G3: Genes|Genomes|Genetics Early Online, published on November 5, 2013 as doi:10.1534/g3.113.008714
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A high-density gene map of loblolly pine (Pinus taeda L.) based on
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exome sequence capture genotyping
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Leandro Gomide Neves1, John M. Davis1,2,3, William B. Barbazuk1,3,4§, Matias Kirst1,2,4§
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Graduate Program in Plant Molecular and Cellular Biology; 2School of Forest Resources
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and Conservation; 3University of Florida Genetics Institute; 4Department of Biology,
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University of Florida, Gainesville, Florida, 32611.
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E-mail addresses:
Corresponding author
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LGN: [email protected]
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JMD: [email protected]
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WBB: [email protected]
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MK: [email protected]
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© The Author(s) 2013. Published by the Genetics Society of America.
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Running title: Loblolly pine gene mapping
Keywords:
Loblolly pine
Exome sequencing
High-throughput genotyping
High-density genetic map
Copy number variation
Corresponding authors:
William B. Barbazuk:
2033 Mowry Road, room 407
Gainesville, FL, 32610
+1 (352) 273-8624
[email protected]
Matias Kirst
2033 Mowry Road, room 320
Gainesville, FL, 32610
+1 (352) 846-0900
[email protected]
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ABSTRACT
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Loblolly pine (Pinus taeda L.) is an important conifer for which a suite of genomic
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resources is being generated for comparative studies. Despite recent attempts to sequence
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the large genome conifers, their assembly and the positioning of genes remains largely
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incomplete. The inter-specific synteny in pines suggests that a gene-based map would be
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useful to support genome assemblies and analysis of conifers. To establish a reference
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gene-based genetic map we performed exome sequencing of 14,729 genes on a mapping
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population of 72 haploid samples generating a resource of 7,434 sequence variants
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segregating for 3,787 genes. Most markers are single nucleotide polymorphisms,
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although short insertions/deletions and multiple nucleotide polymorphisms were also
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utilized. Marker segregation in the population was used to generate a high-density, gene-
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based genetic map. 2,841 genes were mapped to pine’s 12 linkage groups with an average
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of one marker every 0.58 cM. Capture data was utilized to detect gene presence/absence
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variations and position 65 genes on the map. We compared the marker order of genes
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previously mapped in loblolly pine and found high agreement. We estimated that 4,123
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genes had enough sequencing depth for reliable detection of markers, suggesting a high
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marker conversation rate of 92% (3,787 / 4,123). This is possible because a significant
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portion of the gene is captured and sequenced, increasing the chances of identifying a
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polymorphic site for characterization and mapping. This sub-centiMorgan genetic map
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provides a valuable resource for gene positioning on chromosomes and guide for the
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assembly of a reference pine genome.
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INTRODUCTION
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Loblolly pine (Pinus taeda L.) covers 11.7 million hectares of natural and planted forests
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in the Southeastern United States and provides 58% of the U.S. and 16% of the world’s
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timber (WEAR and GREIS 2002). Loblolly pine is also an important species for
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comparative studies between gymnosperms and angiosperms, and genomic resources are
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becoming increasingly available to enable these studies (MACKAY et al. 2012). For
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instance, single-nucleotide polymorphisms (SNP) and microsatellites have been identified
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and applied to generate genetic maps (ELSIK and WILLIAMS 2001; ECKERT et al. 2009;
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ECHT et al. 2011), identify population genetic parameters and associations to phenotype
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(GONZALEZ-MARTINEZ et al. 2007; ECKERT et al. 2010; STEWART et al. 2012), and
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develop genomic selection prediction models (RESENDE et al. 2012). However, the
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number of available genetic markers remains small, particularly considering the large size
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of the loblolly pine genome.
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Advances in high-throughput DNA sequencing and genomic tools are making it
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possible to genotype large numbers of individuals by sequencing reduced representations
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of the genome (DAVEY et al. 2011). For example, targeted resequencing after in solution
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sequence capture (GNIRKE et al. 2009) has proven useful in variant detection. Using this
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method, probes complementary to the target regions of the genome are designed and
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hybridized to genomic DNA for sequence capture and subsequent sequencing. Sequence
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capture is being optimized for an increasing number of plant species (SAINTENAC et al.
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2011; BUNDOCK et al. 2012; ZHOU and HOLLIDAY 2012) including conifers (NEVES et al.
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2013). To evaluate the potential of targeted resequencing for genotyping in loblolly pine,
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we used sequence capture to detect polymorphisms based on the segregation of alleles in
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a recombining population.
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The analysis of a recombining population allows for the construction of genetic
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maps where markers are grouped and ordered based on observed recombination
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frequencies. When the markers are located within genic regions, the genetic map provides
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the relative position of genes in chromosomes (DROST et al. 2009; NEVES et al. 2011),
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which is particularly relevant for loblolly pine or other species without a reference
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sequence. High-density, gene-rich genetic maps are useful to examine genome synteny
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with other species (KRUTOVSKY et al. 2004); for detection of quantitative trait loci (QTL)
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and identification of candidate regulatory genes (BERNARDO 2008); and to support
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genome assembly (TUSKAN et al. 2006; SATO et al. 2012). Currently, the best resolved
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loblolly pine genetic map was reported by Eckert et al. (2010), which positioned 1,635
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genes, genotyped from a starting SNP panel of 7,535 markers. More recently, Echt et al.
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(2011) reported the identification of large numbers of microsatellites and their use to
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create a moderately dense map with 429 markers. A subset of 311 of these microsatellites
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was derived from cDNA and, therefore, reflects the position of the corresponding genes.
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The goal of this work was to genotype a loblolly pine mapping population using
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sequence capture to analyze the efficiency of this method for polymorphism detection,
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and to create a high-density, gene-rich genetic map with the markers obtained. The
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mapping population consisted of 72 haploid megagametophytes extracted from seeds of a
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single tree. In pine seeds, the haploid DNA from individual megagametophyte represents
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the product of recombination of the maternal genomic DNA. Sequence capture was
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performed in each haploid sample and 7,842 markers were detected that segregate in the
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population, representing 4,195 genes. The best marker for each gene was selected and
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2,843 genes were ordered in a genetic map, with an average of one marker every 0.58 cM.
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Sequencing depth data was utilized to detect 408 genes segregating for presence/absence
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variation of which 65 were ultimately mapped in the genetic map. The comparison of our
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genetic map with the previous map of Eckert et al. (2010) via 397 common genes showed
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high levels of agreement at the grouping and ordering level between the two maps.
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RESULTS
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Genotyping of a mapping population with sequence capture
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We used a multiplexed sequence capture method (NEVES et al. 2013) to characterize
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genetic variants in 14,729 genes. Seventy-two seeds were collected from a single female
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tree and the maternally inherited, haploid megagametophyte tissue was used for library
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preparation. The use of DNA from the seed megagametophyte provides an effective way
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to assess recombination frequency between genetic markers, without the need for crosses
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(ADAMS and JOLY 1980). The 72 haploid samples were captured in nine pools of eight
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megagametophytes each, and sequenced with a median depth of 2.8× across 54,773
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probes (average of 3.7 probes/genes). Of the 14,729 genes targeted, 6,283 (43%) were not
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captured and/or sequenced in any sample. This occurred either because these probes
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failed to hybridize to genomic DNA, or because the captured fragments were not sampled
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in the sequencing process due to the shallow sequencing depth utilized. Probes were
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designed primarily based on a transcriptome assembly, and in a previous analysis of
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probe performance (NEVES et al. 2013) we discovered that a significant number of them
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were complementary to the boundary of two exons, justifying the high failure rate and
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illustrating a limitation of designing probes for species with uncharacterized genomes.
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For the remainder 8,446 genes (57%), sequence capture was successful for at least one
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probe, for at least one individual. Of these, 4,123 (28%) were sequenced in 50 or more
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haploid samples of the population. If sequencing data complementary to a given probe
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was available for fewer than 50 samples, the locus was excluded from further analysis.
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Therefore, from the 14,729 genes initially considered for capture, 28% (4,123) were
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actually captured in enough individuals, and sufficient sequencing depth was generated to
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allow identification of segregating sequence variants.
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The analysis of the sequence data identified 7,434 markers segregating 1:1 in at least 50
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haploid samples of the population. These markers represented 3,787 genes (Table 1). The
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majority of these markers were SNPs (6,857), while short deletions, short insertions and
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multiple nucleotide polymorphisms accounted for 210, 208 and 159 markers, respectively.
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The average sequencing depth for each gene was utilized to identify possible
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presence/absence variants (PAVs) (SWANSON-WAGNER et al. 2010). The average
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sequencing depth at the gene level was used to compensate for variations that exist at the
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probe level and 408 putative PAVs segregating 1:1 in the mapping population were
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considered for mapping (Table 1). Altogether, 7,842 segregating markers were identified
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for 4,195 genes, with an average of 2 markers per gene and a range of 1 to 13 (Table 1).
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A list of the markers and their allele representation within the population is described in
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Table S2. Because the presence of multiple markers per gene provided redundant data for
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mapping, the marker with the least missing data was selected for the gene, resulting in a
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final set of 4,195 markers for mapping.
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A P. taeda genetic map with 2,841 gene markers
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The segregation of 4,195 markers in 72 haploid samples was used to construct a high-
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density, gene-based genetic map (Figure 1, Table S3). The final map comprises 2,841
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genes mapped to the expected 12 linkage groups of the species. The number of genes
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mapped in each linkage group varied from 193 genes for LG 1, to 291 genes in LG 8,
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with an average of 237 genes per linkage group. Out of the 2,841 markers mapped, 2,622
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(92%) were SNPs, 63 (2%) were short deletions, 53 (2%) were short deletions, 38 (1%)
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were multiple nucleotide polymorphisms and 65 (2%) were gene PAVs (Table 1). The
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final map spans 1637.4 cM, which is comparable with previous estimations of map length
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for the species (REMINGTON et al. 1999; ECKERT et al. 2010; ECHT et al. 2011). LG 11
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spanned the longest genetic distance with 190 cM, while LG 5 spanned the shortest, with
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105.1 cM. On average, linkage groups spanned 132.9 cM (Table 2). The average interval
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between markers was 0.58 cM, across 12 linkage groups, with 95% of the intervals
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smaller than 1.5 cM, and a maximum interval of 10.9 cM at the end of linkage group 2.
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Following previous methods (REMINGTON et al. 1999; ECHT et al. 2011) we estimated the
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genome length at 1651.5 cM, and the map coverage as 99%. For this marker density, the
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estimated map coverage (c) predicts that any locus on the genome has 99% probability of
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being within 3 cM of a marker and 82% probability of being within 1 cM of a marker. A
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map with a more conservative marker order, obtained by stopping the mapping process at
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Round 2 of mapping on JoinMap, was generated and is provided in Table S4. When
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considering this map, there are still 1,371 genes mapped with an average of one marker
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every 1.3 cM, providing an estimated map coverage (c) that predicts that any locus on the
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genome has 98% probability of being within 5 cM of a marker.
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Map validation
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The high-density map developed provides an overview of the grouping and orientation of
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2,841 genes in each loblolly pine chromosome. However, because the number of markers
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far exceeds the number of meiotic recombinations sampled to generate the map, it can be
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expected that their order might present incongruences with the physical position on the
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genome, particularly between closely mapped markers. To analyze the consistency of our
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map, we compared it with the genetic map built by Eckert et al. (2010), hereafter referred
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to as “Eckert map”, since a large number of genes were shared between the two. Ideally,
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this comparison should be done relative to the genome sequence of loblolly pine, but the
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current assembly (version 0.8) is unordered and still largely fragmented. Thus, the
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comparison relative to another gene-based genetic map remains the only viable option to
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validate our results. The Eckert map was generated using SNPs detected using an
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Illumina Infinium genotyping assay with markers selected from a unigene dataset that
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overlapped with the one used in our sequence capture probe design. Although the SNPs
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analyzed within the genes were not the same, a total of 397 genes had markers in
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common between the two maps and could be used for a comparative analysis. The
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analysis occurred in two steps: (1) First, we analyzed whether the common genes mapped
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to the same linkage group. (2) Next, we evaluated if the order of the genes within the
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linkage group were similar between the two maps.
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In the first analysis, out of the 397 genes positioned in both maps, 392 (99%) mapped to
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the same linkage group as that observed in the Eckert map. Thus, there is a nearly perfect
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agreement between the two maps at the grouping level (Table 3). For the comparison of
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the genetic position of genes within linkage groups, the data is illustrated by plotting the
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normalized position of the common genes on our map (X) relative to the normalized
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position on the Eckert map (Y) (for instance, LG 2 and LG 4 in Figure 2). In the
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comparative analysis, the position of each gene was normalized to control for the
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different length of the two maps. For example, a gene mapped to position 61.56 cM on
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LG 2, which has a length of 123.12 on our map (Table 2), would have a normalized
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position of 50%. The same approach was used for genes in the Eckert map. Assuming
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gene synteny between the populations used for mapping, the expected results would be a
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linear relationship between the markers. For LG 1, 3, 4, 8, 9, 10 and 11 all the common
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genes were collinear across the entire extension of the group (Figure 2, Figures S1 and
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S2). For LG 2, 6, 7 and 12, most of the genes were collinear, but a block defined by
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several genes shows incongruences between the two maps (Figure 2, Figures S1 and S2).
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Interestingly, in all cases, these blocks were previously mapped to the beginning or end
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of the linkage group in the Eckert map and were mapped towards the middle of the
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linkage group in our map. In only one linkage group, LG5, there is low agreement
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between the two maps (Figures S1 and S2). Overall the results show a high agreement
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between the genes mapped in the two maps, both at the grouping level and at the ordering
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level.
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DISCUSSION
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The two main objectives of this study were to evaluate the potential of targeted
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resequencing based on sequence capture for polymorphism detection in loblolly pine, and
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to expand our knowledge about the position of genes in the pine genome. Loblolly pine is
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one of the most economically and ecologically important species within the conifers, and
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its genome is currently being sequenced (http://pinegenome.org/pinerefseq). The large
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and complex pine genome, characterized by large retrotransposons expansions and high
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nucleotide diversity (BROWN et al. 2004; BURLEIGH et al. 2012), offers challenges for
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polymorphism genotyping using traditional platforms such as SNP chips, resulting in low
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rates of SNP conversion and high rates of missing data (ECKERT et al. 2009). From
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previous work (NEVES et al. 2013) we expected that probes utilized for sequence capture
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could be capturing non-unique regions in the pine genome (e.g. paralogs and
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pseudogenes), hampering the detection of site-specific polymorphisms. The use of a
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mapping population provides a method to verify detected polymorphisms because
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alternative alleles are expected to segregate in equal proportion (1:1) in a sample of
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megagametophytes. Even adopting a shallow sequencing depth, a total of 7,434 sequence
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variant markers for 3,787 genes (Table 1) were detected segregating in the population.
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From the 14,729 genes initially considered for capture, we estimated that 4,123 (28%)
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had sequence properties that allowed us to detect sequence variation markers, namely
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having at least 50 individuals with one or more reads aligned to the gene in each
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individual. Thus, given appropriate sequence capture efficiency and sequencing depth,
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92% (3,787 / 4,123) of the genes contained at least one segregating marker that was used
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for mapping in the population. This is a high SNP conversion rate considering that no
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prior information was available for the population and a greater number of genes likely
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could have been mapped if we had sequenced the population at higher sequencing depth
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or increased the number of haploid samples sequenced to sample more meiotic events.
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The megagametophyte used as source of DNA sequence capture is a haploid,
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maternally inherited tissue that is commonly used in conifer mapping studies
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(REMINGTON et al. 1999). Thus, a single haplotype was anticipated for any given locus.
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The pipeline for detection of sequence polymorphism required that the most common
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base detected in a given megagametophyte be present in at least 80% of the reads that
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overlap the variant locus. In other words, for a particular position with sequencing depth
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of 10×, eight or more reads need to have the same base to be considered. This step was
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implemented to exclude possible errors added during library construction and sequencing,
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and to avoid cases where unspecific paralogs and pseudogenes were also being captured,
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which is likely considering their abundance in the pine genome (KOVACH et al. 2010).
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Although we did not formally quantify instances where the latter happens, we observed it
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while optimizing the bioinformatics pipeline. For example, only reads aligned to the
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unigene reference with less than 5% mismatch were considered for polymorphism
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detection and mapping. At this threshold, we detected the 7,434 sequence polymorphisms
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reported. When up to 10% mismatch to the unigene reference were permitted for use of a
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sequencing read, there was a significant reduction in the number of polymorphisms
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detected that segregated in the expected ratio in the population. Accepting 10% of
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mismatches lead to the detection of only 4,563 polymorphisms, represented within 2,210
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genes, that segregate in a 1:1 ratio. This suggests that bona fide polymorphisms were
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confounded by alleles from paralogs and pseudogenes mapped with less stringent
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alignment criteria.
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The large size of the pine genome of pine is attributed to retrotransposon activity,
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rather than whole-genome duplications (MACKAY et al. 2012). Based on the dynamic
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nature of retrotransposons, we hypothesized that a subset of the pine genes might be
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present in multiple copies in the genome, as observed in maize and another genomes
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characterized by retrotransposon genome expansions (BELO et al. 2010; SWANSON-
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WAGNER et al. 2010). To test this hypothesis, we aimed to detect presence/absence
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variants (PAV). In the specific case of a maternally-derived haploid segregating
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population, these are hemizygous genes that segregate in the population with 50% of the
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haploid samples containing the gene in their genome, and 50% with the gene absent in
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their genome. We focused on PAVs, because these events are easier to detect than copy
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number variations (TEO et al. 2012), particularly considering that samples were
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sequenced at low depth. In our analysis, a gene was considered absent when no reads
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aligned throughout the entire gene region, and present when at least one read was aligned
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to the gene region. Using this simple analysis criteria allowed us to detect 408 putative
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PAV. However, only 16% (65) of the PAVs could be mapped, suggesting that many of
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PAVs were false positives or contained genotyping errors that excluded them from
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mapping. These false positive or genotyping errors were expected as a consequence of
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the low sequencing depth, and we anticipated that the genetic mapping process would be
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able to filter those out, providing a more accurate subset of PAVs in P. taeda.
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The final genetic map was obtained by analyzing the segregation of 4,195 markers,
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each representing a unique gene of the P. taeda genome, in 72 haploid samples.
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Ultimately, 2,841 (68%) genes were mapped in the expected 12 linkage groups. To the
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best of our knowledge, this is the most dense genetic map for conifers or trees in general,
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although other highly saturated maps are becoming available (MARTINEZ-GARCIA et al.
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2013). Because this map was constructed using gene-based marker, it provides links with
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other published maps for loblolly pine, allowing map cross-checking and eventual
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consolidation. The genes mapped were not selected based on any specific criteria. Thus,
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the map provides an unbiased representation of the loblolly pine gene set that can be used
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for studies that benefit from a preliminary ordering of the genes until a reference genome
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sequence of reasonable quality becomes available. This high-density map should also be
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valuable when anchoring and orienting scaffolds in the assembly of the reference genome
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(TUSKAN et al. 2006; SATO et al. 2012).
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The comparison to a previous map of P. taeda (ECKERT et al. 2010) that shares
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397 common genes with our new map revealed a high-level of agreement at the grouping
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and ordering level (Table 3). These results provide strong evidence that markers were
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correctly identified and genotyped in the mapping population, despite the complexity of
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the genome of pine. For some linkage groups (2, 6, 7, 8 and 12; Figure 1 and Figures S1
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and S2), blocks of adjacent markers that were previously assigned to the edge of the
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linkage group in the map of Eckert et al. were mapped towards the center of the linkage
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group in our map. Such discrepancies between blocks of genetic markers have been
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observed in other studies in Pinaceae (KRUTOVSKY et al. 2004; ECKERT et al. 2009;
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ECHT et al. 2011) and were usually attributed to genotyping errors. Although we cannot
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test for genotyping errors, we did not observe higher rates of missing data in the markers
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that are out of order compared to the collinear ones (data not shown). Also, from
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simulated experiments, it is expected that genotyping errors would inflate the length of
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the genetic map (HACKETT and BROADFOOT 2003), but this was not observed in our map.
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Therefore, we are unable to conclusively identify the causes for non-collinear blocks of
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markers. Attributing such discrepancies to real genome rearrangements will require
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further experiments based on larger numbers of individuals or whole-genome
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resequencing.
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Genotyping samples of a large population using sequence capture presents some
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advantages over conventional genotyping methods. For example, sequence capture does
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not require one to previously characterize the polymorphisms to be detected. We show
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that this is of greater importance for a segregating population because even if previously
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characterized polymorphisms are available, it is not known which of those sites will
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segregate in a single bi-parental segregating population and have the potential to be
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mapped. In this study, the probes used for capture were designed to span the entire
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unigene length, and we were able to detect an average of two polymorphic sites
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segregating in the population for every gene. Thus, the chances of detecting a marker to
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map the gene increased and we were able to map 68% of the genes containing a
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segregating marker (2,841/4,195), even adopting an overall shallow depth of 2.8×. For
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several genes we detected additional polymorphic sites within 60 bp of the position used
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for mapping, without impairing the efficiency of the capture. This is an usual observation
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in pine and other highly polymorphic species, and a common limitation for developing
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traditional genotyping methods, such as SNP chips (ECKERT et al. 2009; GRATTAPAGLIA
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et al. 2011). Finally, another advantage illustrated is the potential to detect other types of
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genetic variants in addition to SNPs, such as the presence/absence markers that we were
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able to detect and map for 65 genes. Although a small percentage of the mapped markers
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were genotyped with PAV, sequencing at higher depth will likely increase this number
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and enable more accurate detection of gene copy number variation (CNV), as achieved
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by other studies in plants (SAINTENAC et al. 2011; VALDES-MAS et al. 2012). With an
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ultimate goal to use genetic markers for phenotypic characterization, recent studies are
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showing that CNVs might explain a portion of phenotypic variation not sampled by SNPs,
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justifying its characterization (MARON et al. 2013).
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CONCLUSIONS
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We report the application of exome capture on loblolly pine for polymorphism detection
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and gene mapping by analyzing a mapping population of 72 haploid samples. An
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important advantage of this approach for polymorphism detection is that it allows for the
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enrichment and sequencing of several segments of the gene being targeted, which
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increases the chances of detecting a marker segregating in the population. As a result, we
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were able to detect at least one segregating marker for 4,195 genes, despite the shallow
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sequencing depth employed. The markers were used to generate a gene-rich genetic map
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that ultimately positions 2,841 genes with an average intergenic distance of 0.6 cM. Also,
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because exome capture is a genotyping by sequencing method, we show that other types
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of segregating markers can be identified in the population, such as gene presence/absence
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variation. This is particularly important, because it highlights the possibility to study gene
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dosage using exome capture in plants, which traditionally has required additional
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experimentation. To our knowledge, this is the most saturated map in conifers and trees
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in general. Because this is a gene-based map, we expect that it will help in the
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construction of the pine reference sequence that is currently being generated, while
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facilitating other basic and applied research efforts that utilize a high-density, gene-rich
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genetic maps.
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MATERIALS AND METHODS
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Mapping population, DNA extraction and sequence capture
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Seventy-two seeds were collected from the reference loblolly pine genotype 17
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(KAYIHAN et al. 2005). Each seed was manually dissected to extract the haploid,
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maternally-inherited megagametophyte tissue. Megagametophytes were lyophilized and
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DNA extracted using the DNeasy Plant Mini Kit (Qiagen) according to the
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manufacturer’s protocol. DNA sample quality and integrity was analyzed by agarose gel
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electrophoresis, and DNA samples were quantified using the Quant-it PicoGreen dsDNA
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Assay Kit (Invitrogen). Libraries for each haploid sample were created as described
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previously (NEVES et al. 2013). Briefly, DNA was sheared, end-repaired, adenylated, and
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ligated to Illumina compatible adapters that contain 5’ barcode sequences. Fragments
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with ligated adapters were size-selected (250-500bp) by agarose gel electrophoresis and
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enriched by PCR using universal primers. Libraries from eight barcoded haploid samples
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were combined in equimolar amounts and used as input for the sequence capture
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hybridization reactions following the Agilent SureSelect protocol. The probe set used for
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sequence capture contained 54,773 probes representing 14,729 Pinus taeda unigenes and
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are available in a previous study (NEVES et al. 2013). Target enriched libraries were
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sequenced using a variety of Illumina machines and outputs (Table S1).
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Sequencing data filtering and alignment
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Raw reads for each sequencing lane were separated based on their individual barcode
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using the FASTX-Toolkit barcode splitter (http://hannonlab.cshl.edu/fastx_toolkit/),
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resulting in one file for each sample. The 3’ ends of the reads were trimmed to remove
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low quality bases using the FASTX-Toolkit fastq quality trimmer with parameters -t 20 -l
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50 (remove 3’ end bases if phread quality score < 20 and discard read if resulting read is
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shorter than 50bp) for reads sequenced for 100 bp or longer, and -t 20 -l 30 for reads
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sequenced for 40 bp. For samples sequenced in paired-end mode, the order of the reads in
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each mate file was re-synchronized using custom scripts, and reads without an
6
appropriate mate were treated as single-end. The filtered reads were aligned to the
7
unigene
8
(http://bioinformatics.bc.edu/marthlab/Mosaik) with parameters as follows: maximum
9
percentage of mismatches 0.05 (-mmp 0.05), unique alignment mode turned on (-m
10
unique), most accurate hashing strategy (-a all), and hash size 15 (-hs 15). Multiple
11
alignments for the same sample were combined using BamTools (BARNETT et al. 2011)
12
into a single alignment.
reference
used
for
probe
design
with
Mosaik
Aligner
13
14
Detection of SNP and PAV
15
Single nucleotide polymorphisms, short insertions and deletions (short indels) and multi-
16
nucleotide polymorphisms (MNPs) were detected in a combined population analysis that
17
use all individuals of the mapping population simultaneously with Freebayes v0.8.7
18
(http://bioinformatics.bc.edu/marthlab/FreeBayes) with parameters as follows: SNP
19
probability 0.75 (--pvar 0.75), pairwise nucleotide diversity 0.01 (--theta 0.01), haploid
20
mode (--ploidy 1), accepting indels (--indels) and multi-nucleotide polymorphisms (--
21
mnps), minimum alternative allele frequency of 0.8 (--min-alternate-fraction 0.8), and
22
minimum alternative allele count of 1 (--min-alternate-count 1). For a polymorphism to
23
be considered for mapping, reference and alternative alleles had to be observed
19
1
cumulatively in 50 or more individuals of the population, and the alleles had to segregate
2
1:1 in the population (χ2-test p-value 0.05). If more than two alleles were observed at a
3
polymorphic position, a problem that might arise due to contamination with diploid tissue
4
(e.g. embryo), paralog capture, and sequencing error, the individuals with the two most
5
frequent alleles in the population were considered in further analysis and the remaining
6
were categorized as missing data. To detect presence/absence variants (PAV), a
7
sequencing depth matrix was calculated with genes as rows, haploid samples as columns,
8
and every observation being the average number of times each position of the gene was
9
sequenced. The observations were coded as 1, when at least one read aligned to the gene
10
region, or 0 when no read aligning to the gene region was detected. A test for 1:1
11
segregation for the presence of the allele was performed (χ2-test p-value 0.05) and PAV
12
markers passing these criteria were used for mapping.
13
14
Genetic map construction
15
The SNP, short indel, MNP and PAV markers that exhibited segregation in the
16
population were combined, and in case of gene redundancy, the marker with the least
17
amount of missing data was used as a representative of the gene. The genetic map was
18
constructed with JoinMap 3.0 (VAN OOIJEN and VOORRIPS 2001) with parameters as
19
follows: population type HAP, grouping LOD score ≥ 6, recombination fraction ≤ 0.4,
20
ripple value =1, using the Kosambi mapping function. JoinMap 3.0 builds a genetic map
21
adding markers in three successive steps. The first and second step removes markers that
22
reduce the map goodness-of-fit or creates negative distances. The third step then adds all
23
markers ignoring the thresholds of goodness-of-fit and negative distances. The complete
20
1
map was generated using all three rounds to order all linked markers to the map. For a
2
conservative map, we stopped the mapping process after round two to provide a better
3
estimation of gene order as this removes markers that contribute to unstable ordering. The
4
group number (1 to 12) and the relative orientation of the markers within the group were
5
defined following the genetic map published by Eckert et al. (2010) to facilitate
6
comparisons between studies. Genome length and coverage were estimated following
7
Echt et al. (2011). The genome length was estimated using method 4 of Charkravarti et al.
8
(1991) where the observed Kosambi genetic distance of each linkage group (LG) was
9
adjusted by multiplying it by (m+1)/(m-1), where m is the number of markers mapped to
10
the linkage group. Genome coverage (c), defined as the proportion of the genome
11
contained within a distance d (cM) from a marker was estimated by 𝑐 = 1 − 𝑒 −2𝑑𝑚/𝐿 ,
12
where, L is the total estimated genome length (LANGE and BOEHNKE 1982; REMINGTON
13
et al. 1999; ECHT et al. 2011).
21
1
FIGURE LEGENDS
2
Figure 1 – High-density genetic map for loblolly pine. The final map is comprised of
3
2,841 genes distributed among the 12 linkage groups with an average distribution of a
4
gene every 0.6 cM and a total length of 1637.4 cM. For easy comparison, the linkage
5
group name and orientation of the map follow that published by Eckert et al. (2009), and
6
common genes between the two maps are shown in green. Genes for which
7
presence/absence variation was identified and used to map the gene is show in green and
8
have suffix PAV (e.g. 2_6131_PAV, for unigene 2_6131). The remaining markers were
9
coded as “unigene_marker position” (e.g. 0_2563_337, for unigene 0_2563 and a
10
polymorphic marker at position 337 of the unigene). The genetic map graphical
11
visualization was prepared using MapChart 2.2 (VOORRIPS 2002).
12
13
Figure 2 – Comparison of the normalized relative order of shared genes used in our study
14
(X-axis) and that of Eckert et al. (2009) (Y-axis). Due to space constraints, only plots for
15
linkage groups 2 (left panel) and 4 (right panel) are shown. Assuming genes syntheny
16
between the two populations, a straight line would illustrate perfect agreement at the gene
17
ordering level between the two maps. Graphs for all 12 linkage groups are provided in
18
Figures S1 and S2.
19
20
Figure S1 – Comparison of the normalized relative order of shared genes used in our
21
study (X-axis) and that of Eckert et al. (2009) (Y-axis) for linkage groups one to six.
22
Assuming genes syntheny between the two populations, a straight line would illustrate
23
perfect agreement at the gene ordering level between the two maps.
22
1
Figure S2 – Comparison of the normalized relative order of shared genes used in our
2
study (X-axis) and that of Eckert et al. (2009) (Y-axis) for linkage groups seven to twelve.
3
Assuming genes syntheny between the two populations, a straight line would illustrate
4
perfect agreement at the gene ordering level between the two maps.
5
23
ADDITIONAL FILES
1
2
File name: Neves_Data_S2_information_on_segregating_markers.csv
3
File format: comma separated values (csv)
4
Tile of data: Table S2
5
Description of data: Additional information about the 7,842 markers that segregated in
6
the population. The last column describes the status of the marker in the mapping process,
7
where “mapped_Rnd2” are markers that mapped at the second and third rounds of
8
mapping; “mapped_Rnd3” are markers that only mapped at the third round of mapping;
9
“not_tested_for_mapping” are markers for which another marker was selected to
10
represent its gene for mapping and were not tested in the mapping step, and “ungrouped”
11
are markers that did not pass the grouping step of JoinMap mapping.
12
13
File name: Neves_Data_S3_map_round3.csv
14
File format: comma separated values (csv)
15
Tile of data: Table S3
16
Description of data: Text representation of the full genetic map with 2,841 genes
17
mapped, containing the linkage group, the marker name and the position that marker
18
mapped in the linkage group.
19
20
File name: Neves_Data_S4_map_round2.csv
21
File format: comma separated values (csv)
22
Tile of data: Table S4
24
1
Description of data: Text representation of the genetic map of 1,371 genes mapped
2
using the more conservative marker ordering of JoinMap Round 2. It contains the linkage
3
group, the marker name and the position that marker mapped in the linkage group.
4
25
1
Table 1 – Description of markers segregating in the population and mapped. Markers
2
identified based on sequence variation were subdivided in single nucleotide
3
polymorphism (SNP), short insertions and deletions (Short Indels) and multiple
4
nucleotide polymorphism (MNP). # Markers represent the number of markers that
5
segregated 1:1 in the mapping population and # Genes is the number of unique
6
genes represented by these markers.
Marker Class
Sequence variation
SNP
Short deletions
Short insertions
MNP
Presence/Absence variation
Total
Segregating 1:1
# Marker
# Genes
7434
3787
6857
210
208
159
408
408
7842
4195
Genes
Mapped
2776
2622
63
53
38
65
2841
7
8
26
1
Table 2 – Description of genetic map provided. A total of 2,841 genes were mapped
2
to the expected 12 linkage groups of loblolly pine based on recombination data from
3
72 haploid samples.
Linkage group No. genes mapped Length (cM)
1
2
3
4
5
6
7
8
9
10
11
12
Average
Total
193
260
205
195
263
230
242
291
223
228
271
240
236.75
2841
144.72
123.12
113.29
124.79
105.07
117.54
125.03
118.53
169.32
165.71
190
140.29
136.45
1637.41
Inter-marker distance (cM)
Mean
SD
0.75
0.65
0.48
0.39
0.56
0.44
0.64
0.54
0.4
0.62
0.51
0.83
0.52
0.71
0.41
0.41
0.76
0.91
0.73
0.58
0.7
0.62
0.59
0.59
0.58
0.61
-
4
5
27
1
Table 3 – Marker validation at the grouping level. Comparison of the maps from
2
Eckert et al. (2010) and the present map. The few genes mapped to different linkage
3
groups between the two studies are provided in the last column.
Linkage
group
1
2
3
4
5
6
7
8
9
10
11
12
Total
No. genes at
same group
23
40
35
24
33
32
31
45
30
35
30
34
392
No. genes at
different
group
0
0
0
1
1
0
1
0
1
1
0
0
5
Genes linked to different groups
Gene=0_9680; Neves=LG 4, Eckert=LG 8
Gene=0_7496; Neves=LG 5, Eckert=LG 11
Gene=0_5740; Neves=LG 7, Eckert=LG 12
Gene=UMN_213; Neves=LG 9, Eckert=LG 7
Gene=0_6106; Neves=LG 10, Eckert=LG 6
4
5
28
1
Table S1 – Sequencing strategy used for each multiplexed pool of eight haploid
2
samples. Illumina’s machine used and the respective cycle length used is shown.
Sample
Pools
1
2
3
4
5
6
7
8
9
Titration run
GAIIx
1x40bp
X
Full runs
GAIIx
1x100bp
GAIIx
1x115bp
X
X
X
X
X
X
GAIIx
2x115bp
X
X
X
X
X
X
X
HiSeq
2x100bp
X
X
3
4
29
1
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36
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39
40
41
42
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32
LG 1
LG 2
0.0
0_15062_534
4.7
2_6495_300
9.1
9.7
2_3850_246
2_447_780
11.2
11.8
0_16700_115
0_5387_782
13.4
0_2579_P AV
14.7
0_12178_548
15.9
16.7
0_4838_266
2_5793_437
19.1
20.2
21.2
21.6
22.5
22.9
24.2
24.9
25.5
26.0
26.7
27.0
28.1
28.5
28.9
29.2
29.5
30.3
31.0
31.3
32.1
32.9
33.9
35.1
36.1
36.5
37.0
37.6
38.0
38.5
39.0
39.7
41.3
UMN_6323_491
0_14764_206
0_5550_374
2_10223_651
0_4894_98
0_9125_505
0_6783_P AV
2_8726_532
0_17002_168
0_9750_161
2_10350_629
2_3200_492
0_2563_337
2_5340_701
2_2374_541
0_1321_85 2_4968_227
0_8329_207 2_2781_547
UMN_2387_276
0_8673_117
UMN_937_141
0_816_254
0_2529_559
0_14094_191
0_10547_201
0_9830_308
UMN_4153_293
2_9586_451
0_13888_222
0_17858_786
0_14398_666
0_15067_550
UMN_2682_447
0_17278_268
42.9
43.4
44.0
44.7
45.5
45.8
47.4
47.6
48.8
49.2
49.9
50.8
51.1
51.5
52.3
52.6
53.9
UMN_4995_247
0_2632_222
UMN_6386_373
0_14470_680
0_9318_540
2_3622_750
0_13162_342
0_12713_581
2_5877_270
0_7636_347
2_4761_507
1_5378_128
0_16691_263
2_916_592
0_17082_397
0_4826_129
0_4121_139
55.0
0_10359_297
0_17154_139
55.9
58.3
58.7
58.9
59.9
61.3
61.6
62.1
63.7
64.4
65.7
67.0
67.3
67.7
67.8
68.4
68.9
69.8
70.3
71.3
71.8
72.6
72.7
72.9
73.5
73.7
74.0
74.5
74.7
75.1
75.4
75.9
76.1
76.5
77.4
78.1
78.3
78.4
78.8
79.0
79.1
79.4
79.5
79.8
80.7
81.7
82.2
82.5
83.1
83.3
83.6
84.6
85.3
85.9
86.8
87.8
88.0
88.7
88.9
89.6
90.0
90.3
91.0
91.6
92.1
93.1
93.3
93.4
94.2
95.1
95.5
95.6
96.5
96.9
97.3
97.9
99.1
100.0
100.9
101.0
102.3
103.4
104.5
104.6
105.1
106.0
106.4
106.8
108.2
109.4
110.8
112.0
112.9
115.2
115.6
116.6
117.6
117.9
118.6
119.0
119.4
119.5
120.5
120.6
122.2
122.8
123.0
124.1
124.7
125.1
126.1
127.2
127.7
127.9
128.7
129.9
131.5
132.7
135.2
136.0
138.6
139.2
140.8
141.8
0_9281_53
0_9287_580
0_3358_169
0_3784_402
0_5281_608
0_12778_389
0_8667_487
0_15763_368
0_6165_182
2_4534_713
UMN_7046_591
0_13843_538
2_4455_192
UMN_791_67
0_17030_741
0_7183_187
2_6536_179
2_2617_201
0_16927_638
0_10476_375
0_10789_125
0_17434_590
0_2231_412
0_12717_277
UMN_1639_137
UMN_755_366
0_4780_70
0_15448_153
0_18884_282
0_9052_319
2_2125_674
0_15194_464
UMN_6664_247
2_9496_346
2_9228_222
0_11442_492
2_3685_335 0_12249_164
2_7289_359
0_18837_628 0_17411_181
0_11992_119
0_3288_426
2_1668_471 0_16175_101
0_14972_463
0_8185_368
2_10327_727
0_9151_167
0_14511_206
2_8880_296
UMN_873_270
0_17880_250
2_5610_348
0_2691_191
2_781_235
0_5747_137
0_8699_583
UMN_1609_187
0_754_290
0_1213_656
2_6137_155
0_7940_484
0_13971_681
0_5575_354
UMN_3326_120
UMN_476_80
0_945_304
2_9053_589
0_1864_253
2_1001_220
0_12577_272
2_4604_177
2_9664_449
0_17776_485
0_10246_348
0_2329_670
0_4904_338
0_16980_374
2_6249_441
2_3747_944
2_9110_131
0_18368_637
0_1521_206
0_3715_368
0_8679_156
0_9600_654
2_6660_825
2_7532_593
0_10195_585
UMN_5947_170
UMN_613_200
0_16955_354
2_7174_497
2_4462_270
0_10540_200
0_17493_372
0_4829_300
0_4435_184
UMN_765_230
2_2099_598 0_5630_427
0_6952_568
UMN_979_146
0_13204_275
0_9515_538
UMN_3466_116
2_8585_261
2_4040_734
0_16206_760
0_18261_714
0_17242_224
UMN_6446_124
0_1849_397
0_17726_498
2_1223_663
2_6131_P AV
0_17140_611
0_13101_568
0_17577_605
2_6018_747
0_3685_172
0_14506_143
0_18313_255
0_12285_535
0_15934_504
2_3177_223 2_9851_487
144.7
0_817_133
0.0
1.6
4.0
5.2
6.2
7.2
8.3
8.7
9.9
11.2
12.4
13.4
14.5
15.1
15.4
16.0
16.6
16.7
17.3
17.8
19.1
19.3
19.6
20.2
21.0
21.9
23.0
23.5
24.9
25.7
26.6
27.1
27.9
28.6
29.6
30.9
31.4
31.8
33.0
33.7
34.3
35.6
36.2
36.4
37.4
37.9
39.2
40.0
40.8
41.7
42.6
42.9
43.3
44.4
45.0
45.6
46.2
46.6
47.2
47.5
48.1
48.7
49.5
49.8
50.5
50.8
51.2
52.1
52.2
52.6
53.4
53.7
53.9
54.6
55.3
55.4
55.9
56.3
57.1
57.9
58.3
58.4
58.8
59.0
59.4
59.8
60.3
60.6
60.9
61.1
61.6
61.7
61.8
62.7
62.8
63.3
63.6
63.9
64.2
64.5
65.1
65.4
65.8
66.2
66.4
66.5
66.8
67.3
67.5
67.8
68.1
68.3
68.5
68.6
68.7
68.9
69.2
69.4
69.5
69.7
70.1
70.3
70.8
71.0
71.1
71.8
72.1
72.6
73.1
73.3
73.9
74.1
74.2
74.4
74.6
75.2
75.3
75.5
75.9
76.0
76.5
76.6
76.7
76.8
77.1
77.7
77.9
78.3
78.4
78.7
78.8
78.9
79.3
79.4
79.7
79.9
80.4
80.7
80.8
81.0
81.3
81.5
81.7
82.0
82.2
82.3
82.4
82.7
83.0
83.1
83.3
83.4
83.9
84.1
84.3
84.7
84.8
85.1
85.4
85.5
85.8
86.3
86.7
86.9
87.0
87.4
87.7
87.9
88.3
89.3
89.5
89.7
90.3
91.0
91.5
91.9
92.0
92.3
93.1
94.3
94.5
95.1
95.9
96.6
97.2
97.8
97.9
98.1
98.2
98.9
100.0
100.5
101.3
101.6
101.8
102.4
103.4
103.8
104.4
104.5
104.6
105.3
105.4
106.0
106.9
107.8
108.5
108.8
109.8
110.1
110.2
110.9
111.6
111.8
112.0
112.3
112.6
113.5
114.7
114.9
115.6
116.7
117.5
119.4
120.8
123.1
0_273_216
0_17554_P AV
0_14509_411
0_1433_157
2_5667_365
0_16872_528
UMN_3505_222
2_9967_651
0_10104_118
0_10920_126
2_1327_218
0_16937_87
0_2955_135
0_7088_387
0_5882_450
2_374_598
0_8281_448 2_3505_273
0_11472_245
0_2019_342
2_9475_170
0_2491_490
2_693_396
2_8643_410
0_14360_538
0_9820_567
2_2681_222
0_18633_648
0_11406_295
0_18470_454
0_6626_81
0_18123_94
0_5565_553
0_12129_180
0_3853_180
0_12568_529
0_12630_344
0_13144_567
UMN_852_207
0_6505_441
0_16267_517
0_10164_694
2_7618_243
0_4032_400
UMN_1443_255
0_16658_340 2_9870_466
0_6206_255
0_13705_578
0_507_170
0_1037_360
UMN_3395_42
2_3626_467
0_1085_244
2_6335_211
0_4050_618
0_17988_220
2_9049_243
0_2222_390
2_6908_144
UMN_607_228
2_2875_230
0_18632_679
2_5743_863
0_6807_321 UMN_858_246
0_9288_419
0_18348_430
2_1094_111
0_16128_239
0_1131_182
0_6234_272
UMN_3020_244
UMN_1597_468
0_17015_176
0_1028_239
UMN_3237_228
0_5145_239
0_7484_333
0_16230_605
0_6607_454
2_6034_653
2_2946_669
0_6510_244
UMN_1309_129
0_15782_517
2_1285_384
0_6606_314
0_1938_219
0_4017_322
UMN_3423_176
1_4996_733
0_18635_609
0_13127_435
0_6976_496
UMN_1678_196
0_975_P AV
2_3222_369
2_220_585
0_13172_291
0_16484_353
0_12206_30
0_12786_238
0_1659_285 0_6257_706
0_4574_117
2_4077_664
0_18437_154
UMN_834_180
0_6939_447
0_9902_152
2_9730_125
0_4520_258
0_9156_655
2_8497_314
2_2483_425
2_2156_515
0_14455_267
2_7351_286
2_8779_265
2_4196_479
2_6434_466
0_978_334
0_4690_335
0_9529_385
0_17884_701
UMN_2367_269
2_7811_454
0_1471_109
0_13832_706
UMN_1345_286
2_620_825
2_6562_621
UMN_490_104
2_7409_185 0_18483_418
0_7072_221 0_3896_519
0_11688_310
0_9533_218
0_15227_339
UMN_553_239
UMN_4665_224
UMN_1689_224
2_2370_380
0_10902_430
UMN_1992_551
0_14227_708
UMN_4089_342
0_13576_141
0_9427_485
2_2450_503
2_6736_89
0_9584_602
UMN_2734_79
0_1730_285
UMN_7156_113
UMN_2610_90
0_1273_430
UMN_4760_416
UMN_3554_216
0_18596_210 0_8963_373
2_9380_124
2_9786_164
0_4394_500
0_18604_580
0_12104_506 0_3353_196
UMN_3454_175 0_7715_283
0_14602_151
2_5479_464
0_9344_210 0_15524_159
0_8735_658
2_8971_545
0_13118_294
0_6354_393
0_14101_97
2_4531_230
0_9055_82
UMN_4297_156 2_3914_304
0_18081_773
0_16537_357
0_16021_183
0_4214_249
0_8441_206
2_3781_900
UMN_1285_87
0_15588_203
2_7207_721
2_9057_295
0_953_286
2_9713_742
0_1845_512
UMN_4346_139 0_5890_510
UMN_3804_197
2_6613_434
0_366_281
0_17539_373
2_358_180
0_9110_298
0_1974_185
UMN_2728_86 0_3544_635
2_383_224
0_13584_586
2_2611_755
0_17304_349
0_13271_310
2_8658_651
0_12675_444
0_335_145
UMN_971_249
2_33_464
UMN_2065_600
0_3458_157
0_6043_172
2_2665_424
2_4919_326
2_289_315
0_6934_214
0_16710_309
UMN_3225_406
0_8475_171
0_1740_456
0_16614_375
0_13833_397
UMN_2355_201
UMN_3992_90
0_10382_356
0_6062_616
2_9485_233
0_13511_600
UMN_7035_121
0_2780_264
2_5919_337
0_1279_230
0_18786_703
0_14696_203
2_9201_455
0_12921_224
0_12964_514
UMN_5942_297
0_13176_127
0_2414_599
0_15987_695
0_18775_144
0_127_517
0_4057_315
0_16769_399
UMN_2026_329
0_18569_514
0_8864_373
0_3191_444
0_262_161
0_12453_245
LG 3
0.0
3.8
4.6
6.5
7.1
8.9
9.7
10.3
11.1
11.3
12.0
13.3
13.6
13.8
14.3
15.0
15.2
16.2
16.3
16.7
16.8
17.3
17.8
19.1
20.1
20.2
20.7
20.9
22.0
22.1
23.1
23.4
23.7
24.0
24.3
24.9
25.1
25.7
26.3
26.5
27.2
27.5
28.1
29.1
29.5
30.0
30.2
30.4
30.8
31.0
31.3
31.6
32.3
32.9
33.0
33.3
33.8
34.2
34.5
34.8
35.5
35.7
36.2
36.3
36.7
37.3
38.2
38.5
38.9
40.0
40.5
40.7
42.1
43.7
44.4
45.2
45.3
45.4
46.0
46.4
47.3
47.6
48.6
49.2
49.5
49.9
50.0
50.5
50.6
50.7
50.8
50.9
51.0
51.1
51.2
51.5
51.7
52.2
52.8
53.6
54.3
55.0
55.6
56.0
56.6
57.0
58.5
59.3
59.5
60.1
60.3
61.0
61.6
62.1
62.9
64.1
64.4
64.6
65.6
65.8
66.2
67.1
67.3
67.6
67.9
68.1
68.9
69.0
69.5
70.2
70.6
71.0
71.4
72.7
73.5
73.7
73.8
74.2
74.9
75.5
76.0
76.7
77.6
78.7
79.2
79.8
81.3
81.4
82.6
83.4
84.1
84.8
85.1
85.4
85.8
86.3
87.3
88.4
88.6
88.8
89.4
89.6
90.1
90.9
91.3
91.6
92.3
92.5
93.7
94.4
94.8
95.8
96.2
97.0
97.5
98.6
99.2
99.6
100.3
100.7
101.8
102.9
104.0
104.8
105.1
105.8
106.3
107.6
108.4
109.5
110.8
111.3
112.3
112.4
113.3
2_8399_678
UMN_6379_106
0_4859_P AV
0_9773_820
2_6329_219
1_3327_428
0_1979_274
0_16974_237
0_13240_663
0_16132_589
0_12267_140
0_3780_226
0_11754_278
2_10111_710
0_4073_165
0_5359_96
2_10066_203
2_138_P AV
UMN_777_308
0_13114_223 0_4077_208
0_8334_542
0_14085_695
0_12552_187
2_9501_229
0_12248_365
0_9323_710
0_8465_300
UMN_660_123
2_8840_743
2_4902_226
0_14868_348
0_6366_761
0_2683_667
0_18885_564
2_2180_207
0_5798_516
2_6050_469
0_5995_141
UMN_4163_439
2_1630_358
0_1852_340
0_2220_90
0_17159_154
0_6072_349
0_5936_457
0_18468_288
2_5911_451
0_13722_456
0_313_488
0_4413_122
0_14228_411
0_2006_220
0_10108_106
0_2055_210
0_14592_560
0_14191_226
2_4021_119
0_4421_195
0_4094_116
0_15300_263
2_3228_727
2_3128_582
2_8507_958
2_2430_404 0_8846_98
0_14740_254
0_11443_348
0_5091_174
0_8404_373
0_16864_567
0_15944_309
0_3963_170
2_2216_380
0_16355_626
UMN_2893_232
2_6533_607 0_14140_357
0_13135_220
UMN_671_240
0_11661_556
UMN_1653_242
0_18003_723
UMN_2701_472
2_2864_504
0_5158_317
0_5146_181
0_1651_277
0_4943_207
2_8922_106
2_2467_654
2_613_389
0_14520_509
0_11339_231
0_17735_519 2_7567_293
0_16300_393 0_1438_135
2_9232_170
0_18636_191
2_9490_515
0_4901_250 0_6364_281
0_2477_585
2_9757_542
2_1114_196
2_10207_523
0_15097_123 0_12048_276
0_18686_513
0_4760_371
0_3933_617
2_2829_201
0_10240_426
0_12307_734
0_16975_138
0_12219_167
0_9601_548 0_16169_479
0_15697_117
0_1511_346
0_18413_552
2_798_272
0_16665_218
2_5021_78
0_12995_501
0_9197_652
2_9733_186
0_14916_159
0_14826_629
UMN_5588_84
0_5613_179
0_1355_241
0_4218_141
UMN_1069_227
2_10349_226
0_13378_389
2_6959_357
0_7688_160
2_7009_498
2_8612_209
2_3131_293
0_1097_366
0_5284_622
2_5420_670
0_7239_597
0_13988_134
0_18100_246
0_12559_142
0_9844_P AV
0_8872_294
0_18478_394
0_8711_579
0_16502_222
2_9235_272
0_5350_109
0_263_362 0_17091_304
0_15382_160
0_16808_352
0_7598_186
2_4557_181
0_16138_133
0_5266_519
0_2885_606
0_17241_259
UMN_3759_385
0_5343_604
0_8876_374
2_2878_448
0_16120_582
UMN_2296_316
0_17432_699
2_5769_334
0_7034_453
0_13401_733
0_1791_218
UMN_6398_194
0_5149_386
0_9380_248
UMN_3165_420
0_6613_P AV
UMN_2675_254
0_14858_111
0_3261_611
0_381_307
0_634_311
UMN_5156_592 0_16330_270
0_8926_187
0_14439_483
2_3144_310
2_521_211
UMN_2688_418
2_2737_725
UMN_6508_359
0_5340_219
0_18911_145
0_7605_101
0_8798_421
0_2858_P AV
0_18497_367
0_7876_435
0_14699_401
UMN_2174_239
LG 4
0.0
5.4
6.4
8.6
9.8
11.0
11.4
12.9
14.4
14.6
15.1
15.4
16.4
18.0
18.4
18.7
19.7
20.3
21.1
21.6
22.4
23.1
23.5
24.1
25.0
25.6
25.9
26.6
26.8
27.4
27.7
27.9
29.0
29.9
30.2
31.0
31.5
32.0
33.0
33.7
34.4
35.1
35.6
36.5
36.9
37.7
38.3
38.9
39.8
40.9
41.2
42.0
43.1
43.8
44.0
44.4
44.9
45.6
45.9
46.4
47.1
48.1
48.4
48.9
49.2
49.6
49.9
50.3
50.5
51.6
52.0
52.5
52.6
53.5
53.8
54.0
54.1
54.3
54.8
55.2
55.9
57.6
57.7
58.2
58.7
59.0
59.4
59.8
60.1
60.5
61.1
61.2
61.3
61.9
62.4
63.4
65.2
65.8
66.3
66.6
66.8
67.1
67.5
68.2
68.3
69.6
69.9
70.1
71.0
71.4
71.9
72.4
73.1
73.4
73.5
74.2
75.0
75.4
75.9
76.5
77.3
77.5
77.7
78.1
78.8
79.5
79.7
80.3
80.8
81.2
82.1
82.3
82.5
83.4
84.2
84.3
85.9
86.1
86.8
87.3
87.7
88.2
88.6
89.4
90.5
91.2
91.6
92.1
93.1
93.5
94.5
95.6
95.9
96.7
97.9
98.7
99.2
100.0
100.2
100.8
101.9
102.9
103.6
103.9
104.4
104.5
104.6
105.4
106.3
107.1
107.7
108.1
109.6
109.9
111.0
111.9
112.7
114.1
114.5
115.2
115.6
117.3
118.0
118.4
119.8
121.0
122.5
124.8
2_944_356
2_9526_689
2_6003_160
0_18482_257
0_607_131
0_18384_570
UMN_2291_100
0_11528_416
2_5746_294
0_5012_435
UMN_5553_170
0_5483_209
0_15866_27
0_14038_652
0_10754_618
2_1790_320
2_8343_601
UMN_2565_98
UMN_2590_280 2_8554_759
0_16012_420
0_14601_632
2_2742_184
0_14949_507
0_15991_724
UMN_4564_390
0_3061_518
2_4263_331
2_3387_121
2_7249_164
0_8350_245
2_5128_232
0_3641_129 0_6832_490
0_9680_400
0_5610_529
0_7479_264
0_8160_162
0_13535_258
0_17139_118
0_11482_661
0_14529_285
2_3646_160
2_1564_P AV
UMN_1229_PAV
2_5868_658
2_6847_725
2_229_243
2_7346_451
0_16998_251
0_5204_526
UMN_4691_148 2_5103_484
0_10449_444
2_378_464
0_10083_351
0_8467_672
0_18063_312
UMN_4552_77
2_9214_643
2_3374_209
0_13153_425
2_1443_364
UMN_695_194
2_2959_72
0_18157_250
0_13947_114
UMN_728_399
UMN_849_246
2_6202_163
0_6435_641
2_2556_563
0_14375_381
UMN_662_336
2_1627_335
0_7344_181
UMN_3823_59
0_17396_305
UMN_4959_217
0_13203_365
0_6860_529
0_14249_205 0_6392_776
0_13040_337
2_1744_214
0_7363_292
2_9014_673
2_1379_129
UMN_2806_84
0_8317_385
2_746_88
0_12917_337
0_6375_498
0_13745_360
UMN_1373_145
0_4792_223
0_5767_570 0_15813_124
0_3748_603
0_12636_369
0_1150_496
2_6518_294
0_4345_180
0_16687_395
UMN_5506_139
2_2501_85
0_17660_207
2_2944_302
2_1870_484
0_2255_567
0_2839_678
UMN_2473_70
UMN_7373_1019
0_2213_201
0_4364_232
0_17557_407
0_2009_224
UMN_3039_342
0_14184_288
UMN_4763_143
0_6306_102
0_849_175
0_12356_460
2_8371_118
0_11184_187
0_6814_290
2_9905_653
0_364_568
0_9588_501
0_10194_258
0_13380_356
2_5983_185
0_4207_83
0_6564_377
0_17559_367
2_8244_376
UMN_2775_151
2_5530_487
UMN_1352_323
2_4749_622
UMN_488_106
2_8105_287
0_1872_638
0_2337_417
0_1729_305
0_17845_617
UMN_5319_213
2_8428_203
2_9916_697
2_8731_423
2_9637_879
0_16785_237
0_8796_628
0_13391_629
0_15142_450
0_16771_174
2_1215_121
0_6083_178
0_13422_425
0_3243_794
0_13515_485
UMN_797_146
1_3187_760
0_13042_339
2_5806_586
2_6424_88
UMN_3551_430
0_8315_159
UMN_2667_154
0_2210_116
0_14812_423
0_4225_102
0_7033_384 2_10295_384
0_15286_211
2_7323_689
0_17830_341
2_8290_67
UMN_3921_251
0_17212_737
0_9893_148
0_10987_725
2_1747_817
0_10892_98
2_6301_616
UMN_2642_47
UMN_2626_171
0_15137_201
2_3926_347
2_8051_155
2_5855_275
2_1250_587
UMN_2368_332
2_1253_521
2_8021_123
LG 5
0.0
7.4
8.5
9.8
10.6
10.8
11.3
12.1
12.3
12.9
13.7
14.0
15.1
15.4
15.9
16.2
16.3
18.2
18.9
19.2
19.5
20.4
20.6
21.1
21.3
21.5
21.8
22.5
23.4
24.0
24.6
24.8
25.2
25.5
25.8
26.1
26.5
27.9
28.2
28.7
28.8
29.3
29.6
29.8
30.0
30.4
31.1
31.4
31.5
31.8
32.0
32.4
32.7
32.9
33.6
33.8
34.1
34.2
34.3
34.5
34.6
34.7
34.8
35.0
35.1
35.2
35.4
35.8
35.9
36.1
36.3
36.4
36.9
37.1
37.6
37.9
38.0
38.3
38.7
39.0
39.2
39.4
39.5
39.6
39.9
40.7
40.8
40.9
41.5
41.6
41.7
42.2
42.5
42.9
43.1
43.2
43.5
44.0
44.2
44.3
44.7
45.5
45.8
46.0
46.1
46.2
46.3
46.5
46.7
46.8
46.9
47.1
47.3
47.4
47.9
48.0
48.2
48.5
48.6
48.9
49.0
49.1
49.3
49.5
49.6
49.8
49.9
50.2
50.3
50.4
50.5
50.6
50.8
50.9
51.1
51.4
51.7
52.1
52.3
52.5
52.8
53.2
53.7
53.9
54.0
54.2
54.6
54.9
55.5
55.9
56.0
56.7
57.0
57.6
57.7
57.9
58.1
58.4
58.8
59.4
60.0
60.8
61.4
61.5
61.6
62.2
62.8
63.4
63.8
63.9
64.0
64.2
64.6
65.2
65.3
65.4
65.5
65.7
66.1
66.5
67.0
67.3
68.0
68.5
68.7
68.9
69.0
69.4
69.8
70.5
70.8
71.0
71.7
71.9
72.2
73.1
73.2
74.0
74.1
74.5
76.2
76.5
76.6
77.8
77.9
78.0
78.5
78.9
80.5
81.2
81.5
82.0
82.8
83.1
83.9
84.2
85.0
86.0
87.3
88.1
89.0
89.7
90.7
92.1
92.8
93.0
93.8
95.0
95.5
96.4
96.7
97.6
100.2
105.1
0_6174_P AV
0_4910_264
0_2530_372
0_8339_251
0_9292_511
0_12765_141
UMN_6022_389
0_14681_138
2_6360_302
2_8908_231
2_10394_109
2_9107_470 0_12885_215
2_9047_462
0_17963_530
0_16878_342
0_7438_256
0_7618_177
0_842_269
2_1071_560
2_10480_515
UMN_1989_154
UMN_2460_144
0_2145_181
UMN_3623_99 0_5366_662
0_11162_213
UMN_823_250
0_11888_188
0_3176_687
0_17145_256
UMN_1378_111
0_8718_166 2_4856_497
0_10210_667
2_4883_484
0_14259_284
0_3499_402
UMN_4170_210
UMN_4870_132
0_15861_116
2_2306_83
0_14741_244
0_9155_574
2_10078_615
2_5429_544
0_2498_163
0_2495_400
0_9301_393
0_8598_441 0_7572_470
0_969_149
2_8860_787
2_8160_236
UMN_2948_182
0_1094_432
0_1804_245
0_7527_532
0_7578_423
2_9132_477
0_12397_85
UMN_737_249
2_218_907
2_6560_113
0_15714_192 UMN_1157_198
0_8359_233
2_3181_348
0_2249_566
UMN_2713_131
0_14129_388
0_2259_183
2_6109_478
0_4465_508 0_12147_475
2_3359_332
0_14585_151 0_2599_485
2_9227_256
0_13710_154
0_8089_116
0_14550_690
2_2194_442
UMN_6629_439 UMN_2753_361
0_16458_343
0_18493_75
0_12686_610
0_1144_329
0_17724_205
0_8764_119
0_5505_326
0_10329_276
0_5467_373 0_2086_294
UMN_3645_124
2_8080_374
0_16574_368
0_11847_65
0_13913_135
UMN_4969_253
0_11141_606
2_3361_319 0_8390_444
2_3497_159
0_10556_333 0_8802_159
UMN_3234_448
0_14421_693
0_13948_174
0_3084_512
0_2323_495
0_7999_46
0_7496_469
UMN_2398_199
2_6686_323
0_3699_583
0_7206_572
2_2866_678
2_5897_654
UMN_3790_108
0_3056_781
2_2333_466 UMN_3640_378
0_17416_297
0_5831_197
0_10651_217
0_15394_155
0_8958_274 2_4311_513
2_7129_120
0_8252_302
0_10171_614
UMN_5077_190
0_14747_524 0_16158_196
UMN_4983_212
0_11020_689 2_9333_703
1_210_646
UMN_1221_74 2_10191_626
UMN_1197_253
0_8028_172 0_8984_526
0_3856_374
0_11492_578
0_9768_336
0_3266_181 0_17863_676
UMN_3789_138
2_323_226 0_6522_474
0_18372_618
0_14252_208
0_1823_455
0_15868_411 UMN_678_331
0_4499_64
2_2815_170 2_9575_713
UMN_7467_142
0_13222_730
UMN_4551_63
0_16495_734
0_9987_389
0_16711_259
0_11525_426
0_13167_405
2_6178_333
0_9477_180
2_1850_359
2_507_711
0_6741_409
0_10732_360
0_16773_139
2_1621_442 UMN_4919_249
2_6328_325
0_18871_315
UMN_2719_PAV
0_18715_160
0_2369_551
UMN_2213_153
0_6233_683 0_10755_P AV
0_2189_524
0_1643_146 0_14919_613
2_4267_218
0_17469_587
0_14559_194
0_13121_215
UMN_6638_84
UMN_1861_265
0_506_475
2_7725_691
0_16220_472
0_18195_382
0_18834_710
0_2038_328
0_9291_243
0_8621_372
0_9396_214
UMN_2847_80
0_706_460
2_5663_827
UMN_1011_132
2_8769_460
0_9779_597
UMN_2007_104
2_5076_625
0_967_314
0_15180_751
0_1396_556
0_18014_692
2_1069_382
0_9383_335 2_9190_834
0_17308_P AV
0_13196_667
0_9946_369 0_12270_162
0_1522_259
0_14850_364
0_18093_344
0_18249_681
0_1622_92
2_3440_489
0_10303_583
2_2217_739
0_13221_227
0_10245_543
0_15220_98
UMN_2789_236
0_5581_117
2_3236_517
0_16609_P AV
UMN_1177_138
2_5921_254
2_10372_685
2_6319_227 0_16332_336
0_12936_136
0_804_234
0_1669_426
2_10130_330
0_15017_360
UMN_4754_129
2_3540_213
UMN_1792_140
0_16841_298
2_4066_574
0_10432_251
0_16639_541
UMN_5750_303
0_16594_269
2_8153_513
0_18405_427
UMN_4266_135
2_1023_903
0_13292_242
0_9456_425
LG 6
0.0
10.6
12.4
14.9
16.6
17.1
17.8
19.1
19.2
20.3
21.5
22.5
23.3
24.3
25.5
26.2
27.3
27.7
28.7
29.2
29.3
29.4
30.2
31.2
31.7
31.8
31.9
32.2
32.5
33.7
34.1
34.8
35.7
36.0
36.8
37.0
37.3
38.9
39.0
39.1
39.8
40.3
41.1
41.2
41.8
42.7
43.0
43.8
44.0
44.5
44.7
45.0
45.6
46.0
47.1
47.2
47.5
48.2
48.4
48.5
48.9
49.3
49.8
49.9
50.4
50.7
51.0
51.6
51.8
51.9
52.1
52.6
53.1
54.0
54.3
54.7
55.2
55.5
55.8
55.9
56.3
56.4
56.8
57.9
58.4
58.6
58.7
58.8
59.3
59.6
60.1
60.4
60.5
60.6
61.2
61.3
61.5
62.3
62.8
63.3
63.5
63.9
64.0
64.3
64.6
64.8
65.3
65.5
66.0
66.4
66.7
67.2
67.3
68.4
68.6
68.9
69.3
69.4
69.9
70.1
70.4
70.5
70.6
70.7
71.2
71.7
71.8
72.0
72.2
73.3
73.4
73.7
73.8
74.3
74.5
74.9
75.3
75.5
76.1
76.5
77.3
77.7
77.9
78.6
79.4
79.5
80.2
80.9
81.2
82.2
82.7
82.8
83.2
83.4
83.6
84.5
84.6
84.8
84.9
85.3
85.5
85.6
85.7
86.1
86.2
86.9
88.1
88.5
89.0
89.5
89.6
90.0
90.3
90.6
91.0
91.4
91.5
91.9
92.0
92.1
92.2
92.3
92.4
92.5
92.7
93.2
93.3
93.5
94.0
94.8
95.0
95.5
95.7
96.7
97.0
97.7
98.1
98.3
98.6
98.9
99.5
100.0
101.2
101.7
103.1
104.4
105.0
106.7
107.8
110.4
111.6
115.2
117.5
0_5484_261
0_11925_147
0_7764_394
0_15621_521
0_8862_299
0_488_351
2_5456_159
2_9928_96
0_7227_669
0_10822_387
0_8703_404
0_14596_P AV
UMN_6650_507
0_6092_557
0_14737_P AV
1_6453_P AV
UMN_1480_292
0_1597_199
0_1713_160
0_9564_690
UMN_2379_115
2_10498_171
2_6245_267
0_2819_360
2_4138_371
0_2011_78
0_16478_406 UMN_4554_149
0_14061_588
2_7489_126
0_14283_626
2_9315_197
0_7753_541
2_1803_673
0_6675_349
0_6216_59
0_12613_746
UMN_3676_173
0_3944_479
0_10746_89
0_2236_167
UMN_3584_211
0_7459_85
0_7584_313
0_8549_244
UMN_1577_361
2_5025_285
UMN_4600_412
0_4284_270
0_7544_507
0_7361_263
0_11626_143
2_1264_635
0_9693_535
2_5673_746
2_4910_669 2_9661_422
0_9244_160
0_5312_368
2_6026_100
0_13850_265
2_3677_104
0_9056_333
0_11017_592
UMN_985_134
0_16068_573
0_8685_289
0_12551_329
0_9082_496
0_13718_768
2_4205_146
0_5379_104
UMN_2464_228
0_8686_287
0_12545_363
2_4996_556
0_9096_286
0_9126_110 0_11417_240
0_17614_489
0_2500_80
0_8224_P AV
0_4519_412 0_2090_454
0_13274_495
2_8487_635
2_2750_611
UMN_5812_93
0_676_466 0_17299_735
0_15759_101
2_9055_520
2_3949_685
0_2071_463
2_1460_505
0_11241_130
0_14013_320
0_18208_454
UMN_6882_122
2_10390_636
0_10266_562
0_3521_705
0_2963_259
0_7700_315 2_8557_641
0_5652_98
0_15726_348
UMN_962_101
UMN_6029_176
UMN_7550_654
2_2240_479
UMN_675_104
0_9034_144
1_2687_P AV UMN_5594_402
0_7248_196
2_5557_562
UMN_4447_173
0_10374_424
0_12944_88
2_4048_119
UMN_3233_256
2_7254_755
UMN_6591_135
0_18645_611
0_14423_347
2_1467_864
2_401_310
2_6205_83
0_13374_325
UMN_1744_239
UMN_3180_163 0_2017_336
0_11819_204
0_11363_83
1_2880_212
0_11971_376
UMN_3760_177
2_6072_490
0_3609_512
2_7194_124
0_12229_571
0_12290_182
2_10092_676
0_17385_417
0_17433_364 2_8527_769
0_7838_322
2_6162_669
0_7914_354
0_2981_665
0_11781_588
2_7754_767
0_8452_212
0_16679_388
2_3984_602
0_5262_448
2_6623_464
0_4223_239
0_16635_189
0_6006_410
UMN_1939_303
0_13050_477
0_5436_432
0_17831_212
2_1081_217 2_10180_435
0_16259_365
0_44_310
0_12080_774
0_7969_191
2_10129_303
0_2590_173
0_14682_520
0_5511_221
2_6346_743 0_15532_121
0_828_87
0_5623_201
0_9523_617
0_9749_336
0_59_458
2_6206_541
UMN_2180_300
0_473_488
0_18507_564 0_9531_78
2_4985_682
2_2891_339
0_8804_98
0_3755_307 0_14736_269
2_2110_836 0_13984_372
0_12423_652
UMN_2470_158 2_5844_427
0_8523_609
0_806_590
0_14157_533
0_3634_129 0_8705_441
0_3688_363
UMN_2907_464
2_9431_515
2_10248_432
2_5843_230
0_3277_280
0_10926_418
UMN_3295_205
0_18848_479
0_16100_151
2_2072_330
0_14128_620
0_14193_548
0_13614_179
UMN_1484_416
2_1465_251
UMN_6442_235
2_6447_596
0_8478_148
0_9989_375
2_2332_216
UMN_1015_227
0_2827_234
0_14655_312
0_3038_610
0_17336_640
0_14146_250
0_12462_114
LG 7
0.0
4.6
6.0
12.7
13.1
14.9
18.1
18.6
18.9
19.0
19.1
21.0
21.9
22.7
23.2
23.7
24.5
24.8
25.6
26.5
27.3
27.8
28.2
28.5
29.0
29.6
30.6
30.9
31.3
31.6
32.6
33.3
33.7
34.0
34.2
34.3
34.5
35.1
35.3
35.5
36.3
36.4
36.6
36.8
37.0
37.3
38.2
38.5
38.6
39.1
39.3
39.8
40.3
40.8
41.0
41.6
42.0
42.5
42.8
43.1
43.4
43.6
44.3
44.7
45.5
45.8
46.2
46.3
46.5
47.2
47.4
47.7
47.9
48.1
48.5
48.6
48.7
48.8
49.0
49.1
49.3
49.4
49.5
49.7
49.8
50.1
50.2
50.3
50.9
51.2
51.8
52.7
52.8
53.9
54.2
54.7
55.0
55.2
55.3
55.8
56.0
56.2
56.6
57.0
57.3
57.4
57.9
59.0
59.2
59.6
60.3
60.8
61.4
61.6
61.7
61.9
62.2
62.5
63.0
63.3
63.5
63.6
63.9
64.0
64.2
64.4
65.0
65.6
65.9
66.5
66.8
67.1
67.8
67.9
68.1
68.4
68.8
69.4
69.8
70.3
70.4
70.6
71.2
71.9
72.5
72.6
72.8
74.2
74.3
74.4
74.5
74.9
75.6
76.0
76.7
76.9
77.3
77.7
78.4
79.2
80.0
80.3
80.5
81.5
81.7
82.0
83.0
83.4
84.0
84.6
85.8
86.1
86.3
86.5
86.7
87.0
87.1
87.9
88.0
88.5
88.6
88.9
89.3
89.8
89.9
90.0
90.4
91.3
92.3
92.9
94.3
95.0
95.6
95.9
96.1
96.5
96.8
97.5
97.9
98.4
99.0
99.1
99.6
100.3
101.1
101.6
102.4
103.5
103.6
104.6
105.2
105.9
106.4
107.9
108.6
109.2
109.3
110.5
111.5
111.9
113.1
113.5
115.2
117.6
119.8
125.0
2_3227_178
0_12624_89
2_4795_377
0_12123_510
2_6464_735
0_10706_711
0_18800_142
0_7592_248
0_14435_234 2_9295_467
0_17213_205
0_12079_464
0_8373_82
0_469_401
0_7095_371
0_18643_405
UMN_4110_230
UMN_5176_809
0_17203_715
0_13239_345
0_18027_462
2_5493_312
0_14408_580 2_8567_PAV
UMN_5743_677
0_12970_507
2_6037_639
0_1105_86
0_11795_127
2_10310_231
0_12250_225
2_4615_188
2_8923_297
0_12641_99
2_6113_PAV
UMN_7051_285
UMN_6338_111
2_8946_349
0_8348_736
UMN_2438_236
0_18101_268
0_15414_153
0_1034_431
UMN_3022_207 0_3336_342
0_10574_409
0_7445_165
0_18489_276
2_5402_911
0_5657_403
0_5022_299
0_8279_72
2_3820_572
0_6683_137
UMN_2595_479
UMN_4082_141
UMN_2869_PAV 0_15164_428
2_6191_625
0_7804_280
0_6061_114 0_6596_PAV
0_7779_390
UMN_7078_240
0_10342_53
UMN_6002_591
0_15107_334
0_18907_185
2_9102_632
0_11927_219
UMN_6123_145
2_2960_470
0_16488_186 0_16446_170
UMN_4710_109
0_15894_301
0_8367_304
UMN_2043_222
0_1649_261
0_12517_502
0_16079_477
0_13096_590 0_2214_274
UMN_5309_152
0_9624_529 2_3176_109
2_2527_227
0_12914_429
0_5897_407
0_5570_565
2_87_702
0_17579_455
0_1798_102
0_17475_124 2_7866_201
0_6046_207
0_8801_228
0_15795_PAV
0_13190_247
0_5572_409 0_18916_206
0_4629_225 0_2150_104
2_7216_132
0_14917_733
UMN_1790_PAV
2_4539_712
UMN_4176_173
0_17485_400
2_10103_390
2_3070_320
0_10787_598
2_5697_406
UMN_5122_155
UMN_2074_315
0_9262_175
0_1772_573
2_6461_647
1_5330_232
2_3776_245
0_14151_205
2_3947_86
2_6590_161
2_4898_287
0_14905_261
0_2641_203
0_15259_154
0_16443_618 0_18854_674
0_9909_573
2_8017_297
2_10009_494
0_18430_225
0_8894_436 0_13856_490
0_10942_684
0_8947_259
2_7649_89
0_1344_451
0_3151_735
2_6149_188
UMN_649_348
0_1310_140
0_18818_194
0_13897_83
0_4443_PAV
0_14043_671
0_14381_141
0_136_157
0_15013_266
UMN_3888_303
2_4905_340
0_1058_208
0_2318_534
0_15483_85
0_17762_196
0_5740_341
2_10270_414
0_1641_245
0_13126_469
UMN_918_124
0_10653_83
2_9633_101
2_5804_472
0_2335_358
UMN_3058_427
0_5963_174
0_1694_80
0_15790_265
0_8694_632
0_2381_357
0_14295_170
0_7858_442
0_12447_108
0_18746_161
0_6031_80
UMN_3613_177
0_771_448
0_11792_418
UMN_5758_574
2_6059_867
0_8908_249
2_7411_332
0_15249_150
0_13946_554
UMN_1072_100
2_2986_164
0_14495_466
2_4356_271
UMN_7021_316
0_10933_558 0_2784_535
0_9417_191
0_6999_444
0_15209_142
0_10488_222
0_12364_479
0_16584_344
0_13785_140
UMN_5447_254
0_2091_485
UMN_3979_247
0_15455_224
0_444_569
0_4591_296
0_830_478
0_17786_396
2_4634_200
2_9246_273
0_16567_285
2_6085_137
0_2092_345
0_9784_340
0_17542_221
UMN_3847_250 UMN_7253_167
0_629_372
0_13319_522
0_15715_217
0_13643_512
2_5227_400
0_16157_335
0_16963_444
2_4032_124
2_8106_428
0_14634_531
0_8122_248
0_9445_728
0_1743_576
0_9641_477
0_7242_334
0_17643_312
0_10602_482
UMN_2525_239
UMN_568_219
UMN_1211_393
2_881_597
0_10320_212
0_1819_420
0_689_396
2_8972_130
0_18910_725
LG 8
0.0
0.9
4.5
5.6
6.3
7.6
8.3
8.7
9.1
10.0
10.2
10.7
10.9
11.1
11.3
11.5
11.8
12.0
12.2
12.3
12.9
13.5
14.1
14.4
14.7
15.5
16.2
16.3
17.3
17.6
18.1
18.5
19.0
19.5
20.0
20.1
21.2
21.3
21.6
21.8
21.9
22.6
23.3
23.6
23.8
23.9
24.0
24.5
24.7
24.8
25.2
26.2
26.6
27.2
27.9
28.0
28.3
28.6
28.7
29.0
29.4
29.7
30.1
31.0
31.2
31.3
31.5
32.0
32.1
32.4
32.8
33.3
34.2
34.6
34.8
34.9
35.6
36.2
36.9
37.4
37.6
38.1
38.4
38.5
38.8
39.6
40.3
40.7
41.1
41.2
42.1
42.2
42.7
44.0
44.4
45.0
45.7
46.1
46.4
47.4
47.5
47.7
48.0
48.5
48.8
49.1
49.4
49.7
50.4
50.6
51.8
52.0
52.3
52.6
53.1
53.2
53.5
53.6
53.7
54.0
54.1
54.4
55.0
55.7
56.2
56.4
56.8
57.0
57.2
57.5
57.6
57.8
57.9
58.6
58.8
59.0
59.2
59.3
59.6
59.7
60.1
61.1
61.3
61.4
61.8
62.3
62.4
63.1
63.7
63.9
64.0
64.2
64.6
64.9
65.1
65.9
66.2
66.3
66.4
66.6
66.7
66.9
67.0
67.3
67.8
68.1
68.2
68.5
69.2
69.5
69.9
70.1
70.3
70.7
70.8
71.6
72.0
72.4
73.0
73.2
73.3
73.7
74.1
74.3
74.6
75.4
75.7
76.2
76.8
76.9
77.4
77.5
77.6
77.9
78.1
78.2
78.5
78.7
79.1
79.2
79.8
79.9
80.7
81.0
81.1
81.6
81.8
82.2
82.4
82.9
83.1
83.2
83.8
84.2
84.5
84.6
84.9
85.5
85.8
86.0
86.3
86.6
86.8
87.3
87.5
87.9
88.0
88.3
88.7
89.5
89.6
89.7
90.0
90.1
90.4
91.5
92.0
92.5
93.8
94.3
95.1
96.2
96.4
96.8
97.4
97.8
98.5
98.9
99.3
99.8
100.4
101.0
101.3
101.4
101.5
101.6
101.9
102.0
102.5
102.7
103.0
103.2
103.7
104.3
104.7
105.2
105.9
106.7
107.2
107.9
108.1
110.3
112.6
113.2
115.5
118.5
LG 9
0_7202_334
UMN_7457_245
0_675_82
0_14705_P AV
0_9119_371
UMN_2646_242
0_5095_239
UMN_4875_186
0_6211_410
0_14973_538
2_8242_413
0_5852_649
0_8366_312
0_7682_160
0_16535_213
UMN_2505_319
0_6389_632
UMN_2875_502
0_16559_187
0_3924_557
2_1736_178
0_2161_301
0_3800_388
0_7418_278
2_7820_139
0_17805_216
0_3019_638
2_3696_656
0_16685_297
0_14339_571
0_919_446
0_9639_226
0_14504_184
0_5780_92
0_4228_389
0_11864_260
0_2264_126
2_1405_447
0_11132_283
0_15364_430
2_4213_488
0_17877_642
0_9023_331
0_3193_183
0_14445_655
2_6126_222
0_12732_635
0_16589_226
0_14500_411
0_4330_444
0_9152_336
UMN_2325_204
2_6136_489
2_9802_808
UMN_2503_255
0_5574_177
2_168_134
0_9198_174
UMN_150_255
UMN_3821_88 UMN_6558_401
2_10366_817
2_5991_295
0_17701_548
0_14377_338
0_12955_715
0_8806_677
0_11367_490
0_16228_393 0_5609_433
0_6906_271
0_14508_199
0_8827_409
0_11081_634
2_1447_256
0_10453_433
2_5655_568 0_9691_332
0_17038_115
2_5845_445
0_9563_496
0_13874_460
2_7655_444
2_5139_462 0_6304_581
0_6334_295
2_8488_150
0_13022_545
0_3808_450
UMN_5514_114
0_302_390
UMN_5677_151
0_17803_194
0_6559_492
0_8327_303
0_14282_587
0_16829_595
0_3830_245
0_10054_749
0_6809_214
2_4514_255
0_11079_495
0_14560_324
2_6031_297
0_14891_231
UMN_5535_322
0_9806_126
0_4086_125
2_8745_563 UMN_6475_137
2_2945_278
2_8700_267
0_11170_422
0_14459_687 0_2862_474
0_12152_239
0_9538_398
0_16363_78
0_4800_381
2_8173_534
2_3589_251
UMN_5978_316
2_10296_72
0_8636_305
0_11246_289
0_14832_232
0_673_447 2_860_294
UMN_6601_120
0_16558_248
UMN_2868_383
2_9901_748
0_10379_473
0_15088_90
0_719_109
0_7465_545
0_5992_121
0_11172_229
0_4147_209
0_9941_613
UMN_3539_290
0_5865_293
0_13394_459
2_10403_226
0_14112_625
UMN_1362_177
UMN_1190_200
0_8535_97
UMN_3636_PAV
0_2305_104
0_160_533
0_6690_249
0_1779_187
0_13955_459
UMN_3299_171
0_5222_270
2_6808_604
UMN_6150_138 0_8842_292
0_4911_240
UMN_2588_108
0_2193_255
0_12896_761 0_16637_424
0_8358_502
2_9521_323
0_1143_227
2_587_619
2_3362_105
UMN_4045_202
UMN_3648_102 0_10278_499
0_3991_628
2_4861_684 UMN_1169_100
2_3941_585
0_4211_271
0_10151_351
0_9064_354
0_9413_124
0_15570_345
0_17150_139
UMN_2513_228
UMN_1356_317
0_4134_265
2_6914_292
0_4850_386
0_3448_268
0_11042_396
0_13836_524
0_4333_PAV
0_3696_301
2_9658_393
0_767_614
0_13369_199
UMN_4048_440
0_236_548
2_9310_441
0_1579_103
0_8976_500
0_850_153
0_9066_546
2_5724_398
0_2098_492
0_16763_566
0_3776_256
0_7696_164
0_4508_207
0_14290_44
0_11407_356
2_3896_385
0_1065_167 2_7269_773
0_164_277
0_8696_480
0_1232_426
0_13259_215
0_9336_254
0_10021_310
0_1982_580
0_15653_170
0_7787_380
2_4600_537
2_3707_233
UMN_3239_92
2_202_737
UMN_3773_499
0_17509_193
0_18588_100
2_10149_348
0_5464_57
0_4563_596
0_1679_415
0_3062_123
0_9341_581
0_1839_78
0_11010_558
UMN_801_233
0_9950_368
0_14587_452
2_1975_429
0_6680_182
2_1941_746
UMN_2422_298
0_18809_607
2_9283_257
0_8899_386
2_3604_222
UMN_1632_483
UMN_3013_125
2_10484_453
0_16293_77
0_11211_235
UMN_958_182
0_1071_630
2_9280_586
0_3054_388
0_17979_227
0_4303_322
2_3496_213
0_14481_355
0_7311_132
UMN_3102_114
0_6521_122
UMN_293_103
0_18481_193
0_16717_190
2_4199_469
0_16766_430 0_15175_339
0_8972_418 UMN_1076_306
0_18317_437
2_10301_810
2_6609_452
0_11012_114
2_9765_240
2_5354_720
0_16508_703
2_8696_310
2_4502_308
0_16967_158
0_16499_272
1_2145_299
2_9474_735
0_2413_325
0_11239_195
0_4103_132
2_2038_262
0_12334_P AV
2_930_208
LG 10
0_13111_390
0.0
UMN_4454_36
3.7
5.7
6.2
6.7
8.0
8.9
9.2
9.5
9.9
10.6
11.2
12.5
13.7
14.0
14.2
15.0
15.7
16.2
16.4
17.2
17.8
18.3
18.4
18.9
19.2
19.3
19.6
20.1
20.5
20.9
21.3
21.7
21.8
21.9
22.1
22.8
23.4
23.9
24.6
25.3
25.8
26.0
26.6
26.8
27.1
27.4
28.1
29.1
29.3
29.6
30.1
30.5
31.2
31.4
31.7
31.9
32.5
32.9
33.5
34.0
34.1
34.6
35.3
35.6
36.4
37.1
37.6
38.0
38.2
38.4
39.0
39.5
40.2
40.5
41.5
41.8
42.0
42.2
42.4
43.0
43.3
43.5
43.6
43.8
44.5
44.9
46.6
48.1
49.9
50.7
50.9
51.8
52.5
53.5
54.6
56.3
57.3
58.8
59.4
60.9
61.8
62.2
62.8
64.2
65.3
67.2
68.0
69.0
69.6
71.1
71.5
73.0
74.2
74.4
75.7
75.9
76.0
76.6
77.1
77.2
77.3
77.4
78.2
79.0
80.0
80.5
80.8
81.2
82.3
82.6
84.2
84.6
85.1
86.0
86.7
87.3
87.9
88.6
89.1
89.3
89.8
90.1
91.3
92.0
92.5
92.9
94.5
95.4
95.9
96.5
97.1
97.4
98.3
99.0
99.2
99.6
99.7
99.9
100.1
100.7
101.1
101.8
102.1
103.1
103.9
104.1
105.2
105.7
106.5
107.2
107.9
108.6
110.0
110.5
111.8
112.5
113.7
114.4
115.7
116.7
117.5
119.3
119.9
121.6
123.4
125.5
126.8
127.7
128.4
129.2
129.5
129.9
130.1
130.5
130.9
131.9
132.5
133.0
133.8
135.1
136.2
137.0
137.1
138.0
139.7
141.1
141.6
143.5
144.9
147.4
0_1500_209
0_15011_309
0_14067_372
2_9551_249
0_5119_P AV
0_2426_P AV
0_2760_146
0_14269_183
0_10429_453
0_16055_289
0_10781_393
0_1557_356
0_14875_481
2_3883_609
0_9080_131
2_6440_289
2_1961_265
2_6220_491
0_6020_215
0_9409_331
0_17590_666
0_18006_75
0_14614_99
2_9833_650
0_10229_319
0_1033_555
2_3389_305
UMN_1030_398
2_6203_273
0_13607_255
0_13608_405 UMN_3925_250
0_14200_222
0_17343_135
0_12110_310
2_6409_194
UMN_1440_271
0_11127_284
0_18199_298
UMN_519_289
0_8074_412
0_7834_298
0_8457_755
0_5239_242
UMN_1835_154 0_14531_221
0_1881_146
2_5809_235
0_15471_142
0_13102_452
2_6355_240
UMN_5610_606
UMN_1409_154
0_18328_260
0_16546_521
0_16934_313
2_9497_587
0_7891_287
2_8073_153
0_11790_234
0_648_314
0_3028_471
0_2723_422
0_7052_246
0_9322_255
0_7125_379
0_14811_728
0_13659_277
0_3415_563
0_3631_330
2_3444_328
0_13402_441
0_7619_525
0_7556_P AV
0_12413_670
0_12488_488
0_2204_318
UMN_1894_444
0_13835_700
0_15166_490
0_3805_286
UMN_4694_364
0_3643_479
0_14303_679
0_11140_57
0_2711_172
0_4537_345
2_6575_305
0_12956_423
UMN_213_406
0_15329_141
0_10500_435
0_6292_253
0_9308_290
0_1573_300
UMN_2354_259
0_10958_603
0_10503_377
0_10124_414
0_17561_575
0_18820_107
0_18610_233
2_1833_342
UMN_4721_259
2_3447_509
2_7965_754
0_5955_75
2_6111_833
0_9012_284
0_12663_398
0_2076_P AV
0_12785_73
2_7083_269
0_17829_617
0_17700_654
0_545_141
0_13063_595
UMN_2993_251
0_3359_294
0_1302_306
0_15124_405
UMN_4134_40
0_4265_107
0_18762_290
0_5361_426 0_12133_310
UMN_5684_530
0_7568_126
2_3868_327
0_12790_377
2_3824_657
0_15985_737
2_2142_294
0_6100_216
UMN_5003_312
0_17916_408
0_17868_178
0_433_93
0_18822_225
UMN_5717_405
0_15547_141
0_683_469
2_4358_98
0_18116_311
0_11642_528
0_7045_269
2_7488_353
0_14622_110 2_10439_334
0_13249_149
0_1868_578
0_705_372
UMN_6317_351
2_8309_512
0_18572_595
0_4922_130
UMN_1767_365
0_9546_245
UMN_5803_524 2_5973_264
0_15713_577
0_1592_198
0_2439_84
0_13957_338
0_17261_288
0_14443_549
0_14660_313
0_17861_705
UMN_3595_301
0_3518_755
0_3069_320
0_11794_626
0_3157_564
0_9862_701
0_12993_582
UMN_2022_PAV
0_17120_596
0_17912_257
0_16213_293
UMN_7016_119
2_3037_387
0_12538_747
0_10943_401
UMN_188_PAV
UMN_5063_306
2_2041_292
2_3541_390
0_2756_403
2_6324_420
0_17655_387
2_2559_607
0_13828_426
UMN_5830_343
UMN_5113_205
0_1896_577 0_14523_733
2_5726_186
0_17123_178
0_3964_659
0_16894_228
2_10028_189
UMN_507_394
UMN_3963_305
2_6731_612
0_18435_560
0_848_397
2_9847_80
0_3_553
0_1722_282
0_2184_404
2_3136_190 0_1691_434
2_10215_401
0_2654_479
UMN_6746_233
0_757_91
0_17149_166
2.4
0_3070_521
3.6
0_15463_227
4.8
UMN_2564_167
6.4
0_18137_503
9.2
2_8266_569
10.5
0_1986_632
12.1
12.4
12.7
0_11082_103
UMN_2621_209
0_18703_210
14.8
15.6
15.9
16.4
17.1
17.7
18.7
19.1
19.7
20.1
20.6
20.7
21.1
22.1
22.3
23.0
23.5
23.8
24.4
25.1
25.5
26.2
26.7
28.5
30.0
30.5
31.1
31.8
32.6
33.0
33.1
33.9
34.4
34.6
34.9
35.2
35.9
150.2
UMN_5072_469
37.3
37.7
38.5
38.9
39.3
40.4
40.7
40.8
41.0
41.3
41.5
41.8
42.4
42.6
43.5
43.9
44.0
44.4
44.7
45.5
45.9
46.6
47.0
47.1
47.9
48.5
48.8
49.1
49.5
49.7
49.8
50.6
50.9
52.1
53.0
53.1
54.4
55.3
55.8
56.4
57.1
57.6
58.5
59.0
59.8
60.2
60.7
61.1
61.7
62.3
63.1
63.4
64.0
64.4
64.5
64.8
65.4
65.6
65.8
67.7
68.1
68.6
69.2
69.6
70.3
70.8
71.9
73.5
73.9
74.6
75.0
75.7
75.9
76.1
76.7
77.3
78.3
78.9
80.0
80.4
80.7
81.5
82.2
82.4
83.0
84.1
85.3
87.2
88.4
90.7
91.4
92.2
92.3
93.3
93.8
93.9
94.2
94.3
95.9
96.4
96.8
97.2
97.5
98.0
98.3
98.9
99.0
99.3
100.5
101.0
101.5
101.7
103.2
104.0
104.9
105.7
107.8
107.9
110.2
112.3
114.1
114.5
115.8
117.2
117.6
118.9
120.3
121.4
122.8
124.6
125.4
127.3
127.6
128.2
129.7
131.1
131.4
133.2
134.8
135.2
136.4
137.1
137.9
138.6
139.2
140.8
141.6
143.0
143.2
144.3
144.8
145.8
146.8
147.3
147.7
148.3
149.3
150.1
151.0
UMN_2253_88
0_17254_661
UMN_4869_226
0_12925_686
0_1745_359
0_11413_394
2_2065_222
2_9649_843
0_14069_506
0_337_311
UMN_3837_756
0_16250_309
0_12301_559
0_6275_653
UMN_6257_226
0_6416_211
0_6242_292
UMN_5001_539
0_5558_390
0_14920_542
2_2923_398
2_6397_216
2_5780_601
2_1822_405
2_4194_359
2_1616_73
UMN_2111_PAV
0_17866_334
0_18130_338
2_4927_101
0_9700_466
UMN_572_70
2_485_664
0_11344_114
UMN_3143_342
UMN_2600_110
2_6046_306
UMN_7613_573 2_9343_280
0_17773_378
UMN_4056_167 2_9422_361
UMN_7361_356
0_12748_207
0_15834_323
0_15560_244
0_823_493
2_4755_348
2_7344_255
0_14629_770 UMN_4382_189
0_13358_647
UMN_4643_160 0_18790_137
0_17501_323
2_10322_237
0_12256_330
2_2020_773
2_7097_134
2_10436_516
0_3213_195
0_17933_608
0_16902_385 2_5055_132
2_2484_287
0_17143_68
2_10515_466
0_6323_383
0_14604_95
0_11214_215 2_4337_734
0_18619_710
0_3787_160
0_7321_145
0_6699_390
0_13755_444
UMN_2557_PAV
UMN_6326_112
0_10729_218
0_3328_593
2_9393_615
0_16837_355
0_2751_478
2_8432_180
UMN_6284_547
0_12526_596
0_16774_189
2_10184_869 0_11382_340
0_5879_P AV
0_18764_495
2_35_309
0_7674_100
2_9122_89
0_727_404
0_14024_343
2_7619_331
0_2277_444
2_4900_239
0_2224_58
2_3869_610
2_1118_660 2_6177_595
2_8935_76
0_11409_189
0_12348_364 0_7127_248
0_7214_397
2_6593_698
0_10968_94
0_9560_728
0_16879_351
0_17611_438
0_13634_330
2_9918_73
0_6106_277
UMN_2862_343
0_13151_227
0_2238_597
UMN_1439_115
2_2149_167 2_3038_695
0_7917_353
0_16697_418
0_10744_244
0_9121_550
2_4405_492
0_4809_173
0_5305_633
0_8287_677
0_15954_506
0_18422_135
2_5487_317
0_17881_524
2_101_625
2_9532_686
0_9720_190
0_14755_271
2_9294_478
0_18843_391
2_8281_720
0_18536_400
0_18187_234
2_5368_380
0_17959_480
0_18835_422
2_5143_204
2_140_171
0_17896_297
0_11281_167
2_5736_618
0_13195_245
UMN_3064_152
0_5346_819
2_7852_645
2_918_355
0_711_238
2_7807_263
2_5382_304
0_1829_389
2_8806_94
0_13455_152
2_5742_498
0_4607_487
UMN_4815_130
0_16891_217
0_4224_142
UMN_1342_474
UMN_3502_217
0_6918_465
2_6159_298
0_1964_342
2_6510_336
0_11576_392
0_4768_78
0_17947_165
UMN_5976_198
0_12818_466
0_1854_493
2_8597_341
2_6344_549
UMN_2957_480
0_16935_459
0_15315_488
2_8665_642
0_17617_642
0_1038_222
2_4486_197 0_13822_517
0_6901_511
UMN_5911_189
0_13892_240
0_3020_208
0_18795_77
UMN_6365_281
0_6774_559
0_7393_439
0_18458_395
0_12973_377
2_7352_430
0_5650_343
2_1458_375
0_13620_226
2_5791_200
0_16375_120
0_6743_148
2_4343_186
0_8482_584
2_9024_206
152.3
0_12055_P AV
152.9
153.6
153.8
0_15970_318
0_7610_298
0_3665_226
155.6
2_1820_119
157.1
0_3121_292
158.2
0_14489_528
161.1
UMN_5956_695
163.5
2_7751_355
165.7
2_5943_527
0.0
156.6
0_14642_102
158.4
2_1894_546
169.3
2_1182_352
36.5
LG 11
0.0
0_17409_479
5.8
UMN_4512_103
9.5
UMN_847_190
10.4
12.3
13.0
14.0
14.7
16.4
17.0
17.5
18.0
19.3
20.0
20.5
21.3
23.8
24.0
24.6
26.0
26.6
27.1
27.8
28.4
29.3
29.7
30.6
31.3
31.8
32.6
32.9
33.3
33.6
33.9
34.9
35.8
36.6
37.8
39.1
40.1
40.4
41.6
42.3
44.8
46.2
46.9
48.1
48.7
49.5
50.0
50.7
50.8
51.4
51.9
52.8
53.0
53.4
53.8
54.4
54.8
55.2
55.6
56.5
57.2
57.9
58.0
58.2
58.3
58.4
59.2
59.6
60.4
60.7
61.2
61.6
62.2
62.8
63.2
63.9
64.7
64.9
65.7
66.0
66.5
67.2
67.9
68.4
68.6
68.9
69.1
69.2
69.4
69.6
70.3
70.6
70.8
71.2
72.2
72.4
72.6
72.9
73.4
73.9
74.1
74.5
75.0
75.8
76.0
76.2
76.5
77.2
77.7
78.7
79.2
81.2
81.6
82.1
82.6
83.0
84.1
85.2
86.0
86.4
86.6
86.7
87.4
87.7
88.3
88.8
89.1
89.6
90.2
90.9
91.6
92.0
92.1
92.6
93.2
94.4
94.7
95.1
95.3
95.5
95.6
95.8
96.0
96.1
96.8
97.0
97.4
98.1
98.2
98.7
98.9
99.2
99.5
99.8
100.5
101.0
101.6
102.8
103.4
103.6
104.3
105.0
105.6
106.1
106.7
106.8
106.9
107.1
107.9
108.9
109.5
109.6
109.8
110.5
111.5
112.5
113.9
114.8
117.2
117.6
118.4
119.2
120.2
120.6
122.5
123.1
123.7
124.9
125.7
126.7
126.9
128.1
128.8
129.3
129.8
131.3
131.7
132.0
133.8
134.8
135.6
136.9
137.7
140.5
142.4
142.9
145.3
146.6
147.0
147.7
148.1
149.0
149.7
150.4
150.8
151.2
151.6
152.2
154.0
155.1
156.4
157.8
157.9
158.9
159.5
160.5
161.8
162.6
163.2
163.6
164.7
165.0
166.1
167.1
167.5
168.3
168.8
169.1
171.8
172.8
173.7
174.1
174.7
174.9
176.0
177.3
177.9
178.9
179.6
180.4
181.7
181.8
183.1
184.9
186.4
187.2
189.1
190.0
0_936_176
0_17892_251
2_10478_46
2_7945_354
0_630_278
0_3260_229
2_9408_165
0_6449_356 0_16801_351
0_4650_277
2_10320_650
2_10116_163
0_731_479
2_6107_607
0_344_603
0_9969_173
UMN_2711_200
2_3239_178
0_856_591
0_8845_673
UMN_887_229
UMN_4193_234
0_11446_168
0_8572_475
0_18128_124
0_10228_242
2_9009_126
2_3445_167
0_15627_129
UMN_1291_86
0_11991_219
0_18750_409
UMN_491_64
0_9473_762 0_10333_271
0_16223_385
0_15371_383
0_1216_235
0_12076_511
0_15639_207
0_11610_425
2_6310_133
0_8351_358
0_7933_365
0_5559_527
2_9772_172
UMN_3258_272
2_8466_238
0_16819_249
0_2999_471
0_2217_289
0_5994_239
2_9546_61
0_6029_102
0_11694_429
0_2527_590
UMN_6009_86
0_13213_268
0_9808_121
0_5323_479
0_188_78
UMN_4514_102
2_2172_282
0_2252_224
2_3871_693
UMN_4239_108
0_18231_194
0_12087_467
2_6180_141
0_17533_135
0_10060_PAV
2_10497_273
2_2669_183
0_10941_PAV
2_4244_124
0_1664_472
0_17083_113
0_12036_135
0_10515_373
2_2572_356
2_9003_496
UMN_527_52
0_10239_142
UMN_4378_110
2_5912_670
0_18785_468
0_9511_185
0_13523_300
0_3300_136
UMN_1866_401
0_8698_331
0_15416_582 UMN_2501_199
0_9949_251
0_17188_PAV
0_15581_132
0_5130_114
2_2795_435
0_14613_766
2_10479_478
2_3641_361 0_5973_218
UMN_1926_380
0_4685_365
0_644_327
2_3554_81
0_14674_356
0_604_296
2_7643_472
0_2946_260
2_6439_106
0_13143_213
2_9899_756
0_14388_406
2_4137_485
0_9163_703
2_7992_164
0_9390_144
0_10772_203
0_17386_512
2_6229_294
UMN_5395_450
0_4803_482
0_8280_229
0_17822_74 0_16430_117
0_14848_344 0_4384_299
0_12228_185
0_16880_444
0_14631_98 2_9435_174
0_3729_721
0_18122_606
2_4815_516
0_13732_408
0_2663_433
0_4170_450
0_14_423
0_1423_P AV
UMN_3523_395
UMN_705_488
0_18624_181
2_7326_371
2_7844_489
0_11153_180
0_6429_262 UMN_4605_296
0_14306_357 0_15938_327
2_5301_321
UMN_4149_148
2_9683_322
0_7881_438
0_5619_717
0_3806_181
0_8499_180
UMN_3241_572
0_10413_174
0_4245_189
0_2077_419
0_538_463
0_11137_PAV
2_5931_435
0_1822_248
2_4230_392 0_16445_270
UMN_437_204
UMN_6290_1091
0_10900_564
0_12282_253
0_2048_205
0_9106_427
UMN_4649_99
0_6552_125
0_13244_424
UMN_3266_220
0_10366_151
UMN_3531_161
0_18069_456
2_5980_469
0_13868_261
2_5545_378
0_14593_466
UMN_5867_139
0_11640_308
0_11_355
0_1402_350
0_421_PAV
0_11902_PAV
2_3332_206
2_5827_164
0_266_PAV
UMN_3316_268
0_14988_559
2_2268_190
UMN_3872_349
2_1909_773
UMN_1271_263
UMN_4224_167
2_10549_744
0_8476_705
UMN_601_350
0_10393_633
2_4579_731
0_7786_104
0_18691_671
UMN_3264_284
0_8053_243
0_14482_502
0_6742_P AV
0_13543_583
0_779_433
UMN_1034_381
2_3287_434
0_1803_265
UMN_280_PAV
0_16524_186
UMN_4818_48
0_5787_185
UMN_874_556
0_7262_368
0_15687_261
2_5345_558
0_13929_200
0_17220_301
2_5730_436
UMN_706_364
0_6196_623
0_2324_P AV
0_9113_P AV
0_487_311
0_18924_382
0_10505_PAV
0_6751_325
0_9242_P AV
2_4888_265
0_15941_636
2_8878_405
2_10380_395
UMN_3384_239
0_5293_P AV
0_18496_676
2_4556_557
2_9514_213
UMN_4111_111
2_9087_506
UMN_2378_389
0_12244_603
0_5461_405
0_6226_311
2_4251_123
0_8229_134
0_10614_457
2_10201_259
0_4924_260
0_14991_457
0_9317_105
0_17937_PAV
UMN_6070_263
0_18451_178
UMN_5166_307
0_13044_383
0_5978_232
0_11736_237
0_1009_P AV
0_6214_695
2_8417_453
LG 12
0.0
3.3
4.2
5.8
7.1
9.0
9.5
10.6
10.7
13.1
13.2
13.7
14.2
14.3
15.1
15.4
15.7
16.8
17.9
18.5
18.6
19.2
20.5
21.4
21.9
22.5
23.0
23.8
24.3
24.9
25.2
25.3
26.0
26.4
26.8
27.3
27.6
27.7
27.9
28.6
28.8
29.5
29.7
30.4
30.5
30.6
31.1
31.3
31.6
32.0
32.2
32.4
32.8
33.0
33.2
33.4
33.7
34.0
34.1
34.3
34.7
34.8
35.1
35.2
35.4
35.6
36.1
36.6
37.2
37.6
37.9
38.4
38.6
39.2
39.5
40.2
40.4
40.8
41.3
41.6
42.0
42.4
42.5
42.6
42.7
42.8
43.0
43.1
43.4
43.8
43.9
44.5
44.7
45.0
45.4
45.5
45.6
46.5
46.7
47.1
47.7
47.8
48.1
49.7
50.4
50.8
51.4
51.6
52.5
52.9
53.5
53.7
54.4
54.7
55.0
55.3
55.6
55.7
56.0
56.3
56.7
56.9
57.3
58.8
59.1
59.3
59.8
60.8
62.7
63.8
64.0
64.8
65.3
65.9
66.7
67.4
68.5
68.8
69.3
69.7
70.1
70.4
71.1
71.5
72.0
72.7
73.1
73.7
73.9
74.6
75.2
75.3
75.7
77.3
77.7
78.4
79.2
79.8
80.8
81.2
81.7
82.0
84.5
85.2
85.9
86.2
87.1
88.9
89.4
91.6
92.0
93.2
94.2
94.5
95.3
97.1
99.2
101.0
102.0
104.1
104.6
105.2
106.0
106.5
106.9
107.2
107.8
108.2
108.7
109.5
109.7
110.7
111.1
111.5
111.8
112.5
113.9
114.2
114.8
115.2
115.6
115.7
116.0
116.3
116.4
117.7
117.8
118.3
118.5
119.0
119.5
120.0
120.6
121.5
122.6
123.6
123.8
123.9
124.1
124.6
124.9
125.3
125.9
126.9
127.6
129.0
130.5
131.3
132.7
133.3
134.7
140.3
UMN_6145_PAV
UMN_1128_109
2_10437_243
0_10427_191
0_9670_557
2_9345_254
0_9160_388
0_3501_169
2_8987_531
UMN_3646_139
0_13769_75
2_4458_351
0_13183_515
UMN_5017_266
0_11078_659
UMN_991_325
2_1164_608
2_2479_716
0_8726_357
0_11835_252
0_16948_349
UMN_1296_614
2_6614_230
UMN_5591_102
0_8936_362
0_10007_309
0_9466_344
UMN_1189_PAV
0_13898_462
0_11772_491
2_1205_279
UMN_702_154
0_13110_327
0_9354_386
0_2616_173
0_11007_358
0_6533_194
UMN_560_126
0_572_156
UMN_3725_174
UMN_4096_339
2_6576_612
UMN_592_287 0_9194_740
2_3223_328
0_13181_437
UMN_5235_260
0_18379_434
0_16860_274
UMN_1150_252
0_6458_422
UMN_6614_79
2_5264_214
0_18742_657 UMN_540_267
UMN_6062_421
UMN_7042_387
0_12284_245
0_5695_356
0_18007_215
2_1093_646
0_13484_569
0_15374_317
0_18676_201
0_13609_471
0_8769_285
0_3314_793
0_17890_295
0_11037_279
0_12853_337
2_5641_112
UMN_1471_96
2_3801_401
0_5614_376
0_18830_460
0_10064_727
0_5038_244
2_3173_881
0_16653_262
2_6298_696
2_4375_590
2_10340_110
0_7167_425
0_12851_89
0_11451_575
0_1611_76 2_3207_388
2_4444_218
0_8870_162
0_9486_753 0_18411_219
0_10739_294
0_15758_492
0_5405_568
0_1319_227
0_12060_99
0_2703_308
UMN_4692_172 0_14894_126
0_18460_642
0_248_666
2_9090_260
0_6983_297
0_10384_343
0_954_378
UMN_3386_127
0_8803_161
0_17454_383
0_9001_61
0_12402_79
0_13315_486
0_11593_321
2_8131_552
0_14093_653
2_6221_466
0_17588_265
UMN_3238_451
2_55_457
0_10113_246 0_14730_267
2_6357_316
0_2295_187
UMN_3497_273
UMN_6280_403
0_14383_379 2_8522_743
0_8284_325
2_4174_99
2_6041_398
0_457_144
0_3251_517
2_3628_229
2_3382_510
0_11195_412
2_6429_235
0_6337_234
0_18322_627
0_17894_347
0_7654_250
0_4356_184
0_10099_195
2_4921_583
2_768_350
0_15885_561
0_1578_339
0_8335_762
2_2348_333
2_5574_470
0_10037_592
UMN_2583_273
0_16350_473
2_10071_281
0_16565_413
0_14944_705
0_14499_230
2_3018_334
UMN_5730_282
0_882_168
2_294_376
0_1069_121
0_1367_104
UMN_495_89
UMN_1974_193
0_2168_204
0_2152_222
2_6278_580
2_2210_184
UMN_2192_112
UMN_6256_135
UMN_1956_79
0_4482_519
0_5306_141
0_7115_415
0_2542_P AV
0_14956_530
0_16098_244
0_17458_292
0_17445_339
0_9201_81
0_7381_567
1_256_440
0_14369_348
0_9450_318
0_3011_434
2_8984_228
0_6456_202
0_5671_245
2_4323_399
0_4793_470
0_9571_348
0_6300_242
0_1345_520
2_9779_602
2_4342_124 0_13907_550
0_8332_390
2_5840_482
0_9865_786
2_9360_435
UMN_6177_172
UMN_5614_306
2_8381_596
0_1022_272
0_15935_227
0_15121_381
0_18457_502
2_6052_256
0_5322_335
0_15704_61
2_6128_492
0_3319_534
0_8504_471
0_12346_305
UMN_2312_300
UMN_2883_787
UMN_5180_395
0_18465_177
0_18535_163
0_13829_694
0_5132_403
0_18698_410
0_6055_394
0_9868_756
0_6536_256
0_352_449
2_9651_408
2_3542_428
UMN_6313_975
0_6738_143
2_10483_474
0_13256_217
0_17800_494
0_13760_289
UMN_2152_67
0_18620_PAV
UMN_56A_58
0_1349_P AV
0_49_175
2_4944_440
0_13093_165
100
Linkage Group 4
60
0
20
40
Eckert relative position (%)
60
40
20
0
Eckert relative position (%)
Spearman cor. = 0.99
80
Spearman cor. = 0.55
80
100
Linkage Group 2
0
20
40
60
Neves relative position (%)
80
100
0
20
40
60
Neves relative position (%)
80
100