomeric breakpoint, TBP (XR_group8: 7,199,440). Primer sequences are available upon request. All individuals were genotyped at the center of the inversion and at least one of the breakpoint markers. Double crossovers were defined as individuals with an allele in the center of the inversion that was different from the alleles at the two breakpoints, indicating exchange within the inverted segment. 3.2.2 Population genetics analysis of interspecies migration rates within inversion Samples: Twelve stocks from D. pseudoobscura, eight stocks of D. persimilis, and three stocks of D. miranda were assayed (Stock names: D. pseudoobscura: Mesa Verde, CO 2-25 [genome line], Mesa Verde, CO 17 [collected 2001], Flagstaff, AZ 16 53 [inbred 15 generations (collected 1997)], Flagstaff 1993 [cross of four lines collected 1993]; Mather, CA lines 17, 32, 48, and 52 [all collected 1997], Baja, Mexico 1 [collected 2001], Sonora, Mexico 3 [collected 2001]; Zapotitlan, Puebla, Mexico [collected 2000], American Fork Canyon 12; D. persimilis: Mt. St. Helena 3 [genome line, collected 1997], Mt. St. Helena 1993 [collected 1993], Mather, CA lines 37, 40, and G; Mt. St. Helena lines 1, 7, and 42; D. miranda: Mt. St. Helena lines 22 and 38 and Mather, CA 28). Sequencing: Seven markers were designed and sequenced in individuals from stocks listed above (Marker names: MidPoint, MP (XR_group6: 12,536,933), Telomeric Breakpoint, TBP (XR_group8: 7,199,934), Quarter 1, Q1 (XR_group6: 9,546,799), Quarter 2, Q2 (XR_group8: 2,402,917), Near-TBP (XR_group8: 6,429,839), XR6_2.5 (XR_group6: 2,493,745) and Far Out (XL_group1a: 4,181,765)). Markers were chosen based on the published D. pseudoobscura genome sequence assembly (Richards et al. 2005) to be ~750bp long within intergenic regions at varying distances along the XR chromosome arm. Each locus was amplified from genomic DNA template in 25 µL reaction volumes using a touch-down thermal cycling profile consisting of an initial denaturation (95°C for 2 min) followed by 12 cycles of a 95°C denaturation for 30 s, a 62°C annealing for 30 s reduced by 1°C/cycle, and a 72°C elongation for 1 min, followed by 25 cycles at an annealing temperature of 50°C, and a final elongation at 72°C for 2min. PCR products were cleaned using ExoSAP-IT (USB Corporation). Sequencing was performed at Duke University's IGSP core sequencing facility on Applied Biosystems 3730xl DNA Analyzer. DNA sequence chromatograms were aligned and edited in Lasergene SeqMan 7.0 (DNASTAR, Inc.). Custom Perl scripts were then used to export sequences and create consensus alignments for importing into other software platforms. 54 Migration estimates and other sequence analysis: In addition to the seven markers designed for sequencing, four markers were selected from GenBank to supplement additional analysis. Statistical analysis of each marker including FST, diversity within each species, and DXY was performed in DnaSp 5.0 (Rozas et al. 2003). Additionally, a coalescent-based migration rate for each marker was estimated using IM (Hey and Nielsen 2004). Sequences were processed to meet restrictions/ assumptions of IM by removing any gaps in the alignments and accounting for intragenic recombination for each marker as in Stevison and Kohn (2009). Input files were executed in IMa using 10 Metropolis-coupled chains with a 300 000-step burn-in followed by 50 million iterations of the Markov chain. Autocorrelation values, effective sample sizes (ESS), and inspection of parameter trend plots by eye indicated adequate convergence of the Markov chain (not shown). 3.2.3 High-throughput genotyping to analyze recombination rate changes throughout the genome outside inversions A subset of 480 individuals from the cross described in “Single generation estimate of inversion crossover rate in hybrids” were genotyped to assay recombination rate changes relative to the within species rate outside of the inversion due to the interchromosomal effect and at inversion boundaries. This cross design did not account for direction of the cross because a previous study in this system showed no difference in ICE between F1 hybrids backcrossed to either D. persimilis or D. pseudoobscura (OrtizBarrientos et al. 2006). DNA was isolated from the 480 backcross offspring individually at the Genomic Sciences Lab at North Carolina State University for subsequent genotyping with 96 single nucleotide polymorphisms (SNPs) (see methods and scripts described in Chapter 2: Stevison and Noor (2010). A subset of 42 markers corresponds to positions on the 55 second chromosome of the within-species recombination map generated previously for D. persimilis (Stevison and Noor 2010). These markers were designed to assess the pervasiveness of the interchromosomal effect on this chromosome due to inversion heterozygosity in the F1 females of this cross. This chromosome was targeted due to the fine-scale nature of the existing within-species map (markers apx. 150-200kb apart) relative to other chromosomes. Another subset of 48 markers was designed to further fine-map recombination reduction at inversion boundaries as in Kulathinal et al. (2009). These markers correspond to 2-3 Mb outside the breakpoints of each of the three major chromosomal inversions differentiating D. pseudoobscura and D. persimilis (six boundary regions total). Of these 48 markers designed for inversion boundaries, there were 7 on each XR boundary, 6 on each XL boundary, 6 on each Chromosome 2 (C2) boundary, 2 within the XR inversion (plus 2 additional markers designed as genotyping replicates at the center of the inversion, see below), 4 within the XL inversion and 4 within the C2 inversion. Finally, six markers were designed to duplicate the genotyping markers used in the inversion recombination survey of XR, so as to include this subset of individuals in the data for the original cross (See: “Single generation estimate of inversion crossover rate in hybrids” section above). SNP markers were screened in all offspring using the Illumina BeadXpress platform (Fan et al. 2003) (Illumina, Inc. San Diego, CA) at the Genomic Analysis Facility within the Duke University Center for Human Genome Variation. The output consisted of raw genotypes at all markers for all individuals. Eleven total markers were not useful for analysis for various reasons such as polymorphism within strains or monomorphism between strains. Further, eight individuals did not amplify at any of the markers and two individuals were removed from two different subsets due to greater 56 than 10% missing data in that subset of markers. One individual was removed from the C2 assembled map and another individual was removed from the XL boundary map prior to assembly. The raw data was processed to assess crossovers at each interval via scripts from Stevison and Noor (2010). For C2, a Kosambi cM/Mb value was calculated and compared to the published rate in D. persimilis (Stevison and Noor 2010) to calculate a fold change difference between the hybrid map and the pure species map (Figure 3.1). Previous studies comparing recombination rates between D. pseudoobscura and D. persimilis have noted very tight correspondence (Stevison and Noor 2010), therefore I limited my pure species comparison to the D. persimilis recombination rates. 3.3 Results 3.3.1 Single generation estimate of inversion crossover rate in hybrids Out of 9,739 individuals screened, 1 male sample was confirmed as a double recombinant across the XR inversion, with the D. pseudoobscura allele at the center of the inversion and the D. persimilis allele at both breakpoints. Confirmation consisted of first repeating the initial genotyping at all three markers spanning the XR inversion. Next, a gene conversion event was ruled out by genotyping the sample at a marker ~360kb from DPSX063, DPSX051 (chromosome scaffold XR_group6: position 12,953,124), much larger than gene conversion tract lengths in Drosophila (Hilliker et al. 1994), and confirming the D. pseudoobscura alleles extended to the adjacent marker. Finally, the sample was confirmed via Sanger sequencing at markers nearby the three genotyping markers, again showing a mismatch between allele at the center relative to the breakpoints. This finding corresponds to a rate of 0.01% or ~10-4 double crossingover across this 12.Mb inversion in a single generation, though this figure may 57 underestimate the total double crossover rate since only a single position within the inversion was surveyed. 3.3.2 Population genetics analysis of interspecies migration rates within inversion Sequencing seven loci together with four markers from GenBank yielded apx. 8 kb of sequence, 416 SNPs, 52 sites fixed between species, and 16 sites shared between species. Earlier studies have noted lower diversity within the inverted segments relative to collinear regions for D. persimilis, consistent with a loss of diversity with the fixation of the novel chromosome arrangement (Hey and Nielsen 2004; Machado et al. 2007). With the limited number of regions surveyed in the collinear region of XR (N=3, Table 3.1), I was unable to detect any significant difference in diversity between these regions and those within the inverted segment (N=5, Table 3.1) (p=0.83). Further, I followed up on previous work in this system, which has hinted at some increased gene flow near the center of the smaller 2nd chromosome inversion (not significant, Machado et al. 2007). Given this observation, I expect the relationship between gene flow and distance from breakpoint to be stronger inside the larger XR inversion. I tested for a decline in population differentiation with increasing distance from the inversion breakpoints, restricting the analysis to markers inside the inverted region (p=0.3; N=5; r2=0.341). I also looked for a decrease in divergence and did not observe a significant trend (p=0.38; N=5; r2=0.26). Conversely, I looked for an increase in estimated gene flow (Nm) and still did not observe a significant trend (p=0.2; N=5; r2=0.47). To further test for a positive relationship between gene flow and distance from the inversion breakpoints, I estimated a locus-specific migration parameter implemented in the software IM. Except one locus (XR6:2.5, outside inversion), all 58 markers had migration estimates not significantly different from zero. This result precluded the option of examining how variation in migration correlates to distance from inversion breakpoints. The single marker to show non-zero migration also has the highest level of detectable diversity within D. persimilis (see Table 3.1). Further, the locus-specific migration estimates of X009 and X010 corresponded with published migration estimates for these markers (Hey and Nielsen 2004). 59 60 In Near Far TBP (XR8:7.2) Near TBP (XR8:6.43) X0102 824 606 592 680 621 669 297 677 1798 612 638 Length 20 11 12 12 12 12 9 18 11 11 11 N 15 10 19 3 16 22 92 38 32 32 7 S 0.0024 0.0046 0.0071 0.0012 0.0077 0.0107 0.0685 0.0146 0.0044 0.0153 0.0047 π D. pseudoobscura 14 7 7 8 7 7 5 14 11 8 8 N 9 11 5 2 11 6 17 36 10 19 4 S 0.0016 0.0061 0.0032 0.0007 0.0064 0.0030 0.0229 0.0158 0.0013 0.0104 0.0024 π D. persimilis 0.010 0.017 0.009 0.006 0.017 0.018 0.097 0.027 0.007 0.025 0.014 DXY 0.50 0.45 0.39 0.48 0.18 0.33 0.43 0.38 0.15 0.32 0.52 FST 0.60% 1.49% 0.00% 0.44% 0.81% 0.75% 2.69% 0.15% 0.39% 0.82% 0.63% Fixed 0.00% 0.00% 0.00% 0.00% 0.48% 0.00% 0.67% 0.89% 0.06% 0.65% 0.00% Shared 0.25 0.31 0.39 0.27 1.15 0.5 0.33 0.42 1.43 0.53 0.23 Nm 0.0076 0.0076 0.0076 0.0076 0.0076 0.0076 0.0076 0.0076 0.0076 9.3068 0.0076 m1 0.0056 0.0056 0.0169 0.0056 0.0056 0.0056 0.0056 0.0056 0.0056 0.0395 0.0056 m2 IM Results 2MACHADO, 1WANG, R. L., J. WAKELEY and J. HEY, 1997 Gene flow and natural selection in the origin of Drosophila pseudoobscura and close relatives. Genetics 147: 1091-1106. C. A., R. M. KLIMAN, J. A. MARKERT and J. HEY, 2002 Inferring the history of speciation from multilocus DNA sequence data: The case of Drosophila pseudoobscura and close relatives. Molecular Biology and Evolution 19: 472-488. 3MACHADO, C. A., T. S. HASELKORN and M. A. F. NOOR, 2007 Evaluation of the genomic extent of effects of fixed inversion differences on intraspecific variation and interspecific gene flow in Drosophila pseudoobscura and D. persimilis. Genetics 175: 1289-1306. 3.5 0.77 0 4.42 In Q3 (XR8:2.4) 3.97 0.78 6.4 In X_21023 1.22 In Near X0092 2.1 MP (XR6:12.54) Near Hsp821 3.67 In Far XR6:2.5 7.69 Q1 (XR6:9.55) Far Far Out (XL1a:4.2) Location Distance to break point (Mb) Table 3.1. Summary of diversity and divergence calculations for each of the 11 markers along the XR chromosome arm. 3.3.3 Analysis of recombination rate reduction in single-generation hybrids at inversion boundaries Genotypes of markers at inversion boundaries and the markers within each inversion were compared for each individual to determine how far complete suppression of recombination extends outside the inversion breakpoints. At the C2 centromeric boundary, no recombinants were recovered as far as 2.4 Mb, and as far as 2.56 Mb on the telomeric boundary. At the XR centromeric boundary, no recombinants were recovered as far as 2.44 Mb and on the telomeric boundary, no recombinants were recovered as far as 2.79 Mb. At the XL boundaries, there was an absence of observed recombination as far 3 Mb on the centromeric side of the inversion and no recombinants were recovered on the telomeric side of the inversion as far as 2.73 Mb. The range between minimum recombination suppression and the maximums presented above are summarized in Table 3.2 and compared to previous results from Kulathinal et al. (2009). The results from the current study used more markers to fine map both the lower and upper limit of this range. The third column represents the narrowest range as summarized from both studies. Table 3.2. Summary of recombination suppression at inversion boundaries as compared to Kulathinal et al. 2009. The refined range combines the results of both studies. Kulathinal et al. 2009 Current study Refined range Centromeric side XL 0 - 2.84 Mb 2.75 - 3 Mb 2.75 – 2.84 Mb Telomeric side XL 0.4 - 2.8 Mb 2.73 Mb + 2.73 – 2.8 Mb Centromeric side XR 0 - 3.35 Mb 2.25 - 2.51 Mb 2.25 - 2.51 Mb Telomeric side XR 2.1 - 2.8 Mb 2.79 Mb + 2.79 – 2.8 Mb Centromeric side C2 0 - 4.55 Mb 2.33 - 2.45 Mb 2.33 - 2.45 Mb Telomeric side C2 0 - 2.77 Mb 2.27 - 2.51 Mb 2.27 - 2.51 Mb 61 3.3.4 Analysis of interchromosomal effect using high-throughput genotyping The recombination rate of hybrids along C2 ranged from 0 (within inverted regions) to 18.92 cM/Mb with an average interval size of 500kb. The comparable recombination rate in the same intervals for pure species ranged from 0.15 - 20.54 cM/Mb. Although the range of recombination rate values was similar, the distribution of these events was significantly different in the hybrids in 31 out 47 intervals. The ratio of cM/Mb in hybrids to cM/Mb in pure species (D. persimilis) was the calculated fold change, which ranged from 0 to 9.14. The fold change is highest (>2 fold) in the first 4 Mb and the last 5.5 Mb of the chromosome. Figure 3.1 shows a plot of the log (base 10)normalized fold-change of recombination rate in hybrids relative to D. persimilis. Because previous studies on ICE have also observed large effects in distal and proximal regions (which normally have low recombination in Drosophila), I tested for an association between recombination rate with D. persimilis and the fold change observed in hybrids. I found that regions with low recombination rate in D. persimilis were among those with the highest log fold change in hybrids (p<0.0001; r2=0.373; N=36), suggesting these regions are most susceptible to changes in recombination. I excluded regions with no observed recombination in hybrids. Next, I tested for an association of nucleotide divergence between species and hybrid recombination rate. I calculated divergence between D. pseudoobscura and D. persimilis at 4-fold degenerate 3rd position codons (C4). I further calculated a corresponding diversity measure along the same regions between two sequences of D. pseudoobscura at C4 sites, to correct for variation in levels of diversity contributing to variation in divergence. In a multiple regression analysis with log-fold change as the response variable and the diversity and divergence calculations as predictor variables, I 62 showed that divergence was a significant predictor of variation in log fold change (p=0.0088; N=31), but that intraspecies diversity did not explain a significant portion of the variance in log fold change (p=0.0831). This result suggests that the correlation observed between divergence and log fold change was not driven by lower segregating ancestral diversity in chromosomal ends (Noor and Bennett 2009). In the above analysis, I excluded regions with no recombination in hybrids, one interval with no C4 bases for the diversity measure, and the last 4 intervals of the chromosome which are misassembled in the D. pseudoobscura genome making it difficult to obtain reliable divergence estimates. When I further reduced the analysis to exclude chromosome ends, the results remained the same with a significant result for divergence (p=0.0049; N=18), but not for diversity (p=0.0569), despite the loss of variation in the log-fold change variation as ICE affects the ends most strongly. Figure 3.1. Inter-chromosomal Effect. A plot of the log (base 10) fold change difference in recombination rates along the 2nd chromosome between the published D. persimilis map and the map of recombination rate in between species hybrids. 63 3.4 Discussion 3.4.1 Exchange across XR inversion detected in one generation, however prolonged evidence of exchange not observed The observed rate of exchange via double crossovers along the largest inversion (XR inversion, 12.5 Mb) differentiating D. pseudoobscura and D. persimilis was empirically determined to be of the same order of magnitude as rates of exchange across inversions segregating within species (Ishii and Charlesworth 1977; Levine 1956), but much lower than recombination in collinear regions greater than ~2.5Mb outside of the inversion or within the same region in homokaryotypes (Kulathinal et al. 2008; Stevison and Noor 2010). My estimate of 10-4 is much higher than estimates previously used in models examining the role of inversions in persistence of species with gene flow (Feder and Nosil 2009; Kirkpatrick and Barton 2006; Navarro et al. 1997). Here, I estimate much higher rates of exchange between inversions empirically, and yet I observe no evidence of prolonged interspecies gene flow within the inversion using population level sequencing at evenly spaced markers within the inverted segment. My results suggest that models determining the role of inversions in maintaining species should consider independently the expected frequency of heterokaryotes as a function of the expected level of migration between species and any potential fitness consequences that prevent the success of progeny that carry exchange products from propagating in the next generation (see also Faria and Navarro 2010; Jackson in press). Further, because the accumulation of genetic incompatibilities occurs gradually over time, selection does not need to be very strong in early generations when migration is nearly zero. However, upon secondary contact, higher rates of migration, and thus selection are likely, indicating that these parameters should be considered non-independent. These three factors – the frequency of heterokaryotypes/migrants, the fitness consequences of 64 exchange in heterokaryotypes, and the rate at which genetic incompatibilities accumulate – are likely the most important factors contributing to the absence of longterm exchange detected between D. pseudoobscura and D. persimilis along the inverted region on the XR chromosome arm. 3.4.2 Recombination suppression extends 2.5-3 Mb at inversion boundaries One of the more puzzling phenomena associated with inversion heterozygosity is the extension of recombination suppression beyond inversions, outside the breakpoints. Here, I refined the known ranges of recombination suppression at inversion boundaries for the three major inversions differentiating D. pseudoobscura and D. persimilis from an average of 2.76 Mb between markers observing no recombination and the first marker to detect any recombination (Kulathinal et al. 2009) to an average of 132 kb between markers (Table 3.2). The results in Table 3.2 clearly show that the level of recombination suppression due to inversion heterozygosity cumulatively extends 2.5-3 Mb beyond the breakpoints of each inversion, adding 5-6 Mb to the total expected size of the inversion itself. Because recombination is not suppressed in these boundary regions in the absence of an inversion (Stevison and Noor 2010), it is not immediately obvious how recombination suppression would extend this far outside of the inversion loop. Previous studies have accredited this result to the difficulty of the synaptonemal complex from forming at inversion boundaries (Roberts 1976). Another possibility is that because inversion boundaries tend to accumulate in unstable/repetitive regions of chromosomes (Andolfatto et al. 1999; Caceres et al. 1999; Ranz et al. 2007), inversion boundaries serve to recruit recombination suppression over the length of the inversion, triggered perhaps by heterozygosity immediately outside the inversion boundary. If inversion boundaries 65 were indeed the molecular trigger for reduced recombination inside inverted regions, it would follow that these regions are also susceptible to recombination suppression. 3.4.3 Interchromosomal effect highest in regions of low recombination along chromosome 2 My study investigates both ‘intra’- and ‘interchromosomal effect’ using ~50 markers along chromosome 2. Because I analyze hybrids between D. pseudoobscura and D. persimilis, the ICE I observe is due to heterozygosity at three inversions – two on the X and one on the 2nd (focal chromosome). Previous studies have observed ICE yielding differences in recombination rates as high as 250% higher than standard map distance (Schultz and Redfield 1951), whereas I observed greater than 800% higher recombination rates with an average of 224%. The higher proportional increase in recombination rate in this study relative to earlier studies is likely due to heterozygosity for more inversions (other studies observe ICE with only 1-2 heterozygous inversions) and/or the scale at which I analyzed recombination in hybrids. Averaging over larger intervals may have masked the effects of smaller regions with very strong ICE in previous studies. However, the average percent change in the first and last 5 Mb is ~250% higher recombination in inversion heterozygotes, suggesting that the size of intervals assayed in the current study is most likely responsible for the higher observed change in recombination rates. Similar to previously published results, I found that interchromosomal effect (ICE) was strongest in regions distal and proximal on chromosome 2. Because these regions are known to have lower recombination rates overall, I showed that, irrespective of chromosome position, regions of low recombination were most susceptible to ICE, supporting that these regions may be less resilient to disruptions in recombination rate. 66 Finally, I examined how changes in recombination rates in hybrids correspond to patterns of gene flow and differentiation, as an extension of how inverted regions (which have low recombination in hybrids) often bear high divergence between species. To test for this, I calculated divergence between D. pseudoobscura and D. persimilis and found a strong correlation with log-fold change of recombination in hybrids relative to pure species. When I corrected for regions of low within species diversity (e.g., chromosome ends), I still observed a significant association, showing that low diversity at chromosomal ends (where ICE is strongest) does not influence the relationship between divergence and recombination rate changes in hybrids. Hence, it appears that the interchromosomal effect may actually increase interspecies gene flow outside of the inverted regions- a factor not considered previously in the effects of inversions on species persistence. In the past ten years, interest in the role of chromosomal inversions in speciation has been rekindled based on the inherent properties of inversions to restrict recombination in heterokaryotypes. This reduced recombination protects existing adaptive complexes and genetic incompatibilities and allows for the accumulation of additional incompatibilities between species. The research presented here confirms that some features found in inversions segregating within species apply to interspecies inversion differences, but also identifies potential differences in long-term effects of inversions differentiating species and hypothesizes their causes. Further research should explore some of the patterns suggested here, particularly considering 1) what factors may maintain divergence in inverted regions between species despite detectable exchange due to double crossover, 2) the relationship between size of recombination intervals 67 assayed on the intensity of inter- and intrachromosomal effects (ICE) observed, and 3) how much ICE increases interspecies gene exchange outside inverted regions. 68 4. Male-mediated effects on female recombination 4.1 Introduction One of the major evolutionary advantages to meiotic recombination, or ‘crossing over’, is the combinatorial effect of shuffling beneficial alleles onto a common genetic background (Crow and Kimura 1970; Fisher 1930), favoring their fixation, and deleterious alleles onto separate genetic backgrounds, facilitating their elimination (Felsenstein 1974; Muller 1964). Mechanistically, meiosis requires crossing over, which is proposed to stabilize chromosomes in metaphase (Kucherlapati and Smith 1988). However, there is an upper limit to the level of crossing over that is favorable. For example, too much recombination can lead to non-disjunction in meiosis and therefore be unfavorable due to offspring with too many or fewer than necessary chromosomes (Roeder 1997). There is also an evolutionary upper limit to how much recombination is selectively advantageous. Although recombination can combine the advantages of two beneficial mutations by putting them on a common genetic background, it can also break them apart. This separation of selectively advantageous alleles can cause ‘interference’ between these alleles, reducing the selective advantage of the allelic combinations and preventing the population from reaching the optimal genotypic combinations (Comeron et al. 2008; Hill and Robertson 1966) and see section “Evolutionary Effects of Recombination on Structure of the Genome” of Chapter 1. Despite the evolutionary importance of recombination, recombination rates vary drastically between species, ranging from no recombination in asexual systems to very high levels of allelic exchange across the genomes of some sexual systems. Further, within any specific genome, meiotic recombination rates are also variable, such that some regions have higher rates of crossing over than others. Additionally, recombination 69 within the genome can vary based on genotype. There are also condition-dependent features that lead to changes in recombination within an individual’s lifetime, such as variation with maternal age whereby eggs laid later in life exhibit higher numbers of crossovers than eggs laid when females are younger (Bridges 1927; Bridges 1929). Attempts to examine plasticity in recombination rates have demonstrated that factors such as temperature (Plough 1917; Plough 1921), nutrition (Neel 1941), age of mating (Redfield 1966), and the number of matings (Priest et al. 2007) also affect recombination rates (for full summary, see section “Variation in Crossing Over” of Chapter 1). One of the more surprising patterns of plasticity in recombination rates is the change elicited in response to mating frequency (Priest et al. 2007; Redfield 1966). An early study on the effects of age on recombination rates focused on the effects of age at the time of mating, comparing recombination rates of females mated at different time points after emergence (Redfield 1966). These results indicated that recombination rates shortly after mating are generally high for up to 2 days (Redfield 1966). Next, the results showed a rapid decline 3-4 days post mating, followed by a recovery period of at least 6-8 days (Figure 4.1). Female age at mating significantly influenced both the time for recombination rates to decline and the recovery period, such that both were longer for females mated on emergence than for older females. These results were the first demonstration that the act of mating may lead to variation in recombination rates. 70 Figure 4.1 Results of Redfield experiment showing changes in postmating recombination rates between markers Stubble (Sb–3R:11.9Mb) and scarlet (st–3L:16.5Mb) on the 3rd chromosome (Modified from Redfield 1966). Priest et al. (2007) followed Redfield’s work with a series of experiments to test the effects of maternal age and number of matings on recombination rate of offspring collected. They confirmed that maternal age impacts recombination rate. Furthermore, they demonstrated that females mated twice (once early and once late) showed no difference in recombination rate from females mated late only, with eggs for both females collected at the same age. This observation suggests that a twice-mated female would exhibit patterns of gametic recombination resembling the patterns in Figure 4.1. Though this result suggested that mating history does not affect recombination rate, they then manipulated the number of matings, where females experienced low, medium or high levels of mating, and compared recombination rate based on levels of mating. The offspring of females in the high and medium treatments did not exhibit significantly different levels of recombination. However, recombination rates observed in both of these treatments differed significantly from the recombination rate of offspring from the 71 females in the low mating treatment. The combined conclusion from these experiments is that mating itself alters the fraction of recombinant progeny produced by the female. This effect of mating on recombination rates could result from the stress of mating on the female in a manner analogous to temperature- or nutrition-based stress, and/or it could indicate that the male is inducing a physiological change in the female, resulting in a short-term change in the proportion of recombinant offspring. To distinguish between the possible causes for differences in recombination rates in response to mating, I present here a test for male-mediated effects on recombination. Given that female meiosis is incomplete at mating, and males induce a suite of known phenotypic effects in females with whom they mate, it is biologically plausible that males could alter female recombination. My experimental test assayed the putative variation in this novel trait among males in two ways – (1) by altering male rearing environments to induce potential changes that vary the degree of male-mediated effects and (2) by examining natural standing genetic variation among males in their ability to alter female recombination rates. Induced differences among males were imposed by altering male rearing temperature and via treatment with Juvenile Hormone. Temperature has been demonstrated to play a major role in physiology (Angilletta et al. 2002; Foguera et al. 2007), circadian rhythms, (Busza et al. 2007), behavior (Herrel et al. 2007), and recombination rates, with changes in recombination rates observed at both high and low temperature in several systems, including Drosophila (Plough 1917; Plough 1921). Juvenile Hormone (JH) affects many aspects of insect development and physiology. Levels of JH trigger transitions between larval stages in insects along with multiple aspects of reproduction, stress, and immunity affected in the adult stage. Specifically, JH 72 regulates protein synthesis in the male accessory gland organ, and oocyte maturation and behavior in the female (Flatt and Kawecki 2007). Both sources of environmental variation could be of evolutionary importance because, unlike other factors known to influence recombination rates, the environmental factor in this scenario would not directly affect the individual (i.e. the female) that exhibits the variation. Thus, if environmental manipulation of the male leads to observable differences in female recombination rates, I can isolate the possible cause of this variation. Further, to examine variation in standing genetic variation, stocks collected from Zimbabwe were used due to their greater genetic variability as compared to stocks collected from the rest of the world (Andolfatto and Przeworski 2001; Begun and Aquadro 1993). Genetic variation in male ability to alter the proportion of recombinant progeny could suggest a link between this trait and male reproductive success, raising the possibility that this trait could be influenced by sexual selection. Lower recombination rates in most Drosophila males than females is consistent with the Haldane-Huxley rule, where, when one sex has no crossing over, that sex is often the heterogametic sex (Haldane 1922; Huxley 1928), see section “Unique features of recombination in Drosophila” of Chapter 1. Because natural selection requires heritable phenotypic variation and females are the only sex undergoing recombination, recombination rates would evolve only in response to associated fitness variation in females. Selection could, however, favor the spread of a trait whereby males could indirectly influence their proportion of recombinant progeny. Due to the evolutionary significance of a male-mediated affect on female recombination rates, it is worth noting that, although crossing over has already occurred at the time of mating, meiosis in females is incomplete (Page and Hawley 2003). The 73 haploid gametes have not yet formed, and none of the daughter cells have been lost due to absorption into the polar bodies (Roeder 1997). At the end of meiosis, 15 of 16 daughter cells become polar bodies (King 1970), which may lead to competition among developing ootids (perhaps based on chiasma frequency) to become the nucleus of the developing egg, altering the outcome of female meiosis (Ashburner and Wright 1980; Buckler et al. 1999; Zwick et al. 1999). Chiasma are retained throughout meiosis I into anaphase I as physical links between chromatids which underwent crossing over (Page and Hawley 2003). This Ootid Competition (OC) model is a unique form of female meiotic drive on the basis of chromosomal features such as chiasma frequency (Zwick et al. 1999), and would lead to with the cumulative affect over many rounds of meiosis of altering the frequency of recombination in the offspring. Therefore one of the potential mechanisms whereby males could elicit the effect proposed here is by manipulation of competition among individual ootids in developing oocytes. Further, experimental evidence of male-mediated influences on rates of non-disjunction in females, demonstrates that males can influence the outcome of meiosis in females, perhaps via influencing this meiotic drive process in females (Gilliland 2003). In many insect species, several male mediated factors have been shown to have strong antagonistic effects on both female behavior and physiology owing to sexual selection. Male seminal fluid proteins mediate some of these traits. These proteins are transferred to the female during mating to elicit a variety of responses that increase male reproductive success, often at the cost of female fitness. For example, some seminal proteins yield drastic effects in females including decreased lifespan, altered receptivity to mating, and changes in oogenesis as well as egg-laying rates (Ram and Wolfner 2007; Wolfner 2002). To date, hundreds of male seminal proteins have been identified, but 74 only some have known functions. Regardless, the possibility exists that male-derived substances act to influence female reproductive mechanisms, potentially including the rate of recombination among the progeny. 4.2 Methods Cross design for both experiments are outlined in Figure 4.2. For experiment 1, stocks of Drosophila melanogaster with homozygous lethal dominant phenotypic mutations Stubble (Sb–3R:11.9Mb) and Glued (Gl–3L:13.9Mb) were maintained in the heterozygous condition using the 3rd chromosome balancer TM6B (developed via stocks obtained from the Model Systems Genomic group at Duke University). Virgin females of this stock were crossed to males from the stock OregonR (wild type) to yield genetically identical female replicates heterozygous for the visible markers (Sb-Gl). These females were used for subsequent mating to either control or treated males from the wild type stock. To vary male rearing temperature, I maintained control and treatment stocks of wild type males at 21°C and 25°C, respectively. Virgin males collected from the treatment group were stored at 25°C prior to mating, while control males were reared and stored at the control temperature. To treat males with JH, the synthetic analog, methoprene (JHa), was suspended in 95% ethanol at a concentration of 2 µg/ml. The JHa solution was then added to standard fly media in a concentration of 1.04 µl/ml as described by previous experimental manipulations to fruit flies by application of JHa to food (Flatt and Kawecki 2007; Flatt et al. 2005). At this concentration, changes to males included decreased courtship display and mating success. Further, the stocks treated with JHa showed highly reduced rates of eclosion relative to control stocks. Care was taken to limit variation in rearing density, which may affect rates of JHa uptake, by only allowing ~575 15 females to lay eggs for no longer than 1 week. Control males for this treatment group were reared on food prepared the same way with 95% ethanol substituted for the JHa solution. To remove residual hormone on the exterior of male flies, virgin males collected from this treatment group were stored on normal media prior to mating. This precaution ensured that any effect in females was due to male treatment and not residual hormone that rubbed off of the male during mating. All other conditions between control and treated males were identical. For experiment 2, strains of D. melanogaster from Zimbabwe, Africa used were Zim30, Zim11, Zim152, Zim6, Zim42, and Zim44 (the latter five provided courtesy of C. Aquadro). Additionally, the strain OregonR was used to increase the variance in genetic background among males. Virgin females from the stock Zim11 were crossed to males from the stock Zim152 to generate genetically identical female replicates heterozygous for zim11 and zim152 alleles. These females were used for subsequent mating to the remaining strains. Stocks used to collect males and females were stored in identical environmental conditions. For both experiments, males and females were collected within eight hours of eclosion and held virgin for four days. Males were isolated 24 hours prior to mating to avoid crowding, which has been previously observed to influence mating rates (Noor 1997). Males and females were singly paired in vials and observed for a period of 60-90 minutes. To control for any effect of mating frequency on recombination, males were removed once mating was complete to prevent remating (Priest et al. 2007). To control for age effects, mated females were transferred every two days up to eight days postmating to ensure offspring in each vial were the result of eggs laid within a specific period of time after mating (Redfield 1966). Based on a pilot experiment (see Appendix 76 A), I aimed for a target sample size of 3,000 offspring per male parent in order to have enough power to detect a statistically significant difference in recombination rates. Figure 4.2. Experimental Design. (A) Experiment 1: Females with homozygous lethal dominant mutants were crossed to wild type males to produce heterozygous females. Genetic replicate heterozygous females were then crossed to either control or treated males from two treatment groups: temperature (25°°) or Juvenile Hormone (JHa). (B) Experiment 2: Inbred lines were crossed to produce heterozygous females. Genetic replicate heterozygous females were then crossed to 1 of 5 males from different genetic backgrounds. For both experiments, proportion of recombinant offspring were compared based on the identity of the male parent. 4.2.1 Experiment 1 – Effect of male treatment on female recombination rate To assay recombination rate, progeny were visually assayed for the presence/ absence of each visible marker. Individuals that were either wild type or mutant for both alleles were counted as parental, and individuals mutant for one allele and wild type for the other were counted as recombinant. The final dataset was analyzed using JMP v8.0.2 statistical analysis software. I conducted a logistic regression to test the effects of day collected relative to mating and paternal treatment, and their interaction term on the binary response variable of whether an individual was recombinant or non-recombinant. 77 4.2.2 Experiment 2 – Effect of male genetic background on female recombination rate Rather than use phenotypic markers, which can have segregation distortion due to viability differences in different genetic backgrounds (Mark and Zimmering 1977), I used molecular markers to assay recombination rate. The microsatellite markers X3821715gtA (X:3,856,931), DMC30B8a (X:2,111,512), dmu14395 (3L:6,691,165), and AC004658 (3L:3,484,107) were used to distinguish alleles inherited from Zim11 versus Zim152. Primers for AC004658 and dmu14395 were previously published, except a modified reverse for dmu14395: 5'-CGTACTTGTATTAAGTCGAGCG-3' was used (Colson et al. 1999). DNA was isolated according to Gloor and Engels (1992) and amplified via touchdown cycle according to Palumbi (1996). To minimize reagent use and time, markers were multiplexed into single PCRs with 2 primer pairs per reaction. PCR products were separated by size on LiCor 4300 genotypers on 6% acrylamide gels. Individuals were scored as recombinant if one allele on a chromosome was derived from Zim11 and the other from Zim152. Results were fitted to a logistic regression model with the following independent variables: days post-mating (D), paternal genetic background (P), and genomic position (G). Rijkl = µ + Pi + Gj + Dk + PGij + PDik + GDjk + PGDijk+ εl, for i = 1..5; j = 1..2; k = 1..4; l = 1..30,553 Genomic position was treated as a repeated measure effect by incorporating a repeated statement in SAS (R-sided random statement in Proc Glimmix) for individual because each individual was assayed for two pairs of markers. All other data were analyzed using JMP v8.0.2. 78 Late into the experiment, when I decided to assay recombination rate in a third region of the genome to determine if recombination rate differences were a global phenomenon, I encountered difficulty in finding markers that distinguished zim11 and zim152, particularly due to higher levels of heterozygosity of the 2nd chromosome within each stock. On the 3rd and X chromosomes, both of these parental strains were homozygous; however, I found that one or both strains were often heterozygous on the 2nd chromosome. Heterozygosity within the parental strains introduced potential variation among the F1 females that must be taken into account. Therefore, I split the F1 females from this cross (see Figure 4.2B) into one of the four possible allelic combinations based on their genotype at the marker CAD (2L: 20,759,256), heterozygous in both zim11[A/A′] and zim152[a/a′]. This marker was chosen based on its putative linkage to segregating inversions within the two stocks. I then entered female genotype as a categorical parameter with four levels (F1 genotypes: Aa, Aa′, A′a, A′a′) into the final logistic regression model (see “Results” section for Experiment 2). 4.3 Results For experiment 1, neither male temperature treatment nor male hormone treatment had a significant effect on female recombination rate (p=0.674 [temperature], Figure 4.3A; p=0.355 [JHa], Figure 4.3B). However, when only considering recombination rate of offspring collected from days four through eight post-eclosion for the male hormone treatment, a weakly significant effect was observed (p=0.0401), suggesting a delayed effect of this hormone on altering recombination rate in females. Though this latter result is not significant after correction for multiple tests using sequential Bonferroni methods. 79 I also noted that males treated with Juvenile Hormone courted less (qualitative observation only) and had marginally significantly lower mating success than control males (p=0.0687; Z=-1.820, Wilcoxon signed rank test). This same trend was observed when comparing the mating success of males reared at higher temperatures relative to control males (p=0.0759; Z=-1.775, Wilcoxon signed rank test). Despite the lack of consistent male-mediated effects on female recombination rate between the two paternal treatments, both experiments revealed compelling variation in recombination rates based on maternal age via days post-mating (p=0.0019 [temperature], p=0.0036 [JHa]). This variation is consistent with the experimental results of Redfield (1966), who assayed nearly the same section of chromosome (Figure 4.1). Despite the sample size difference between the hormone treated and control group shown in Figure 4.3B, these numbers reflect variation in the total number of crosses set up, not differences in fecundity between treatment and control vials. Indeed, there was no difference between the numbers of progeny per vial between the control versus hormone treated crosses (p=0.81; F=0.0596). These lower sample sizes came from unanticipated limitations in percentage of virgin males eclosing from treated vials and percentage of successful matings in crosses involving hormone treated males. 80 Figure 4.3. Results for experiment 1. Results of (A) male temperature treatment and (B) male hormone treatment on female recombination rate between markers Stubble and Glued on the 3rd chromosome (~17cM apart). The numbers next to each data point indicate the sample size for each male treatment group per two-day collection period and the bars represent standard error values. For experiment 2, I detected a significant difference in the proportion of recombinant offspring based on paternal genetic background (p=0.0192; Figure 4.4C), which I initially interpreted as suggesting that males vary in their ability to influence recombination rate in their mates. Females showed up to 18% higher recombination rates based on their mated partner (zim6 vs. zim30 in Figure 4.4C). Model effects of days 81 post-mating and genomic position were not significant (p=0.579, p=0.992), nor were any interaction terms. The lack of an effect of days post-mating on recombination rates was surprising given previous literature on the topic (Redfield 1966); however, the Xchromosome data analyzed by itself does exhibit a significant day effect (Figure 4.4A). The full model most likely did not exhibit a significant day effect because the autosomal region analyzed did not show any variation in days post-mating (Figure 4.4B). I observed no difference in the number of progeny per vial based on paternal genetic background (ANOVA p=0.173; N=300), suggesting the effect observed is not due to differences in offspring viability. There was also no association between recombination rate and mating success among the crosses (p=0.82; r2=0.021; N=5). 82 Figure 4.4. Results from Experiment 2 prior to accounting for female variation. Recombination rate variation as a function of male parent for sexlinked markers (A) and autosomal markers (B) and a summary (C) showing the total results of the experiment. The numbers next to each data point refers to the number of offspring genotyped to obtain the mean and standard error values plotted. 83 Upon identifying genetic variation among the female parents in this cross, I genotyped the mothers to add their genotype as a parameter in the logistic regression model. Based on genotyping within each parent strain (zim152 and zim11), I determined that the 2nd chromosome was being inherited nearly as a single unit (data not shown). This indicated that variation among the females is most likely due to the presence of inversions, which are common in African lines of D. melanogaster. Therefore, I account for the known genetic variation using a single marker located centrally along chromosome 2. Incidentally, none of the females with genotype Aa was mated with OregonR males (Figure 4.5); therefore, to test more accurately for an interaction effect between male and female genotype, I excluded the ~4,000 offspring from this maternal genotype. The final logistic regression model, therefore, followed the equation below: Rijklmnp = µ + Mi + Pj + V[M,P]k + Sl + Gm + Dn + MPij + εp, for i = 1..4; j = 1..5; k = 1..200; l = 1..2; m = 1..2; n = 1..4; p = 1..22,364 where R represents the binary response variable of whether an individual was recombinant at the markers assayed or not, M represents maternal genotype, P represents paternal genotype, V represents F2 vial as a nested parameter within both maternal and paternal genotype, S represents sex of each individual, G represents genomic position assayed (X-linked markers or autosomal markers), and D represents days post-mating. Once this model was fitted to the data, only M, maternal genotype (p=0.0003) and V[M,P] (p=0.0253) were found to significantly influence offspring recombination rate Figure 4.5. 84 The model parameters sex, genomic position, days post-mating, and paternal genotype, as well as the interaction between female and male genotype did not significantly contribute to variation in offspring recombination rate. Figure 4.5. Results for Experiment 2 accounting for variation in female genotype plotting recombination rate of offspring based on the genotypes of both the male and female parents. Numbers indicate the sample size of offspring genotyped at each category on both the 3rd and X chromosomes. Female genotype was split based on the 4 possible allelic combinations at the marker CAD (heterozygous in both parental stocks) on chromosome 2L (20.76Mb). One notable difference between Figure 4.4 and Figure 4.5 is the differences in samples size among male genotypes in the latter figure. The uneven sample sizes of male genotypes when split by female genotypes were the result of females not being genotyped prior to mating. Therefore, the split of females by genotype into male treatment was random, and likely reflects the frequency of maternal alleles segregating within the parent stocks. 85 4.4 Discussion The experiments described here were designed to test for male mediated effects on recombination by examining both environmental and genetic variation among males in their ability to influence recombination rates in females. The results of Experiment 1 showed that while male temperature treatment does not influence offspring recombination rates, male hormone treatment may alter recombination rate of offspring from eggs laid 5-8 days post-mating (Figure 4.3). However, differences in mating success between control males and hormone treated males suggest differences in levels of promiscuity among the females who mated with males treated with JHa. If there is a physiological association between female promiscuity and recombination rate, then the variation in recombination rates seen in the treatment group relative to the control may not be directly attributable to the male treatment. Given that at the time of mating, the physical act of crossing over is completed, the only potential influence males can have on crossing over involves altering the competition between individual chromatids. The previously proposed OC model describes how competition might take place on the basis of chiasma frequency to become the nuclear component of the developing egg, or the pronucleus, via female meiotic drive (Zwick et al. 1999). Further, experimental evidence suggests that males may influence the outcome of this competition altering the rate of non-disjunction in females (Gilliland 2003). The experimental results here outline how males potentially affect the outcome of this competitive process, leading to variation in the frequency of chiasmate tetrads that ultimately become the pronucleus. The results of Experiment 2 showed that, once female genotype was considered, male genotype did not significantly influence differences in offspring recombination 86 rates (Figure 4.5). Complications with experiment 2 resulted in unanticipated genetic variation among the maternal parents in the cross. Although my attempt at accounting for this variation indicates that female genotype is a strong predictor of recombination rate, there is no guarantee that I have accounted for all possible genotypic variation among females. A lack of male genotype on female recombination rates suggests that, although there may be environmental variation among males in their ability to alter female recombination rate, there is no detectable genetic variation for this trait among the males assayed in this experiment. 4.4.1 Experiment 1 – Male hormone treatment alters female recombination rate The decreased recombination rate in the offspring of hormone treated males is delayed until four days after mating. This delayed effect is compatible with the length of time it takes for a oocyte to progress through the ovariole to oviposition (King 1970; pg. 50-54), and corresponds then to the event which happened four days prior (i.e. mating). Although there were differences in mating success among males treated with Juvenile Hormone and control males, this difference may not influence the difference observed in recombination rates between the two groups. First, if the effect were associated with promiscuity differences among the females, the conclusion from my experiment then, would be that the more promiscuous females who mated with the hormone treated males (males in poorer condition with lower mating success) have lower recombination rates. This interpretation is perhaps inconsistent with previous research showing higher rates of recombination in females experiencing higher levels of mating (Priest et al. 2007). Second, a similar difference in mating success was observed between temperature treated and control males, but without a corresponding change in 87 recombination rate, suggesting this difference does not contribute to differences in recombination rates. An influence of male condition on the outcome of crossing over is evolutionarily significant in the context of sexual selection, providing direct evidence of how environmental factors may influence sexually selected traits. Further, this result suggests that male condition, such as hormone treatment, may have novel repercussions for a female’s reproductive fitness by altering the recombination rate of her offspring. 4.4.2 Experiment 2 – Variation in male genotype does not contribute to variation in female recombination rate As seen in Figure 4.4, the initial results of experiment 2 (before accounting for variation of female genotype) highlighted strains zim6 and zim30 as most different in their effect on recombination rate in their mated partner. As an attempt to identify candidate proteins involved in eliciting the response, I performed comparative proteomic analysis on the male reproductive tissue of these two strains which identified three candidate proteins which are differentially expressed and transferred to females during mating (detailed results presented in Appendix B). However, the results accounting for female variation do not identify these two strains as having any difference in their effect on recombination (though recombination rates of females mated to zim6 vs. zim30 are significantly different within female genotype A′a′). The results of the experiments presented here show that environmentally induced changes in male condition influence their ability to alter the level of genetic diversity in their offspring. This result is the first demonstration of males exerting a direct effect on recombination rate in females with whom they mate. Because females often select mates based on their condition, females could select mates that generate fitness-enhancing recombination rates in their offspring, especially in populations experiencing parasitism 88 or fluctuating environments where novel genetic combinations are favored (Keightley and Otto 2006; Parsons 1988). Although a verbal mechanism has been previously described for such a male-driven meiotic drive, future work is required to explore the physiological means through which males mediate recombination rate differences. Further, future research should evaluate the role of this effect, if any, in sexual selection or sexual conflict over recombination rates within populations. 89 5. Major Conclusions A classic struggle in evolutionary biology has been coming to a consensus on what conditions are necessary for new species to arise and persist. In this struggle, some scientists have invoked the necessity for groups of organisms to be in isolation to grow apart (Mayr 1942; Mayr 1963). Darwin himself struggled with the idea that species could be kept distinct while being allowed to interbreed, as expressed below in the excerpt from the Origin of Species: “The view commonly entertained by naturalists is that species, when intercrossed, have been specially endowed with sterility, in order to prevent their confusion. This view certainly seems at first highly probable, for species living together could hardly have been kept distinct had they been capable of freely crossing.” Charles Darwin (1859), Ch. 9 Indeed, the widespread adherence to the biological species concept (BSC) aided the persistence of isolation as key to species maintenance through the modern synthesis (Hey 2006). However, modern genetics revealed the importance of migration between populations to best explore the adaptive landscape. This discordance was presented clearly by Dobzhansky in this excerpt from Genetics and the Origin of Species: “We are confronted with an apparent anitomy. Isolation prevents the breakdown of the existing gene systems, and hence precludes the formation of many worthless gene combinations that are doomed to destruction. Its role is therefore positive. But on the other hand, isolation debars the organism from exploring greater and greater portions of the field of gene combinations, and hence decreases the chance of the discovery of new and higher adaptive peaks. Isolation is a conservative factor that slows down the evolutionary process. The anitomy is removed if one realizes that an agent that is useful at one stage of the evolutionary process may be harmful at another stage…isolation is necessary but it must not come too early.” Theodosius Dobzhansky (1937), pg. 228-9 Although isolation allows locally adapted groups to form co-adapted gene complexes that function optimally in a given environment, novel alleles from 90 neighboring groups may provide new combinations that may have even higher adaptive values. This ‘anitomy’ was influential to Wright’s shifting balance theory (Wright 1982), presented around the same time period, which outlined a key role for migration between populations to the process of species formation. Although this particular idea has since come under scrutiny (Coyne et al. 2000), scientists held tightly to the idea that gene flow could play a role in speciation. Modern texts on the topic of speciation, although continuing to adhere strongly to the BSC, note the importance of understanding isolation barriers that specifically act at the forefront of species formation – those existing while species are in sympatry – as is presented in the following excerpt from Coyne and Orr’s Speciation book: “If one accepts some version of the biological species concept, then the central problem of speciation is understanding the origin of those isolating barriers that actually or potentially prevent gene flow in sympatry… First, one must determine which reproductive barriers were involved in the initial reduction of gene flow between populations…Second, one must understand which evolutionary forces produced these barriers.” Jerry Coyne and Allen Orr (2004), p. 57 More recently, through genetic research of hybrid zones analyzing many hybrids at multiple loci, we have learned that gene flow varies considerably across the genome (Arnold 1997; Arnold 2006; Stevison 2008). This variability suggests that there are many battles being fought between the build up of barriers to reproduction and opposing gene flow, with regional variation in the outcome of this battle across the genetic landscape. Further, this unexpected variability shifted thought about speciation as a simple battle between isolation and gene flow to realizing that the relative strength of these two forces, rather than the complete absence of gene flow, was critical to the outcome of the process of species formation. 91 Much work has been focused on the 2nd task suggested by Coyne and Orr (see excerpt above), to understanding those factors that produce barriers to reproduction, or more broadly which shape the variation in gene flow across the genome. Focusing particularly on regions where ‘speciation is winning’ (or regions of low gene flow between species), we expect to find genes responsible for speciation. This approach has been useful in identifying several loci important in the process of speciation between various species. However, much of the variation in estimates of gene flow can be attributed to the sensitivity of migration estimates to within species nucleotide diversity. With a tight association between recombination rates and genetic diversity (see below), regions of low within species recombination tend to have very low genetic diversity, leading to artificially low estimates of between species gene flow (such as near centromeres: Strasburg et al. 2009; Stump et al. 2005). However, this underestimate of gene flow is simply an artifact of low diversity and does not necessarily point to any specific sets of genes in these regions contributing to speciation (Noor and Bennett 2009). Genetic mapping studies attempting to dissect regions of low gene flow between species have often been unable to pinpoint genes conferring differences between species because these genes are frequently located within chromosomal inversions. Regions within chromosomal inversions show reduced gene flow/higher nucleotide divergence due to lower between species recombination (Kulathinal et al. 2009; Machado et al. 2007). Although these regions may indeed contain (and often do contain) isolation loci (Noor et al. 2001), mapping these loci has proven difficult due to the inherent low recombination along inverted segments. Collectively, this research has highlighted the 92 impact recombination rate differences both within and between species have on influencing genomic structure. My doctoral work, then, has been focused on understanding what shapes variation of recombination across the genome by focusing on: (1) what shapes variation in recombination rates within species, specifically understanding the ubiquitous correlations between recombination rates and both genetic diversity and GC content, and which evolutionary forces are more likely to contribute to these patterns (see Chapter 2: “Genetic and Evolution Correlates of Fine-Scale Recombination Rate Variation in Drosophila persimilis”), and (2) what shapes variation in recombination rates between species, particularly focusing on how chromosomal inversions influence changes in recombination rates in hybrids (see Chapter 3: “The effect of chromosomal inversions on recombination in hybrids between Drosophila pseudoobscura and D. persimilis”). Finally, I examined plasticity of recombination rates by examining potential novel forces that disrupt recombination patterns within individuals, mainly malemediated effects and interchromosomal effects (see Chapter 4: “ Male-mediated effects on female recombination”). Perhaps owing to the elusive properties of the mechanism of recombination, classical theory in population genetics began by assuming recombination rates were uniform throughout the genome, as is revealed in the classic paper by Hudson and Kaplan on the derivation of the four-gamete test, “Since recombination is assumed to occur uniformly across the genome…” (Hudson and Kaplan 1985). In 1992, a critical association was observed between recombination rate and genetic diversity, that would open a new line of research into understanding how selection versus neutral forces could predict changes in recombination rates (Begun and Aquadro 1992). 93 My research attributes the association between recombination and diversity mainly to selective processes, whereas both selective and neutral processes explain the association with GC content and recombination rates. Further, I was able to show that recombination rates tend to be conserved between species, by comparing my fine-scale recombination map in D. persimilis to a previously published recombination map in D. pseudoobscura (Kulathinal et al. 2008). Finally, I identified fine-scale structuring of recombination rates where recombination rates were highly correlated with one another on a 1 Mb scale. Still, the struggle to understand by such different conclusions come out of various studies on recombination rates continues, and while I provide additional empirical evidence on the topic, I think we have much to learn of what dictates changes in recombination rates. While chromosomal inversions were initially discovered and intended for use of their supposed neutral features, these structural variants were later discovered to be far from neutral. Indeed we now know that chromosomal inversions preserve co-adapted gene complexes by restricting recombination within inverted segments along genomes. Previous research on inversions segregating in Drosophila has focused on inversions segregating within populations, while overlooking those that segregate between species, which are key to our understanding of the process speciation. While recombination suppression at inversion boundaries and inside inversions tend to be similar for inversions segregating within and between species, my results suggest that the frequency of inversion heterozygotes dictates the strength of each of these patterns. Because the frequency of inversion heterozygotes is almost always lower between species, the impact of double crossovers within the inverted segment is much lower over long-term timescales between species. 94 Given that recombination rates can be significantly altered within an individual over the course of its lifetime was unexpected, it is equally surprising then that recombination rates are considerably variable across the genome and among individuals. It is not clear why scientists were open to drastic changes in recombination rates with simple changes in temperature and not with the idea that one chromosomal region could have differences in the relationship between map distance and physical distance. Even recently developed population genetics software testing for gene flow does not require any information on the recombination rate of the surrounding genomic region (but see: Becquet and Przeworski 2007; Hey and Nielsen 2004; Hey and Nielsen 2007). Finally, in an experiment to test for environmental variation in male ability to alter female recombination rates, I have shown that males influence the rate of recombination in the offspring of their mates perhaps through some sort of segregation distortion based on chiasma frequency. This kind of segregation distortion, while not a new concept, is particularly confusing when one considers the selective implications of males altering the recombination frequency of their progeny. Finally, I have shown that inversions further act to disrupt recombination rates elsewhere in the genome, affecting those regions with lowest recombination most, suggesting these regions to be more susceptible to changes in recombination rates. Further, I have shown that these disruptions lend themselves to correspondence with levels of interspecies divergence, suggesting that inversions not only shape patterns of gene flow and divergence within inverted segments, but that their effects on recombination disruptions in hybrids also contribute to variation in gene flow between species across the genome. 95 The research that I have done during my graduate work has shaped my own ideas about what are the central questions in speciation and the best approaches to answer these questions using available experimental and genetic data. For my post-doctoral research, I would like to investigate how changes in recombination profiles in interspecies hybrids (inferred using genomic sequences of genetically admixed individuals) relative to pure species contributes to nucleotide variability between species in a context that is independent of the presence of chromosome inversions. Furthermore, I plan to survey existing evidence from available research systems with genetic data where recombination rates and gene flow have been extensively documented (e.g. mice, sunflowers, tomatoes) to develop a generalized understanding of the role of between species recombination and hybridization. 96 Appendix A Pilot experiment for Chapter 4: Experiment 2 – Test if standing genetic variation of males affects the proportion of recombinant offspring The purpose of the pilot experiment was to determine the degree of natural variation in male ability to influence recombination rates in offspring. The use of genetic markers allowed the measurement of recombinational distances in offspring of different males. For this experiment, I used 5 lines of D. melanogaster stocks from Zimbabwe (4 additional lines courtesy of C. Aquadro lab). These stocks, though inbred, have heterozygosity rates higher than many other D. melanogaster stocks (Begun and Aquadro 1993). By screening microsatellites, I identified two markers that differentiate the two Zimbabwe stocks zim11 and zim152 (see below). These markers are the same X chromosome markers assayed in the full-scale experiment (see Chapter 4). Similar to the cross design for the full-scale experiment described within Chapter 4 (see Figure 4.3), a heterozygous female was generated by crossing 2 of the Zimbabwe lines: Zim 11 and Zim 152. Each heterozygous female was then mated to a single male from 1 of 3 Zimbabwe lines to generate an outbred generation with 3 categorical response variables that were compared. Original parents were not used in the subsequent cross to avoid inbreeding. In this pilot study, I genotyped 2521 individuals at both markers for the 3 Zimbabwe crosses: Zim 6, Zim 30, and Zim 42. 97 Figure A.1. Preliminary results for experiment 2. Contingency tables of the number of recombinant (R) and non-recombinant (NR) offspring of males from three different Zimbabwe stocks split into offspring from (A) first 1-4 days post-mating and (B) next 5-8 days post-mating. These were analyzed separately because maternal age affects recombination rate. I obtained a G-squared p-value for each comparison using a 2x3 contingency table of the counts of recombinant and non-recombinant offspring of males from each of the lines. The two comparisons were between recombination rates of the offspring generated 1-4 days post-mating and 5-8 days post-mating respectively. This separation was done to account for changes in recombination rate with maternal age (Bridges 1929). The p-value for the comparison for the first four days after mating was 0.07 (Figure A.1), whereas the p-value for the second four days after mating was 0.36 (Figure A.2). The marginally significant effect in the first four days after mating was evidence that there may be heterogeneity in recombination rates among crosses due to the genetic background of the father. Evaluating the 4-day age pools from these crosses in light of the data from the Redfield experiment suggested that 2 day pools of progeny would be more informative than four day pools in this pilot experiment, especially in the initial 4 day period post-mating (Redfield 1966). 98 A post-hoc power analysis indicated that based on the sample sizes in this preliminary data, I had as much as 60% power to detect a 2% recombination rate difference in the first set of day 1 – 4 offspring (Figure A.1A) (Faul et al. 2007). The difference in sample size and the smaller effect size (minimum effect size 1.2%) may explain the reduced power (only 26%) in the next four days (Figure A.1B). Increased egglaying rate soon after mating explained the difference in sample size between the 1-4 days post-mating and 5-8 days post-mating offspring pools. Published results from the Priest et al (2007) study measured around 4% difference in recombination rate between categories, whereas my analysis suggested effect sizes as small as 2%. To detect this small of an effect size, I would need approximately 3000 individuals for each cross (Faul et al. 2007). In addition to increasing the sample size of each cross, I decided to add an additional non-Zimbabwe D. melanogaster line into the experiment. Due to the larger expected divergence between lines, higher levels of genetic variation among males will potentially increase the effect size difference in recombination rates due to malemediated influences. Therefore, the pilot experiment aided the design of the full-scale experiment, where I transferred females every 2 days rather than every 4 days, and increased the divergence among the males by adding the OregonR males to the experiment, with a target sample size of ~3,000 offspring per male treatment group. 99 Appendix B Proteomic analysis to follow-up on Experiment 2 from Chapter 4: Test for quantitative differences between zim6 and zim30 in male reproductive proteins transferred to females during mating Based on the initial results of experiment 2 (Figure 4.4), a follow-up proteomic analysis was designed to test for differences between males with the largest effect size difference (zim6 vs. zim30). Male reproductive tissue, including both testes and accessory glands, was dissected from 4-day old virgin males in Ringer’s solution. Tissue was then transferred to ~50 uL of 50 mM Ammonium Bicarbonate with 0.1% Rapigest solution to preserve proteins and stored at -80° prior to protein analysis. Approximately 20-30 males (all collected on the same day) were pooled to generate enough protein for each sample. A total of three samples per Zimbabwe stock (six total) were generated for quantitative comparison of proteins with each stock and between the two stocks. The proteomic analysis took place at the Duke IGSP Proteomics Facility. Organs were lysed by sonication with a probe sonicator, followed by a Bradford assay to any remove debris. Approximately 10 µg of each of six samples was used for quantitation via Mass Spectrometry. The software Elucidator was then used to compare the three zim6 samples to the three zim30 samples, examining differences within the pooled samples to the differences between zim6 and zim30. Once a report of proteins differentially expressed between the two samples was received, I cross-referenced this list with a list of proteins known to be transferred to Drosophila melanogaster females during mating (Findlay et al. 2008). The results of this cross-referencing (Table B.1) revealed three significant candidate proteins that could contribute to the effect difference between these males on female recombination rate. 100 Figure B.1 shows graphically the variation within and between each sample for the candidate proteins identified in Table B.1. As discussed within Chapter 4, there is no difference in offspring recombination rate between zim6 and zim30, once female genotype is considered. Therefore, these proteomic results do not reflect differences in the phenotype originally intended. However, these results may be useful in highlighting male reproductive proteins that are commonly differentially expressed between closely related Drosophila males. 101 102 # of Peptides 1 4 2 1 1 11 7 1 3 3 1 1 1 10 9 2 1 1 12 1 13 1 2 2 9 2 2 1 Fold Change 14.473 5.291 2.129 1.856 -1.361 -1.507 -1.675 -2.137 -2.18 -3.122 -3.636 -119.514 2.789 -1.312 -1.395 -1.422 -1.516 4.272 -1.292 -1.451 -1.483 10.818 2.51 1.639 1.522 -1.274 -1.406 -1.692 Log (Intensity) 4.353*** 4.536*** 4.215*** 4.37*** 4.246*** 5.414*** 5.113*** 3.856*** 4.91*** 4.865*** 3.784*** 2.437*** 4.124*** 5.575*** 5.431*** 4.831*** 4.319*** 4.285** 5.506** 4.21** 5.639** 3.709* 4.474* 4.521* 5.49* 4.44* 4.869* 4.151* Intensity Zim30 5932.124 14922.787 11250.851 17201.17 20544.299 318473.375 167892.531 10481.583 120158.273 129338.656 11605.611 2990.176 7965.793 430387.188 318651.219 80778.953 25656.068 9318.477 364257.031 19536.213 530716.625 1555.772 18820.738 25918.08 250486.297 31115.043 87709.117 18435.418 Intensity Zim6 85855.281 78955.406 23947.736 31918.424 15100.542 211277.484 100208.977 4905.57 55106.445 41427.551 3192.271 25.019 22219.812 328065.031 228490.844 56825.219 16924.465 39807.637 281918.438 13466.661 357863.125 16830.027 47235.199 42470.418 381213.188 24418.756 62378.391 10895.5 2 1 2 2 2 2 2 2 2 2 2 1 2 2 Ref P<0.0001***; P<0.01**; P<0.05*; Bolded proteins identified as being transferred to females during mating (references: 1-Wolfner et al. 2006; 2-Findlay et al. 2008). 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"Studying Patterns of Recent Evolution at Synonymous Sites and Intronic Sites in Drosophila melanogaster." Journal of Molecular Evolution 70(1): 116-128. Zenger, K. R., L. M. McKenzie and D. W. Cooper (2002). "The first comprehensive genetic linkage map of a marsupial: The tammar wallaby (Macropus eugenii)." Genetics 162(1): 321-330. 120 Zwick, M. E., J. L. Salstrom and C. H. Langley (1999). "Genetic variation in rates of nondisjunction: Association of two naturally occurring polymorphisms in the chromokinesin nod with increased rates of nondisjunction in Drosophila melanogaster." Genetics 152(4): 1605-1614. 121 Biography I was born on July 10, 1982, in Dallas, Texas. I received a Bachelor of Science in Biophysics from Centenary College in Shreveport, LA. I obtained a Master of Arts in Ecology and Evolutionary Biology at Rice University, completing my thesis in May 2007. As a masters student, I served as a teaching assistant (TA) for Animal Behavior and Evolution of Genes and Genomes and gave guest lectures for both courses as well as the Evolution course. I continued on to Duke University where I have been pursuing a PhD in the Biology Department. As a PhD student, I served as a TA for the courses Genetics and Molecular Biology and Introduction Biology and guest lectured for the Evolutionary Genetics course. I have maintained memberships in the Society for the Study of Evolution and the Society for Molecular Biology and Evolution, as well as served as an associate member of the Faculty of 1000. Fellowships, Grants, and Awards: 2010 Katherine Goodman Stern Fellowship, Duke University 2010 Dean's Award for Excellence in Mentoring, Duke University 2009 NSF Doctoral Dissertation Improvement Grant ($15,000) 2009 Walter M. Fitch Student Award Finalist, SMBE Meeting, Iowa State University 2008 Duke Biology Dept. Grant-in-aid of Research ($1,000) 2007 Sigma Xi Grant-in-aid of Research ($400) 2007 Summer Institute in Statistical Genetics Scholarship, University of Washington ($2,500) 2006 Rice EEB Travel Grant, Rice University ($500) 2005 Bob E. & Lore Merten Watt Fellowship, Rice University ($1,000) 2005 Texas Rodeo and Livestock Fellowship, Houston, TX ($2,000) 2005 Outstanding Biophysics Student of the Year, Centenary College Publications: 1. L.S. Stevison and M.A.F. Noor. 2010. Genetic and evolutionary correlates of finescale recombination rate variation in Drosophila persimilis. Journal of Molecular Evolution. 71(5): 332-345. 2. Kulathinal R.J., L.S. Stevison, M.A.F. Noor. 2009. The genomics of speciation: Diversity, divergence and introgression on a genome-wide scale. PLoS Genetics. 5(7): e1000550. 3. Stevison, L.S. and M.H. Kohn. 2009. Divergence population genetic analysis of hybridization between rhesus and cynomolgus macaques. Molecular Ecology. 18(11): 2457-2475. 122 4. Fitzpatrick, C.F., Stevison, L.S. and M.A.F. Noor. 2009. Fine-scale crossover rate and interference along the XR-chromosome arm of Drosophila pseudoobscura. Drosophila Information Service, 92:27-29. 5. Stevison, L.S. and M.A.F. Noor. 2009. Recombination Rates in Drosophila. In: Encyclopedia of Life Sciences, Chichester: John Wiley & Sons, Ltd. 6. Stevison, L.S. and M.H. Kohn. 2008. Inferring genetic background of cynomolgus macaques (Macaca fascicularis). Journal of Medical Primatology. 37(6): 311-7. 7. Stevison, L.S. 2008. Hybridization and gene flow. Nature Education 1(1). 8. Bourgeois G., L.S. Stevison, D. Ortiz-Barrientos, and M.A.F. Noor. 2005. Characterization of UDP-glycosyltransferase genes in Drosophila pseudoobscura. Drosophila Information Service, 88: 12-15. 9. Stevison L.S., B.A. Counterman, and M.A.F. Noor. 2004. Molecular Evolution of Xlinked Accessory Gland Proteins in Drosophila pseudoobscura. Journal of Heredity 95 (2): 114-118. 123
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