Genetics: Published Articles Ahead of Print, published on December 15, 2005 as 10.1534/genetics.105.051227
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Pleiotropic quantitative trait loci contribute to population divergence in traits
associated with life history variation in Mimulus guttatus
Megan C. Hall*1, Christopher J. Basten†, and John H. Willis*
*
Department of Biology
Duke University
Durham, NC 27708
†
Syngenta Biotechnology
Research Triangle Park, NC 27709-2257
1
Present address:
Department of Genetics
North Carolina State University
Raleigh, NC 27695
running head: pleiotropic QTLs in Mimulus guttatus
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key words: pleiotropy, QTL mapping, complex phenotypes, comparative mapping
Address for correspondence:
Megan C. Hall
Department of Genetics
Box 7614
100 Derieux Pl.
North Carolina State University
Raleigh, NC 27695-7614
phone: 919-515-5738
fax: 919-515-3355
email: [email protected]
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ABSTRACT
Evolutionary biologists seek to understand the genetic basis for multivariate
phenotypic divergence. We constructed an F2 mapping population (N = 539) between
two distinct populations of Mimulus guttatus. We measured 20 floral, vegetative, and
life-history characters on parents, F1 and F2 hybrids in a common garden experiment.
We employed multitrait composite interval mapping to determine the number, effect,
and degree of pleiotropy in quantitative trait loci (QTLs) affecting divergence in floral,
vegetative, and life-history characters. We detected sixteen QTLs affecting floral traits,
7 affecting vegetative traits, and 5 affecting selected floral, vegetative, and life-history
traits. Floral and vegetative traits are clearly polygenic. We detected a few major
QTLs, with all remaining QTLs of small effect. Most detected QTLs are pleiotropic,
implying the evolutionary shift between these annual and perennial populations is
constrained. We also compared the genetic architecture controlling floral trait
divergence both within (our intraspecific study) and between species, based on a
previously published analysis of M. guttatus and M. nasutus. Eleven of our sixteen
floral QTLs map to approximately the same location in the interspecific map based on
shared, collinear markers, implying there may be a shared genetic basis for floral
divergence within and among species of Mimulus.
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Evolutionary biologists have long sought to understand the genetic basis for
adaptive divergence between populations with complex multivariate phenotypes.
Adaptation to a novel environment may involve evolutionary change of multiple
genetically correlated traits as the population approaches a new phenotypic optimum
(FISHER 1930; ORR 2000). If variation in individual traits is governed largely by traitspecific loci, the selected traits may be able to evolve independently (unless they are
constrained by linkage disequilibrium); whereas those that are governed by pleiotropic
loci are going to be evolutionarily constrained. The degree of pleiotropy can have
profound effects on the evolutionary trajectory of particular traits (LANDE 1979) and
therefore the nature of divergence of multiple traits between populations.
An understanding of the degree of pleiotropy affecting multiple traits sheds light
on one of the classic debates in evolutionary biology—whether phenotypic divergence
is the result of fixation of one or two mutations of large effect or due to many mutations
of small effect. Each of these two options could have different effects on the nature of
evolutionary divergence. One of the earliest views of the genetic basis of adaptation
was that phenotypic divergence was extremely gradual, consisting of many genes, each
having an infinitesimally small effect on the trait (FISHER 1930). This view of
adaptation had widespread support among early empiricists (DOBZHANSKY 1937;
HUXLEY 1942; MULLER 1949), though it was later challenged in favor of the alternate
view that adaptations were largely the result of substitutions of single genes with large
effects (GOULD 1980; GOTTLEIB 1984; TURNER 1985). The debate continued (COYNE
AND LANDE 1985; ORR AND COYNE 1992) until
more recent evidence based on
quantitative trait locus (QTL) mapping analyses allowed more rigorous testing of this
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hypothesis. To date, genetic mapping studies have provided support for both
possibilities, some where few large-effect QTLs underlie divergence (DOEBLEY AND
STEC 1991; BRADSHAW et al. 1995,1998; SUCENA AND STERN 2000; COLOSIMO et al.
2004). Other studies have demonstrated that divergence can result from many QTLs of
small effect (LIU et al. 1996; LAURIE et al. 1997; ZENG et al. 2000; FISHMAN et al.
2002). However, these patterns are generally interpreted with respect to individual
traits rather than accounting for correlations among traits. For example, consider a pair
of populations divergent for two traits, and the difference in each trait is controlled by
five quantitative trait loci (QTLs). Phenotypic divergence in these traits could be
explained by as many as ten QTLs (if each QTL were completely independent) or as
few as five QTLs (if all QTLs were pleiotropic). Because phenotypic divergence
between populations generally involves changes in multiple traits for complex
organisms, it is particularly important to account for pleiotropic QTLs. Examining
traits individually to uncover the number of QTLs controlling phenotypic divergence
could be potentially misleading if pleiotropy exists.
QTL mapping can serve as a powerful way to understand whether multiple traits
have diverged together, and it can also be used to distinguish the number and effects of
genetic factors controlling divergence in individual traits. Numerous studies have found
evidence for pleiotropy affecting multiple traits (TRUE et al. 1997; JIANG et al. 1999;
CUI et al. 2004; WESTERBERGH AND DOEBLEY 2002). Of course, fine-mapping of
pleiotropic QTLs can reveal separate, tightly linked QTLs (i.e., KNIGHT et al. 2001).
Most evidence for pleiotropy is based on overlapping genetic regions detected in
separate QTL mapping analyses for individual traits, rather than accounting for the
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correlated structure of multiple traits in a joint QTL mapping analysis. By jointmapping QTLs affecting multiple divergent traits, we can directly address whether the
genetic correlations we see are the result of broadly pleiotropic QTLs versus unlinked
QTLs affecting separate traits (JIANG and ZENG 1995).
In order to understand the genetic basis of complex adaptations, we examine the
genetic architecture of phenotypic and life-history divergence between two wild,
primarily outcrossing populations of Mimulus guttatus (yellow monkeyflower) that
differ dramatically in floral, vegetative, and life-history characters. One of the
populations we analyze consists of small annual plants that produce thin stems and
small flowers (Figure 1; Table 1), and inhabit a high elevation environment on Iron
Mountain, OR (IM). These annuals flower rapidly and a large proportion of their
meristems are reproductive (i.e., they have flowers or buds, rather than leaves). The
other population consists of large perennial plants with comparably thicker stems and
larger flowers (Figure 1; Table 1) that live in a coastal, temperate environment in the
Oregon Dunes National Recreation Area (DUN). These plants flower later than the
annuals and a large proportion of their meristems are vegetative (i.e., they have leaves
rather than flowers or buds). A reciprocal transplant study reveals evidence for strong
local adaptation in each of these two populations (HALL 2005).
The transition between annual and perennial life-histories is common among
plants, it is associated with multiple phenotypic differences, and it is the likely response
to different ecological conditions (STEBBINS 1974). Do life-history characters such as
timing of flowering and proportion of floral versus vegetative meristem growth have a
genetic basis? Or are these characters affected only by different environmental
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conditions? Many life-history characters clearly have a genetic basis (PATERSON et al.
1995; HU et al. 2003; WESTERBERGH AND DOEBLEY 2004). In addition, life-history
characters can respond plastically to changing environmental conditions, including the
allocation to sexual and vegetative reproduction (OGDEN 1974; SCHMID AND HARPER
1985; VAN KLEUNEN et al. 2001) and timing of flowering (ALONSO-BLANCO et al. 1998;
WEINIG et al. 2002). One of the questions we address in this study is whether lifehistory divergence between two populations of M. guttatus has a genetic basis or is
completely environmentally dependent.
We employ a QTL mapping analysis to understand the genetic basis of
divergence in characteristic floral, vegetative, and life history traits between these two
populations. Specifically, we address whether the individual traits differentiating the
two populations are affected by many or few QTLs, and whether these QTLs are of
large or small effect. We also use multitrait composite interval mapping to clarify
whether multiple traits are controlled by pleiotropic (or tightly linked ) loci or are
controlled by separate genetic loci. Finally, our analysis offers the opportunity to
compare the genetic architecture controlling floral trait divergence both within species
(our intraspecific study) and between species, based on a previously published analysis
that examines many of the same floral traits in an interspecific map of M. guttatus and
M. nasutus (FISHMAN et al. 2002). Floral traits are excellent candidates for
investigating the genetics underlying divergence. Floral characters show tremendous
variation within and between species, much of which is heritable (CAMPBELL 1996;
GALEN 1996; FISHMAN et al. 2002; HANSEN et al. 2003). Variation in floral
morphology is often the result of adaptive divergence (GRANT and GRANT 1965; KIM
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and RIESEBERG 1999; FISHMAN et al. 2002). Here we compare the number, effects, and
locations of QTLs controlling floral traits and infer whether or not there is a shared
genetic basis controlling floral traits between and within species.
MATERIALS AND METHODS
Study System: The M. guttatus species complex (historically Scrophulariaceae,
order Lamiales) is highly polymorphic and geographically widespread throughout
western North America (PENNELL 1951; VICKERY 1978; SWEIGART and WILLIS 2003).
Populations differ in morphology, mating system, life history strategy, and habitat type.
Though widely studied in ecology and evolutionary biology, taxonomic classification of
the M. guttatus species complex has been inconsistent. Some authors have sub-divided
this taxon into seventeen morphologically distinct species (PENNELL 1951), while others
designate just a few subspecies within the complex (HITCHCOCK and CRONQUIST 1973).
Mimulus guttatus (2n =28) is the most common and variable species in the complex.
Populations of M. guttatus can exist as either annuals or perennials, with
perennial populations widespread along the Pacific coast. Perennial plants can also be
found inland along streams, rivers, and drainage ditches where there is year-round
moisture. Some authors consider the coastal perennial forms, M. guttatus var. grandis
Greene, to be distinct varieties from the inland perennials, M. guttatus var. guttatus
(HITCHCOCK and CRONQUIST 1973). Annual populations are typically located at inland
sites like seepy hillside meadows, rocky cliff faces, or road-cuts that have abundant soil
moisture in the spring and early summer, but little during the late summer. These small
annuals are called M. guttatus var. depauperatus (Gray) Grant by some authors
(HITCHCOCK and CRONQUIST 1973). Plants from these populations are facultative
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annuals due to seasonally dry environmental conditions, and they can be maintained
indefinitely in standard greenhouse conditions. Flower size and vegetative traits differ
dramatically between annual and perennial populations, with annuals typically being
substantially smaller than perennials for most size-related traits in the field (M. HALL,
unpublished results) and in common garden experiments (Figure 1; Table 1). Annual
plants flower earlier than perennial plants. Annuals also produce more floral than
vegetative meristems compared to perennial plants, which we refer to as proportion of
reproductive allocation.
For this analysis, we focus on two populations of M. guttatus that have a high
degree of divergence in overall size, habitat, and life history. The well-studied IM
population consists of small-flowered, diminutive annuals that live on Iron Mountain, in
Oregon’s western Cascades (WILLIS 1993). These plants are predominantly outcrossing
(WILLIS 1993; SWEIGART et al. 1999) and have a short period of growth and
reproduction, with germination occurring in either the fall or spring, flowering
occurring over a 3-5 week period in June through early July. All plants at this site die
by mid July. The montane environment experiences fluctuations in temperature and
precipitation ranging from below freezing and over six meters of snow in the winter to
well above 40° with little or no rainfall in the late summer months (HALL 2005). The
DUN population consists of large-flowered perennial plants with larger, nearly
succulent leaves that inhabit the temperate environment of Oregon’s coastal sand dunes
south of Florence in the Oregon Dunes National Recreation Area. At this site,
temperatures vary less than 20˚ from summer to winter, and there is continual moisture
available to plants from heavy rain (up to 2000 mm in the winter months) and coastal
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fog (HALL 2005). DUN plants typically germinate in the fall and flower from early
June through October or November.
Generation of F2 mapping population: We generated an F2 mapping
population from IM and DUN parents to investigate the genetic basis for quantitative
trait differences between these populations of M. guttatus. The IM parent is a highly
fertile inbred line (IM62) derived from the Iron Mountain site. This parental line is the
same parental line used to construct the previous interspecific map (FISHMAN et al.
2001). Two separate wild-collected plants (DUN1 and DUN2) were used as parents
from the DUN perennial population. Each of the DUN parents was reciprocally crossed
to IM62 to produce four sets of F1 individuals, and one plant from each class was
selected at random to produce the F2 generation. One F1 plant (IM62 maternal parent,
DUN1 paternal parent) was reciprocally crossed to another F1 plant (DUN2 maternal
parent, IM62 paternal parent) to produce two sets of F2 seeds. The other two F1 plants
were also reciprocally crossed to each other to produce two other sets of F2 seeds, for a
grand total of four sets of F2 seeds. Each F2 individual therefore has a nuclear genome
derived from contributions of three individuals (IM62, DUN1, DUN2) and a
cytoplasmic genome derived from either the DUN or IM population. Notice that this
crossing design enforces outbreeding with respect to alleles derived from the DUN
population, but allows for homozygosity of alleles from the highly viable and fertile
highly inbred IM62 line, thereby reducing the potential for transmission ratio distortion
in F2 progeny to be caused by inbreeding depression. All seeds used in the common
garden experiment described below were the same age: the F1 plants and the parental
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plants were recreated by selfing IM62 and reciprocally crossing DUN1 and DUN2 at
the same time as the creation of the F2 lines.
In June 2000, we grew 100 IM62 plants, 50 each of the DUN1 x DUN2 plants
and their reciprocal crosses (see below), and 200 F1 plants along with the F2 mapping
population (N = 600 total, with each of the four F2 classes equally represented) in
individual pots in a common garden experiment at the University of Oregon Department
of Biology greenhouse. Plants were grown in 4-inch pots filled with sand over a thin
layer of hemlock bark on the bottom, to prevent sand from escaping the pot. A thin
layer of organic potting mix (Black Gold potting soil, Sun Gro Horticulture, Bellevue,
WA) was sprinkled on top to prevent seed dessication. We planted five seeds of the
same class per pot on June 12, 2000, and pots were placed in flats in a fully randomized
design in the greenhouse during the long days when flowering begins for each of the
native populations. Plants were watered as needed 2-3 times daily and left unfertilized.
Germination rates were measured per pot; and seedlings were thinned to the centermost
individual after germination, two weeks after planting.
Phenotypic analyses: We measured 20 floral, vegetative, and life history traits
on all plants that flowered using an engineering ruler with gradations to the nearest
100th of an inch. All measurements were converted into millimeters. To estimate
overall plant size (vegetative characters), we measured the length, width, and thickness
of the first two leaves on each plant at the time of its first flower. At this time, we also
measured stem thickness at the base of the plant (between the first true leaves and the
cotyledons) and the internode length between the first and second set of true leaves.
Stem and leaf thickness were measured with digital calipers to the nearest hundredth
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millimeter. If the vegetative traits continued to grow after flowering, there could be an
association of these traits with flowering time. However, we chose to measure traits for
all plants at a defined developmental stage. We also recorded the date of flowering for
the first two flowers per plant and used the average of these two days, and for each of
these flowers we measured six floral size traits (corolla width, corolla length, corolla
tube length, style length, stamen length, and distance separating stigma and nearest
anther). For a diagram of these floral traits, see FISHMAN et al. (2002).
In addition to date of first flowering, we measured a number of other life-history
traits. After 10 weeks from planting, we counted the total number of floral and
vegetative meristems on each plant. Floral meristems were scored as any stem bearing
flowers or flower buds, and vegetative meristems were lacking any flowers or flower
buds. Percent reproductive allocation was estimated for each plant by dividing the
number of floral meristems by the total number of meristems (floral + vegetative).
After 16 weeks, the soil and sand were washed from the roots of each plant and the
plants were placed in labeled paper bags. Each bag was placed in a drying oven for 3
days on lowest heat to remove all of the moisture from the plants. Dried plants were
then weighed on an electronic balance to the nearest tenth of a gram with their roots
(total mass) and with the roots removed just below the cotyledons (above-ground mass).
The soil granules were nearly impossible to remove from the roots, particularly for the
annual plants, therefore the total mass was not included in the data set.
We measured male fertility by collecting the anthers from the first two flowers
on each plant and placing them in 60 µl of lactophenol aniline blue stain (KEARNS and
INOUYE 1993). We counted the number of viable (darkly stained) and inviable pollen
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grains in a 0.8 µl subsample of each collection under a compound microscope. The
aniline blue dye stains intact (starch-filled) cytoplasm, which may also be present in
some inviable grains, therefore our estimates of pollen fertility may be slightly relaxed.
Total number of pollen grains was also calculated as a summation of viable and inviable
pollen grains. For each plant, we divided the total number of viable pollen grains by the
total number of pollen grains measured to estimate the percent viable pollen per flower.
For each trait measured, we calculated the mean and variance for each class (IM
parent, DUN parent, F1, and F2) and for each of the F2 classes separately. The F1
hybrids are mostly genetically homogeneous, so the phenotypic variance of this class
reflects just environmental variance, whereas the F2 phenotypic variance reflects both
environmental variance and the segregation of alleles at genetic loci differentiating the
parental lines. For the environmental variance (VE), we used the F1 phenotypic
variance. We also estimated the average variance within F1 classes to account for any
differences among classes. These estimates were very similar, though slightly smaller,
on average, to the VE calculated from the F1 phenotypic variance, therefore we simply
used the latter. The environmental standard deviation (ESD) for each trait was
calculated as the square root of VE. We calculated the genotypic variance as VG =
Var(F2) - VE, and then estimated the broadsense heritability for each trait as H2 = VG/
Var(F2). Genotypic correlations were estimated by calculating covE from the F1 class
and for each pair of traits and then estimating covG = cov[F2] - covE. Genetic
correlations (rG) among traits were calculated as covG (i, j)/sisj, where covG (i, j) is the
genetic covariance between traits i and j and si and sj are the square roots of the
genotypic variances of the two traits, respectively. Genetic correlations were not
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calculated for traits with negative estimates of H2. Phenotypic correlations were also
calculated among traits for the F2 hybrids.
We performed a simple statistical test for epistasis (LYNCH and WALSH 1998)
using analysis of variance (ANOVA) to calculate the class means and sampling
variances for each trait. These were used to calculate the test statistic,
∆ = z (F2) -
 z ( P1) + z ( P2 )

+

4

z (F1) 
,
2 
(1)
where z is the trait mean for each class. In the absence of epistasis, ∆ is expected to be
zero. The ratio ∆ / Var (∆) is a t-test for epistasis, or a rejection of a purely additivedominance model. In addition, the ratio of ∆ to the F2 mean expected under the
additive-dominance model (E[F2]) is a relative measure of the severity of hybrid
breakdown.
Linkage map construction: In a previous analysis, we constructed a linkage
map for this F2 population (N = 539) at 154 AFLP, microsatellite, and gene-based
markers (HALL and WILLIS 2005). The linkage map spans 1482 cM Kosambi, includes
14 linkage groups (which presumably correspond to the 14 pairs of chromosomes in M.
guttatus), and has an average interval length of 15 cM. We detected transmission ratio
distortion in nearly half of all markers, though it was not severe enough to eliminate
entire genotypic classes. Our most distorted marker, LFY, had a normal percentage of
DUN homozygotes, excess numbers of IM homozygotes (216 observed vs. 119
expected), and fewer than expected heterozygotes (143 observed vs. 238 expected).
Although the presence of distorted markers diminished some of our power to detect
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QTLs, distortion was not so severe as to eliminate entire genotypic classes, therefore we
feel it had a minimal impact in this study.
Quantitative Trait Locus analyses: We mapped QTLs for 20 single traits
using composite interval mapping (CIM; ZENG 1993, 1994) and for subsets of traits
using multitrait composite interval mapping (MCIM; JIANG and ZENG 1995) using QTL
Cartographer v. 1.17 (BASTEN et al. 2002) and QTL Cartographer Windows 2.0 (WANG
et al. 2005). For each trait, the CIM procedure tested the hypothesis that a test site in an
interval between adjacent markers had a QTL affecting the trait, while accounting for
genetic background by using multiple regression on additional markers as cofactors.
The cofactors included in each CIM model were determined by forward-backward
stepwise regression, with the critical P-values set at 0.05. Tests were performed at 2cM intervals with a flanking window size of 10 cM. The likelihood ratio (LR) test
statistic is –2 ln(L0/L1), where L0/L1 is the ratio of the likelihood under the null
hypothesis (there is no QTL at the test site) to the alternative hypothesis (there is a QTL
at the test site). Experiment-wise significant levels (α = 0.05) were determined by
permuting the phenotypes against the genotypes 1000 times for each trait (CHURCHILL
and DOERGE 1994).
Because many of the traits (particularly the floral and vegetative characters)
were highly correlated and the single-trait CIM analyses identified QTLs for multiple
traits mapping to the same interval, multitrait composite interval mapping (MCIM) was
used to jointly map QTLs affecting a) six floral traits (corolla width, corolla length,
corolla tube length, stamen length, and style length), b) four vegetative traits (stem
thickness, leaf width, internode length, and leaf thickness), and c) six general traits
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(including representative floral [corolla width, corolla tube length], vegetative [stem
thickness, leaf width], and life-history traits [days to flower, percent reproductive
allocation]). These traits were chosen based on their relatively high heritabilities and
genetic correlations. The MCIM procedure is similar to single trait CIM, but the LR
test statistic is -2 ln(L0/La), where La is the likelihood under the alternative hypothesis
that the test site is a QTL affecting any of the included traits. MCIM provides
additional power and accuracy for mapping QTLs by taking into account the
correlational structure of the phenotypic data (JIANG and ZENG 1995). Experiment-wise
significance levels (α = 0.05) were determined by permuting the phenotypes against the
genotypes 1000 times so that the correlations between traits were maintained
(CHURCHILL and DOERGE 1994).
To determine if QTLs detected by MCIM had pleiotropic effects on the traits in
each analysis, individual MCIM likelihood ratio test values were examined for each
position where joint mapping indicated the presence of a QTL (JIANG and ZENG 1995).
Pleiotropy was indicated by the rejection of the null hypothesis of no more than one
trait having a LR test value greater than a significance threshold value of 5.99 (X20.05,2)
at a particular QTL position as determined by the model parameters estimated jointly by
MCIM. This test does not require corrections for multiple tests along the genome
because each position is fixed prior to the test, which increases the power to detect QTL
effects on multiple traits (JIANG and ZENG 1995).
RESULTS
Phenotypic analyses: Plants from the two M. guttatus populations grown in a
common garden were highly divergent for many of the traits measured, indicating that
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these differences have a genetic basis (Table 1). For floral characters, the parental lines
differed by 4-11 environmental standard deviations (ESDs), and the mean of the DUN
plants was greater than the IM line. The parental lines differed by 2-7 ESDs for
vegetative traits. The DUN plants were larger for all vegetative characters, with the
exception of internode distance. All of the floral and vegetative traits appeared to be
additive, with the F1 and F2 means nearly intermediate between the two parental means.
Broadsense heritabilities (H2) were small to moderate for the floral and vegetative
characters (0.22-0.65; Table 1). The variance of the DUN parents was typically larger
than either the IM parents or the two hybrid classes, consistent with the variance scaling
with the mean.
Life-history and male fertility traits showed less difference compared with floral
and vegetative traits between parental lines (0.4-3 ESDs) and had primarily low
heritabilities (0.031-0.61; Table 1). The IM plants flowered earlier, produced many
floral meristems and few vegetative meristems, had reduced mass, and produced less
total pollen on average than the DUN parental lines. The life-history and fertility traits
do not appear to be entirely additive. For example, for flowering date, both F1 and F2
hybrid classes flowered early like the IM parent, which is consistent with partial
dominance towards the IM parent. However, this trait also deviated significantly from
the predictions of an additive-dominance model of inheritance (Table 1), suggesting
some level of epistasis controlling flowering time. All of the meristem traits show
partial dominance in the hybrids towards the parent with more meristems (IM for floral
meristems and DUN for vegetative meristems). For male fertility traits, the DUN plants
made more pollen grains (both viable and inviable) than the IM parents (Table 1)
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though the fraction of viable pollen produced did not markedly differ between the
parents or the hybrid classes. The F1 hybrids produced more viable and less inviable
pollen than expected under a strictly additive model, while the F2 hybrids had the
opposite pattern—they made less viable pollen grains, more inviable pollen grains, and
less total pollen than expected. All of the pollen grain measures (with the exception of
fraction of viable pollen) were inconsistent with the additive-dominance model (Table
1) which implies that epistatic interactions are involved in control of pollen production.
Genetic and phenotypic correlations: All of the floral size traits, with the
exception of stigma-anther separation (SA), were strongly and positively correlated
with each other, both genetically and phenotypically (Table 2). The total number of
pollen grains produced (TP) was also highly positively correlated both genetically and
phenotypically with the floral size traits. The vegetative traits had weak to moderate
positive genetic correlations with each other, though internode length was negatively
correlated with other vegetative traits. Phenotypic correlations between vegetative traits
were moderate to high, with the exception of internode length. Life-history traits were
not strongly genetically correlated with each other, though flowering date and percent
reproductive allocation had modest negative genetic and phenotypic correlations with
each other, and they represent important indicators differentiating our annual and
perennial populations. Flowering time had a strong positive genetic correlation with
both corolla width and stem thickness, and floral and vegetative traits were highly
correlated (both genetically and phenotypically) in a positive direction. Due to the
nature of the genetic correlations, we chose to group our traits in three sets for further
investigation of the cause of genetic correlations among multiple traits. First, we
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grouped six floral traits, then we grouped four vegetative traits, and finally, we grouped
six representative floral, vegetative, and life-history traits to determine the extent of
pleiotropic QTLs affecting multiple traits.
Quantitative trait locus analyses
Floral QTLs: We identified 16 putative QTLs affecting one or more floral
traits based on the likelihood-ratio statistic (LR) profile of the joint MCIM model (Fig.
2A). Twelve LR peaks exceeded the threshold of 43.52 (estimated by permutations, α =
0.05), though we accepted five lower peaks that all had highly significant LR profiles
for one or more floral traits based on the single trait analysis produced by CIM and
MCIM (data not shown). These peaks are located on linkage group (LG)4, LG6, LG8,
LG11, and LG12. One significant peak on LG1 did not affect any of the six floral traits
and was therefore not included.
All of the individual floral traits were polygenic (mean number of detected
QTLs per trait = 8.7, range = 5-12). The direction of allelic effects was consistent, in
general, with the phenotypic differences between parents (i.e., the larger flowered DUN
carried the "plus" allele). All seven QTLs affecting stamen length were positive, and
most other floral traits had the majority of QTLs in the positive direction with just a few
negative QTLs (i.e., eight of twelve QTLs affecting corolla width were positive). Many
of the QTLs showed partial dominance of one parental allele, but there was no overall
pattern of directional dominance (Table 3A). One QTL (QTL10 f) appeared to be
overdominant and another QTL (QTL11f) appeared to be underdominant. These results
could reflect true overdominance or underdominance, but they could also result from a
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low density of markers (particularly codominant markers) in these regions that makes it
difficult to detect QTLs tightly linked in repulsion.
We used two different methods to estimate the magnitude of effects of
individual QTLs on each trait. One biologically relevant measure of QTL size is to
scale the effect of substituting a single QTL by the difference between populations.
Here, we standardized 2a by the difference in the parental means (Table 3B). Using
this method, the floral QTLs we detected had a range of individual effects from very
small (QTL11f; <1% of the parents' difference in corolla tube length) to large (Table
3B; QTL5f; 24% of the populational difference in stamen length). Each floral trait had
at least one QTL that explained more than 17% of the species difference, but the
remaining QTLs were small. QTL5f on LG8 had a consistently large effect on multiple
traits (Table 3B). We also estimated the magnitude of QTL effects relative to the
environmental standard deviation (ESD; Table 3B). This method reveals that most
QTLs have small effects, though several have larger effects. In 45 of 52 of the
QTL/trait combinations, the substitution of one parental genotype for the other caused a
change in phenotype equivalent to less than one ESD. The remaining seven QTLs
caused a change of greater than one ESD for individual traits, most of which is
attributed to the effect of QTL5f, which affected all six floral traits, four of which had
homozygous affects greater than one ESD.
We used JIANG AND ZENG's (1995) test for pleiotropy to determine which traits
each floral QTL affected. Nearly all of the floral QTLs (14 of the 16) identified by
MCIM had significant effects on multiple traits (Table 3). One of the exceptions
21
identified only affected style length (QTL12f), and the other affected only total pollen
production (QTL1f).
Vegetative QTLs: We identified seven putative QTLs affecting one or more
vegetative traits based on the LR statistic profile of the joint MCIM model (Figure 2B).
Four LR peaks exceeded the threshold of 40.18 (estimated by 1000 permutations),
though we also accepted three marginally significant peaks (on LG6, LG7, and LG11)
based on single trait LR profiles produced by CIM and MCIM (data not shown). As
with the floral traits, the direction of QTL effects on vegetative traits was consistent
with the phenotypic differences between parents. No overall pattern of directional
dominance was obvious, nor was there evidence for overdominance (Table 4A). We
detected fewer QTLs for vegetative traits than floral traits (mean number of detected
QTLs per trait = 4.3, range = 3-5; Table 4A).
Our two methods of estimating QTL effect on vegetative traits produced similar
results to the floral traits analysis. Using the method where we standardized the
additive effect of a QTL by the difference in parental means, we detected QTLs with a
broad range of effects from very small (QTL1v; 7.7% of the parental difference in stem
thickness) to very large (Table 4B; QTL7v; 91.2% of the parental difference in
internode length). This same very large QTL also had a pronounced effect when we
analyzed the difference with respect to the ESD, with a substitution of one parental
genotype for the other caused a phenotypic change equivalent to 1.4 ESDs. Though the
extent of the effect is difficult to define, in both cases this QTL appears to be sizeable.
Overall, when the additive effect of a QTL is scaled relative the ESD, we found that six
22
of 17 vegetative QTLs had effects greater than one ESD, while the remaining QTLs had
small effects.
Six of the seven vegetative QTLs detected affected multiple traits using JIANG
and ZENG's (1995) test for pleiotropy (Table 4). One exception affected only leaf
thickness (QTL3v).
Multiple trait QTLs: Five QTLs were identified that affected one or more of
our six representative floral, vegetative, and life-history traits based on the LR statistic
profile of the joint MCIM model (Figure 2C). These five LR peaks exceeded the
permutation threshold of 55.97.
We found that relatively few QTLs explained the trait differences between
parents (mean number of detected QTLs per trait = 2.5, range = 2-4). The direction of
QTL effects was generally consistent with the phenotypic differences between parents
(Table 5A), and there was no pattern of directional dominance. By scaling the additive
effect of a QTL by the difference in parental means, we detected QTLs with a broad
range of effects from very small (QTL5 m; <1.0% of the parental difference in corolla
tube length) to large (Table 5B; QTL5m; 36.1% of the parental difference in leaf width).
Most of these individual QTL effects were moderate, explaining between 10-20% of the
phenotypic differences between parents. Alternatively, when scaling each QTL's
individual additive effect by the ESD, 5 of fifteen QTLs had homozygous effects
greater than one ESD, which we consider to be moderate to large QTLs.
All five QTLs affected multiple traits (JIANG and ZENG 1995: Table 5). This
analysis confirmed that two of the major QTLs detected separately in the floral and
vegetative traits analyses (both on LG8), affect both floral and vegetative traits. These
23
two QTLs also affect life history traits (QTL3m affects percent reproductive allocation,
QTL4m affects days to flowering).
DISCUSSION
The two populations of M. guttatus studied in this common garden experiment
differed markedly in many phenotypic traits associated with life-history and
morphology, indicating a genetic basis for the divergence. As expected from
observations of the phenotypes in nature, the annual plants from Iron Mountain in
Oregon’s western Cascades (IM) had smaller flowers and vegetative traits, flowered
earlier, and produced more floral meristems relative to vegetative meristems on average
than the perennial plants from the coastal Oregon sand dunes (DUN). Our investigation
of the genetic basis for floral, vegetative, and life-history divergence revealed
substantial numbers of pleiotropic quantitative trait loci governing complex phenotypic
divergence and also indicated that these classes of traits have different genetic
architectures. Overall, all of the traits were controlled by at least two QTLs, and we
detected several large-effect QTLs.
Number of quantitative trait loci: The divergence in floral and vegetative
traits between populations of M. guttatus is controlled by many QTLs. Despite the
large number of QTLs detected, the sum of QTL effects for each floral and vegetative
trait is less than 75% of the difference between parents. The remaining unexplained
difference suggests that there are many QTLs that were not detected in our study. If
true, then the divergence involves a much larger number of genes controlling
phenotypic divergence. Alternatively, epistatic interactions among detected QTLs may
be responsible for the unexplained difference. Because each QTL may contain multiple
24
linked genes, and methods of estimating gene number are inherently biased towards
underdetection of QTLs and overestimation of QTL effect (BEAVIS 1994; ZENG 1994),
the 16 floral QTLs and seven vegetative QTLs detected in this study are minimum
estimates of gene number. In addition, each gene could contain multiple substitutions
that affect different traits.
When we combined a subset of the floral and vegetative traits with two lifehistory traits in a multitrait analysis, we detected fewer QTLs overall and fewer
affecting each trait. This contradicts our previous results for four of the six traits. For
example, we detected only three QTLs affecting corolla width using multiple-trait
analysis, compared to 12 QTLs in the floral traits analysis, where we concluded that this
is likely to be a minimum estimate of gene number. In this third multi-trait analysis, the
lower genetic correlations among traits (particularly the life-history traits) may inhibit
our power to detect QTLs of smaller individual effect on certain traits. While we are
certain that floral and vegetative traits are governed by many loci, the number of QTLs
controlling life-history traits is less clear, though our results suggest the number is
small.
Our results of mostly polygenic trait divergence between populations are
consistent with other studies among different accessions of Arabidopsis, where floral,
vegetative, and life-history traits are mostly polygenic, with 2-15 QTLs detected per
trait (MITCHELL-OLDS 1996; ALONSO-BLANCO et al. 1998; JUENGER et al. 2000; PÉREZPÉREZ et al. 2002; UNGERER et al. 2002). Unfortunately, no consistent patterns emerge
to explain why certain traits are more or less polygenic. One possibility is that different
traits may have experienced different patterns of selection, which could affect the
25
numbers of QTLs responsible for trait divergence. In a review of the literature where
selection differentials were measured, KINGSOLVER et al. (2001) found that the strength
of directional selection differed between morphological and life-history traits, where
selection was generally stronger on morphology. We need more studies that investigate
both the genetic basis controlling trait divergence and the ecological significance of
particular traits in the wild to better understand how different patterns of selection might
affect the total number of genes controlling adaptive trait divergence.
Comparative mapping of floral QTLs within and between Mimulus species:
To what extent is the genetic architecture controlling floral divergence shared within
and between species? Comparative mapping of the same traits can reveal whether there
are shared QTL locations and numbers within and beween species. In this
interpopulational study we measured five of the same floral traits as those studied in an
interspecific QTL mapping study of M. guttatus and closely related self-fertilizing M.
nasutus (FISHMAN et al. 2002). These two studies have 27 markers in common (HALL
and WILLIS 2005). To understand the extent to which there were shared QTLs affecting
floral divergence between and within species of Mimulus, we made two comparisons.
First, we compared the total number of QTLs detected in each study. We found a total
of 16 floral QTLs compared to the 24 detected interspecific floral QTLs, lending
support for the hypothesis that QTL number is positively correlated with genetic
divergence (KIM and RIESEBERG 1999). However, the smaller number of QTLs
detected in this study may also be caused by slightly reduced statistical power due to the
smaller number of codominant markers.
26
Unfortunately, there are very few existing systems where QTL analyses have
examined divergence between both populations and species, particularly for comparable
traits. QTLs affecting grain weight were mapped in both intraspecific (YU et al. 1997;
XING et al. 2002) and interspecific crosses (MONCADA et al. 2001; LI et al. 2004) of
rice, where more QTLs for grain weight were detected in the intraspecific crosses
relative to the number detected between species. However, it is difficult to directly
compare patterns of phenotypic divergence between any pair of studies (including
Mimulus), as they were not conducted in the same environment. Furthermore,
domesticated and wild systems have experienced very different evolutionary histories,
therefore direct comparisons between the two may be limited. Clearly, we need more
studies that compare the genetic basis for phenotypic divergence both at the intra- and
interspecific level in order to understand whether a pattern exists between the degree of
genetic divergence and QTL number.
Second, when we compared the locations of floral QTLs between both maps, we
find that 10-11 QTLs map to approximately the same locations (Figure 4). These
shared QTLs suggest the possibility that some of the same underlying genes could be
responsible for divergence in floral traits between and within species of Mimulus. One
shared QTL involves QTL5f and the interspecific QTL13, both tightly linked to marker
CYCB on LG8. Interestingly, these floral QTLs actually affect different sets of traits in
the two studies. If these QTLs are caused by the same genes, then they seem to have
very different effects on floral traits within versus between species of Mimulus. Of
course, each QTL spans a fairly broad genomic region that may contain hundreds of
genes, and the co-localization of the QTLs may simply be due to chance. Fine-scale
27
mapping with additional markers and ultimately, positional cloning, may help
distinguish whether some of these "shared" QTLs affecting floral divergence are truly
controlled by the same underlying genes. However, some QTLs clearly mapped to
different locations in the two maps, indicating that floral divergence may be
evolutionarily labile with multiple alternative genetic changes involved in different
lineages.
In rice, there is evidence for shared QTLs affecting grain weight in either intraor interspecific crosses (YU et al. 1997; MONCADA et al. 2001; XING et al. 2002; LI et
al. 2004) In one interspecific study, fine-scale mapping demonstrated that one of the
potentially "shared" QTL regions affecting grain weight in rice contained 14 genes (LI
et al. 2004). There are currently no similar studies in intraspecific rice that determine
whether any of these genes are actually shared between and within species. Clearly,
this is an important avenue for further research.
Effects of quantitative trait loci: There is much interest in understanding
whether adaptive divergence is the due to major or minor genes (ORR AND COYNE
1992). However, there is no standard criterion for defining major versus minor QTLs.
Furthermore, QTL effect sizes can differ dramatically depending on how they are
estimated (LEXER et al. 2005). TANKSLEY (1993) characterized QTLs as potentially
major if they explained >10% of the phenotypic variation in the segregating population,
generally referred to as PVE (percentage of variance explained). This is the most
typical measure used to estimate QTL effect, and it may be particularly appropriate for
lab or agricultural systems. However, a more useful measure for understanding
adaptive divergence in the wild may be to estimate QTL effect in terms of the
28
difference between parental populations or relative to the phenotypic variation within
populations. For example, TRUE et al. (1997) uses a fairly stringent criteria by defining
a major QTL as one for which the distributions of alternative homozygotes for a
particular QTL show little overlap, so that the probability of misclassification of
phenotype is less than 5%, equivalent to 3.28 environmental standard deviations. For
this study, we have represented QTL effects both in terms of the mean difference
between parents and relative to the ESD, as we are most interested in the whether
substitution of alternative QTL alleles generates visible differences in phenotype
relative to the two parents.
In this intraspecific study, we detected several sizeable QTLs, the largest of
which is on LG8. This QTL alone was responsible for divergence in floral, vegetative,
and life-history traits. Although this and a few other fairly large QTLs do not change
the phenotype more than 3.28 ESDs, we argue that these QTLs are major, particularly
because they affect multiple traits. This QTL has a very large LOD score and a sharp
peak, indicating that the QTL interval is fairly small, and it is very tightly linked to a
single codominant marker, CYCB. A 2-LOD support interval around this QTL spans
less than 10 cM (LG8: from 68 to 77 cM). Fine-scale mapping of this interval, followed
by positional cloning may enable us to uncover the gene or genes responsible for
divergence of multiple traits at this locus.
Most traits had at least one QTL that explained more than 10% of the species
difference or that changed the phenotype more than one ESD, and the remaining QTLs
were of small effect. Overall, this is consistent with evolutionary predictions (ORR
1998) and a few other empirical examples (JUENGER et al. 2000; PÉREZ-PÉREZ et al.
29
2002) of the distribution of QTL effects, where the evolutionary shift in divergent
characters between populations of Mimulus is likely to involve a major genetic change
with most of the remaining divergence due to many minor allelic changes.
Pleiotropic QTLs: The floral, vegetative, and life-history traits measured in
this study are governed largely by pleiotropic QTLs. We define a pleiotropic QTL as a
genomic region that affects multiple traits. This region could contain multiple tightly
linked trait-specific genes or single genes that have multiple substitutions affecting
different traits. Of the 28 total QTLs we detected in this study, all but three affected
multiple traits, pointing to a pleiotropic basis for genetic associations we observed
(Tables 3-5). Fine-scale mapping with additional markers and larger mapping
populations are needed to distinguish truly pleiotropic loci from tightly linked loci.
Other studies have consistently found evidence suggesting individual QTLs have
pleiotropic effects on multiple floral, vegetative, or life-history characters (MITCHELLOLDS 1996; JUENGER et al. 2000; UNGERER et al. 2002; CUI et al. 2004; WESTERBERGH
AND DOEBLEY
2004), though all of these studies rely on QTL mapping on individual
traits, rather than using our approach of joint mapping. The joint mapping approach
offers the advantage of allowing us to directly test whether different traits are affected
by a particular QTL at that position, and provides greater power to detect pleiotropy
(JIANG AND ZENG 1995). For this reason, previous studies may have underestimated the
degree of pleiotropy.
For example, using joint QTL mapping, we identified 16 QTLs underlying
divergence in one or more of the six floral traits. Nearly all (14) of these QTLs had
significant effects on more than one floral trait for a total of 52 significant QTL-trait
30
effects. If we had analyzed each floral trait in separate single-trait composite interval
mapping (CIM) analyses instead of in a joint trait analysis, we would have detected 12
total floral QTLs. Reliance on single trait analysis has several limitations, based on our
results. First, it would have led us to overestimate the total number of QTLs, because
most of the 12 single trait QTLs would have mapped to the same genomic regions.
Second, it has substantially less power to detect pleiotropic QTLs than joint mapping
analyses (in our case a 4-5 fold difference in total QTL-trait effects); so we would have
also grossly underestimated the total number of QTL-trait effects. For highly correlated
traits, joint trait QTL analysis provides a more comprehensive view of the genetic
architecture underlying multivariate phenotypic divergence.
To better understand the role that pleiotropic QTLs can have on our view of trait
divergence, we examined the joint effect of the major QTL on LG8. In the floral traits
analysis, this QTL had a large effect on both corolla tube length and stamen length. The
direction of change at this QTL was almost perfectly correlated between traits (Figure
3A), demonstrating that substitution of this QTL alone into one parent can shift the
phenotype roughly one quarter of the way towards the alternate parent for both of these
floral traits. Evolutionary divergence for either of these two floral traits is highly
constrained. One might also expect overall "size" QTLs to affect both floral and
vegetative traits. We therefore examined the joint effect of this QTL on corolla tube
length and stem thickness. These two traits are also positively correlated, though not as
strongly as the two floral traits, and the QTL had a major effect on both traits (Figure
3B). Based on the effect of this QTL, flower size and plant size are likely to evolve
jointly, and in the same direction in Mimulus, which fits with a common observation
31
that larger plants tend to produce larger flowers. Not all of the detected QTLs affected
traits in the same direction. We plotted the antagonistic effect of this major QTL on
corolla tube length and leaf width. Increases in both of these traits are likely to be
adaptive in both environments (HALL 2005), thought the evolutionary response to
selection operating on either trait will necessarily be constrained.
The large number of pleiotropic QTLs detected in this study sheds light on our
understanding of the genetic basis for multivariate divergence. If we were to examine
each of these traits separately, we would mis-estimate the total number of QTLs.
Furthermore, any one QTL with modest individual effects on multiple traits can actually
have a fairly large effect on the overall phenotype. Therefore a few pleiotropic QTLs
can play an important role in phenotypic divergence between populations or species.
The evolution of life-history strategy: The divergence between the annual and
perennial forms studied here is complex, consisting of differences in multiple floral,
vegetative, and life-history characters. The life-history traits we measured were
controlled by few QTLs, suggesting that the evolution of differences in timing of
flowering and allocation of floral and vegetative meristems would require only one or
two genetic changes. For both life-history characters we measured, we showed that the
genetic control of these individual traits is not independent of other morphological
traits, which can have important implications for the potential for evolutionary
divergence.
In QTL mapping experiments of other plant species, life-history variation is
governed mainly by multiple genetic loci. In Arabidopsis thaliana, timing to flowering
is controlled by 5-12 QTLs (MITCHELL-OLDS 1996; ALONSO-BLANCO et al. 1998;
32
UNGERER et al. 2002), and the particular QTLs detected can differ when plants are
grown in different environments (WEINIG et al. 2002). In crop plants, traits
differentiating annual and perennial forms are mostly polygenic (PATERSON et al. 1995;
WESTERBERGH and DOEBLEY 2004; HU et al. 2003; CUI et al. 2004). Without further
genetic dissection of genomic regions in Mimulus and comparisons in different
environments, it is difficult to determine if our results are inconsistent with polygenic
inheritence of life-history traits.
In this study, we have begun to understand the genetic basis of phenotypic traits
associated with life-history divergence. Future studies aimed at understanding how
alleles at each QTL affect fitness in the wild will be particularly informative. We have
developed recombinant inbred lines between these two divergent populations which
have been placed into each of the native environments. Understanding the role of QTL
genotype x environmental interactions and the particular QTLs affecting fitness in these
two diverse sites will further our understanding of the genetic basis of adaptation in the
wild.
33
ACKNOWLEDGEMENTS
The authors would like to thank M. Rausher, W. Morris, P. Manos, R. Vilgalys,
A. Case, A. Sweigart, A. Cooley, Y.-W. Lee, D. Lowry, S. McDaniel, and J. Kelly for
advice on earlier drafts of this manuscript. Also thanks to L. Fishman for advice on
QTL mapping and for providing updated M. guttatus x M. nasutus map data. Abundant
thanks to E. Gilliam and K. Sullivan for assistance with quantitative measurements and
to A. Bissell for graphical assistance. This material is based upon work supported by
the National Science Foundation under Grant Nos. 9727578, 0075704, 0328636, and
010577, by Sigma Xi GIAR, and by the National Institute of Health Grant Number
GM045344.
34
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44
Table 1: Phenotypic data for 20 traits measured in M. guttatus on parental lines and hybrids in a common garden.
Class
IM62
F1 hybrids
F2 hybrids
DUN
MPD/ESD
H2
∆/E(F2)
Corolla width
16.47 ± 0.23
24.61 ± 0.19
22.94 ± 0.13
30.56 ± 0.33
5.39
0.34
-0.047
Corolla length
20.65 ± 0.23
29.69 ± 0.17
29.17 ± 0.13
37.40 ± 0.33
7.47
0.48
0.0062
Corolla tube length
11.39 ± 0.13
16.31 ± 0.074 16.24 ± 0.066
20.09 ± 0.17
8.72
0.61
0.013
Stamen length
11.78 ± 0.12
16.90 ± 0.068 16.64 ± 0.064
20.21 ± 0.15
9.37
0.65
0.012
Style length
13.75 ± 0.13
19.08 ± 0.076 19.16 ± 0.065
24.40 ± 0.17
10.58
0.59
0.0041
Stigma-anther distance
1.96 ± 0.083
2.19 ± 0.044
2.52 ± 0.038
4.19 ± 0.097
3.79
0.58
-0.043
Stem thickness
0.82 ± 0.020
1.97 ± 0.032
2.00 ± 0.024
3.93 ± 0.077
7.34
0.46
-0.08
Leaf width
12.13 ± 0.38
20.54 ± 0.39
18.60 ± 0.20
24.38 ± 0.50
2.37
-0.13
-0.041
Leaf thickness
0.27 ± 0.0066 0.35 ± 0.0039 0.34 ± 0.0025 0.40 ± 0.0055
2.61
0.22
-0.021
Internode length
22.40 ± 0.77
16.63 ± 0.67
15.78 ± 0.42
8.96 ± 0.52
-1.50
0.24
-0.023
Days to flower
30.22 ± 0.40
33.24 ± 0.33
33.56 ± 0.19
43.69 ± 0.59
3.03
0.081
-0.044
Character
45
Above-ground mass
0.98 ± 0.065
0.94 ± 0.044
1.05 ± 0.027
1.21 ± 0.087
0.40
0.23
0.032
Floral meristems
3.68 ± 0.21
3.26 ± 0.12
3.15 ± 0.069
2.03 ± 0.17
-1.01
0.031
0.031
Vegetative meristems
2.22 ± 0.17
8.80 ± 0.36
6.08 ± 0.18
6.24 ± 0.31
0.84
-0.23
-0.066
Total number meristems
5.78 ± 0.26
11.92 ± 0.38
9.05 ± 0.19
8.27 ± 0.36
0.48
-0.27
-0.045
Percent reproductive allocation
0.63 ± 0.026
0.30 ± 0.012
0.39 ± 0.0090 0.25 ± 0.0017
-2.40
0.48
0.05
131.52 ± 10.05 403.12 ± 13.80 312.25 ± 8.63 434.22 ± 27.37
1.65
0.2
-0.09
241.28 ± 8.92 237.27 ± 6.14 480.84 ± 28.91
3.27
0.34
-0.1
225.05 ± 12.33 644.40 ± 16.62 549.52 ± 10.02 915.05 ± 30.74
3.13
0.14
-0.095
-0.42
0.61
-0.037
Viable pollen grains
Nonviable pollen grains
Total pollen grains
Fraction viable pollen
93.53 ± 6.06
0.54 ± 0.025
0.62 ± 0.011
0.54 ± 0.010
0.48 ± 0.025
Floral and vegetative traits are in millimeters. Mass is in grams. Means and standard errors are given for each class. The mean
populational difference (MPD) for each trait was standardized by its environmental standard deviation (ESD). To test for epistatic
breakdown, we estimated the ratio ∆/E(F2), which indicates the relative magnitude of F2 breakdown (see Materials and Methods). The
ratio of t = ∆ / Var(∆ ) tests the null hypothesis that ∆ = 0, which is the expectation under a purely additive-dominance model of
inheritance (LYNCH and WALSH 1998). The values of ∆/E(F2) in bold are those where we rejected ∆ = 0 (P < 0.05).
46
Table 2: Genotypic (above diagonal) and phenotypic (below) correlations in intraspecific F2 hybrids.
Trait
WW
Corolla width (WW)
FL
TL
AL
SL
SA
ST
0.59
0.533
0.567
0.671
0.152
0.812
0.751
0.816
0.808
LW
LT
IL
FT
AM
FM
0.487
-0.03
-0.16
0.628
-0.02
0.072
0.591
0.203
-0.26
0.309
0.802
-0.06
0.45
0.221
-0.12
0.814
-0.29
0.565
0.257
0.247
0.569
-0.03
VM
TM
RA
VI
NV
TP
PV
-0.95
-0.1
0.027
0.307
0.32
-0.14
0.014
0.127
-0.05
0.26
0.383
0.631
-0.06
-0.02
0.013
-0.12
-0.1
0.331
0.279
0.605
-0.02
-0.21
0.146
-0.01
-0.52
-0.22
0.436
0.254
0.689
0.105
0.085
-0.26
0.299
-0.16
-0.46
-0.07
0.299
0.291
0.583
0.031
-0.32
-0.09
0.26
-0.27
0.126
0.264
-0.26
0.051
-0.22
-0.14
0.307
-0.27
0.642
-0.39
-0.17
-0.2
-0.18
0.429
0.226
-0.24
-0.09
0.901
-0.37
1.548
0.021
0.007
0.201
0.199
-0.14
0.609
-0.3
0.413
-0.07
0.24
-0.67
-0.39
0.341
0.23
1.611
-0.32
-1.08
0.927
-0.23
-0.82
-2.73
-0.37
-0.22
-0.02
-0.25
-0.07
0.668
-1.12
0.037
-1.12
-0.45
-0.25
0.047
-0.21
-0.1
-0.69
0.768
1.259
0.138
-0.63
Corolla length (FL)
0.75
Corolla tube length (TL)
0.632
0.839
Stamen length (AL)
0.641
0.768
0.812
Style length (SL)
0.708
0.825
0.818
0.834
Stigma-anther distance (SA)
0.147
0.136
0.048
-0.25
0.331
Stem thickness (ST)
0.461
0.506
0.452
0.461
0.466
0.03
Leaf width (LW)
0.344
0.293
0.266
0.202
0.26
0.111
0.376
Leaf thickness (LT)
0.228
0.269
0.257
0.191
0.21
0.042
0.442
0.545
Internode length (IL)
0.065
0.007
0.036
-0.04
-0
0.061
-0.14
0.455
0.025
Days to flower (FT)
-0.02
-0.01
-0.09
-0
-0.04
-0.06
0.34
-0.03
0.169
-0.32
Above-ground mass (AM)
0.026
0.007
-0
0.044
-0.01
-0.09
-0.05
-0.04
-0.08
0.016
-0.02
Floral meristems (FM)
0.189
0.218
0.202
0.125
0.181
0.103
0.202
0.343
0.223
0.117
-0.28
-0.09
Vegetative meristems (VM)
0.033
0.072
0.097
0.146
0.059
-0.15
0.191
-0.15
0.041
-0.13
0.213
0.002
-0.1
0.1
0.149
0.167
0.187
0.123
-0.1
0.258
-0.02
0.12
-0.08
0.106
-0.03
0.264
0.933
Percent reproductive allocation (RA)
0.121
0.101
0.063
-0.01
0.088
0.17
0.019
0.294
0.077
0.159
-0.32
-0.05
0.598
-0.69
-0.45
Viable pollen grains (VI)
0.138
0.153
0.133
0.265
0.179
-0.14
0.039
0.103
0.062
0.031
-0.1
0.057
0.07
-0.02
0.01
0.033
Nonviable pollen grains (NV)
0.241
0.229
0.202
0.202
0.245
0.084
0.276
0.068
0.159
-0.12
-0.01
-0.03
0.177
-0
0.062
0.1
-0.14
Total pollen grains (TP)
0.27
0.276
0.241
0.358
0.308
-0.07
0.204
0.133
0.152
-0.05
-0.1
0.03
0.171
-0.02
0.046
0.09
0.792
Total number meristems (TM)
0.493
0.694
47
Fraction viable pollen (PV)
-0.07
-0.04
-0.04
0.08
-0.01
-0.15
-0.15
0.025
-0.07
0.103
-0.11
0.057
-0.07
-0.01
-0.03
-0.04
0.75
-0.62
0.276
48
Table 3: QTL number for vegetative traits from LR profile (Fig. 2), position (LG, marker, cM), and A) additive (a) and dominant (d)
effects. B) homozygous effect (2a) of each QTL standardized by the difference in the parental means and by the ESD. QTL effects
are only shown in the single-trait LR at a QTL located by joint mapping (MCIM) exceeded the significance threshold of 5.99. IM
homozygous genotypes were scaled to zero and DUN homozygotes to 2a, so negative values of a indicate that IM carries the minus
allele. WW, corolla width; TL, corolla tube length; FL, corolla length; AL, stamen length; SL, style length; TP, total pollen grains.
*Each QTL is numbered and labeled with a subscript corresponding to each of the three analyses (floral traits analysis)
A.
WW
QTL* Position
a
TL
d
1f
3, 4, 55
2f
4, 7, 89
0.80
-0.33
3f
6, 1, 0
0.60
0.17
4f
8, 1, 0
5f
8, 3, 75
1.08
0.51
FL
a
d
0.53
0.0072
0.22
1.05
0.13
0.21
a
0.44
1.84
AL
d
0.094
0.33
SL
TP
a
d
a
d
0.26
0.36
0.38
-0.045
0.53
-0.45
0.036
1.03
0.37
0.13
-0.50
0.77
0.88
0.18
a
d
82.28
-126.36
-7.98
53.33
36.16
-33.04
49
6f
8, 9, 155
1.20
-0.41
7f
10, 3, 20
0.71
-0.34
8f
11, 1, 14
-1.05
9f
11, 4, 38
0.57
-0.49
1.08
-1.06
-0.43
0.71
10f
11, 6, 100 -1.11
1.82
11f
12, 1, 10
-2.88
12f
12, 8, 158
13f
13, 1, 10
14f
13, 3, 49
0.51
15f
16f
B.
0.17
-0.28
-0.24
1.42
-0.18
0.79
-0.60
0.76
0.031
0.43
-0.93
1.82
-0.048
-1.18
0.58
-0.68
0.43
0.42
0.18
13, 5, 169 1.08
-0.54
0.44
-0.25
14, 7, 42
-0.84
0.23
-0.27
0.36
-0.31
-0.095
0.46
-0.71
0.28
0.33
-0.00002
-1.22
-0.22
3.01
0.33
-0.36
0.62
-0.57
0.21
-0.51
0.76
-0.55
0.10
0.38
-1.01
-0.34
-6.48
128.04
44.73
-48.50
50
WW
QTL* Position 2a/diff
TL
2a/ESD
1f
3, 4, 55
2f
4, 7, 89
0.11
0.61
3f
6, 1, 0
0.085
0.46
4f
8, 1, 0
5f
8, 3, 75
0.15
0.82
6f
8, 9, 155
0.17
0.92
7f
10, 3, 20
0.10
0.54
8f
11, 1, 14
-0.15
9f
FL
2a/diff
2a/ESD
0.12
1.06
0.051
0.24
2.10
0.56
0.059
0.22
2a/ESD
0.44
1.64
SL
TP
2a/diff
2a/ESD
2a/diff
2a/ESD
0.062
0.58
0.071
0.75
0.10
1.05
0.0085
0.080
0.24
2.29
0.078
0.73
0.040
0.38
-0.094
-0.99
0.17
1.74
0.086
0.91
0.068
0.51
-0.80
-0.13
-0.95
-0.034
-0.36
11, 4, 38 -0.061
-0.33
-0.072
-0.54
0.0058
0.061
10f
11, 6, 100 -0.16
-0.85
-0.11
-0.83
11f
12, 1, 10
-0.54
-0.026
-0.20
-0.0090
-0.095
12f
12, 8, 158
0.074
0.55
-0.10
0.064
0.44
2a/diff
AL
-0.000005 -0.00004
0.076
0.66
0.024
0.22
2a/diff
2a/ESD
0.24
0.75
-0.023
-0.072
0.10
0.33
-0.019
-0.059
51
13f
13, 1, 10
0.11
1.15
14f
13, 3, 49 0.072
0.39
0.079
0.83
15f
13, 5, 169 0.15
0.82
0.083
0.87
16f
14, 7, 42 0.051
0.27
0.043
0.46
0.048
0.42
0.091
0.68
0.090
0.84
0.13
0.41
52
Table 4: QTL number for vegetative traits from LR profile (Fig. 2), position (LG, marker, cM), and A) additive (a) and dominant (d)
effects. B) homozygous effect (2a) of each QTL standardized by the difference in the parental means and by the ESD. QTL effects
are only shown in the single-trait LR at a QTL located by joint mapping (MCIM) exceeded the significance threshold of 5.99. IM
homozygous genotypes were scaled to zero and DUN homozygotes to 2a, so negative values of a indicate that IM carries the minus
allele. ST, stem thickness; IL, internode length; LW, leaf width; LT, leaf thickness.
*Each QTL is numbered and labeled with a subscript corresponding to each of the three analyses (vegetative traits analysis)
A.
ST
QTL* Position
IL
LW
LT
a
d
a
d
a
d
-0.47
1.60
1v
4, 2, 17
0.12
-0.065
-2.98
1.91
2v
4, 5, 58
0.23
-0.22
-5.07
0.85
3v
6, 3, 42
4v
7, 1, 0
0.26
-0.25
5v
8, 5, 79
0.24
-0.059
-1.34
2.91
0.81
-1.67
-0.89
0.23
a
d
0.011
0.010
-0.018
-0.011
0.014
-0.023
53
6v
8, 9, 163
7v
11, 5, 86
0.32
-0.28
6.11
-5.46
0.90
-1.13
2.29
-0.91
B.
ST
QTL* Position
IL
LW
LT
2a/diff
2a/ESD
2a/diff
2a/ESD
2a/diff
2a/ESD
2a/diff
2a/ESD
-0.077
-0.18
0.16
0.42
-0.26
-0.69
0.21
0.54
1v
4, 2, 17
0.077
0.57
0.44
-0.66
2v
4, 5, 58
0.15
1.10
0.76
-1.13
3v
6, 3, 42
4v
7, 1, 0
0.17
1.24
5v
8, 5, 79
0.15
1.14
6v
8, 9, 163
0.21
1.52
7v
11, 5, 86
0.20
-0.91
-0.30
1.37
0.13
0.31
-0.15
-0.34
0.15
0.35
0.37
0.89
54
Table 5: QTL number for vegetative traits from LR profile (Fig. 2), position (LG, marker, cM), and A) additive (a) and dominant (d)
effects. B) homozygous effect (2a) of each QTL standardized by the difference in the parental means and by the ESD. QTL effects
are only shown in the single-trait LR at a QTL located by joint mapping (MCIM) exceeded the significance threshold of 5.99. IM
homozygous genotypes were scaled to zero and DUN homozygotes to 2a, so negative values of a indicate that IM carries the minus
allele. ST, stem thickness; LW, leaf width; WW, corolla width; TL, corolla tube length; RA, percent reproductive allocation; FT, days
to flower.
*Each QTL is numbered and labeled with a subscript corresponding to each of the three analyses (multiple traits analysis)
A.
ST
QTL* Position
a
LW
d
a
WW
d
a
TL
d
RA
a
d
a
FT
d
1m
3, 2, 52
0.52
-0.16
2m
3, 5, 59
0.24
0.58
0.045
-0.15
3m
8, 3, 77
1.07
0.059
-0.039
0.0039
0.26
0.0022 -0.84
0.29
1.18
0.31
a
d
-0.93
-0.029
55
4m
8, 9, 165
5m
11, 6, 106
0.30
-0.22
2.21
-0.77
1.38
-0.31
-0.64
0.94
2.04
-0.016
-2.19
0.65
B.
ST
QTL* Position
2a/diff
LW
2a/ESD
2a/diff
WW
2a/ESD
2a/diff
TL
2a/ESD
RA
2a/diff
2a/ESD
2a/diff
FT
2a/ESD 2a/diff 2a/ESD
1m
3, 2, 52
0.12
1.04
2m
3, 5, 59
0.055
0.48
-0.24
0.56
3m
8, 3, 77
0.17
1.24
0.25
2.14
0.21
-0.49
4m
8, 9, 165
0.19
1.43
5m
11, 6, 106
-0.14
0.36
-0.32
0.85
0.17
0.90
0.20
1.05
-0.091
-0.49
-0.0037
-0.032
-0.14
-0.42
0.30
0.92
57
Figure 1: Parental representatives of M. guttatus populations grown in a common
garden greenhouse. On left, M. guttatus from the Oregon Dunes National Recreation
Area (DUN); right, M. guttatus from Iron Mountain, OR (IM).
Figure 2: Likelihood-ratio (LR) test statistic profile from multitrait composite interval
mapping of: A) six floral traits, B) four vegetative traits, and C) six representative
floral, vegetative, and life-history traits in the intaspecific F2 individuals of M. guttatus.
The solid line indicates the LR significance threshold for joint mapping generated by
permutation analyses (α = 0.05, experimentwide). LR threshold is 43.52, 40.18, and
55.97 for floral, vegetative, and multiple traits analyses, respectively. All linkage
groups are along the x-axis, with vertical double lines separating them. The positions of
mapped markers (∆) are shown along each linkage group. QTLs detected were labeled
numerically and with a subscript (floral, vegetative, multiple traits) for each separate
analysis (see text for explanation of individual peaks).
Figure 3: Additive effect and direction of effect of major QTL (on LG8, marker 3) on:
A) corolla tube length and stamen length from the floral traits analysis (QTL5 f), B)
corolla tube length and stem thickness from the mulitrait analysis(QTL3 m), and C)
corolla tube length and leaf width from the multitrait analysis(QTL3 m). Parental means
for each trait are plotted with bars indicating standard deviation. QTL additive effect is
58
positioned at the midparent with the homozygous effect of substitution indicated for
each trait.
Figure 4: Comparative map of floral QTLs within and between species of M. guttatus
and close relative M. nasutus. The linkage group is indicated above both intraspecific
(gxg, corresponding to M. guttatus x M. guttatus) and interspecific (gxn, corresponding
to M. guttatus x M. nasutus; FISHMAN et al 2001) maps. Hatchmarks indicate marker
placement. Only terminal markers and common markers are labeled on each map, with
thin lines connecting markers in common. For more detailed description of map
comparisons, see HALL and WILLIS (2005). LGs with a single common markers are
matched up arbitrarily, note the orientation could be rotated. Arrows point to location
of QTLs affecting one or more floral traits. Numbers alongside arrows correspond to
the QTL number given (see Table 3 for intraspecific numbers, and FISHMAN et al 2002
for interspecific numbers). Shaded solid gray bars are putative shared floral QTLs
between the two maps. Gradient bars (LG14) are alternative putative shared floral
QTLs, depending on orientation of LGs.
A.
5f
147
118
88.2
1f
13f
6f
58.8
2f 3f
8f
11
10f f12 14f 15f
9
f
f
7f
16f
4f
29.4
0
87.0
5v
Likelihood ratio (LR)
B.
69.6
6v
1v 2v
52.2
3v 4
v
7v
34.8
17.4
0
177
3m
C.
142
106
4m
1m 2m
70.8
5m
35.4
0
1
2
3
4
5
6
7
8
9
10
Linkage groups
11
12
13
14