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Lineage specific inference about QTL evolution among three Mimulus species of contrasting relationship… Chen, Charles 2009

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Lineage specific inferences about QTL evolution among three Mimulus species of contrasting relationship and inbreeding  by  Charles Chen A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES  (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  June 2009 © Charles Chen, 2009  ABSTRACT Complex traits including those involved with natural adaptation are determined by the contributions of numerous genes, the environment, and their interactions. Although quantitative trait locus (QTL) mapping approaches have been successful in dissecting complex traits, few studies have adopted a comparative approach of contrasting species pairs that differ in relationship, for the purpose of dissecting evolutionary changes of QTL. Furthermore, no QTL mapping approaches have explicitly inferred QTLs along lineages in a species network. This thesis brings such a comparative approach into QTL mapping. The evolution of inbreeding in the Mimulus guttatus species complex provides an excellent system where lineage-specific QTL changes can be inferred. Three intercrossable species were chosen: M. guttatus, M. platycalyx and M. micranthus, the latter two taxa being independent derived inbreeders from the first one. Five floral characters were selected as representative traits for the evolution of inbreeding in these species. A three-species crossing design was implemented, upon which QTL analyses were conducted. As expected in QTL mapping studies, the estimated number of genetic factors varies among crosses. An important role of dominance in the evolution of selfing from outcrossing taxa is supported by the data, owing to the consistency of directional dominance towards selfing taxa. The extensiveness of epistasis identified in this study suggests that in Mimulus, genes related to floral characters are co-adapted gene complexes, where genetic interdependency evolves as species diverge. Moreover, such genetic interdependency may be a key element in the evolution of stable mixed mating systems. A model for the inference of lineage specific QTL in a three-taxon network is described, and used to infer lineage-specific changes for floral traits among the three  ii  Mimulus taxa. After mapping QTL onto lineages, one can determine if QTL at the same map position are homologous (arising in an ancestral lineage leading to two taxa) or nonhomologous (arising independently in derived lineages or via convergent evolution). In Mimulus, shared negative QTLs of dominant effect that arise from convergent evolution seem to play a prominent role in the early evolution of inbreeding; then derived, independent changes fine-tune further evolutionary changes of inbreeding.  iii  TABLE OF CONTENTS ABSTRACT ................................................................................................................................. ii TABLE OF CONTENTS .......................................................................................................... iv LIST OF TABLES ................................................................................................................... viii LIST OF FIGURES ................................................................................................................... ix ACKNOWLEDGEMENTS ...................................................................................................... xi CO-AUTHORSHIP STATEMENT ........................................................................................ xii  CHAPTER 1. GENERAL INTRODUCTION ..........................................................................1 THE BIRTH OF QUANTITATIVE GENETICS......................................................................1 EVOLUTIONARY QUANTITATIVE GENETICS .................................................................4 MAPPING QUANTITATIVE TRAIT LOCI (QTL) AND TRAIT EVOLUTION IN PLANTS ....................................................................................................................................5 Genetic basis of adaptation ...................................................................................................5 Genetic basis of species differentiation .................................................................................8 The unanswered questions in the study of evolutionary quantitative genetics ....................10 STUDY SPECIES ....................................................................................................................12 OBJECTIVES ..........................................................................................................................15 The specific objectives of each thesis chapter .....................................................................16 REFERENCES ........................................................................................................................20  CHAPTER 2. EFFECTIVE NUMBER OF GENETIC FACTORS SEPARATING INBREEDING VS OUTBREEDING SPECIES IN THE MIMULUS GUTTATUS COMPLEX ................................................................................................................................28 INTRODUCTION ...................................................................................................................28 MATERIALS AND METHODS .............................................................................................31 Study species ........................................................................................................................31 Pairwise crosses between Mimulus species .........................................................................32 Gene number estimation ......................................................................................................33 RESULTS ................................................................................................................................36  iv  Degree of dominance ...........................................................................................................37 Gene number estimates with the correction of uniform dominance ....................................37 Number of genetic factors in relation to species evolutionary divergence versus morphological divergence ...................................................................................................38 DISCUSSION ..........................................................................................................................39 Violations of the Wright-Castle estimator ...........................................................................39 Comparisons to previous QTL studies in Mimulus ..............................................................40 Dominance and the evolution of inbreeding ........................................................................43 REFERENCES ........................................................................................................................53  CHAPTER 3. LINEAGE SPECIFIC INFERENCES ABOUT QTL EVOLUTION AMONG AN OUTCROSSING AND TWO DERIVED INBREEDING TAXA OF YELLOW MONKEYFLOWERS ...........................................................................................57 INTRODUCTION ...................................................................................................................57 MATERIALS AND METHODS .............................................................................................61 Hypotheses about the homology of QTLs ............................................................................62 Three-taxa crossing design and quantitative trait measurement .........................................64 AFLP (amplified fragments length polymorphism) genotyping...........................................65 Inferred parent genotype and linkage map construction via joint analysis.........................66 The analysis of lineage specific QTL genetic effect .............................................................67 RESULTS ................................................................................................................................71 AFLP marker distribution and linkage map ........................................................................71 Pairwise genetic distance between Mimulus parents ..........................................................71 The analysis of lineage specific QTL effects ........................................................................72 DISCUSSION ..........................................................................................................................75 The reconstruction of the evolution of inbreeding via the analysis of lineage specific QTL effects ...........................................................................................................................75 Directional selection and the evolution of selfing ...............................................................77 The novelty of lineage specific QTL inference .....................................................................78 The homology of QTL among lineages ................................................................................79 Conclusion ...........................................................................................................................81  v  REFERENCES ........................................................................................................................91 CHAPTER 4. EPISTATIC INTERACTION OF QTLS INVOLVED IN THE EVOLUTION OF FLORAL TRAITS IN THE MIMULUS GUTTATUS SPECIES COMPLEX ................................................................................................................................96 INTRODUCTION ...................................................................................................................96 MATERIALS AND METHODS .............................................................................................99 Study species ........................................................................................................................99 Backcrosses between Mimulus species ..............................................................................101 Measurement of floral traits ..............................................................................................102 DNA isolation and AFLP (Amplified Fragments Length Polymorphism) genotyping ......103 Joining linkage map construction using joint likelihood function .....................................104 QTL mapping using R/QTL genome scan ..........................................................................105 Pairwise R/QTL genome scans ..........................................................................................106 RESULTS ..............................................................................................................................108 QTL analysis using single locus R/QTL genome scans .....................................................108 Pairwise QTL genome scans ..............................................................................................110 Pairwise QTL genome scans: corolla width ......................................................................110 Pairwise QTL genome scans: corolla length .....................................................................112 Pairwise QTL genome scans: pistil length ........................................................................112 Pairwise QTL genome scans: stamen length .....................................................................113 Pairwise QTL genome scans: stigma-anther separation ...................................................113 DISCUSSION ........................................................................................................................115 Statistical detection of epistasis .........................................................................................117 Epistasis found in previous studies ....................................................................................119 Epistasis in the evolution of selfing in Mimulus ................................................................120 Epistasis in stable mixed mating systems...........................................................................121 Conclusion .........................................................................................................................122 REFERENCES ......................................................................................................................139  vi  CHAPTER 5. GENERAL CONCLUSION ...........................................................................147 REFERENCES ......................................................................................................................153  vii  LIST OF TABLES Table 1-1. Recent Mimulus genetic analyses of species differences ...........................................19 Table 2-1. Generation means and sample sizes for each character..............................................45 Table 2-2. Estimates of degree of dominance (D/A ratio) in F1, F2 and backcross generations .......................................................................................................................46 Table 2-3. Estimates of effective number of genetic factors underlying Mimulus floral character divergence. .......................................................................................................47 Table 2-4. Estimates of effective number of genetic factors, morphological divergence and genetic distance in Mimulus F2 crosses ....................................................................48 Table 3-1. AFLP primers and polymorphism of primer pairs. ....................................................82 Table 3-2. Probabilities of bandless (first number) vs. banded progeny (second number), conditioned on the genotypes of the two parents of a cross . ..........................................83 Table 3-3. Estimates of lineage specific QTL genetic effects . ...................................................84 Table 4-1. Generation means and sample sizes for each corolla trait in Mimulus.....................123 Table 4-2. AFLP primers used in this study and the extent of polymorphism. .........................124 Table 4-3. Significant QTL identified from R/QTL single locus and pair-wise genome scans.. .............................................................................................................................125 Table 4-4. Significant interacting pairs of loci found by R/qtl pairwise genome scans. ..........128  viii  LIST OF FIGURES Figure 2-1. Distribution of corolla lengths for parental, F1, F2 and backcrosses in the cross of M. guttatus and M. micranthus. ..........................................................................49 Figure 2-2. The distribution of means and variances of corolla length trait variation among parental, F1, F2 and backcrosses in the cross of M. guttatus and M. micranthus........................................................................................................................50 Figure 2-3. The pattern of estimated number of genetic factors upon species evolutionary relationship .......................................................................................................................51 Figure 2-4. The distribution of degree of dominance among Mimulus F2 crosses......................52 Figure 3-1. The three intercrossable Mimulus taxa used for our QTL phylogenetic analysis .............................................................................................................................85 Figure 3-2. Possible patterns of QTL evolution and homology...................................................86 Figure 3-3. A central dissection of a Mimulus guttatus flower and the floral traits measured in this study. .....................................................................................................87 Figure 3-4. Crossing design and expected means for a given quantitative trait. .........................88 Figure 3-5. AFLP linkage map as inferred from segregating progeny in 6 backcrosses involving 3 Mimulus taxa.................................................................................................89 Figure 3-6. Estimated genetic distances between M. guttatus, M. platycalyx and M. micranthus........................................................................................................................90 Figure 4-1. The crossing scheme and mapping populations for QTL phylogenetic analysis.. .........................................................................................................................131 Figure 4-2. A central dissection of a Mimulus guttatus flower and the floral traits measured in this study.. ..................................................................................................132  ix  Figure 4-3. The results in pairwise genome scan for the variation of corolla width in Mimulus crosses.. ...........................................................................................................133 Figure 4-4. The results in pairwise genome scan for the variation of corolla length in Mimulus crosses.. ...........................................................................................................134 Figure 4-5. The results in pairwise genome scan for the variation of pistil length in Mimulus crosses.. ...........................................................................................................135 Figure 4-6. The results in pairwise genome scan for the variation of average stamen length in Mimulus crosses.. ............................................................................................136 Figure 4-7. The results in pairwise genome scan for the variation of stigma-anther separation in Mimulus crosses. . ....................................................................................137 Figure 4-8. Epistasis detected in the Mimulus crosses, and the degree of genetic divergence between species as estimated with AFLP markers. . ..................................138  x  ACKNOWLEDGEMENTS Many people have helped me in the course of my research in writing this book, and any merit in it is in large measure due to them. First and foremost, I gladly acknowledge my debt to Dr Kermit Ritland of the University of British Columbia. Without his constant encouragement and advice over many years neither the original thesis nor my degree would ever have been completed. Sincere thanks are extended to my committee members: Dr. Cindy Prescott, Dr. Dolph Schluter, Dr. Michael Whitlock and Dr. Jeannette Whitton, for being so supportive and taking an intense interest of this research. Dr. Yousry El-Kassaby, of University of British Columbia, has seen showing his encouragement in many different ways. He is more than a mentor, more than a boss and more than a friend. My thanks are due to my colleagues: members in Ritland group, Genetic Data Centre and Office 3324 in Forestry Building. Finally, I would like to express my special thank to my friends- my extended family in Vancouver. Actually, in all these years, I have never felt alone.  xi  CO-AUTHORSHIP STATEMENT  For this thesis, Dr. Carol Ritland supervised all the steps from optimizing the protocol for AFLP (amplified fragment length polymorphism) primer testing, genotyping and genotype scoring. I, Charles Chen, conducted all the intercrosses and backcrosses, phenotyping and AFLP genotyping and data analyses. Charles Chen also prepared the manuscripts as his PhD dissertation. Dr. Kermit Ritland supervised the research, the crossing design as well as data analyses. Dr. Kermit Ritland also edited the manuscripts. In Chapter 3 Dr. Kermit Ritland produced the computer programs for the inferred parental genotype and lineage specific QTL inference.  xii  CHAPTER 1. GENERAL INTRODUCTION  The beauty of biological diversity has been more than impressive to all of us. Underlying the morphological differences among species are fundamental genetic changes. One of the ultimate questions in biology, then, is what is the nature of these genetic changes responsible for the evolution of morphological divergence. The questions surrounding numbers of genes, sizes of gene effects, and dominance and epistatic effects is one of the oldest problems in evolutionary biology: the complexity of the genetic changes underlying phenotypic evolution (Orr 2001).  THE BIRTH OF QUANTITATIVE GENETICS The study of inheritance and evolution began at the end of nineteenth century (Barton and Keightley 2002). The early research of evolutionary genetics started from a motivation to understand the genetic basis of complex traits, particularly for those relating to humans, such as intelligence, temper and artistic faculty. Without an explicit understanding of inheritance, Francis Galton and his student Karl Pearson first established the approaches to statistically describe continuously varying characters (Provine 1971). The multivariate statistical tools of correlation and regression that they developed laid the foundation of the Biometrical school for much of modern statistics (Mauricio 2001). On 8 February and 8 March of 1865, Gregor Mendel described the results of his research at meetings of the Brunn Natural Science Society (Orel 1984). The following year, in a paper entitled “Experiments in plants hybridization”, Mendel (1866) published  1  his now famous work in the society’s journal, Proceedings of the Brunn Society for the Study of Natural Science (Mendel 1866; Sterb and Sherwood 1966). That paper reported research done by Mendel from 1854 to 1863, involving almost 28,000 plants, in which he claimed he “carefully examined” 12,835 plants. This famous yet under-appreciated experiment involved crosses of two pure-breeding varieties of garden pea (Pisum sativum) that differed in many phenotypic character traits. However, the world did not regain its appreciation of Mendel’s contribution until 1900, 16 years after Mendel's death. Although the words “heredity” or “inheritance” were not even used in his 1866 report, the results of his work have been credited in the discovery of the first two laws of inheritance, which form the basis of “Mendelian genetics” (Gliboff 1999). Despite the elucidation and extension of Mendel’s laws by the Morgan school with Drosophila melanogaster, and ultimately the discovery of the structure of DNA by Watson and Crick, many historians set the year of 1900 as the birth of genetics because the rediscovery of Mendelian inheritance (Zwick et al. 2000). The most successful applications of Mendelian genetics involve traits in which genotypic changes result in large, discrete phenotypic differences (“Mendelian traits”). These include trait differences such as white vs. pink flower color, or smooth vs. wrinkled seed coat, and these differences are primarily governed by the segregation of single genes. During the late 19th and early 20th centuries, a conflict arose between Mendel's principles of inheritance for discrete variation and the Biometrical principles for continuously varying characteristics. The debate became fierce in the early 20th century, over whether discrete and continuous traits shared the same hereditary and evolutionary  2  properties. By 1910, it had been shown that continuous variation could result from the action of the environment on the segregation of many Mendelian loci (East 1910, Provine 1971). Nearly a decade after, in 1918, the publication of Ronald Fisher provided a comprehensive framework to unite particular inheritance with continuous variation in evolutionary content, in which Fisher demonstrated that many Mendelian factors of small effect, together with natural environmental variation, could explain continuous trait variability in natural populations (Fisher 1918). This 1918 publication convincingly reconciled discrete Mendelian inheritance with the inheritance of continuous traits. Even now, modern quantitative genetics is mostly based on the statistical foundations laid in 1918 by Ronald A. Fisher (Roff 2007).  After the 1920's, quantitative genetics as we know it today was developed by Fisher and Wright as a synthesis of statistics, Mendelian principles, and evolutionary biology. Quantitative genetics was also embraced by plant and animal breeders after this time for several decades. In 1975, Russell Lande wrote the first in a series of papers that brought quantitative genetics into evolutionary biology (Lande 1975, 1981; Roff 2007). Throughout the 1980s, while quantitative genetics was still increasingly applied in agriculture, Lande developed a comprehensive theory of evolutionary quantitative genetics, including that inferences we might expect about evolution at specific gene loci underlying quantitative traits (Barton and Turelli 1989). With the advent of highthroughput genomic approaches, the genetic basis and evolutionary forces underlying quantitative variation and evolution are now receiving renewed attention, and the wealth of genetic information obtained is having impact upon evolutionary genetics, human health, agriculture and molecular phenotyping (Gibson and Mackay 2002).  3  EVOLUTIONARY QUANTITATIVE GENETICS  If R.A. Fisher’s 1918 publication is taken as the beginning of quantitative genetics, then the centenary of quantitative genetics is only a few years away (Roff 2007). In the 21st century, modern quantitative genetics is considered as the fusion of Mendelian inheritance, biometry and mathematics encompassing this science of heredity (Mauricio 2001). Up until the 1980s, quantitative genetics assumed that phenotypic variation was static, and that the response to natural selection was based upon the standing genetic variance distribution. The "new" evolutionary quantitative genetics seeks to model or infer the underlying genetic architecture that underlies the divergence between individuals, populations and species (Lynch et al. 1999). "Genetic architecture" refers to the pattern or the collection of genetic effects. That includes allele numbers and effects, genomic distribution, allelic frequency, patterns of pleiotropy, dominance and epistatic interactions of genes. All these build and control a given phenotypic character and its variation (Remington and Purugganan 2003). The genetic architecture of phenotypic variation among individuals within population is typically complex, often with multiple interacting genetic factors that are also sensitive to the external environment (Lyman et al. 2002). Molecular studies of genetic architecture have become very feasible over the last two decades, largely because of the revolution in DNA marker technologies. In genetic mapping, association of phenotypic trait variation with DNA marker alleles has been successfully used to identify chromosomal regions harbouring individual genes responsible for quantitative variation. These genetic factors, genes, or loci, are referred  4  to as "quantitative trait loci", or QTL (Geldermann 1975), and constitute the genetic diversity that directly and indirectly contribute to the vast majority of phenotypic variation in natural populations, and perhaps represent the main sites at which selection acts upon phenotypic variation (Price 2006). Most human diseases (diabetes, asthma, arteriosclerosis), agricultural production and yield characters, as well as adaptive traits in wild species, are quantitative traits (Cordell 2002; Mackay 2001a).  MAPPING QUANTITATIVE TRAIT LOCI (QTL) AND TRAIT EVOLUTION IN PLANTS  The basic principle behind finding QTL in experimental organisms was first proposed in 1923, in plant research (Sax 1923). Since then, QTL mapping has been applied to agriculture (Roff 2007), plant domestication (Paterson 2002), and studies of adaptation (Alonso-Blanco et al. 1999), hybridization (Rieseberg et al. 1996) and speciation (Bradshaw et al. 1995).  Genetic basis of adaptation The genetic basis of adaptation has been one of the most intriguing questions in evolutionary biology (Orr and Coyne 1992). Integrated with modern genetics, the new evolutionary synthesis of neo-Darwinism states that evolution is a gradual continuous process of natural selection acting on small phenotypic variations, and that adaptation results from the fixation of alleles with individually small effects at many loci. This micromutationist viewpoint was first advocated by Fisher (Fisher 1930), suggesting that  5  adaptation is a process where organisms are fitted to the environment simultaneously for a large number of characters. And, Fisher (1930) also famously demonstrated that, while small mutations are favourable, mutation with large effects are possible but improbable (Orr 1999). Although Fisher’s infinitesimal model of genetic basis of adaptation has been challenged almost since its inception (Kimura 1983; Orr and Coyne 1992; Wright 1952), the model still provides a fundamental testable hypothesis. By dissecting the genetic architecture underlying variation of functional traits at the molecular level, QTL mapping can test this hypothesis about infinitesimal genetic changes in adaptation. Molecular markers have made it possible to map and characterize genetic changes underlying domestication (Tanksley et al. 1982; Paterson et al. 1988; Paterson et al. 1991). QTL mapping studies on crop plants have found that domestication often involves major alleles at genes with pleiotropic effects and epistatic interactions (Doebley et al. 1990; Doebley et al. 1995; Tanksley 1993). Comparative mapping also has further indicated that the same genetic loci are involved in adaptation to domestication (e.g. cereals, Paterson 1995). Domestication has correlated with dramatic increases in fruit size. For example, modern tomatoes (Lycopersicon esculentum) can weigh as much as 1,000 grams and exceed 15 cm in diameter, compared to their progenitor (L. pimpinellifolium), which had fruits less than 1 cm and only a few grams in weight (Smartt and Simmonds 1995). In fact, these two species have served as a model system for the study of the genetic basis of domestication. QTL analysis involving a cross between these two species suggested a polygenic system responsible for the domestication process of modern tomatoes (Grandillo et al. 1999). They identified at least 28 QTL responsible for  6  tomato fruit size, and some of these QTL contribute to over 20% of the phenotypic variance; one of the QTL, fw2.2, affecting the size change on tomatoes, accounts for 30% of the variation (Grandillo et al. 1999). Using a transgene complementation as a proof, Frary et al. (2000) utilized a chromosome dissection to identify a 150 kb region that contains the QTL of fw2.2, transformed into a large fruited cultivar and one of the cosmids derived from the fw2.2 region of a small fruited wild species reduced the fruit size by the predicted amount. The cause of the effect of QTL fw2.2 was determined by a single gene, ORFX, which expresses early in floral development (Frary et al. 2000). The adaptive importance of flowering phenology has long been recognized, and climatic factors, pollinator adaptations, or deleterious effects of interspecific gene flow may all function as selective mechanisms (Rathcke and Lacey 1985). More importantly, the difference of flowering time can also result in prezygotic isolation, even if they are not selectively advantageous (Remington and Purugganan 2003). A QTL mapping study, using different ecotypes of Arabidopsis thaliana, revealed four major QTL responsible for the variation of flowering time with a number of minor QTL (Alonso-Blanco et al. 1998). More interestingly, the four major QTL found in flowering time variation also contribute the variation in Arabidopsis shoot architecture, indicating a polygenic system with possible pleiotropic actions and the complexity of flowering phenology in plants (Ungerer et al. 2002). In a recent review, Ratcliffe and Riechmann (2002) list 38 flowering time genes that have been isolated from Arabidopsis, primarily by mutant analysis. These loci include CONSTANS, a zinc-finger transcription factor gene (Putterill et al. 1995), the MADS-box transcription factor gene FLOWERING LOCUS C or FLC (Michaels and  7  Amasino 1999). Results from molecular studies have shown that flowering time in Arabidopsis plants is under complex genetic control (Simpson and Dean 2002). Among those large number of known candidate genes, positional cloning based on QTL mapping research later identified the FRIGIDA locus as a major locus affecting flowering time variation among A. thaliana ecotypes (Johanson et al. 2000), and EDI locus corresponding to a blue-light receptor protein (Alonso-Blanco et al. 1998). These results illustrate the potential power of QTL mapping to address fundamental evolutionary questions (Price 2006).  Genetic basis of species differentiation A species, as the basic unit of biodiversity, is defined as a discrete interbreeding group of individuals, reproductively isolated from other such groups (Dobzhansky 1935; Mayr 1942). The first genetic survey of species differences appeared in 1938, with J. B. S. Haldane’s paper, “The nature of interspecific differences”. Current questions about how species differ morphologically largely remain the same as in Haldane’s day, involving questions about the genetic architecture of species differentiation (Orr 2001). The continuing genetic work with sunflower species (Kim and Rieseberg 1999; Rieseberg et al. 1996; Rieseberg et al. 1999b) and Louisiana irises (Arnold 2000; Cruzan and Arnold 1993; Hodges et al. 1996; Hodges et al. 1996; Martin et al. 2005) have demonstrated aspects of the genetic basis of speciation. In both study systems, natural hybrid populations can be found. These hybrids can serve as a genetic bridge between species. In sunflower, studies have shown the evolutionary dynamics of colonizing ability in Helianthus annuus via the acquisition of advantageous alleles from the locally  8  adapted H. debillis. Also, “transgressive segregation” in hybrids occurs when new combinations of parental alleles are formed, which enable the survival in novel ecological niches unavailable to either parent (Rieseberg et al. 1999a). This process has also been suggested to arise from non-additive gene action of adaptively important alleles inherited from each parent (Monforte et al. 1997). In addition, by mapping QTL in parents that are responsible for ecological traits such as flood-tolerant versus dry-adapted genotypes, Arnold (2000) and Martin et al. (2005) showed that the survivorship of Iris hybrids was strongly influenced by the presence of a number of introgressed alleles with significant epistatic genetic effect throughout the genome. Their results explicitly suggest that introgressive hybridization is an important evolutionary mechanism in Iris. Furthermore, the evolutionary inferences obtained by QTL mapping can be extended beyond a pair of species, yielding more insight into the evolutionary dynamics of species divergence. Diverse taxa in common taxonomic groups often share gene order over large chromosomal segments, and aligned QTL maps of different taxa can reveal important patterns of evolution. For example, the grasses sorghum, rice and maize were each independently domesticated about 10,000 year ago (Mauricio 2001). Each species has been selected to have large seeds, daylength-insensitive flowering and reduced fruit shattering. QTL have been mapped for these traits (Paterson et al. 1995b). Interestingly, the approximate locations of these QTL for each trait were resided in similar map locations in all three species, despite their having 65 million years of reproductive isolation. This conservation of chromosomal regions containing QTL indirectly indicates evolutionarily important genes, upon which selection can act independently across species (Paterson et al. 1995b).  9  Finally, the most well studied plant species for the genetic basis of reproductive isolation is, perhaps, Mimulus. Toby Bradshaw, Douglas Schemske and their colleagues pioneered the QTL approach with the study of speciation (Bradshaw et al. 1998). M. lewsii is a bumble-bee pollinated species with pink petals, contrasting yellow nectar guides, wide corolla opening and inserted anthers and stigma. M. cardinalis is pollinated by hummingbirds and has red petals, narrow tubular corolla, copious nectar and exerted anthers and stigma (Bradshaw et al. 1998). Although these two Mimulus species grow together and flower at the same time, hybrids are not commonly observed in nature. B y crossing these two Mimulus species, the study of underlying genetic architecture revealed QTL accounting for more than 25% of the phenotypic variance in floral morphology, and suggested that the evolution of reproductive isolation might involve genes with large effects, representing “speciation genes” (Bradshaw et al. 1998). In contrast, from the study of floral characters affecting the differences of selfing rate between M. guttatus and M. platycalyx, Lin and Ritland (1997) found a different result: genes of small effect are responsible to the evolution of mating system in the M. guttatus species complex (Lin and Ritland 1997). Table 1.1 lists the studies of genetic architecture of Mimulus species differences: 4 out of 24 floral traits were estimated to be one single QTL underlying the divergence; more than 50% of trait difference in these genetic studies had at least 5 QTL loci involved.  The unanswered questions in the study of evolutionary quantitative genetics Although it is clear that, in theory, gradual micro-evolutionary processes can explain abrupt macro-evolutionary patterns (Charlesworth et al. 1982; Lande 1983) and  10  genetic dissection techniques like QTL mapping can be used to understand the genetic architecture that underlies trait variation among plant species, the empirical problem remains greatly unsolved. We know little about the actual evolutionary processes and pattern of genetic materials that underlies phenotypic divergence between populations and species. The quantitative genetic scheme of using just a single cross only allows estimates of differences that arise along a single lineage – that separating the two species. In QTL mapping, it does provide fundamental information about the size, location and effects of individual QTL underlying a given species pair difference. However, no information is provided about the timing of QTL evolution; whether the measured QTL arose quite distantly in the past, or are recent. In this thesis, at the simplest, by bringing just one more species into QTL mapping scheme, and using this third species as an outgroup, a phylogenetic approach to QTL mapping can be developed, in which one can infer the QTL changes along each of the two lineages descending from the outgroup, and also infer QTL changes to the outgroup. The advantage of adding one more species as reference is that with such an approach to QTL mapping, we can go from a directionless comparison to directed comparison, in lineage-specific QTL can be identified. A three-species approach was recently used in genomics to detect non-neutral evolution. From a three-species phylogeny of human, chimpanzee and mouse, several genes related to physiological function like olfaction and nuclear transport were identified as undergoing positive selection along the human-chimp lineage, using the chimpanzee and mouse lineages as outgroups (Clark et al. 2003).  11  STUDY SPECIES  Owing to the diversity of life history, mating system, and adaptation to novel environments, the genus Mimulus has been a model system in plant evolution since 1940 (Clausen et al. 1940). Mimulus species occupy habitats from desert to aquatic to alpine, and contain a great degree of genetic diversity (Vickery 1978). More importantly, all species in Mimulus are self-compatible and interspecific crossing barrier range from nothing to complete (Vickery 1978). As a result, the Mimulus genus is also emerging as a model system for ecological functional genomics (Wu et al. 2007). Studies using Mimulus species a model system include those involving the genetics of speciation (Brunet and Eckert 1998; Hiesey et al. 1971; Sweigart et al. 2006), inbreeding depression (Darwin 1876; Dudash and Carr 1998), mating system evolution (Leclerc-Potvin and Ritland 1994; Fishman et al. 2002; Sweigart and Willis 2003), ecological adaptation (Macnair et al. 1993; Angert and Schemske 2005) and cytology (Beardsley et al. 2004; Vickery 1978). The Mimulus genus contains about 160 to 200 species, belonging to two large radiations centered in western North America and Australia (Grant 1924; Beardsley and Olmstead 2002; Beardsley et al. 2004; Vickery 1978). Systematic study has shown that the rapid radiate adaptation in Mimulus genus in west North America created approximately 75% of the species of this genus (Whittall et al. 2006), and among those, species in Section Simiolus display a high degree of morphological complexity and environmental plasticity (Beardsley et al. 2004). The Mimulus guttatus species complex lies within Section Simiolus and has about 8 to 12 intercrossable species members  12  (Campbell 1950; Grant 1924). Natural hybrids are sometimes found co-occupying at wild populations. In this species complex, all of the taxa have a haploid chromosome number of n = 14 (Campbell 1950; Dole and Ritland 1993; Vickery 1964; Vickery 1978). Yellow monkeyflowers show wide evolutionary changes of mating system, from predominantly selfing to predominantly outcrossing (Ritland and Ritland 1989), but are also intercrossable and of high fecundity, making them valuable material for genetic analysis (Vickery 1978). In a study of 8 different species from M. guttatus species complex, Ritland and Ritland (1989) documented a wide range of shift of mating system, and morphological variation related to species reproduction system. Allozyme and chloroplast DNA (cpDNA) RFLP analyses have suggested that among these closely related taxa, inbreeding has multiple, independent origins (Ritland and Ritland 1989; Fenster and Ritland 1992). The centerpiece of the yellow monkeyflower species complex is M. guttatus Fischer ex DC (commonly known as yellow monkeyflower). It is an herbaceous annual and perennial plant that has an extensive distribution throughout western North America in wet, semi-dry meadows and along small streams. M. guttatus is often considered the most polytypic species in this species complex, and has been thought as the center of this actively evolving species complex. The diversity within this species complex reflects a rapid adaptation radiation, which has also caused taxonomic confusion, with up to 21 species identified in the group by Pennell (1951), but only four species identified by Campbell (1950). Regardless of this confusion about taxonomy, M. guttatus has at least three, independently derived selfing relatives: M. micranthus, M. nasutus and M.  13  laciniatus (Leclerc-Potvin and Ritland 1994; Ritland and Ritland 1989; Fishman et al. 2002), which can form the basis of replicated studies of the evolution of inbreeding. The large-flowered M. guttatus is herkogamous with high levels of outcrossing (Wright’s inbreeding coefficient, F = 0.38); and as expected by its small-flower size, the predominantly selfing M. micranthus shows high inbreeding (Wright’s inbreeding coefficient, F = 0.73) (Ritland and Ritland 1989; Fenster and Ritland 1994b; Dudash and Carr 1998). M. micranthus Heller is ecologically monotypic and also endemically restricted to the Coastal Range of northern California. As a primarily selfer, M. micranthus shows reduced allocation to a number of floral traits that contribute to male function including corolla size and pollen number (Ritland and Ritland 1989). The magnitude of inbreeding depression in outcrossing M. guttatus is much greater than in selfing M. micranthus in several respects, such as above-ground biomass, pollen production and ovule production (Dudash and Carr 1998). M. platycalyx, typically annual, is a mixed-mating derivative of M. guttatus with an inbreeding coefficient of F = 0.54 (Ritland and Ritland 1989). M. platycalyx and M. guttatus are sometimes sympatric. Natural hybrids have been identified along Sausal Creek in Marin County, California (Dole and Ritland 1993). Grown in uniform conditions, M. platycalyx has floral characters intermediate between M. guttatus and M. micranthus (Ritland and Ritland 1989). In this study, we used capsule samples collected in Ritland and Ritland 1989) as parental materials. All details about species collection locations, morphological variation, mating system coefficients, phylogenetic genetic distance are in Ritland and Ritland 1989).  14  OBJECTIVES  The principle objective of this thesis was to develop and implement methodology that can use the information from multiple species comparisons to infer the evolutionary pattern of QTLs. This involves two major components: (1) development of a statistical approach where, within a three-species phylogenetic network, QTL genetic effects residing on specific lineage are inferred and homologous vs. non-homologous QTL identified; and (2), a empirical study with Mimulus to demonstrate this approach and address hypotheses about the nature of QTL evolution along lineages that change in inbreeding. More specifically, the distribution of QTL effects in each of the three lineages of a three-species network allows tests of hypotheses about the pattern of evolution among species. The “null” hypothesis would be that position and effects are randomly distributed among the three branches of an unrooted three species network, and essentially the outcome of mutation and genetic drift. QTLs that occur on a phylogenetic lineage in a non-random way serve as footprint for natural selection. Various hypotheses about the evolution of mating systems and adaptive evolution will be addressed in the following thesis chapters. These include: are phenotypic differences among taxa for the mating system governed by many or few genes? Are these genes recessive in the selfing taxa? Do changes in position of stigmas and anthers result in pleiotropic changes in other floral characters? Are the same processes evident along independent phylogenetic lineages? Also, because the genetic distance between QTL can be inferred from their  15  phylogenetic map, one can examine the relationship between gene-gene interaction (epistasis) and evolutionary separation. The specific objectives of each thesis chapter  Chapter 2: Effective number of genetic factors separating inbreeding vs outbreeding species in Mimulus guttatus complex Prior to the development of QTL mapping with molecular markers, Castle and Wright (Castle 1921; Wright 1952, 1968) provided a biometrical method to estimate the number of effective genetic factors underlying quantitative genetic variation. The estimated numbers of genes underlying trait difference are based on the segregational variance of the F2 and the means of the two parental taxa. It provides an estimate of the minimum number of genetic factors, all fixed in the same evolutionary direction that differentiate two morphologically distinct taxa. However, gene number is a result of species evolutionary history, where many different evolutionary forces can alter species morphology. Also, the longer the evolutionary lineages of species are, the greater the opportunity for mutation accumulation at QTL. I therefore expect, as species are separated by longer phylogenetic distances, to detect more genetic factors. In this chapter, evolutionary distance will be estimated for all pairs of Mimulus taxa from neutral genetic markers (AFLPs). The number of genetic factors that govern phenotypic changes in pairwise crosses will be obtained by using formula of Fenster and Ritland (1994b), based upon the phenotypic measurements made upon the segregating crosses.  16  Chapter 3: Lineage specific inferences about QTL evolution among an outcrossing and two derived inbreeding taxa of yellow monkeyflowers The quantitative genetic scheme of using just a single cross only allows estimates of differences that arise along a single lineage – that separating the two species. In QTL mapping, it does provide fundamental information about the size, location and effects of individual QTL underlying a given species pair difference. However, no information is provided about the timing of QTL evolution: whether the measured QTL arose quite distantly in the past, or are recent. At the simplest, by bringing just one more species into QTL mapping scheme, and using this third species as an outgroup, one can infer the QTL changes along each of the two lineages descending from the outgroup, and also infer QTL changes to the outgroup. Taking the advantage of adding one extra species as reference in classic QTL mapping experiment, one now can go from directionless comparison to having reasonable ability to infer the lineage-specific genetic changes. In this chapter, I first describe an analysis to identify the independent origin of QTL in a species network. An empirical study using Mimulus species will then carried out to demonstrate this novel method.  Chapter 4: Epistatic interaction on QTLs involved in the evolution of floral traits in the Mimulus guttatus species complex From QTL-based evidence, epistasis appears to be fairly common in segregating crosses within/between species, suggesting its significance in studying quantitative variation (see review in Mackay 2001a and Malmberg and Mauricio 2005). However,  17  the importance of epistasis varies among studies (Cockerham and Zeng 1996; Li et al. 1997; Xiao et al. 1995). In theory, epistasis is a property of genetic complexity (Sanjuan and Elena 2006): it is more likely to detect epistasis when there are more genetic loci involved. I expect epistasis to be detected in the comparison of taxa with different mating systems, given the genetic architecture that underlies the difference of selfing rates of Mimulus species is polygenic in nature (Fishman et al. 2002; Lin and Ritland 1997). In this chapter, the extent of epistasis for mating system traits between Mimulus species will be examined. The major expectation is that species that are more highly diverged will show greater epistasis in the progeny of crosses between them. This is because of the introduction of genes into foreign genetic background, or equilivalently, the disruption of co-adapted genes within species.  18  Table 1-1. Recent Mimulus genetic analyses of species differences  Species  Traits  M. lewisi- M. cardinalis  Number of genes  References  >1  Bradshaw et al. (1998)  Anthocyanin concentration Carotenoid concentration Lateral petal width Corolla width Corolla projected area Upper petal reflexing Lateral petal reflexing Nectar volume Stamen length Pistil length Corolla aperture width Corolla aperture height M. guttatus- M. platycalyx Flower length Pistil length Long stamen length Short stamen length Anther-stigma separation M. guttatus- M. nasutus Throat width Corolla width Tube length Corolla length Styla length Stamen length Stigma-anther separation M. guttatus- M. micranthus Bud growth rate Flowering time  > 14 > 14 > 13 > 11 > 12 > 13 > 15 >8 >1  Corolla width Corolla length Stamen length Pistil length Stigma-anther separation  >9 > 10 >8 > 13 >5  19  >3 >8 >8 >7 >5 >4 >3 >7 >7 >8 >4 >1 >1 >3 >3 >2  Lin and Ritland (1997)  Fishman et al. (2002)  Fenster et al. (1995) Fenster and Ritland (1994b)  REFERENCES Alonso-Blanco, C., H. Blankestijn-de Vries, C. J. Hanhart, and M. Koornneef. 1999. 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H. Willis. 2007. Mimulus is an emerging model system for the integration of ecological and genomic studies. Heredity. Xiao, J., J. Li, L. Yuan, and S. D. Tanksley. 1995. Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics 140:745-754. Zwick, M. E., D. J. Cutler, and A. Chakravarti. 2000. Patterns of genetic variation in Mendelian and complex traits. Annual Review of Genomics and Human Genetics 1:387-407.  27  CHAPTER 2. EFFECTIVE NUMBER OF GENETIC FACTORS SEPARATING INBREEDING VS OUTBREEDING SPECIES IN THE MIMULUS GUTTATUS COMPLEX 1  INTRODUCTION Over evolutionary time, the quantitative genetic bases of morphological evolution may involve the accumulation of quantitative trait locus (QTL) differences of either small effect or larger effect. Fisher (1930) argued that adaptation through microevolution should involve the accumulation of many favourable mutations of all small effect, since mutations of large effect less likely to accurately match the optimum. Kimura (1983) reconsidered Fisher’s infinitesimal model and derived an expected distribution mutational effect. He noted that while mutations with minor effect may often be favourable, mutations of favourable larger effect are more likely to escape stochastic loss, and hence that mutations with intermediate effect are most likely to be involved in adaptation (Kimura 1983; Griswold and Whitlock 2003). Later, Orr (1999) studied the effects of changes in the distribution of mutational effects and predicted that the genetic basis of phenotypic changes often involves a modest number of factors of large effect and a greater number of factors of small effect. There have been many empirical studies of the genetic basis underlying species phenotypic differentiation. The genetic basis of phenotypic evolution can be simply controlled by one or a few major genes, or be complex involving many genes with interactions. Orr (2001) reviewed the results studying genetic basis of morphological divergence between species,  1  A version of this thesis will be submitted for publication. Chen, C. and K. Ritland. 2009. Effective number of genetic factors separating inbreeding versus outbreeding species in the Mimulus guttatus complex.  28  comparing 22 studies of 54 traits involving both plants (mostly Mimulus spp.) and animals (mostly Drosophila spp.). The most striking feature was the range in the numbers of genetic factors (loci) that distinguish species. For example, Zeng et al. (2000) showed that the difference between D. simulans and D. mauritinan in the size/shape of the posterior lobe of the male genital arch involved at least 19 loci, where Sucena and Stern (2000) reported the difference in larval morphology between D. simulans and D. sechellia to be due to as few as one gene. Similarly, wide differences in the numbers of detected genetic factors are also found in plant studies. Studies using genetic analysis of segregation in crosses have shown that many genes are responsible for mating system differences within and among species in Turnera (Shore and Barrett 1990), Clarkia (Holtsford and Ellstrand 1992) and Mimulus (Macnair and Cumbes 1989; Fenster and Ritland 1994b; Fishman et al. 2001). However, studies using the same type of genetic analysis found only a few major genes in Senecio (Marshall and Abbott 1982), Ipomea (Clegg and Epperson 1988) and Mimulus (Bradshaw et al. 1998). Although further studies are needed to gauge the number of genes typically involved with species divergence, different types of inferences, not just on gene number, can also be made. On such inference is the extent of dominance. Dominance of alleles that confer selfing, over alleles that confer outcrossing, is known to facilitate the evolution of selfing (Haldane 1927). Overdominance was also proposed to facilitate the evolution of a stable, intermediate outcrossing rate (Charlesworth 2006). The extent of dominance for mating system traits has been explored in Mimulus, using both QTL mapping techniques (Lin and Ritland 1997; Fishman et al. 2002) and biometric approaches (Macnair and Cumbes 1989; Fenster and Ritland 1994a). However, the extent of dominance has not been systematically examined in a multiple species comparison involving different degrees of evolutionary divergence.  29  In this study, we first employed the traditional biometric analysis to examine the quantitative genetics basis of the floral character variation associated with the evolution of mating system. The number of genetic factors and the dominance effect were examined on the comparison of one outcrossing M. guttatus species and two independent derived inbreeding relatives. With the number of the crosses involving in this Mimulus study, we then furthermore addressed the question about the number of genetic factors in relation to species evolutionary divergence. We hypothesized that, if species adaptation is a process of accumulating mutations with different effects, with the drift-mutation balance, the numbers of genetic factors should be greater with greater evolutionary distance. Alternatively, the number of effective genetic factors may only be proportional to the morphological divergence between taxa. The extent of dominance is also examined in this multiple species comparison involving different degrees of evolutionary divergence.  30  MATERIALS AND METHODS  Study species Section Simiolus in the Mimulus genus consists of both predominantly selfing and predominantly outcrossing species (Ritland and Ritland 1989). Many species in Section Simiolus are inter-crossable and produce F1 and F2 progeny that are easy to raise and maintain in controlled environments. Those advantages have enabled Mimulus species to be the subject of systematic and genetic studies for decades (Vickery 1978). The M. guttatus species complex lies within the Section Simiolus and has about 8 to 12 inter-crossable species members (Campbell 1950; Grant 1924). Natural hybrids are sometimes found in the field. All taxa have a haploid chromosome number of n = 14 (Campbell 1950; Dole and Ritland 1993; Vickery 1964; Vickery 1978). Species in this complex have a wide range of natural self-fertilization rates. In a study of 8 different species from M. guttatus species complex, Ritland and Ritland (1989) documented such variation, as well as morphological variation related to shifts in allocation to male vs. female reproductive effort. Allozyme and chloroplast DNA (cpDNA) RFLP analyses have also indicated that among these closely related taxa, inbreeding has multiple, independent origins (Ritland and Ritland 1989; Fenster and Ritland 1992). M. guttatus Fischer ex DC, also known as yellow monkeyflower, is an herbaceous annual and perennial plant that has an extensive distribution throughout western North America in wet, semi-dry meadows and along small streams. M. guttatus is the most polytypic species in this species complex, and has been thought as the center of this actively evolving species  31  complex. M. guttatus has at least three, independently derived selfing relatives: M. micranthus, M. nasutus and M. laciniatus (Ritland and Ritland 1989; Fishman et al. 2002). The largeflowered M. guttatus are herkogamous with a relatively higher degree of outcrossing rate (Wright’s inbreeding coefficient, F = 0.38), compared with other smaller-flowered Mimulus and predominantly selfing M. micranthus (Wright’s inbreeding coefficient, F = 0.73) (Ritland and Ritland 1989; Fenster and Ritland 1994a; Dudash and Carr 1998). M. micranthus Heller is ecologically monotypic and also endemically restricted to the Coastal Range of northern California. As a predominant selfer, M. micranthus shows reduced allocation to a number of floral traits that contribute to male function including corolla size and pollen number (Ritland and Ritland 1989). The magnitude of inbreeding depression in outcrossing M. guttatus is much greater than in selfing M. micranthus for several fitness components, including above-ground biomass, pollen production and ovule production (Dudash and Carr 1998). M. platycalyx, an annual like the other two species, is a mixed-mating derivative of M. guttatus with an inbreeding coefficient of F = 0.54 (Ritland and Ritland 1989). M. platycalyx and M. guttatus are sometimes sympatric. Natural hybrids have been identified along Sausal Creek in Marin County, California (Dole 1992). Grown in uniform conditions, M. platycalyx has floral characters intermediate between M. guttatus and M. micranthus (Ritland and Ritland 1989). In this study, we used seed collected in Spring 2001 from the same locations as given in Ritland and Ritland (1989).  Pairwise crosses between Mimulus species All nine pairwise reciprocal backcrosses and F2 intercrosses were conducted between M. guttatus, M. platycalyx and M. micranthus (sample sizes are given in Table 2-1). Parents were  32  simultaneously grown in different growth chambers. F1 crosses were performed by intercrossing parents, and maintained in other chamber. F2 progeny were produced by selfing F1 individuals. Backcrosses were performed in both directions to both parents (BC1 and BC2; BC1 is the backcross back to larger flower parent and BC2 is the backcross to smaller flower parent). All plants were grown at 18C/14C day/night temperature, with 18-hour daylight in growth chambers, in the same batch of Pro-Mix soil. To avoid pollen contamination, flowers were bagged after manual pollination. Crosses between parents were performed in 2000; the F2 and two backcrosses from M. guttatus x M. platycalyx were performed in 2001 under the same growth chamber conditions. Crosses from M. guttatus x M. micranthus and M. platycalyx x M. micranthus were performed in the same growth chamber in continuous years. During the BC and F2 generations, and for parents, the following characters were measured on individuals: (1) corolla length, (2) corolla length, (3) pistil length, (4) stamen length (averaged over the low and high anthers), (5) stigma-anther separation, (pistil length minus the average stamen height). A digital calliper was used to take dimensional measurements.  Gene number estimation The most widely used method for estimating the effective number of factors (Ne) was developed by Castle (1921) and his graduate student Sewall Wright (Castle 1921; Wright 1968). It utilizes information on the phenotypic means and variance of two parental lines, and their line-cross derivatives. It is known as the Castle-Wright estimator (Eq. 1),  NE  U P1 U P 2  2  8 s F2 2  s E2  33  (Eq. 1)  where the Us are estimated means in the two parental taxa, and the s F2 2 and s E2 are the segregational variance in F2 progeny and the environmental variance, respectively. The estimator has a number of assumptions, including additivity of gene effects, equality of allelic effect and unidirectional gene effect (Lynch and Walsh 1998). In actuality, there is often dominance of gene effect, which can also vary among loci. If the F1 is not exactly intermediate between the parent means, there is some dominance present (Wright 1968). Here, we also incorporated dominance in our gene number estimation, following Fenster and Ritland (1994b). However, we still assume a uniform dominance coefficient for all loci (as in Fenster and Ritland 1994b). To incorporate dominance, the ratio of dominance to additive effect (D/A) is first estimated as: D  A  2 U F 2 EF 2 , U P1 U P 2  (Eq. 2)  and the gene number estimate, corrected for dominance effects, is then obtained as: 2 NE d  D  2  A  U P1 U P 2  2  C  (Eq. 3)  16 S  In these expressions, C is a correction factor that equals to the statistical variance of 2  D  A  U P1 U P 2  U P1 U P 2 , and E F 2  and E B1  2  3U P1 U P 2  4  are the averages of  the two parental means or that expected in the absence of dominance (Fenster and Ritland 1994b). The ratio of dominance to additive effect (D/A) estimators for backcross progeny are:  D  4 U B1 A  E B1  (Eq. 4)  U P1 U P 2  34  1 NE d  D  2  A  U P1 U P 2 16 S  2  C  .  (Eq. 5)  The estimators for the backcross to second parent are the same except that B1 is replaced by B2 and P1 and P2 are interchanged, resulting in a degree of the sign of degree of dominance ( D A ).  35  RESULTS  Table 2-1 lists the means and the sample sizes for each floral character calculated from each of the parental generation, F1 generations, F2 generations and backcross generations. M. guttatus is the most outbreeding in this study and it also shows the largest floral characters. For corolla width and length, M. guttatus is about twice as large as the intermediate inbreeder M. platycalyx, and almost three times larger than the highly inbreeding M. micranthus. Correspondingly, M. guttatus has the longest pistil and stamens. M. guttatus also shows the greatest separation between the height of stigma and anther (character stigma-anther separation mean = 2.02). The intermediate inbreeder, M. platycalyx, shows very little stigma-anther separation (mean = 0.26), while for inbreeding M. micranthus, stigma-anther separation was negative (mean = -1.35). Figure 2-1 shows distribution of corolla length across generations for the cross of M. guttatus with M. micranthus. The F1 distribution is slightly skewed toward M. micranthus parents, but the distribution of F2 falls, as expected, in between the two Mimulus parents. Figure 2-2 plots the mean vs. the variance for corolla length for the same cross. With respect to the mean, the F1 and the backcrosses were approximately intermediate, while the variances were inflated for the backcross to M. guttatus and especially the F2, suggesting transgressive segregation against the M. guttatus genetic background.  36  Degree of dominance Estimates of dominance based on Eq. 2 (for F2s) and Eq. 4 (for backcrosses) are given in Table 2-2. The differentiation of floral characters among species within M. guttatus species complex was governed by a great degree of dominance. Among all characters, except the character of stigma-anther separation, estimates of dominance are mostly negative, suggesting that the direction of dominance is that inbreeding alleles are dominant over outbreeding alleles (the more outbred species was parent #1, the more inbred parent #2, so that this direction of dominance would give a negative D/A ratio). For example for corolla length, dominance estimates range from almost zero (-0.06; F1 of M. platycalyx - M. micranthus) to strongly negative (–1.40; backcross M. platycalyx - M. micranthus). There are some exceptions: in the cross of M. platycalyx and M. micranthus dominance shows changes of direction between the F1 and F2 generations for corolla width and pistil length (Table 2-2). More importantly, not only did stigma-anther separation show some extreme variation for dominance, but all estimates were positive (Table 2-2).  Gene number estimates with the correction of uniform dominance Estimates of the number of genetic factors underlying the differentiation of taxa are listed in Table 2-3. The numbers of factors ranged from 13.96 (pistil length in the backcross of M. guttatus and M. micranthus to M. micranthus) to 0.45 (pistil length, M. guttatus x M. platycalyx backcross to M. platycalyx). A low number of genetic factors was also found between M. guttatus and M. platycalyx, ranging from 4.73 (in the backcross to M. guttatus for corolla length) to as little as 0.82 (in the backcross to M. platycalyx for corolla length). Larger estimates were found between M. guttatus and M. micranthus. The lowest estimate in this cross  37  was 2.14 genetic factors (in the backcross to M. guttatus for stigma-anther separation) and the greatest one was 13.96 (in the backcross to M. micranthus for pistil length).  Number of genetic factors in relation to species evolutionary divergence versus morphological divergence Figure 2-3 shows the results for the number of effective genetic factors with the species genetic divergence. The genetic divergence between Mimulus species was estimated from AFLP (amplified fragment length polymorphism) variation (see Chapter 3). The estimated genetic distance between M. guttatus and M. micranthus was 0.08 (S.E.=0.01), between M. guttatus and M. platycalyx was 0.19 (S.E.=0.02), and between M. platycalyx and M. micranthus was 0.20 (S.E.=0.02) (Table 2-4). This three species phylogeny is displayed in Figure 3-6 (Chapter 3). Euclidean distances between Mimulus species for each trait were given as morphological divergence. Standard errors of morphological divergence were estimated from taking squared root of variance among the 1,000 bootstraps. To properly compare crosses with different degree of genetic divergence, we only select the data from F2 crosses in this analysis. In the cross of M. guttatus x M. micranthus, the species with greatest difference in floral morphology but least degree of genetic differentiation, shows the largest number of estimate number of effective genetic factors (Table 2-4). The number of genetic factors is smallest in the cross between M. guttatus and M. platycalyx, which paradoxically had the greatest genetic differentiation (Figure 2-3). Summarized in Table 2-4, the hypothesis of increasing number of genetic factors with genetic distance is not supported; however, the species with greatest morphological divergence does still show the largest number of effective genetic factors (the cross of M. guttatus x M. micranthus in Table 2-4).  38  DISCUSSION  In this study, we followed the traditional biometric approach to estimate the number of effective genetic factors differentiating floral characters among Mimulus taxa. The individuals used as parents in this study were all collected directly from field populations. F1 and later generation crosses were manipulated and grown under consistent growth chamber conditions. We then applied Fenster and Ritland's (1994b) formula to correctly estimate the number of genetic factors by incorporating dominance into the estimation procedure. We found the numbers of genes underlying floral morphological difference among Mimulus species to range from 1.11 to 6.53 (for corolla width) and 0.7 to 7.04 (for corolla length) (Table 2-3). Although the numbers estimated in our study assumed a uniform dominance, our results here still reveal a small to intermediate number of effective genetic factors underlying Mimulus species phenotypic differentiation. A similar result of few genetic factors was also found for floral traits that cause autogamous selfing in M. cupriphilus, as well as for traits associated with the adaptation to heavy-metal sites (Macnair and Cumbes 1989).  Violations of the Wright-Castle estimator A variety of statistical approaches have been proposed for estimating the effective (or minimum) number of genetic loci contributing to a quantitative trait (Serebrovsky 1928; Tan and Chang 1972; Wright 1952, 1968). Among those, the original method of Wright (in Castle 1921; Wright 1952, 1968) is most commonly used. While the requirement of inbred lines in Wright method is sometimes violated in practice, and may produce unwanted complications of  39  inbreeding depression on the mean and developmental stability of the lines. However, Wright’s method can be applied to crosses between genetically heterogeneous populations (Lande 1981); this minimizes the extent of inbreeding depression and reduces the total time necessary to perform the experimental crosses. In addition, even though the effect of dominance was discussed in his later review (Wright 1968), the original Wright-Castle estimator assumed additivity among loci underlying the given morphological divergence. Non-additive genetic effects may either increase or decrease the estimate of gene number, but is perhaps small compared with the effect of linkage, which could possibly lead to a serious underestimation of the number of gene controlling a trait difference (Zeng et al. 1990). To ignore the effect of dominance would certainly underestimate the gene number separating taxa (Wright 1968). In the current work, we have dealt with the issue of dominance via using the estimators of Fenster and Ritland (1994b).  Comparisons to previous QTL studies in Mimulus The M. guttatus species complex is a well-known system to study mating system divergence. The most common species in this complex, M. guttatus, is a predominantly outcrossing species (Latta and Ritland 1994b), and self-fertilization appears to have evolved several times within this species complex (Vickery 1978; Fenster and Ritland 1994b). The autogamous self-fertilization species, M. micranthus and M. nasutus, have striking reductions in corolla size and stigma-anther separation, as well as with changes in the production of male and female gametes (Fishman et al. 2002). The molecular evidence for the independent origin of selfing in this group suggests that evolution of selfing in this group may involve different genes or different genetic mechanisms (Ritland and Ritland 1989; Fenster and Ritland 1992, 1994a).  40  Because of the intercrossibility of species in this species complex, the genetic architecture of their difference has been characterized with both traditional and advanced molecular quantitative genetic approaches (Fenster and Ritland 1994a; Fishman et al. 2002; Macnair and Cumbes 1989). However, the estimated number of effective genetic factors underlying the mating system difference was not consistent across studies. Fenster and Ritland (1994a) analyzed the effective number of genetic factors differentiating 6 floral traits among 4 Mimulus taxa, and reported that the mean number of genes separating selfer and outcrosser Mimulus in all F2 generations and backcrosses was ranged from 5.3 (for stigma-anther separation) to 12.8 (for pistil length). The effective number of genes estimated by the biometric approach cannot exceed the number of chromosomal segments segregating independently in one generation, which equals to, in most cases, the haploid number of chromosomes (Darlington 1937; Lande 1981). The estimated numbers of genetic factors using biometric approach are close to the upper limit (n = 14 for Mimulus, Vickery 1978). A polygenic evolutionary system has also been shown in studies using molecular quantitative genetic approaches by Fishman et al. (2002) and Fishman and Willis (2001), who used 255 AFLP and microsatellite markers and constructed a framework genetic linkage map of hybrid genome of M. guttatus and M. nasutus. They then analyzed the genetic basis of 16 floral characters in a large segregating F2 population and identified 24 QTL underlying interspecific differences in seven floral traits (Fishman et al. 2002). In the absence of epistasis, at least 18 additional QTL could be added on to the effect responsible to corolla width differentiation. It is seemingly conceivable that, based on both biometric and molecular quantitative genetic models, divergence between outcrosser M. guttatus and selfer M. nasutus is a polygenic system.  41  However, in the comparison of the studies targeting the same interest, studies have identified relatively small numbers of effective genetic factors responsible for the divergence of Mimulus mating system. Mimulus floral size features, such as floral width and floral length, are known to influence mating system through pollinator attraction during reproduction (Karron et al. 1997; Chang and Rausher 1998). An early QTL study of floral divergence between M. guttatus and M. platycalyx, a selfer with relatively large flowers but no degree of stigma-anther separation, found a relatively low number of QTLs (one to three) affecting five mating system characters, and each QTL explained 7.6% to 28.6% of the phenotypic variation (Lin and Ritland 1997). The genetic control of 12 morphological differences on floral characters between the bumblebee-pollinated M. lewisii and hummingbird-pollinated M. cardinalis was carried out in a large linkage mapping population of F2 plants by Bradshaw et al. (1998), who identified one to six QTLs for each trait with most traits appearing to have at least one major QTL explaining larger than 25% of phenotypic variation. This research implied an oligo-genetic model, where single genes of individually large effect or clusters of tightly linked genes with large cumulative effect play important role in the evolution of floral characters in Mimulus. The incongruent results of gene numbers vs. morphological divergences raises questions about the strength of selection vs. the amount of standing variation (Orr 2001). In this thesis, a wide range of effective number of genetic factors was identified (Table 2-3). Given the assumptions of additivity and uniformity of gene action in the traditional biometric method, our findings can only suggest the minimal mutational steps that allow populations to reach another fitness peak. The actual number of genetic changes is undoubtedly larger than what we have estimated in this thesis. Also, the divergence of traits driven by natural selection, i.e. mutations toward selfing favoured on inbreeding lineage, could generate a covariance of alleles at  42  underlying QTL/effective genetic factors. This would decline the expectation in gene number estimates with greater species evolutionary divergence under the drift-mutation hypothesis. Dominance and the evolution of inbreeding We observed a consistent dominance of alleles for inbreeding over alleles for outbreeding. In our study, significant dominance was found for almost all crosses and floral traits (Table 2-2). As illustrated in Figure 2-4, a general pattern of directional dominance was identified. In Figure 2-4, three F2 crosses between Mimulus were chosen for the comparison, (1) the cross between highly outbreeder M. guttatus and highly selfer M. micranthus, (2) the cross between M. guttatus and intermediate outbreeder M. platycalyx, and (3) the cross between intermediate outbreeder M. platycalyx and highly selfing M. micranthus. A directional dominance towards selfing taxa of Mimulus is clearly evident, with the exception being stigmaanther separation where dominance was found in the direction of outcrossing over selfing (large separation over small separation). In a large outcrossing population, the probability of fixation of completely recessive advantageous new mutations is much less than that for favorable mutation with some expression in heterozygotes, making dominance important in the evolution of selfing at early stage (Haldane 1927). Dominance therefore increases the probability of the evolution for selfing. Our results support this hypothesis. Our study further suggests the initial stage of evolving selfing is likely caused by a small number of major genetic factors with dominance (Chapter 3). Such findings of partial dominance towards inbreeding characters was also found in Fenster and Ritland (1994b). Further, as depicted in Figure 2-4, dominance is greater when comparing species with high outcrossing rate with species of intermediate outcrossing (e.g. M. guttatus vs. M. platycalyx), compared to the intermediate outcrossers vs. high selfers (e.g. M.  43  platycalyx vs. M. micranthus). Our result also supports the theoretical work of Latta and Ritland (1994a), who demonstrated a stable intermediate outcrossing is more likely to occur if selfing alleles were dominant and when multiple genes are involved in controlling outcrossing rate. In theory, characters that closely associated with fitness are expected to be controlled by genes with non-additive genetic effects (Falconer 1981); and nevertheless, dominance has also been recognized its importance role in shaping mating system characters in Mimulus study systems. For example, using QTL mapping between Mimulus species pair, M. guttatus and M. nasutus, Fisherman et al (2002) identified a polygenic system with many QTL; partial dominance was observed and the directionality of dominance was found nearly equal between the two parental species. Lin and Ritland (1997) also found mixed results, in that dominance was found toward both parents; however a greater number of QTL had dominance towards the selfing taxa. In the M. guttatus species complex, the reduced stigma-anther separation is directly indicative of increased selfing (Carr and Fenster 1994; Dole 1992; Ritland and Ritland 1989). Stigma-anther separation in our study interestingly shows dominance of outcrossing alleles over selfing alleles (Figure 2-4). This type of dominance would initially maintain stigma-anther separation at the early stages of the evolution of inbreeding, which may be advantageous, since it helps to maintain the standing genetic variation. However, as flowers evolve towards smaller size, the stigma-anther separation would likewise become smaller, leading to a higher selffertilization rate. Also, when species become more inbreeding, genes and the associated genetic dominance towards selfing taxa that reduce the size of flowers make self-fertilization more efficient, due to changes of sex allocation (Ritland and Ritland 1989). Thus the dominance for stigma-anther separation may be a transient phenomenon.  44  Table 2-1. Generation means and sample sizes for each character Characters Cross  Sample size  Corolla length  Corolla width  Pistil length  Stamen length  Stigma-anther separation  M. guttatus X M. platycalyx 10 P- M. guttatus 9 P- M. platycalyx 20 F1 100 F2 216 BC to M. guttatus 173 BC to M. platycalyx  34.20 (1.07) 23.23 (0.57) 24.08 (0.60) 24.70 (0.3) 29.83 (0.19) 23.71 (0.32)  30.25 (0.79) 17.77 (0.40) 19.61 (0.54) 19.88 (0.3) 24.16 (0.18) 18.58 (0.28)  18.96 (0.53) 13.15 (0.31) 14.72 (0.20) 14.80 (0.2) 18.09 (0.11) 14.35 (0.18)  16.95 (0.25) 12.89 (0.29) 12.52 (0.22) 12.98 (0.2) 14.64 (0.10) 12.03 (0.16)  2.02 (0.39) 0.26 (0.26) 2.20 (0.14) 1.81 (0.07) 3.4 (0.07) 2.24 (0.10)  M. guttatus X M. micranthus 10 P – M. guttatus 11 P – M. micranthus 35 F1 184 F2 149 BC to M. guttatus 122 BC to M. micranthus  34.20 (1.07) 11.39 (0.36) 14.88 (0.84) 16.97 (0.23) 26.71 (0.33) 13.86 (0.22)  30.25 (0.79) 8.10 (0.30) 12.26 (0.82) 13.71 (0.22) 22.34 (0.33) 10.67 (0.22)  18.96 (0.53) 6.38 (0.22) 9.17 (0.52) 10.41 (0.25) 16.09 (0.15) 8.51 (0.11)  16.95 (0.25) 7.21 (0.23) 8.82 (0.33) 9.19 (0.09) 13.86 (0.14) 7.89 (0.10)  2.02 (0.39) -1.35 (0.15) 0.34 (0.23) 1.22 (0.05) 2.21 (0.07) 0.62 (0.05)  M. platycalyx X M. micranthus 9 P – M. platycalyx 11 P – M. micranthus 36 F1 221 F2 72 BC to M. platycalyx 166 BC to M. micranthus  23.23 (0.56) 11.39 (0.36) 16.58 (0.23) 11.74 (0.22) 16.13 (0.28) 11.74 (0.25)  17.77 (0.40) 8.10 (0.30) 16.98 (0.23) 9.95 (0.14) 11.66 (0.22) 8.13 (0.20)  13.15 (0.31) 6.38 (0.22) 10.39 (0.09) 8.43 (0.08) 9.82 (0.14) 7.09 (0.14)  12.89 (0.29) 7.21 (0.23) 9.76 (0.11) 7.76 (0.12) 10.03 (0.17) 7.76 (0.14)  0.26 (0.26) -1.35 (0.15) 0.63 (0.06) -0.31 (0.04) -0.2 (0.09) -0.67 (0.06)  Values in parentheses are standard errors to the mean  45  Table 2-2. Estimates of degree of dominance (D/A ratio) in F1, F2 and backcross generations  Characters Cross  Corolla length  Corolla width  Pistil length  Stamen length  Stigma-anther separation  M. guttatus X M. platycalyx -0.42 (0.07) F1 -0.73 (0.11) F2 -0.59 (0.26) BC to M. guttatus -0.82 (0.22) BC to M. platycalyx  -0.30 (0.05) -0.66 (0.08) -0.95 (0.14) -0.74 (0.14)  -0.23 (0.06) -0.43 (0.10) 0.40 (0.30) -0.17 (0.21)  -0.59 (0.09) -0.96 (0.15) -1.28 (0.21) -1.85 (0.37)  0.60 (0.27) 0.89 (0.41) 4.52 (1.80) 3.74 (1.11)  M. guttatus X M. micranthus -0.35 (0.04) F1 -0.51 (0.04) F2 -0.31 (0.14) BC to M. guttatus -0.57 (0.07) BC to M. micranthus  -0.31 (0.04) -0.49 (0.03) -0.43 (0.11) -0.54 (0.66)  -0.28 (0.05) -0.36 (0.05) -0.09 (0.13) -0.32 (0.07)  -0.34 (0.04) -0.68 (0.05) -0.32 (0.10) -0.93 (0.10)  0.01 (0.09) 0.39 (0.15) 1.23 (0.45) 1.34 (0.27)  M. platycalyx X M. micranthus -0.06 (0.03) F1 -0.61 (0.02) F2 -1.40 (0.04) BC to M. platycalyx -0.88 (0.15) BC to M. micranthus  0.42 (0.04) -0.62 (0.06) -1.53 (0.14) -0.98 (0.13)  0.11 (0.03) -0.39 (0.06) -0.97 (0.13) -0.58 (0.14)  -0.05 (0.04) -0.58 (0.08) -1.22 (0.16) -0.98 (0.20)  0.74 (0.21) 0.32 (0.22) -0.10 (0.53) 0.72 (0.37)  Values in parentheses are standard errors to the mean  46  Table 2-3. Estimates of effective number of genetic factors underlying Mimulus floral character divergence, assuming an uniform correlation of uniform dominance coefficient among loci. Characters Cross  Corolla length  Corolla width  Pistil length  Stamen length  Sigma-anther separation  M. guttatus X M. platycalyx 2.08(1.03-3.37) F2 2.79(1.05-3.3) BC to M. guttatus 0.82(0.48-1.19) BC to M. platycalyx  2.90 (1.51-4.19) 4.73 (2.88-7.84) 1.59 (0.92-2.03)  2.66 (1.27-5.81) 4.44 (1.09-8.67) 0.45 (0.28-0.71)  1.54 (0.89-2.87) 1.78 (1.17-2.74) 1.37 (0.89-2.21)  2.13 (0.64-4.9) 4.26 (1.27-11.07) 2.10 (0.58-18.0)  M. guttatus X M. micranthus 5.17 (3.42-6.38) F2 2.89 (1.8-3.78) BC to M. guttatus 9.87 (6.06-12.08) BC to M. micranthus  5.12 (3.47-6.19) 3.08 (1.98-3.99) 9.07 (5.92-10.69)  10.2 (8.69-15.14) 7.58 (3.07-13.56) 13.96 (8.76-32.35)  8.42 (6.82-10.55) 2.14 (1.53-3.05) 8.27 (6.01-11.86)  2.85 (2.64-2.91) 2.57 (1.54-2.64) 6.93 (4.13-7.17)  M. platycalyx X M. micranthus 5.23 (3.32-6.51) F2 7.08 (3.79-9.72) BC to M. platycalyx 1.7 (1.2-2.29) BC to M. micranthus  4.58 (3.27-6.3) 8.90 (4.66-10.93) 2.04 (1.52-2.91)  5.8 (4.07-8.5) 5.24 (3.24-10.31) 1.23 (0.92-1.74)  3.47 (2.15-6.65) 4.27 (2.64-8.06) 1.18 (0.83-1.74)  2.35 (1.01-5.97) 0.46 (0.2-1.11) 0.67 (0.29-1.38)  Values in parentheses are 95% confidence intervals.  47  Table 2-4. Estimates of effective number of genetic factors, morphological divergence and genetic distance in Mimulus F2 crosses  Cross  Estimate number of effective genetic factors in F2 cross1  Morphological divergence2  Corolla width M. guttatus x M. micranthus M. guttatus x M. platycalyx M. platycalyx x M. micranthus  2.90 (1.5-4.2) 5.12 (3.5-6.2) 4.58 (3.3-6.3)  22.15 (0.08) 12.48 (0.08) 9.67 (0.05)  Corolla length M. guttatus x M. micranthus M. guttatus x M. platycalyx M. platycalyx x M. micranthus  5.17 (3.2-6.4) 2.08 (1.0-3.4) 5.23 (3.3-6.5)  22.81 (0.11) 10.98 (0.11) 11.83 (0.06)  Pistil length M. guttatus x M. micranthus M. guttatus x M. platycalyx M. platycalyx x M. micranthus  10.2 (8.7-15.1) 2.66 (1.3-5.8) 5.80 (4.1-8.5)  12.59 (0.05) 5.81 (0.05) 6.78 (0.03)  Stamen length M. guttatus x M. micranthus M. guttatus x M. platycalyx M. platycalyx x M. micranthus  8.42 (6.8-10.6) 1.54 (0.9-2.9) 3.47 (2.2-6.7)  9.20 (0.04) 4.02 (0.04) 5.17 (0.03)  Stigma-anther separation M. guttatus x M. micranthus M. guttatus x M. platycalyx M. platycalyx x M. micranthus  2.85 (2.6-2.9) 2.13 (0.6-4.9) 2.35 (1.0-5.0)  3.39 (0.04) 1.78 (0.04) 1.61 (0.03)  1. Values in parentheses are 95% confidence intervals. 2. Values in parentheses are standard errors to the mean.  48  Genetic distance2  0.08 (0.01) 0.19 (0.02) 0.20 (0.02)  Figure 2-1. Distribution of corolla lengths for parental, F1, F2 and backcrosses in the cross of M. guttatus and M. micranthus. 49  Figure 2-2. The distribution of means and variances of corolla length trait variation among parental, F1, F2 and backcrosses in the cross of M. guttatus and M. micranthus.  50  Effective number of genetic factors  16  M. guttatus x M. micranthus  0.2  M. platycalyx x M. micranthus  14  M. guttatus x M. platycalyx 12  0.15  10 8  0.1  6 4  0.05  2  0  0 Corolla length  Corolla width  Pistil length  Stamen length  Stigma-anther separation  Genetic divergence  Figure 2-3. The pattern of estimated number of genetic factors upon species evolutionary relationship  51  Corolla length  Corolla width  Pistil length  Average stamen length  S/A separation  1.5  Degree of Dominance  1  0.89  0.5  0.39 0.32  0  -0.36  -0.5  -0.43  Guttatus-Micranthus  -0.39  -0.49  -0.51 -0.61  -0.66  -0.58  -0.62  Guttatus-Platycalyx  -0.68  -0.73  Platycalyx-Micranthus -1  -0.96  Figure 2-4. The distribution of degree of dominance among Mimulus F2 crosses  52  REFERENCES  Bradshaw, H.D., Otto, K.G., Frewen, B.E., McKay, J.K., and Schemske, D.W. 1998. Quantitative trait loci affecting differences in floral morphology between two species of monkeyflower (Mimulus). Genetics 149(1): 367-382. Campbell, G.R. 1950. Mimulus guttatus and related species. El Aliso 2: 319-337. Carr, D.E., and Fenster, C.B. 1994. Levels of genetic variation and covariation for Mimulus (Scrophulariaceae) floral traits. Heredity 72: 606-618. Castle, W.E. 1921. 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The genetics of quantitative variability. In Quantitative inheritance. Edited by E.C.R. Reeve, and C.H. Waddington. Agriculature Research Council, London. pp. 5-41. Wright, S. 1968. Evolution and genetics of populations. I. Genetic and biometric foundations. University of Chicago Press, Chicago.  55  Zeng, Z.B., Liu, J.J., Stam, L.F., Kao, C.H., Mercer, J.M., and Laurie, C.C. 2000. Genetic architecture of a morphological shape difference between two Drosophila species. Genetics 154(1): 299-310.  56  CHAPTER 3. LINEAGE SPECIFIC INFERENCES ABOUT QTL EVOLUTION AMONG AN OUTCROSSING AND TWO DERIVED INBREEDING TAXA OF YELLOW MONKEYFLOWERS 1  INTRODUCTION  A major challenge of evolutionary biology is to understand the molecular genetic basis of complex traits that differentiate taxa. A crude characterization of the genetic architecture of species differences can be obtained with “classical” biometric approaches, where the number of genetic factors that distinguish two taxa is estimated using the segregation variance in artificial crosses and the difference of parental means (Wright 1968; Chapter 2). With developments in genotyping technologies and statistical genetics, quantitative trait locus (QTL) mapping has become a powerful means of ascertaining the genetic architecture of species differences (Tanksley 1993; Westerbergh and Doebley 2004). Regardless, the identification of genes affecting complex traits, including those of evolutionary significance, is considered to be one of the most challenging tasks of genetics (Risch 2000). Although problems exist with the accuracy of QTL mapping, QTL analysis still provides fundamental information about the size, location and effects of individual QTL that differ between the two parents of the cross (Broman 2001; Price 2006). QTL mapping techniques have been used for a large variety of traits, including those involved with human diseases (Cardon and Bell 2001), adaptation in natural populations (Slate 1  A version of this thesis will be submitted for publication. Chen, C. and K. Ritland. 2009. Lineage specific inferences about QTL evolution among an outcrossing and two derived inbreeding taxa of yellow monkeyflowers. Evolution.  57  2005) and breeding of animals (Mott et al. 2000). QTL mapping is also used to dissect the genetic architecture of complex traits in model organisms such as Arabidopsis (Ungerer et al. 2002), Drosophila (Mackay 2001b), maize (Westerbergh and Doebley 2002) and mouse (Cheverud et al. 1996). In plants, a classic example of QTL mapping for adaptive traits has involved the comparison between bumblebee pollinated M. lewsii and hummingbird pollinated M. cardinalis (Bradshaw et al. 1995; Schemske and Bradshaw 1999). These studies found that 9 of 12 traits related to the shift of pollination syndrome have at least one QTL that explained more than 25% of variation between species. At the yup locus, a single QTL accounted for 83% of phenotypic variation for carotenoid concentration between species. This is probably the best known case of a major QTL for morphological differentiation between species (Orr 2001). However, studies such as this mainly use pairwise comparisons, which allow estimates of those differences only along a single lineage. Within this lineage, no information is available about the evolutionary pattern and process of genetic changes, such as time of appearance of new QTL along the lineage. In this paper, we develop a phylogenetic approach for QTL mapping, in which the genetic effect of QTL along phylogenetic lineages is inferred. At the simplest, by bringing in a third taxa, one can infer the QTL changes that have occurred along the two lineages that lead to the two most closely related taxa. The third lineage traces from the common ancestor of these two taxa, back to the common ancestor of all three taxa, and forwards again to the third taxa, making lineage specific inferences more complicated. In the case of a star phylogeny, QTLs can be unambiguously identified in all three lineages. After mapping QTL onto lineages, we can determine if QTL at the same map position are  58  homologous (arising in an ancestral lineage leading to derived taxa) or non-homologous (arising independently in derived lineages). The distribution of homologous QTL on a species network can also be used to examine hypotheses such as drift-mutation balance versus directional selection model in the evolution of quantitative trait variation. A related approach of using a three- taxa phylogeny was recently applied to mammals. From a three-species phylogeny involving human, chimpanzee and mouse, several genes related to physiological function like olfaction and nuclear transport were identified as undergoing positive selection along the human-chimp lineage, using the mouse lineage as an outgroup (Clark et al. 2003). In the yellow monkeyflower (Mimulus guttatus) species complex, selffertilization has presumably arisen several times from on outcrossing ancestor. The evolution of selfing is accompanied by changes of an entire syndrome of floral traits, including male allocation, reduced size of floral characters, reduced attraction to pollinators and reduction of the spatial and temporal separation of male and female reproductive organs within the flower (Jain 1976; Ritland and Ritland 1989). We selected five quantitative floral characters as representative traits for the evolution of inbreeding in Mimulus species and focused on three intercrossable taxa: M. guttatus, M. platycalyx and M. micranthus, the latter two being presumed inbreeding derivatives of the first taxa. Specifically we expect that non-homologous QTLs, e.g., those shared between M. platycalyx and M. micranthus via convergent evolution, will be of larger effect as compared to those that occur later in derived lineages, as initial evolution towards selfing is more likely to occur with few loci as major genes allow associations to easily develop  59  between loci affecting inbreeding depression and loci controlling selfing (Holsinger 1991; Uyenoyama and Waller 1991).  60  MATERIALS AND METHODS  The genus Mimulus (Scrophulariaceae) consists of about 160 species in 10-12 taxonomic sections (Grant 1924; Pennell 1951; Beardsley and Olmstead 2002; Vickery 1995; Beardsley and Olmstead 2002). In the section Simiolus, the M. guttatus species complex consists of 8-12 inter-crossable species mainly occurring in California (Vickery 1964, 1978). Its populations mostly occur in stream edges and wet meadows and grow at a variety of elevations (Fenster and Ritland 1994b). A previous study of reproductive traits and isozymes in 8 taxa of this species complex found that inbreeding, and a suite of traits associated with inbreeding, has evolved at least twice in this group (Ritland and Ritland 1989); evolutionary increases of selfing also correlated with decreases of maleness (flower size, pollen number) (also see Fenster and Ritland 1992). For this study, we selected three inter-crossable, herbaceous annual Mimulus species from the M. guttatus species complex, M. guttatus, M. platycalyx and M. micranthus, based on their morphological and mating system differences. M. guttatus is extensively distributed in western North America, with a relatively low inbreeding coefficient of 0.38 (Ritland and Ritland 1989). M. platycalyx occurs in the coast ranges north of San Francisco (Dole 1992) and is a moderately inbred species with an inbreeding coefficient of 0.54 (Ritland and Ritland 1989). M. micranthus is endemic to the Coast Range foothills of California, and is a highly selfing species with an inbreeding coefficient of 0.73 (Ritland and Ritland 1989). The shape and size of the flowers of these species is illustrated by Figure 3-1.  61  Hypotheses about the homology of QTLs Homology is the common ancestry of a trait or gene, as opposed to functional similarity. It is mainly applied to gene sequences and gene products (Fitch 1970), but in the case of quantitative trait loci, homology has been thought as the sharing of QTL in a genome interval between related taxa. In Ritland and Ritland (1989), M. tilingii appears to be the outmost lineage in the dendrogram of this M. guttatus complex. Owing its large sized floral characters, outcrossing mating system and closer relationship to the abovementioned three Mimulus species, we considered the positive QTL genetic effect that increases the size of traits to be the ancestral. Together with the common assumption that inbreeders are “dead end” species and hence recent derivatives from outbreeders, and also because of the observation that M. platycalyx has a very restricted distribution (Marin Co., CA) while M. guttatus is distributed throughout western North America, we consider that M. platycalyx (as well as M. micranthus) are taxa derived from a M. guttatus type ancestor. In this study, we infer QTL homology using a well supported Mimulus phylogeny and map QTLs on species lineages. In Figure 3-2 (using results from Figure 3-6, which shows the ancestor lies in the M. platycalyx lineage), we depict the scenarios that QTLs can evolve at a single homologous chromosomal interval. In Figure 3-2A, a positive (larger flowered) QTL (bestowing larger flowers) appears in the common ancestor and the two larger flower species. This represents a true orthology of shared positive QTL between the two derived species, M. guttatus and M. platycalyx. The negative QTL found on M. micranthus is then an independent arrival QTL change. Distinguishing ortholog and paralogy can be difficult (Petsko 2001) and the current confusion about the meaning of these terms has  62  not gone unnoticed (Fitch 2000). With only QTL data, a clear distinction of paralogy from orthology would be difficult to draw, owing to the lack evidence of QTL duplication event. However, ancestral type of QTLs occurs when a positive QTL arises by the shared common ancestry between the two derived lineages, and this is represented by Figure 3-2A. Assuming ancestral status being positive QTL, Figure 3-2B and Figure 3-2C depict the contrasting case of QTL homology of shared negative QTL between derived relatives. As in our Mimulus study, in Figure 3-2B a negative QTL genetic change (promote the reduced size of flower) found on derived selfer lineages, M. platycalyx and M. micranthus, but they are unrelated QTL. A case of homoplasy is thus defined in Figure 3-2B. In contrast, Figure 3-2C is the parsimonious scenario when negative QTL found on selfer lineages (M. platycalyx and M. micranthus) could possibly be orthologous; a negative QTL change has to happen onto ancestral lineage and the possibility of positive QTL, by a reverse mutation, on the larger flower lineage of M. guttatus. A phylogenetic reference, such as ancestral status and the evidence of +QTL versus –QTL on species specific lineage, is required in distinguishing scenarios in Figure 3-2B from Figure 3-2C. More complicated scenarios are possible. As those cases are not the most parsimonious scenarios, we regard such patterns as much rarer than the single changes in Figures 3-2A to 3-2C.  63  Three-taxa crossing design and quantitative trait measurement All three inter-taxon crosses were performed (M. guttatus x M. platycalyx, M. guttatus x M. micranthus and M. platycalyx x M. micranthus). F1’s were maintained in the same growth chamber, with the parents. Backcross progeny were produced by crossing F1 individuals back with both parent species, using parent individuals as pollen resource. Overall, 6 reciprocal backcrosses were generated in this experiment. To avoid the pollen contamination, flowers were bagged immediately after crossing. All plants, including parental, F1, and backcrosses, were grown on the same batch of Pro-Mix soil in the growth chambers in the Forest Sciences Centre, University of British Columbia, with growing conditions of 14C/8C day/night and 18 hours of daylight. To avoid a possible block effect, the seedlings from all 6 reciprocal backcrosses were labelled and sowed on growing trays, each of the growing trays contained the same number of seedlings from every backcross and we also periodically rotated the growing trays among growth chambers. Five floral traits were measured, as diagrammed in Figure 3-3: corolla width, corolla length, pistil length, stamen length (there are two sets of anther that differ in length), and stigma-anther separation (the difference between the previous two traits). In a combined-cross analysis, crosses with greater variability in the phenotype will have a greater influence (Li et al. 2005). Hence, prior to QTL analysis, we standardized the corolla width measurements by its standard deviation to stabilize the variance.  64  AFLP (amplified fragments length polymorphism) genotyping In total, 675 individuals from all of six reciprocal backcrosses were used. Specifically, the number genotyped for each backcross was: (GxP)xG=135, (GxP)xP=112, (PxM)xP=68, (PxM)xM=135, (GxM)xG=124 and (GxM)xM=121. Fresh leaf tissue from every offspring was collected while the floral sizes were measured. Leaf tissue for DNA isolation were immediately stored at - 80°C. Genomic DNA was isolated from frozen leaf tissue of all backcross families via the CTAB method (Doyle and Doyle 1990). AFLP was performed as described in Vos et al. (1995) and Remington et al. (1999) as modified for the LiCor 4200 DNA sequencer. Templates for AFLP reactions were prepared using 500 ng of genomic DNA for restriction enzyme digests with EcoRI and MseI, and ligation adapters Remington et al. (1999). The restriction-ligation mixture was diluted 1:100 in deionized water prior to preamplification. Preamplification was carried out using standard AFLP EcoRI and MseI primers containing the selective nucleotides Eco + C and Mse + CC. Selective final amplifications were conducted by combinations of Eco primers with three nucleotides and Mse primers with three nucleotides as listed in Table 3-1. Reaction products were resolved on denaturing gel containing 3.5% of Long Ranger polyacrylamide, 7.5 M urea and 1X TBE. Loading buffer (10 μl) consisting of 95% deionised formamide, 20 mM EDTA pH8.0, and 1 mg/ml bromophenol blue (USB) was added to each amplification product. Prior to loading the gel, the mixture of amplified product and loading buffer was heated at 94C for 3 minutes and then quickly cooled down on ice. Electrophoresis was carried out on the Li-Cor 4200 sequencer using 1X TBE running buffer. IRD-labelled molecular weight markers were loaded in the first  65  and last lane as standards. Polymorphic fragments were first scored by eye in TIFF image files for primer selection and repeatability test of primers. RFLPscan Version 3.0 (Scanalytics) program scored segregating loci. AFLP markers were tested for a departure from the 1:1 (backcross) and 3:1 (intercross) ratios for presence:absence of bands. Markers showing significant segregation distortion (p <0.05) were excluded. Out of 614 polymorphic loci scored, a total of 368 polymorphic loci were obtained in later analyses.  Inferred parent genotype and linkage map construction via joint analysis Owing to the fact that heterozygous parents cannot be identified by direct genotyping of AFLPs, but are essential for QTL mapping, we inferred parent genotype using the progeny of all backcrosses. For a single locus with two alleles (one recessive), Table 3-2 lists the segregation probabilities for bandless vs. banded progeny, conditioned on parent genotype (AA, Aa or aa). Let these probabilities be defined as pu (i, j ) for the bandless (unbanded) phenotype and pb (i, j ) for the banded phenotype. Any given parent has three possible genotypes and across all six parents, there 36 = 729 possible configurations of parent genotypes. For any particular genotypic configuration, depicted as g(k), for k=1,6, the likelihood of the observed data across the six crosses is 6  p u ( g (k1 ), g (k 2 ))  Nu ,k  p b ( g (k1 ), g (k 2 ))  Nb,k  Eq. 1  k 1  where Nu,k is the number of bandless progeny and Nb,k is the number of banded progeny in cross k; and k1and k2 are the male and female parents of cross k. A computer program was written that enumerated all 729 possible parental genotype configurations, and chose the most likely configuration of the six parents for each AFLP locus. The likelihood of  66  the second most likely parent genotype configuration for each locus was also examined, which allows reassuring the uncertainty of the inferred parent genotypes. To account for genotyping error, we first found parental genotypes using the above procedure. Then at each cross, the less frequent phenotype is truncated to zero and the likelihood of the data again estimated. If the increase in likelihood is greater than expected by a 5% genotyping error, the data was modified to assume the less frequent category were genotype errors (this normally occurs with rather extreme ratios). This procedure was repeated six times at each locus to ensure convergence (as the crosses are interdependent for parental inference). A joint likelihood function, which combines information across the six crosses, was used to estimate pair-wise recombination fractions between dominant markers. This approach not only efficiently combines genotype information across crosses, but also infers linkage phase of parents, and is particularly more informative with dominant markers (Hu et al. 2004). Estimated recombination fractions were then converted to map distances using Kosambi mapping function, and the linkage groups constructed with JoinMap (Stam 1995; Stam and van Ooijen 1995).  The analysis of lineage specific QTL genetic effect Figure 3-4 illustrates the crossing design employed in this study. In the figure, the expected QTL effect along each branch emanating from the ancestral taxa is denoted as UG, UP and UM, for M. guttatus, M. platycalyx and M. micranthus, respectively. Note that UG includes any effect on the ancestral lineage leading to M. platycalyx and M. micranthus. Now, arrange the expected means into the matrix  67  u  UP UM  UM UP  UM UG  UG UM  UG UP  UP UG  where columns index the six crosses, and for the backcross denoted as AAxAB, the first row is the mean for the species "A" and the second row is the mean for species "B". These means within this matrix are indexed as Ujk (j=1..2, k=1..6). At a marker locus, for cross k and progeny i in cross k with quantitative trait Qik, and with the total number of progeny in cross k being nk, the joint likelihood is 6  nk  L(u)  exp( (Qik  Eik ) 2 / 2)  k 1 i 1  where Eij  U 1k if the progeny ik has marker genotype aa, or Eij  (U 1k  U 2 k ) / 2 if the  progeny ik has marker genotype A_ (dominant phenotype). This assumes a normal distribution of quantitative traits with unit variance; as discussed earlier we did such a transformation. Percentage variance explained was calculated as the differences between the two 6  ni  variances  exp( (Qik  Eik ) 2 / 2) for Eik = 0 (no model) vs. Eik = estimated model  k 1` i 1  parameters. Explained variance is (no model – estimated model)/(no model) variances. Note that for this crossing design to be informative for all UG, UP and UM, at least two crosses involving expected means must be segregating. For example, the first and the third crosses, just by themselves, are informative about all three means, while the first two crosses are not (Figure 3-4). Simpler designs involving fewer backcrosses are possible, but to maximize the chance of having an informative cross, we assayed all six backcrosses.  68  The above estimation formula assumes that the magnitude of QTL effect between any two species is the sum of the two lineage specific QTLs which lie between each of the two species, i.e., that QTLs evolve in an additive manner. More elaborate designs involving dominance and epistasis would be worthwhile to research and implement, but are outside the scope of our current experimental design involving the three Mimulus taxa. The formula also assumes that QTLs are fixed between taxa, and none are segregating within taxa. This is justified because we are examining QTL differences that distinguish phylogenetic lineages, and also these QTLs are likely of much greater effect than QTLs that are segregating within contemporary populations. Furthermore, since we are using dominant genetic markers, in the particular cross design given in Figure 3-4, we can only estimate QTLs when the F1 is heterozygous and the backcross parent is homozygous recessive (if both parents of the backcross are heterozygous, the cross is non-informative for QTLs, and if the parent taxa is heterozygous and the F1 homozygous, the cross is also non-informative for QTLs that differentiate taxa). Unfortunately, this limits the numbers of loci which are informative for mapping QTL. More informative markers such as single nucleotide polymorphisms (SNPs) or particularly microsatellites, would of course be desired. Finally, because of the relative complexity of the analysis and the lack of any previous computer programs developed for this type of work, we use single-marker QTL detection (as opposed to interval mapping or other multi-marker analyses for QTL). In effect we employ a "genome scan", examining markers one-by-one down a genetic map. To avoid problems with numerical estimation of the maximum likelihood, we used a "brute force" evaluation of the likelihood surface across all possible values of UG, UP and UM, each ranging from -  69  1 to +1 in increments of 0.05. The joint estimate was chosen as that combination of the three values that gave the highest likelihood. Statistical significance was ascertained in two ways. First, we permuted quantitative traits and markers 1000 times (in this procedure, traits are randomized among genotypes, and estimates redone). The likelihood of these permutated data were compared to the original unpermuted data; in general, if 50 or less of the permuted data were more likely than the original data, the estimates are deemed significant. In the second way, we use the bootstrap to estimate standard errors of individual branches. Progeny were re-sampled within crosses, estimates recalculated, and the square root of the variance among the 1000 bootstraps was found. Also, if more than one significant QTL was detected within a window of 20 cm, the QTL with the highest percentage variance explained was chosen, and other adjacent markers showing QTL excluded. We placed no constraints upon the joint space of UG, UP and UM. In the case of a two taxa comparison, say between taxa G and P, then obviously UG=1-UP (no lineage specific estimates can be obtained). We tried the constraint UG = 1-UP- UM, but this model as compared to the full model of jointly estimating UG, UP and UM gave a much worse fit as revealed by variance explained. In the three taxa case, some type of constraint does exist. However, for our purposes, the relative QTL changes among taxa do reveal the changes during the evolution of selfing from outcrossing.  70  RESULTS  AFLP marker distribution and linkage map A total of 8 AFLP primer pairs were used to genotype all offspring of 6 backcrosses in the study (Table 3-1). From these, 614 polymorphic AFLP loci were genotyped and scored. After excluding the AFLP markers with unexpected segregation ratios, 368 AFLP loci were used for linkage map construction. We identified 14 linkage groups containing 99 markers covering 482 cM (Figure 3-5), and the average marker spacing was 4.9 cM.  Pairwise genetic distance between Mimulus parents Over 8 AFLP primer pairs, a total of 368 AFLP loci were selected for further analyses. Using the likelihood method described in this chapter, Mimulus parent genotypes for these 368 AFLP loci were inferred from the information given by all 6 backcrosses. It appears that parentage inference was quite reliable, as relative to the most likely set of six parent genotypes, the next most likely set of six-parent genotypes were 1000 times less likely 90% of the time, and 10 times less likely 97% of the time. All 368 AFLP loci were included in the analysis in estimating Nei’s genetic distance (Nei 1972) between Mimulus parents. Standard errors were estimated from taking squared root of the variance among the 1000 bootstraps over the 368 AFLP loci. The estimated genetic distances between M. guttatus and M. micranthus was 0.08 (S.E.=  71  0.01), between M. guttatus and M. platycalyx was 0.19 (S.E.=0.02), and between M. platycalyx and M. micranthus was 0.20 (S.E.=0.02). Figure 3-6 shows the branch length and the three species network. The branch length for each of the Mimulus lineage was calculated from the genetic distance between species pairs. The standard errors were also estimated from the bootstraps (numbers in parentheses). We also estimated the genetic distance with band sharing index (Nei and Li 1979), and the same topology of Figure 3-6 was obtained, with only slightly larger genetic distances between taxa.  The analysis of lineage specific QTL effects Table 3-3 lists the markers that gave significant lineage specific QTL genetic estimates for all three lineages. Among the 99 markers in the genetic map, 9 markers had significant estimates of lineage specific QTLs. Across the six quantitative traits, 24 QTL were found: 7 for corolla width, 6 for corolla length, 4 for pistil length, 5 for stamen height, and 2 for stigma-anther separation (Table 3-3). The percentage variance explained by each marker ranged from 1.5 to 9.6, and averaged about 5. QTL of positive effect increases the size of the trait (promoting outcrossing), while negative QTL effect decreases it (promoting selfing). In general, we expect the M. guttatus lineage to show positive QTL effects, and the M. platycalyx and M. micranthus lineages to show negative effects. By and large this was true; for corolla width, 5 of 6 significant QTL were of positive effect in the M. guttatus lineage, while 10 of 12 significant QTL were of negative effect in the other two lineages. However, there were exceptions; for example, for corolla width, marker C1_200 showed a significantly  72  negative QTL genetic effect of -0.22 on the M. guttatus lineage, and significantly positive QTL genetic effect of 0.83 on the M. platycalyx lineage. Many of the markers showed QTL genetic effects for several of the floral traits. For example, QTL B2_229 affected all five traits on all three lineages, and QTL C1_378 and QTL C7_210 affected four of five traits (the exception being stigma-anther separation). The sign of lineage specific effect was also consistent among traits. Also, one marker (B4_410) exhibited a significant QTL in just one lineage: the M. micranthus lineage. Based on allozymic variation, Ritland and Ritland (1989) presented a phylogenetic dendrogram of taxa in M. guttatus species complex, in which the larger flowered M. tilingii was the most outlying species indicated that outbreeding is the ancestral condition of our currently studied species. We therefore expect the sign of lineage specific QTL effect to be positive in the M. guttatus lineage (evolution towards larger flowers) and negative in the other two (evolution towards smaller flowers). This was the classic expectation of our study, and this occurs with markers QTL B2_229, B5_292 and B5_535 for many of the traits including corolla width (Table 3-3) and also presents the most common case in our study. Regardless, many of these QTL also appear to be of relatively large effect (absolute values of 0.5 or greater). However, contrasting cases occur with QTL C1_200 and QTL B5_394, for corolla width and corolla length, respectively. Both cases showed the opposite: negative effects in the M. guttatus lineage and positive effects in the M. platycalyx lineage. For corolla width, QTL B4_410 was significant only in the M. micranthus lineage, conferring smaller size, and QTL C7_210 was of significant opposite sign between the two  73  inbreeding lineages, with smaller size conferred in M. platycalyx and larger size conferred in M. micranthus. These results also hold for the other four floral traits. Because the floral characters analyzed in this study are all dimensional traits, and are likely under same type of selection, a consistent homology pattern for a given QTL locus would be expected among all floral traits, and indeed this was generally observed. An interesting exception was QTL locus B3_166 for stamen length and stigma-anther separation (Table 3-3).  74  DISCUSSION  The reconstruction of the evolution of inbreeding via the analysis of lineage specific QTL effects Here we have presented a new approach for QTL mapping, "lineage specific QTL mapping". In addition to inferring number of genes and magnitudes of gene effects, we infer the lineages where QTL changes occur in a network. We considered only the simplest of phylogenies, that of three species, and where the ancestral state is represented by one of the three species. Nevertheless from the results of our QTL analysis of three Mimulus taxa, we infer that the evolution of inbreeding in two derived inbreeding Mimulus taxa involved major genes causing reduced floral size (increased inbreeding). Independent QTL substitutions of smaller effect can also subsequently occur on species with higher selfing rate (for example, QTL B4_410 for corolla width and corolla length). Not all QTL involving with the evolution of inbreeding were of smaller flower size effect, however. Our results accord with the expectation of Holsinger (1991) and Uyenoyama and Waller (1991). They worked with models for the evolution of selfing where inbreeding depression must be purged before genes favouring self-fertilization can spread. They found that initial evolution towards selfing is more likely to occur with few loci of major effect because associations easily develop between loci affecting inbreeding depression and loci controlling selfing.  75  In a study of the genetic architecture of floral differences between M. guttatus and M. micranthus, Lin and Ritland (1997) suggested that genes with small to intermediate effects were considered responsible to the evolution of mating system. They speculated that the evolution of self-fertilization in Mimulus involves the initiation of selfing by a few genes with relatively larger effects and followed by subsequent minor changes of minor modifiers loci (Lin and Ritland 1997). In accordance with this expectation, our lineage specific QTL mapping shows that the QTL appears on both inbreeding lineages, for example of B5_535, explains the largest percentage of variance for corolla width (9.6%, Table 3-3). The largest QTL effect for pistil length was also found significant on all lineages, B2_229 (9.6% variance explained, Table 3-3). For stamen length, B5_535 and B2_229 each explained 6.4% and 6.6% of total variance, respectively, also the two largest QTL. There are only two QTL for stigma-anther separation: QTL B2_229 and QTL B3_166. The percentage of variance explained by shared B2_229 is higher than B3_166 (Table 3-3). Moreover, independent derived QTL that arrives on one lineage, such as B4_410 in M. micranthus, shows a smaller genetic effect of 2.1% variance explained in the variation of corolla width (Table 3-3). The hypothesis of Lin and Ritland (1997) is thus supported by the analyses of our lineage specific QTL mapping. One result that is somewhat paradoxical is the large distance inferred for the M. platycalyx lineage (Figure 3-6). It is over twice the length of the other two lineages. Under neutrality and a molecular clock, this topology implies that the common ancestor of the three taxa is on the M. platycalyx lineage. If so, then M. platycalyx is an independently derived inbreeder from M. micranthus, assuming M. guttatus is the progenitor. Together with the common assumption that inbreeders are “dead end” species  76  and hence recent derivatives from outbreeders, and also because of the observation that M. platycalyx has a very restricted distribution (Marin Co., CA) while M. guttatus is distributed throughout western North America, we consider that M. platycalyx (as well as M. micranthus) are taxa derived from a M. guttatus type ancestor, despite the results of Figure 3-6. We also note that because inbreeding causes increased rates of evolution and loss of homozygosity, and because estimators of genetic distance assume constancy of population size and homozygosity, that these estimates of genetic distance may not reflect evolutionary time among species that vary for levels of inbreeding.  Directional selection and the evolution of selfing The signs of QTL can be used to indicate whether the trait variation has been under selection, as opposed to the neutrality of antagonistic QTL in a given line (Orr 1998; Rieseberg et al. 2002). Under random genetic drift, there should be roughly equal numbers of “+” and “–“ QTL between taxa (Orr 1998). However, we observed an excess of one over the other, indicated a role of natural selection in selective pressure the evolution of inbreeding in Mimulus. Specifically, in the M. guttatus lineage five QTL for corolla width show significant lineage specific effect, one negative and four positive. In the M. micranthus lineage, six QTL for corolla width were identified, one positive and five negative. The rapid change of directionality of lineage specific QTL effects between M. guttatus and M. micranthus suggests a role of directional selection in the shift of mating system in Mimulus.  77  Our novel approach for lineage specific QTL mapping allows for a second type of Orr-type non-neutrality test. We note that if the genetic basis on the evolution of mating system was solely based on drift-mutation balance, the lineages with larger branch length (such as M. platycalyx lineage in Figure 3-6) should have more QTL. For corolla width, the number of lineage specific QTL identified on each of Mimulus lineages are five in M. guttatus, six in M. platycalyx and six in M. micranthus, yet M. platycalyx has a significantly longer lineage as estimate from the AFLP data. Results from our lineage specific QTL mapping do not support the pure drift-mutation model in the evolution of mating systems.  The novelty of lineage specific QTL inference In Chapter 4 of this thesis, we used the classical QTL mapping method, involving crosses between a single pair of taxa, for example, between M. guttatus and M. platycalyx. Indeed, the same marker in both this and that study, C1_200, exhibited a QTL for corolla width. Here, by adding a third species (M. micranthus) and adopting our new analysis which maps QTL onto species lineages, we further found that QTL C1_200 was an independent QTL mutation, one arising after speciation of M. micranthus from M. platycalyx, as there was no sign of significance of QTL effect in the M. micranthus lineage (Table 3-3). Moreover, one cannot detect QTL of the same sign between two taxa; only by introducing a third taxon (of the opposite QTL sign) can QTL of the same sign between two taxa be identified. For example, QTL were found on linkage group 8, around marker C7_210 for two of the crosses in Chapter 4: M. guttatus x M. platycalyx and M.  78  platycalyx x M. micranthus (QTL8_38, Table 4-3 in Chapter 4). On the same homologous chromosomal position, there was no QTL identified between M. guttatus and M. micranthus (Chapter 4). However, lineage-specific QTL mapping revealed QTL C7_210 in all lineages (Table 3-3). Lineage specific QTL are both positive in M. guttatus and M. micranthus lineages, but negative in the M. platycalyx lineage (Table 33). Thus lineage specific QTL analysis can reveal QTL not seen in two-taxon crosses.  The homology of QTL among lineages The term homology was introduced by Richard Owen in 1843 as the similarity of characters due to shared ancestry (Panchen 1999; Owen 1848). This concept of “derived from an equivalent characteristic of the common ancestor” has been extensively applied in classical phylogenetic comparisons, where homology is the opposite of analogy and characters can therefore be similar without being homologous or homologous without being identical. Ancestral QTL effects are those that arise prior to speciation, and share true common ancestry between derived taxa. They are not detected by crosses between these derived taxa, but require a third taxa, representing the ancestral outgroup. Figure 3-2A represents such scenario of a true orthology of positive QTL on homologous chromosomal location of derived lineages. In this study, we however suspect that this true common ancestry of lineage specific QTL genetic effects is in fact not as common as we thought. With our Mimulus data, the supporting evidence of such type of QTL is not found (Table 3-3).  79  In Table 3-3, we found that the M. guttatus lineage harbours several positive QTLs, and most of negative QTL were on the selfer lineages, M. platycalyx and M. micranthus. These negative QTLs that arrive on the same chromosomal location in derived selfer lineages show interesting but conflicting cases in homoplasy versus homology (Figure 3-2B versus Figure 3-2C). In the previous section of this thesis, an important role of directional selection was suggested by the rapid change of directionality of QTL genetic effect found on inbreeding lineages. The scenario described in Figure 32B is likely, when selection favours negative QTL changes (that reduce flower size and thus increase inbreeding) on inbreeding lineages. Without the evidence of negative mutation that occurred before spreading out into selfer lineages (Figure 3-2C), those shared negative QTLs on both M. platycalyx and M. micranthus could have arisen through convergent evolution. Such QTLs are however common. In our study, 4 out of 7 lineage specific QTL genetic effects are under this category (Table 3-3). In addition, these shared negative QTLs on derived selfer lineages are often larger in size, suggesting an important role of convergent evolution of derived genetic changes in the evolution of inbreeding. Our findings in convergent evolution of QTL genetic changes also raise the question about inferences of QTL orthology, where orthologous QTLs are thought to be the common key genetic regulators of morphological development (Pereira and Lee 1995; Hu et al. 2003). Moreover, those that arise subsequent to speciation, control the same quantitative trait but locate solely on one of the lineages are the cases of independent arrival of QTL. In our case, most of those on the derived lineages are often found with smaller genetic effect, to "fine tune" the trait. This is supported by the QTL B4_410 in M. micranthus  80  lineage (Table 3-3). In the evolution of inbreeding of these two related Mimulus taxa, we found that shared QTL were predominant. Our analysis shows as well the strength in quantifying the role of lineage specific genetic changes in the evolution of quantitative variation.  Conclusion The basic idea in the analysis of lineage specific QTL effect allows integration of QTL with species evolutionary history. Superimposing QTL changes on species evolutionary history helps to re-examine important evolutionary and biological phenomenon such as differential adaptation of speciation (Wu 2001), evolution of selfing (Charlesworth et al. 1993), the origin of disease-producing allelic variation in human population (Kidd et al. 2000) and the evolution of Dobzhansky-Muller hybrid incompatibility (Brideau et al. 2006). This approach will gain more statistical and biological power as additional lineages are added, especially when crosses to more evolutionarily distant species are available, and when more informative markers are used.  81  Table 3-1. AFLP primers and polymorphism of primer pairs. Primer sequence of EcoRI is GACTGCGTACCAATTC. Primer sequence of MseI is GATGAGTCCTGAGTAA  AFLP Primers for pre-amplification: Primer names and sequences  Primer name  AFLP primer pair for final amplification  Number of amplified polymorphic fragments  Primer B set: EcoRI* + AC / MseI** + CC  B2 B3 B4 B5  Eco + ACA / Mse + CCT Eco + ACA / Mse + CGC Eco + ACA / Mse + CCA Eco + ACT / Mse + CGG  25 54 59 38  Primer C set: EcoRI + C / MseI + CC  C1 C3 C4 C7  Eco + CA / Mse + CCG Eco + CCG / Mse + CCG Eco + CCA / Mse + CCT Eco + CTC / Mse + CCT  61 31 54 46  Total number of polymorphic fragments  368  82  Table 3-2. Probabilities of bandless (first number) vs. banded progeny (second number), conditioned on the genotypes of the two parents of a cross (bandless is the recessive condition).  Parent 1 AA  AA 0, 1  Parent 2 Aa 0, 1  Aa  0, 1  ¼, ¾  ½, ½  aa  0, 1  ½, ½  1, 0  83  aa 0, 1  Table 3-3. Estimates of lineage specific QTL genetic effects (significant effects, as determined by bootstrapping, are indicated by standard errors, SE, in parentheses and by asterisks, *).  Traits Corolla width  Corolla length  Pistil length  Stamen length  Stigma-anther separation  AFLP marker C1_200 B2_229 C1_378 B5_292 B4_410 B5_535 C7_210 B2_229 C1_378 B4_410 B5_535 B5_394 C7_210 B2_229 C1_378 B5_535 C7_210 B2_229 C1_378 B5_535 B3_166 C7_210 B2_229 B3_166  Linkage group 1 1 2 3 3 4 8 1 2 3 4 5 8 1 2 4 8 1 2 4 7 8 1 7  cM 13.95 29.34 22.30 19.40 39.78 0.00 41.18 29.34 22.30 39.78 0.00 60.81 41.18 29.34 22.30 0.00 41.18 29.34 22.30 0.00 0.00 41.18 29.34 0.00  Lineage specific QTL effect M. guttatus M. platycalyx M. micranthus effect (SE) effect (SE) effect (SE) -0.22 (0.11) * 0.83 (0.25) * 0.19 (0.25) 0.55 (0.13) * -0.94 (0.12) * -0.74 (0.21) * 0.02 (0.10) -1.00 (0.00) * -0.15 (0.06) * 0.46 (0.12) * -0.62 (0.24) * -0.70 (0.22) * -0.09 (0.18) 0.14 (0.20) -0.60 (0.33) * 0.22 (0.08) * -0.95 (0.10) * -0.75 (0.24) * 0.46 (0.27) * -0.37 (0.21) * 0.28 (0.18) * 0.49 (0.13) * -0.87 (0.16) * -0.66 (0.21) * 0.05 (0.09) -1.00 (0.00) * -0.17 (0.07) * -0.06 (0.17) 0.07 (0.19) -0.51 (0.35) * 0.22 (0.08) * -0.95 (0.10) * -0.63 (0.25) * -0.55 (0.26) * 0.18 (0.13) * -0.07 (0.11) 0.68 (0.24) * -0.51 (0.20) * 0.34 (0.17) * 0.51 (0.13) * -0.97 (0.07) * -0.63 (0.25) * 0.05 (0.09) -1.00 (0.00) * -0.15 (0.06) * 0.19 (0.09) * -0.94 (0.11) * -0.50 (0.30) * 0.60 (0.25) * -0.51 (0.17) * 0.34 (0.17) * 0.47 (0.14) * -0.86 (0.19) * -0.63 (0.25) * 0.04 (0.11) -1.00 (0.00) * -0.17 (0.07) * 0.20 (0.08) * -0.87 (0.15) * -0.62 (0.32) * 0.15 (0.14) * -0.29 (0.16) * 0.40 (0.38) * 0.72 (0.24) * -0.54 (0.21) * 0.38 (0.17) * 0.35 (0.14) * -0.84 (0.18) * -0.31 (0.27) * -0.06 (0.13) 0.19 (0.13) * -0.82 (0.24) *  .  84  % of variance explained 4.5 9.1 1.7 5.3 2.1 9.6 2.0 7.1 2.3 1.5 9.1 1.6 3.4 9.6 2.1 7.7 3.5 6.4 2.0 6.6 2.3 4.8 5.3 4.3  Permutation probability 0.044 0.009 0.003 0.006 0.029 0.000 0.053 0.015 0.002 0.077 0.000 0.045 0.002 0.022 0.001 0.001 0.007 0.041 0.003 0.003 0.058 0.000 0.025 0.008  Figure 3-1. The three intercrossable Mimulus taxa used for our QTL phylogenetic analysis  85  +QTL  +QTL  -QTL  -QTL  -QTL  (A) -QTL  M. micranthus  +QTL  M. guttatus  (B)  +QTL  M. platycalyx  -QTL  M. micranthus  +QTL  M. guttatus  -QTL  M. platycalyx  +QTL - QTL  +QTL  (C) -QTL  M. micranthus  +QTL  M. guttatus  -QTL  M. platycalyx  Figure 3-2. Possible patterns of QTL evolution and homology. The dashed lines indicate lineages that have evolved towards QTL of smaller effect (towards inbreeding).  86  Figure 3-3. A central dissection of a Mimulus guttatus flower and the floral traits measured in this study.  87  Expected mean for a quantitative trait Conditioned on progeny genotype and parental configuration of the backcross  Backcross  Parent M. platycalyx (UP)  Genotype Aa  aa  BC(PxM)  P  1 (U P U M ) 2  UP  BC(PxM)  M  1 (U P U M ) 2  UM  BC(GxM)  M  1 (U G U M ) 2  UM  BC(GxM)  G  1 (U G U M ) 2  UG  BC(GxP)  G  1 (U G U P ) 2  UG  BC(GxP)  P  1 UG U P 2  UP  F1PxM M. micranthus (UM) F1GxM M. guttatus(UG) F1GxP M. platycalyx (UP)  Figure 3-4. Crossing design and expected means for a given quantitative trait.  88  1  2  3  4  5  6  9  10  LG 1  LG 2  LG 3  LG 4  LG 5  LG 6  LG 7  LG 8  0  0  0  0  0  B4_106  11  C1_200  15  C1_404  C1_274 B4_171 B5_468 B2_229 B3_291 B3_256 B5_099 C1_189 B2_130 B5_222 C4_221  26 27 29 30 31 32 35 38 41  C1_594  5  B3_465  11 12 15  C1_247 C4_446 C1_306  19  C1_622  22  C1_378  26  C1_522  30  C1_343  B5_186  17 19  C7_390 B5_292  27  B4_104  34  B4_454 B3_322  38 40  B4_432 B4_410  43  B5_442  47  B2_210  47  B4_181  50 52  C4_177 B4_084  51  B5_286  54  B4_262  57  B4_439  58  C1_088  B5_535  7  C4_288  18  B3_485  28  B4_341  C7_459  18  C4_416  35  0  0  B3_166  3 5  B3_168 B2_141  23  B4_320  39  40  B5_390 43  B5_394  14  15  LG 9  LG 10  LG 11  LG 12  LG 13  0  0  0  C7_230  0  C7_245  0  C4_253  12  C7_461  29  C7_310  35  B4_166  18  19  LG 14  B5_070  26  C7_224  31  31  B5_180  41  C7_210  C1_292  B5_052  13  C4_147  C4_182  C3_341  12  C1_295  21  B5_339  11  C7_312  C3_307  B3_588  28  61  0  0  C4_188  Figure 3-5. AFLP linkage map as inferred from segregating progeny in 6 backcrosses involving 3 Mimulus taxa.  89  M. guttatus 0.035 (0.01) 0.155 (0.02) 0.045 (0.01)  O  M. platycalyx  M. micranthus  Figure 3-6. Estimated genetic distances between M. guttatus, M. platycalyx and M. micranthus..  90  REFERENCES  Beardsley, P. M., and R. G. Olmstead. 2002. Redefining Phrymaceae: The placement of Mimulus, tribe Mimuleae and Phryma. Am. J. Bot. 89:1093-1102. Bradshaw, H. D., S. M. Wilbert, K. G. Otto, and D. W. Schemske. 1995. Geneticmapping of floral traits associated with reproductive isolation in monkeyflowers (Mimulus). Nature 376:762-765. Brideau, N. J., H. A. Flores, J. Wang, S. Maheshwari, X. Wang, and D. A. Barbash. 2006. Two Dobzhansky-Muller genes interact to cause hybrid lethality in Drosophila. Science 314:1292-1295. Broman, K. W. 2001. Review of statistical methods for QTL mapping in experimental crosses. Lab Animal 30:44-52. Cardon, L. R., and J. I. Bell. 2001. 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EPISTATIC INTERACTION OF QTLS INVOLVED IN THE EVOLUTION OF FLORAL TRAITS IN THE MIMULUS GUTTATUS SPECIES COMPLEX 1  INTRODUCTION  Complex traits such as human disease, growth rate, or crop yield are polygenic, or determined by the contributions from numerous genes in a quantitative manner. Although many studies have successfully identified quantitative trait loci (QTL), our knowledge of QTL underlying complex traits is largely constrained to QTL with relatively large effect. In addition, QTL are often inferred without incorporating genetic background, or the effects of a combination of other loci (Carlborg and Haley 2004). Genetic interaction is often ignored, in part due to the difficulty of analysis (Barton and Turelli 2004; Cheverud 2000). In fact, the statistics used to detect single QTLs can mask the significance of interaction terms, as the genetic effects of interacting loci are summed and overall have no influence on the prior (Holland 2001), and can therefore be biased against detecting significant interactions (Templeton 2000). There have been a number of recent methods proposed to infer the epistatic interaction, including (1) the one dimensional scan that searches for genetic interactions of a given allele with the genetic background (Jannink and Jansen 2001), (2) simultaneous two-way searches at multiple, selected pair of loci (Kao et al. 1999) and more recently, (3) a genome-wide scan that simultaneously considers all locus pairs  1  A version of this thesis will be submitted for publication. Chen, C. and K. Ritland. 2009. Epistasis interaction on QTLs involved in the evolution of floral traits in the Mimulus guttatus species complex.  96  (Broman et al. 2003; Sen and Churchill 2001). With genomic approaches, more comprehensive inferences are possible. Examples can be seen in Arabidopsis thaliana, where epistasis has been shown to underlie genetic determination of flowering time (Juenger et al. 2005), juvenile growth rate (Kroymann and Mitchell-Olds 2005), and response to water availability (Hausmann et al. 2005). In Drosophila, a major QTL on chromosome 3 and minor QTL on chromosome 2 were initially identified affecting variole number between D. sechellia and D. simulans (Jones 2005). With a rather fine physical map and a large selection of 1038 additional recombinants in the chromosome regions of interest, the study also discovered that a previously identified major QTL on chromosome 3 was, in fact, a pair of epistatically interacting QTL (Orgogozo et al. 2006). How much is this epistatic genetic variance involved in local adaptation, population differentiation, and speciation? This question dates back to 75 years ago, to the differing viewpoints about the genetic basis of evolutionary change that Fisher (1958) and Wright (1984) held. Their famous debate on the important of epistasis in adaptation and population differentiation has greatly influenced theoretical studies and our understanding of evolutionary, population and quantitative genetics (Malmberg and Mauricio 2005). When strong selection acts on the additive component of the genetic variance, it will eventually exhaust the overall additive genetic variance and leave segregating loci with primarily dominance and epitasis effects (Wade 1992; Roff and Emerson 2006). Although mutational variation is commonly thought to be the primary source to the long term selection response (Keightley 2004), one long term selection experiment found a surprisingly important role of epistasis, where genetic interactions  97  among four loci mediated a considerably larger response to selection than predicted by a single locus model (Carlborg et al. 2006). Lately, the role of epistasis has re-gained attention in theoretical evolutionary biology research, with the analytical tools and experimental approaches having been improved (Wolf et al. 2000). The resurgent interest in the role of epistasis has included how epistatic variance can be converted into additive variance after a population bottleneck (Barton and Turelli 2004), and the role of epistasis in the evolution genetic recombination (Otto and Lenormand 2002). It is important to empirically study the "character" of genetic interaction, specifically the role it plays in the evolution of complex traits. In this chapter, we study the genetic architecture of epistasis for mating system traits that differ among three monkeyflower species (Mimulus), using quantitative trait locus (QTL) mapping. Our study examined the ubiquity of epistasis using all pairwise comparisons (crosses) of three Mimulus species that differ for selfing rate. Our three species phylogenetic comparison further allows us to examine the relationship between the degree of genetic divergence among species and the extent of epistasis.  98  MATERIALS AND METHODS  Study species Species in the genus Mimulus have become model systems for the study of evolutionary processes in nature due to the diversity of life histories and mating systems in the genus, as well as the ease with which they can be grown and manipulated. The Mimulus genus currently contains about 160 species, of which approximately 75% occur only in western North America (Beardsley and Olmstead 2002; Grant 1924). Mimulus species vary with respect to ploidy level (Vickery 1978), breeding system (including frequent shifts among pollinators and to self-fertilization), and acclimation to extreme environments. Species within Mimulus genus species are model systems to study the genetics of speciation (Bradshaw et al. 1998), inbreeding depression (Darwin 1876; Dudash and Carr 1998), mating system evolution (Leclerc-Potvin and Ritland 1994), the evolution of heavy metal tolerance (Macnair et al. 1993) and cytological pattern of evolution (Vickery 1978). Studies have shown that Mimulus has likely gone through an adaptive radiation in western North America (Whittall et al. 2006). Species in Section Simiolus display a high degree of morphological complexity and environmental plasticity (Beardsley et al. 2004). With dramatic differences in mating systems, from predominantly selfing to predominantly outcrossing (Ritland and Ritland 1989), the compatibility of crosses among species, and the large numbers of seeds produced by artificial crosses, species  99  from Section Simiolus in Mimulus genus are ideal material for genetic analyses of evolutionary changes (Vickery 1978). The Mimulus guttatus species complex lies within the Section Simiolus and is comprised of 8 to 12 inter-crossable species (Campbell 1950; Grant 1924). Although each species is morphologically distinct, natural hybrids are sometimes found (Vickery 1978). All taxa in this species complex have a haploid chromosome number of n=14 (Campbell 1950; Dole and Ritland 1993; Vickery 1964;Vickery 1978). Species in this section display a wide range of mating system and floral morphology variation (Ritland and Ritland 1989). M. guttatus has at least three, independently derived selfing relatives: M. micranthus, M. nasutus and M. laciniatus (Ritland and Ritland 1989, Fenster and Ritland 1992, Leclerc-Potvin and Ritland 1994; Fishman et al. 2002). The large-flowered M. guttatus is a herkogamous species with a fairly high outcrossing rate (Wright’s inbreeding coefficient, F = 0.38), while the smaller-flowered M. micranthus is predominately selfing (Wright’s inbreeding coefficient, F = 0.73; Ritland and Ritland 1989). As a predominant selfer, M. micranthus shows reduced allocation to the floral traits that contribute to male function, including corolla size and pollen number (Ritland and Ritland 1989). In addition, the magnitude of inbreeding depression in selfing M. micranthus is much lower than in outcrossing M. guttatus, based upon fitness measures such as biomass, pollen production and ovule production (Dudash and Carr 1998). Lastly, M. platycalyx, is intermediate between these two species in terms of outcrossing (Wright’s inbreeding coefficient F = 0.54) and floral size traits (Ritland and Ritland 1989). M. platycalyx and M. guttatus are sometimes sympatric. Natural hybrids have been identified along Sausal Creek in Marin County, California  100  (Dole and Ritland 1993). In this study, we used samples collected in Ritland and Ritland (1989) as parents of the crosses we conducted. Further details about locality, morphological variation, mating system coefficients, phylogenetic genetic distance are given in Ritland and Ritland (1989). The shift of mating system in M. guttatus species complex has been discussed widely and thought to be associated with the change of an entire syndrome, including male allocation, reduced size of floral characters, reduced attraction to pollinators and reduction of the spatial and temporal separation of male and female reproductive organs within the flower (Jain 1976; Ritland and Ritland 1989). Thus, measures of a relatively few floral traits (corolla width, anther length, etc.) capture the majority of evolutionary differences between species.  Backcrosses between Mimulus species Three interspecific crosses were performed using three Mimulus species as parental material, M. guttatus, M. platycalyx and M. micranthus. All parent plants in this study were simultaneously grown in the same growth chamber. Three intercross F1 were produced, M. guttatus x M. platycalyx, M. guttatus x M. micranthus and M. platycalyx x M. micranthus. F1s were maintained in the same growth chamber, while all the parent material were still kept growing. Backcross progeny were produced by crossing F1 individuals back with both parent species, using parent individuals as pollen resource. As a result, a total of 6 reciprocal backcrosses were established. Figure 4-1 shows the crossing scheme and the composition of mapping populations in this study. BC1 refers to a backcross to the larger flower size parent and BC2 for the smaller flower size parent.  101  To avoid the contamination from inter-pollination, flowers were bagged right after crossing. All plants, including parental, F1, and backcrosses, were grown on the same batch of Pro-Mix soil in the growth chambers in Department of Forest Sciences, University of British Columbia, where the growing conditions was kept as 14C/8C day/night temperature with 18 hours daylight for all the chambers. To avoid the possible block effect, the seedlings from all 6 reciprocal backcrosses were labelled and sowed on the growing trays, each of the growing trays contained the same number of seedlings from every backcross and we also periodically rotated the growing trays among growth chambers. The pairwise crosses between parents were performed in the year 2000; due to the limitation of growth chamber space, the backcrosses from M. guttatus x M. platycalyx were conducted in 2001 under the same growth chamber conditions. Crosses from M. guttatus x M. micranthus and M. platycalyx x M. micranthus were done in the same growth chamber in the continuous years. The Mimulus parents used to generate later crosses in this chapter and therefore offspring are the same individuals that are described in Chapter 3.  Measurement of floral traits The following traits were measured on individuals, with a digital calliper used in taking measurements: (1) widest corolla width, (2) corolla tube length, (3) pistil length, (4) stamen height (averaged over the low and high anthers), (5) stigma-anther separation (pistil length minus the average stamen height) (Figure 4-2). Those 5 floral characters are correlated with the evolution of mating system in the M. guttatus species complex (Carr  102  and Fenster 1994; Dole 1992; Ritland and Ritland 1989). The means and standard errors of these floral characters are given in Table 4-1.  DNA isolation and AFLP (Amplified Fragments Length Polymorphism) genotyping Fresh leaf tissue from every individual in the crossed progeny was collected while the floral trait data was collected. Leaf tissues for DNA isolation were immediately stored in the minus 80°C fridges in Department of Forest Sciences, University of British Columbia. Genomic DNAs were isolated from frozen leaf tissue via the CTAB method (Doyle and Doyle 1990). Table 4-2 lists the sample size for each backcross family. The cross made in between M. platycalyx and M. micranthus and then backcross to M. platycalyx had reduced progeny number so we collected all available seeds. AFLP assay was performed as described in Vos et al. (1995), Remington et al. (1999) and modified for the LiCor 4200 automatic DNA-sequencer. Templates for AFLP reactions were prepared using 500 ng of genomic DNA for restriction enzyme digests with EcoRI and MseI, and ligation adapters (Remington et al. 1999). The restriction ligation mixture was diluted 1:100 in deionised water prior to preamplification. Preamplification was carried out using standard AFLP EcoRI and MseI primers containing selective nucleotide Eco + C and Mse + CC. Selective final amplifications were conducted using various combinations of Eco primers with three nucleotides and Mse primers with three nucleotides. The primers in preamplification and selective amplification are given in Table 4-3.  103  Joining linkage map construction using joint likelihood function AFLP markers were tested for a departure from the 1:1 and 3:1 Mendelian ratio for the presence:absence of a fragment (according to whether the cross was a backcross of F2 with respect to the markers), using chi-square test with α =0.05. Markers showing segregation distortion (α <0.05) were excluded and not used to construct the linkage map. Out of 614 polymorphic AFLP loci scored, 368 are included in later analyses. A joint likelihood function using combined information across crosses was used to estimate the pair-wise recombination fractions between AFLP markers. This recently developed approach is not only able to use the genotype information from crosses when the knowledge of parental genotype, or linkage phase, is absent, but also improves the estimates with higher precision and accuracy, particularly when dominant markers are used (Hu et al. 2004). Recombination fractions were then converted to map distances using the Kosambi mapping function, then linkage groups were found using the JoinMap program (Stam 1995; Stam and van Ooijen 1995). Prior to the combined cross analysis, a process of “calc.genoprob” in R using a hidden Markov model to calculate the probabilities of the true underlying genotypes was performed. We then examined the segregation of every marker used on the genetic map in each pair of backcross progeny, re-coded the genotypes in the progeny that represent the parental configuration with the reference from all the 6 crosses together and the function “c.cross” of R/QTL to combine the two backcrosses from the same parents as one intercross (Broman et al. 2003; Sen and Churchill 2001). Among the three Mimulus species, M. guttatus is the most outbreeding and indeed shows the largest flowers, averaging 30.25 mm on corolla width (Table 3-1). In general,  104  M. guttatus corolla size is about twice the size of the intermediate inbreeder M. platycalyx, and almost three times larger than the highly inbreeding species of M. micranthus (Table 3-1). In a combined-cross analysis, crosses with greater variability in the phenotype would have a greater influence (Li et al. 2005). As a result, we then standardized the corolla traits with their standard deviations, in order to stabilize the variance before jointly analyzing the QTL that contribute to the variation among Mimulus species.  QTL mapping using R/QTL genome scan Interval mapping (IM) was used to find QTL at each map position across the whole genetic map (Lander and Botstein 1989). At each position, the likelihood of a QTL was estimated as the product of the prior probability of the QTL, times the likelihood of the QTL effect given the genotype of the flanking markers. The QTL effect is estimated as that maximizing the likelihood; it is numerically estimated using the expectation-maximization (EM) algorithm. The logarithm of odds (LOD) is estimated as the log10 of the likelihood of the estimated effect, minus the likelihood under zero QTL effect. The genome-wide scans of QTL were implemented with the “scanone” function in R/QTL software (Broman et al. 2003; Sen and Churchill 2001). Prior to the execution of scanone, the required multipoint genotype probabilities for EM algorithm were first calculated using the “calc.genoprob” function in R/QTL. The probabilities were calculated at 1 cM intervals for the maximum distance between positions.  105  A proper LOD threshold, which identifies statistically significant QTL, should take into account genome size, progeny number, and the density of the markers genotyped on the map (Churchill and Doerge 1994). In each mapping population, significance thresholds to identify QTL presence were estimated through tests of 10,000 permutations. The permutation thresholds were determined by the “n.perm” command in the scanone, as described in Churchill and Doerge (1994). In this study, cut-off threshold genome wide significance of α =0.05 was used. The upper 5% bound of estimated QTL effects under this null condition of no QTL effect (as simulated by permutation) gives the significance threshold.  Pairwise R/QTL genome scans To infer interactions between QTL, pairwise genome scans were implemented using the “scantwo” function of R/QTL (http://www.rqtl.org; Broman et al. 2003). As the number of pairwise comparisons between QTL is enormous, to reduce computational load a 5 cM interval scanning interval was used. The scantwo function searched through all pairs of loci with a two-way ANOVA model. The likelihood under the full regression model with interaction terms is Y  m b Q1  b Q2  b Q1 Q2  Ag  Z d Q1  Z d Q2  Z d Q1 Q2  .  This was first compared to the null model with no genetic effects, Y  m Ag  where Q1 and Q2 are unknown QTL genotypes at two map locations, A is a matrix of covariates and Z is the matrix of covariates that interact with QTL genotypes. This jointLOD likelihood is the “full” model of the joint effect of two QTL underlying floral trait  106  variation. The “joint LOD” score provides an estimate of the entire suite of two-locus effects. R/QTL then considers another linear model that includes only additive effects (no interaction): Y  m b Q1  b Q2  Ag  Z d Q1  Z d Q2  .  Comparing the full vs. reduced model results in an “interaction LOD”; this statistic measures the statistical significance of the two locus interaction (epistatic) effect. Genome-wide significance thresholds for the interaction LOD score were established through permutation tests by n.perm involving 1,000 permutations by scantwo. Following Lander and Kruglyak (1995), the cut-off threshold was set at p < 0.63, to pre-select possible interacting pairs of loci. This procedure in using less stringent significance threshold to identify “suggestive linkage” was also used in other combined cross QT: analysis (Li et al 2005). In this procedure, as there are many suggestive estimates of QTLs, all of the pre-selected QTL loci and chromosome regions then subject to a backward selection procedure with interaction terms, to search the best fitting model involving the fewest parameters, using function “stepwiseqtl” and “makeqtl” in R/QTL. Details of the methods can be seen in http://www.rqtl.org/manual/html/stepwiseqtl.html. First, the suggestive QTLs as well as interacting pair of QTL were estimated. Then, the suggestive QTL or interaction that is least insignificant in the overall ANOVA result was eliminated. Recalculating the overall ANOVA, insignificant QTL and interacting gene pairs were successively eliminated, until only highly significant main and interaction effects remained. The same procedure of model selection for the best fit QTLs and interacting pairs can be seen in Zou and Zeng (2008) and Tsudzuki et al. (2007).  107  RESULTS  QTL analysis using single locus R/QTL genome scans In the single-locus R/QTL genome scan of QTL for the 5 floral traits, 21 QTLs were found between M. guttatus and M. platycalyx, 12 QTLs between M. platycalyx and M. micranthus and 19 QTLs between M. guttatus and M. micranthus. The number of QTLs differentiating M. platycalyx and M. micranthus is the least of the three pairwise comparisons. Table 4-3 gives results from both the single locus and locus-pair analysis (twoway interaction ANOVA model). With the resolution offered by R/QTL pairwise genome scans, a few previously undetected QTLs were identified. In total, 34 QTLs were identified between M. guttatus and M. platycalyx, 31 QTLs between M. platycalyx and M. micranthus and 27 QTLs between M. guttatus and M. micranthus (Table 4-3). The largest QTL was found in M. platycalyx and M. micranthus, for stamen length (QTL8_38 in Table 4-3). QTLs were named according to their linkage group and chromosome location (in map units). For example, for corolla width, one QTL was found in linkage group 1, on chromosome location of 19 cM around AFLP marker C1_200, so it is termed QTL1_19. QTL for two different floral traits that reside on the same location were considered the same QTL, and thus will be given the same name, to account for probable pleiotropy (Togawa et al. 2006). The QTL locus QTL1_19, for example, was found to control both stamen length and corolla length between M. guttatus and M. micranthus (Table 4-3).  108  Our analyses identified QTLs in almost all 14 linkage groups, the exception being group 5. Moreover, linkage group 2 and linkage group 8 have considerably more QTLs than other linkage groups. On average, QTLs in linkage group 8 were found to account for 14.4% of PVE across all crosses (Table 4-3). QTLs in linkage group 2 were found associated with almost every floral trait in all crosses, except for the corolla length and stigma-anther between M. guttatus and M. platycalyx and corolla width and length in M. guttatus and M. micranthus. Between M. guttatus and M. platycalyx, linkage group 8 showed significant QTLs for every floral trait in the analysis. QTL10_26 was, in contrast, a minor QTL with a marginal but significant effect of 3.7% PVE (pistil length) to 2.8% PVE (corolla length) which was revealed only when epistasis was included (Table 4-3). Between M. platycalyx and M. micranthus, linkage groups 2, 8 and 11 nevertheless showed important genetic roles not only in terms of number of QTLs, but also by PVE explained by each QTL (Table 4-3). For example, two corolla width QTLs were found in group 8, QTL8_0 and QTL8_38, with 2.7% of PVE and 21.2% of PVE, respectively. Together, they also displayed significant epistatic genetic interaction (Table 4-4). Stigma-anther separation showed fewest QTL, with only 3 (QTL2_28, QTL8_38 and QTL13_25). A less polygenic system with multiple QTL between M. guttatus and M. micranthus, with number of QTL detected in the range of 3 (stamen length) to 6 (corolla width and stigma-anther separation). Interestingly, more QTL had negative genetic effect of reducing floral size. In comparison, QTL genetic effects also were more minor, with PVE ranging from 1.2% (QTL13_18 for stigma-anther separation) to 10.1% (QTL1_1 and QTL9_19 for corolla width).  109  Pairwise QTL genome scans Among the variation of 5 floral traits in three Mimulus species, up to 167 pairs of possible gene-gene interaction were suggested by the pairwise genome scans (Figure 4-3 to Figure 4-7). Table 4-4 summarizes significant epistatic locus pairs for each of the three Mimulus pairwise comparisons, tested by fitting two way ANOVA models. Overall, 57 pairs were significant across the five floral traits. In general, results from the two-way ANOVA vary among crosses. Between M guttatus and M. micranthus, only 4 epistatic pairs were found, while 25 were found between M. guttatus and M. platycalyx and 28 between M. platycalyx and M. micranthus (Table 4-4). The percentage of variance explained by epistasis also varied among crosses. For example, for corolla width, the full ANOVA model explained 71.61 percent of overall variance of the corolla width between M. guttatus and M. platycalyx, and of this, 6.21 percent of overall variance was explained by epistasis. Between M. platycalyx and M. micranthus, the full ANOVA model explained 76.45 percent of overall variance while the interaction effect was stronger with 16.03% variance explained. For the same trait, the variance explained by epistasis was only 6.18% in the cross between M. guttatus and M. micranthus. Pairwise QTL genome scans: corolla width Between M. guttatus and M. platycalyx, 4 interacting locus pairs for corolla width were identified. The most significant involved QTL8_38 (Table 4-3) and a new QTL (undetected in the single-locus analysis) on linkage group 3, with 2.3% of variance  110  explained by this interaction (Table 4-4). Two mapped QTL genes, QTL4_32 and QTL8_38, both significant in the single-locus analysis, had epistasis that explained 1.37% of the variance. In addition, epistasis was also found between chromosome regions that had no QTL in the R/QTL single-locus analysis. For example, the two-way ANOVA detected epistasis between linkage group 9 and linkage group 12, which had no QTLs detected by the R/QTL analysis (Table 4-3 and Table 4-4). Seven pairs of interacting loci were found in the cross of M. platycalyx and M. micranthus. Epistasis appears to be an important genetic character in this cross, giving that there were originally only 3 QTL genes mapped by R/QTL with single locus genome scans, but with the two-way ANOVA analysis, 8 QTL were found. Two of the mapped QTL loci, QTL2_28 and QTL8_38, not only showed additive effects, but jointly demonstrated epistasis that accounted for 4.66% of the segregational variance, the largest degree of epistasis found in this study. Also, a great proportion of epistasis was found to involve QTL8_38, with 5 out of 7 epistasis interactions involving this QTL (Table 4-4). Again, R/QTL failed to detect additive QTL that interact epistatically; e.g., epistasis number (2) and number (3) between M. platycalyx and M. micranthus, although each explained a relatively small amount of trait segregation, 1.48% and 1.85%, respectively (Table 4-4). In the cross of M. guttatus and M. micranthus, two epistatic interactions were found, both involving previously mapped QTL (Table 4-4).  111  Pairwise QTL genome scans: corolla length Although the R/QTL single locus scans revealed similar QTL for corolla length and corolla width between M. guttatus and M. platycalyx (Table 4-3), the analysis of epistasis revealed detailed differences for QTL for corolla length. Eight pairs of epistatic QTL were found and epistasis explained up to 10.5% of the segregational variance (Table 4-4). Two significant epistasic pairs were found in linkage group 10 (QTL10_26) and linkage group 13 (QTL13_25); both were not initially revealed by R/QTL analysis (Table 4-4). Similar results were also found between M. platycalyx and M. micranthus. Epistasis was mostly found between mapped QTL and the chromosome regions (Table 44). No epistasis was found between M. guttatus and M. micranthus.  Pairwise QTL genome scans: pistil length Five cases of epistasis were found between M. guttatus and M. platycalyx, with PVE ranging from 1.51% to 3.32%. Interestingly, the highest epistasis was found between two chromosome regions that contained no significant QTL in the single-QTL R/QTL analysis (epistasis number (3) between M. guttatus and M. platycalyx, Table 4-4). Between M. platycalyx and M. micranthus, we identified six epistatic QTL most of them between mapped QTL and previously unidentified QTL (Table 4-4). Between M. guttatus and M. micranthus, we found two significant epistatic QTL between previously identified QTL loci, with a combined PVE of 5.24% (Table 4-4).  112  Pairwise QTL genome scans: stamen length In total, 13 cases of epistasis were identified for stamen length among all comparisons; 7 between M. guttatus and M. micranthus, 6 between M. platycalyx and M. micranthus, and none between M. guttatus and M. micranthus. Although the analysis of R/QTL single locus genome scans did not suggest significant major QTL on linkage group 10, actually in this group a few epistatic interactions were identified with the twoway ANOVA. For example, between M. guttatus and M. platycalyx, genetic interaction was found within linkage group 10 between chromosome region 0 cM (QTL10_0, nearby marker C1_295) and 26 cM (QTL10_26, nearby marker C7_224). Epistasis from two interacting chromosome regions on the same linkage group can also be seen on linkage group 11: epistasis number (6) between M. platycalyx and M. micranthus. However, the epistasis found within linkage group 11 was generated between a previously mapped QTL (QTL11_19) and the chromosome region identified QTL related gene, QTL11_13 (around 10 cM and the nearby marker is C4_147), in which R/QTL failed to detect a QTL. Interestingly, this QTL11_13 was a QTL for other floral traits (Table 4-3). This epistasis on linkage group 11 between M. platycalyx and M. micranthus could be due to a pair of closely linked genes, or several genes over a larger chromosome region, with pleiotropic effects upon floral traits.  Pairwise QTL genome scans: stigma-anther separation Compared to the other floral traits, epistasis for stigma-anther separation was much less. Between M. guttatus and M. platycalyx, we found only one significant epistasic pair of QTL, that involving QTL8_38 and a location in linkage group 5 (Table  113  4-4), even though we found several (8) QTL previously via single-locus analyses (Table 4-3). Between M. platycalyx and M. micranthus, again only one was detected, between QTL8_38 and QTL13_25 (Table 4-4). Between M. guttatus and M. micranthus, no significant epistasis was found.  114  DISCUSSION  The focus of this study was to determine the extent of epistasis for QTL underlying mating system variation in the Mimulus guttatus species complex. A basic understanding of genetic architecture not only involves the number of QTL and their magnitude of effect, but also their genetic interactions. This provides a complete understanding of the evolutionary transition of mating system from outcrossing to selffertilization. In our analyses, both “joint” and “interaction” LOD scores were calculated for all pairwise combinations of QTL loci. The joint LOD score provided an estimate of the entire two-locus interaction, and the interaction LOD score specifically indicated epistasis. Genome-wide significant thresholds identified significant interactions via permutation tests. We found 25 pairs of QTL to exceed the threshold of both joint and interaction LOD scores between M. guttatus and M. platycalyx, 28 between M. platycalyx and M. micranthus and 4 between M. guttatus and M. micranthus. The importance of epistasis in population differentiation has been emphasized by Sewall Wright in a series of papers. It was summarized in 1980 by Wright (1980), represented in his “shifting balance” theory that explained selection as a counterpoint to the selection of single mutations according to classical Darwinian theory. By his interpretation, a selective advantage is attained when a particular combination of alleles is created by population admixture, rather than by single point mutations. His idea was originally founded on experimentation, not theory. Observations of domestic livestock suggested that overall improvement takes place not from within a particular herd, but  115  when interbreeding takes place between herds, thus generating novel combinations of alleles that foster interacting co-adapted gene complexes. A number of theoretical models have demonstrated that favourable interacting co-adaptive gene complexes can evolve with similar selective pressures. Breaking the co-adaptive gene complex by intercrossing populations or species can decrease fitness, and this phenomenon of hybrid-breakdown is the basis of the Dobzhansky-Muller speciation model (Dobzhansky 1937). For example, in the genetic architecture of population differentiation of Chamaecrista fasciculate, the enhanced performance of F1 to parents suggestively represented that increased fitness (heterosis) was due to dominance that masked deleterious recessive alleles and the expression of positive epistasis (Fenster and Galloway 2000; Lynch 1991; Lynch and Walsh 1998; Lynch et al. 1999). More interestingly, these studies documented a consistent hybridbreakdown in the F3, suggesting that combining genes from different populations can decrease vigour beyond what was due to the expected loss of heterozygosity (Fenster and Galloway 2000). Such findings of epistasis contributing to population divergence in a local scale can also be seen in a number of other studies (Templeton et al. 1976; Price and Waser 1979; Waser and Price 1989, 1994; Burton 1987, 1990; Deng and Lynch 1996). In Drosophila, the incongruence of epistasis estimates, both between studies and traits, can be generally related with the degree of differentiation presented by the populations in each trait (Rego et al. 2007), while considering studies involving populations from the same species (Edmands 1999; Bieri and Kawecki 2003; Teotónio et al. 2004).  116  Statistical detection of epistasis An interesting observation from studies that address the importance of epistasis was that while some studies have shown little evidence for significant epistasis (Edwards et al. 1987; Xiao et al. 1995), others have reported major interaction effects (Cockerham and Zeng 1996; Li et al. 1997). It is often emphasized that the most important problem in studying epistasis in QTL experiments is the relatively poor statistical power, owing to the small sample size, limiting numbers of individuals in each of the genotype classes, and questionable level for declaring significance of epistasis (Tanksley 1993; McMullen et al. 2001, Yi and Xu 2002). In this study of floral trait variation among Mimulus species, we noticed that there were only a few cases of epistasis found in between the QTLs as identified via singlelocus analyses (Table 4-4). Given the nature of the R/QTL single locus genome scan that we used to identify QTL and the calculation of significance threshold by the permutation method, we could have likely missed QTL due to the stringent cut-off threshold. Such results could have biased us towards detection of too few QTL loci with too large effects (Beavis 1998). As shown in Figures 4-3 to Figure 4-7, our multi-dimensional genome scans revealed a higher degree of complexity of the genetics in floral character evolution. On those graphical examples of two-dimensional linkage maps, the highlighted (darker) points in the figures are indicative of the potential genetic interactions in Mimulus. For the example in the cross of M. guttatus and M. micranthus, additively, both QTL1_1 and QTL12_8 affect the variation of corolla width, with significant amount of genetic effect (Table 4-4). Together, Figure 4-3(C) displays a highlighted LOD score to genetic  117  interaction in between QTL1_1 and QTL 12_8 ((1) in Figure 4-3(C)), in the comparison with other genome regions. In addition to the epistasis found in between mapped QTL, much epistasis was found between mapped QTL and previously unidentified chromosome regions in these two dimensional analysis (Table 4-4). These chromosome regions with only little marginal genetic effect, the potential QTL, were the chromosome regions that only showed their genetic effect while epistasis is considered. Without the restriction to just the "known" QTL (as inferred by single QTL analysis), the multidimensional genome scan technique we utilized allows a more systematic and powerful search for interacting locus pairs (Malmberg and Mauricio 2005). This is because of the well-known fact that in two-way ANOVA models, main effects may not be present but interactions terms can be. For example, on linkage group 10, 26 cM nearby AFLP marker C7_224, using R/QTL single locus genome scan with the threshold set up by 10,000 permutations, the linkage group did not succeed in supporting significance of QTL10_26 in the cross between M. guttatus and M. platycalyx. Next, by fitting in a two-way ANOVA, this locus later reveals its genetic effect by exhibiting an epistatic effect jointly with another locus locating on 0 cM position of the same linkage group (QTL10_0) in the variation of corolla width (1.53% of PVE), corolla length (1.15% of PVE) and average stamen length (1.57% of PVE) (Table 4-4). QTL10_26 was then reckoned to be likely involved in the two-locus epistatic interaction. In the final ANOVA model together with all the interaction terms, some visible genetic effect was also uncovered associated with this QTL10_26 locus, contributing to 3.09 % PVE for corolla width, 2.38% PVE for corolla  118  length and 3.02% PVE for stamen length. Such additively silent but epistatically acting genetic loci are also frequently observed on linkage group 7, 12 and 13 from the comparison of M. platycalyx and M. micranthus (Table 4-4).  Epistasis found in previous studies The same QTL mapping technique as we have employed have also been used in studies of genome-wide epistatic interactions that determine the genetic basis of circadian behaviours (Shimomura et al. 2001) as well as the diabetes related phenotypes in mice (Togawa et al. 2006). In plants, Malmberg et al. (2005) studied fruit number, germination and seed size in field-grown Arabidopsis thaliana. The number of additive QTL varied from 2 to 4; in each case the number of QTL loci estimated with epistatic interactions was approximately double, varying from 5 to 8. In a maize domestication study, residing on the long arm of chromosome 3, the effect of QTL-3L was found only weak or non-significant when transferred into a NIL background (Doebley and Stec 1993; Doebley et al. 1995). The significance of QTL-3L can however only be detected when the epistasis with QTL-1L was also considered (Lukens and Doebley 1999). The interesting findings of QTL on maize domestication traits, the silent loci (e.g. QTL10_26) involved in Mimulus mating system evolution or the additional QTLs found in Arabidopsis suggests that the alleles like QTL10_26 in Mimulus case could have resided in natural populations without affecting the fitness of the population, while not segregating. The joint presence of teosinte and maize alleles in the domestication process, or the QTL10_26 of Mimulus on different genetic background, allows detectable genetic effects of QTL-3L and QTL10_26, and thus is indicative that  119  selection in the evolution of mating system shift (or during domestication of maize species) was acting on a gene complex, rather than single locus in strictly additive fashion.  Epistasis in the evolution of selfing in Mimulus Kelly (2005) found considerable epistasis for flower morphology in Mimulus. However, epistasis in Mimulus might be variable across genetic loci underlying these traits, as well as among different genetic crosses (Fishman et al. 2002; Kelly 2005). Here, in the cross of M. guttatus and M. micranthus, not only was the proportion of variance explained by epistasis small, but the two-way ANOVA model also failed to find epistasis in a number of floral traits, including corolla length, stamen length and stigma-anther separation (Table 4-4). Given that inbreeding depression is high in M. guttatus population (Dole and Ritland 1993; Latta and Ritland 1994), the partially recessive mating system modifier loci likely drive the evolution of selfing from outcrossing in Mimulus (Lin and Ritland 1997). Although we did not explicitly estimate dominance genetic variance in this study, directional dominance was evident in the biometric comparison of M. guttatus and M. micranthus (Chapter 2). With the little proportion of epistasis variance found in this analysis, we speculate the genetic basis of evolution of selfing from outcrossing, in M. guttatus species complex, involves genes with predominantly additive and dominance effect, with few epistatic interactions. Moreover, in theory, population or species that have longer independent evolution histories should also evolve more closely co-adapted gene complexes owing to the lack gene flow and recombination. Thus one could expect a greater degree of epistasis when  120  crossing populations or species with greater degree of genetic divergence, because of the novel combinations of alleles from different loci brought by recombination. Estimated by 368 neutral AFLP markers, M. guttatus and M. micranthus are about twice as closely related to each other compared to the M. guttatus-M. platycalyx and M. micranthus-M. platycalyx species pairs (Chapter 3). Our findings of little epistasis in between closely related M. guttatus-M. micranthus taxa are compatible with Moller et al. (1965), who found that crosses between more distant populations had a favourable epistatic effect upon grain yield (Lynch 1991). Finally, the differentiation of M. micranthus from M. guttatus might have been a rapid event with only a few co-adapted gene complex involved. This is supported by the evidence that these interacting QTL pairs, involving epistasis found in the cross of M. guttatus and M. micranthus, are among those homologous lineage specific QTLs found in these Mimulus lineages (e.g., QTL B5_535 in Chapter 3).  Epistasis in stable mixed mating systems The genotypic association between inbreeding depression loci and modifiers of mating system, as studied by numerous workers (Campbell 1986; Holsinger 1991 and Uyenoyama et al. 1993), offers a scenario that may help explain stable mixed mating systems. As overdominance cannot be purged, and therefore inbreeding depression maintained, the evolution of selfing from outcrossing is prevented and stable partial selfing maintained. The heterozygous modifier loci needed for such a condition should occur in either outcrossing and intermediate selfing species, like M. guttatus and M. platycalyx, respectively, and not in a fully selfing species like M. micranthus. Now, in  121  the multi-locus model, when there is partial selfing, it is known that different loci do not behave independently, a situation referred as “identity disequilibrium” (Charlesworth and Charlesworth 1990; Ohta and Cockerham 1974; Tachida and Cockerham 1989). When loci are in identity disequilibrium, heterozygosity is correlated among loci. Higher than expected multilocus heterozygosity might lead to elevated epistasis. This genetic scenario is supported by our results: epistasis was greater in crosses with M. platycalyx, the intermediate selfer, suggesting a role of co-adapted gene complex and epistasis in maintaining mixed mating systems.  Conclusion Epistasis was suggested here by the pairwise genome scans for floral character evolution in Mimulus. It was further revealed by the two-way ANOVA model, where epistasis was further supported between M. guttatus and M. platycalyx and between M. platycalyx and M. micranthus. The comparison between M. guttatus and M. micranthus however failed to show epistasis, although results from single locus genome scans did identify polygenic genetic basis with averaged 5.8 QTL genes for each floral trait variation. Interesting, these two species have half the genetic distance of the other two pairings of taxa, suggesting that epistasis increases in the progeny of wider crosses (Figure 4-8).  122  Table 4-1. Generation means and sample sizes for each corolla trait in Mimulus pedigrees (P=parental species, F1=first generation cross, BC=backcross to parental species). Corolla trait Sample Population size Cross: M. guttatus X M. platycalyx P- M. guttatus P- M. platycalyx F1 BC to M. guttatus BC to M. platycalyx  10 9 20 216 173  Corolla width  Corolla length  Pistil length  Stamen length  Stigma-anther separation  30.25 (0.79) 17.77 (0.40) 19.61 (0.54) 24.16 (0.18) 18.58 (0.28)  34.20 (1.07) 23.23 (0.57) 24.08 (0.60) 29.83 (0.19) 23.71 (0.32)  18.96 (0.53) 13.15 (0.31) 14.72 (0.20) 18.09 (0.11) 14.35 (0.18)  16.95 (0.25) 12.89 (0.29) 12.52 (0.22) 14.64 (0.10) 12.03 (0.16)  2.02 (0.39) 0.26 (0.26) 2.20 (0.14) 3.4 (0.07) 2.24 (0.10)  30.25 (0.79) 8.10 (0.30) 12.26 (0.82) 22.34 (0.33) 10.67 (0.22)  34.20 (1.07) 11.39 (0.36) 14.88 (0.84) 26.71 (0.33) 13.86 (0.22)  18.96 (0.53) 6.38 (0.22) 9.17 (0.52) 16.09 (0.15) 8.51 (0.11)  16.95 (0.25) 7.21 (0.23) 8.82 (0.33) 13.86 (0.14) 7.89 (0.10)  2.02 (0.39) -0.35 (0.15) 0.34 (0.23) 2.21 (0.07) 0.62 (0.05)  23.23 (0.56) 11.39 (0.36) 16.58 (0.23) 16.13 (0.28) 11.74 (0.25)  13.15 (0.31) 6.38 (0.22) 10.39 (0.09) 9.82 (0.14) 7.09 (0.14)  12.89 (0.29) 7.21 (0.23) 9.76 (0.11) 10.03 (0.17) 7.76 (0.14)  0.26 (0.26) -0.35 (0.15) 0.63 (0.06) -0.2 (0.09) -0.67 (0.06)  Cross: M. guttatus X M. micranthus P – M. guttatus P – M. micranthus F1 BC to M. guttatus BC to M. micranthus  10 11 35 149 122  Cross: M. platycalyx X M. micranthus P – M. platycalyx P – M. micranthus F1 BC to M. platycalyx BC to M. micranthus  9 11 36 72 166  17.77 (0.40) 8.10 (0.30) 16.98 (0.23) 11.66 (0.22) 8.13 (0.20)  Values in parentheses are standard error to the mean All measures are in mm  123  Table 4-2. AFLP primers used in this study and the extent of polymorphism.  AFLP Primers for pre-amplification: Primer names and sequences  Primer name  AFLP primer pair for final amplification  Primer B set: EcoRI* + AC / MseI** + CC  B2 B3 B4 B5  Eco + ACA / Mse + CCT Eco + ACA / Mse + CGC Eco + ACA / Mse + CCA Eco + ACT / Mse + CGG  25 54 59 38  Primer C set: EcoRI + C / MseI + CC  C1 C3 C4 C7  Eco + CA / Mse + CCG Eco + CCG / Mse + CCG Eco + CCA / Mse + CCT Eco + CTC / Mse + CCT  61 31 54 46  Total number of polymorphic fragments  * **  Number of amplified polymorphic fragments  368  Primer sequence of EcoRI is GACTGCGTACCAATTC. Primer sequence of MseI is GATGAGTCCTGAGTAA.  124  Table 4-3. Significant QTL identified from R/QTL single locus and pair-wise genome scans. The significance was determined by calculating the genome-wide thresholds of α = 0.05 by 10,000 permutations. The genetic effect in percentage of variance explained and significance of selected genetic loci were all tested in a two-way ANOVA model when interaction was taken account. The nearest marker denoted the AFLP marker residing closest to the position where exhibits the maximum LOD score that defines the QTL gene on the linkage group. Name of QTL was given by the linkage group and the chromosome location of the resulting QTL. QTL genes that are identified from the same linkage group and share the nearest AFLP marker are considered to be the same genetic locus, and therefore given the same name.  Cross: M. guttatus x M. platycalyx Linkage Trait group 1 Corolla width 2 4 8 93 103 123 1 Corolla length 4 83 8 103 103 11 133 1 Pistil length 23 4 8 9 103 11 1 Stamen length 2 4 8 93 103 103 11 123 6 Stigma-anther separation 8 11  Name of QTL QTL1_19 QTL2_12 QTL4_32 QTL8_38 QTL9_0 QTL10_26 QTL12_12 QTL1_20 QTL4_32 QTL8_10 QTL8_38 QTL10_0 QTL10_26 QTL11_25 QTL13_25 QTL1_20 QTL2_12 QTL4_32 QTL8_38 QTL9_0 QTL10_26 QTL11_25 QTL1_19 QTL2_12 QTL4_32 QTL8_38 QTL9_0 QTL10_0 QTL10_26 QTL11_25 QTL12_12 QTL6_43 QTL8_38 QTL11_25  QTL map position cM (nearest marker) 19.1 (C1_200) 12.0 (C4_446) 32.0 (C3_341) 38.2 (C7_210) 0.0 (C7_312) 26.0 (C7_224) 12.0 (C7_461) 20.0 (B4_106) 32.1 (C3_341) 10.0 (C3_307) 38.0 (C7_210) 10.0 (C1_295) 26.0 (C7_224) 25.0 (C4_188) 29.0 (C7_310) 20.2 (B4_106) 12.0 (C4_446) 32.1 (C3_341) 38.0 (C7_210) 8.0 (C7_312) 26.0 (C7_224) 25.0 (C4_188) 20.0 (C1_200) 12.0 (C4_446) 30.0 (C3_341) 41.0 (C7_210) 5.0 (C7_312) 0.0 (C1_295) 26.0 (C7_224) 25.0 (C4_188) 12.0 (C7_461) 43.0 (B5_052) 41.0 (C7_210) 29.0 (C4_188)  125  LOD score 31.1 33.4 35.6 50.6 3.51 7.95 5.6 46.1 52.8 32.2 63.3 3.3 7.7 67.5 2.9 44.4 31.9 49.4 59.5 4.4 7.9 64.6 28.6 30.0 29.2 46.6 4.5 2.7 7.8 45.8 6.3 3.8 17.5 12.2  Genetic effect 1, PVE 2 4.7, 1.7% -4.5, 1.8% 5.5, 1.7% 4.5, 12.9% 1.6, 1.8% -1.1, 3.1% -1.8, 2.6% 6.4, 1.7% 7.0, 2.8% 0.2, 3.7% 5.9, 18.4% 2.2, 2.8% -1.2, 2.4% 6.5, 2.8% 2.0, 2.6% 6.3, 2.9% -5.1, 3.3% 6.0, 1.9% 5.4, 9.3% 2.1, 1.9% -1.2, 3.7% 6.3, 1.8% 4.4, 2.5% -4.1, 2.3% 5.0, 5.0% 1.4, 11.5% 1.8, 2.3% 1.7, 2.0% -1.0, 3.0% 5.1, 4.5% -1.9, 2.4% -0.6, 3.0% 0.1, 11.5% 1.4, 3.3%  Cross: M. platycalyx x M. micranthus Linkage Trait group 13 Corolla width 2 73 83 8 11 123 133 2 Corolla length 63 8 93 11 123 133 23 Pistil length 73 8 93 11 123 23 Stamen length 43 8 93 113 11 123 2 Stigma-anther separation 8 133  Name of QTL QTL1_58 QTL2_28 QTL7_0 QTL8_0 QTL8_38 QTL11_13 QTL12_5 QTL13_25 QTL2_28 QTL6_35 QTL8_38 QTL9_0 QTL11_13 QTL12_12 QTL13_5 QTL2_28 QTL7_0 QTL8_38 QTL9_0 QTL11_13 QTL12_12 QTL2_28 QTL4_0 QTL8_38 QTL9_0 QTL11_13 QTL11_25 QTL12_12 QTL2_28 QTL8_38 QTL13_25  QTL map position cM (nearest marker) 58.0 (C1_088) 28.5 (C1_522) 0.0 (B3_166) 0.0 (C3_307) 41.2 (C7_210) 13.0 (C4_147) 5.0 (C7_230) 25.0 (C7_310) 28.2 (C1_522) 35.0 (B5_339) 41.2 (C7_210) 5.0 (C7_312) 15.0 (C4_174) 12.0 (C7_461) 5.0 (C7_245) 30.0 (C1_522) 0.0 (B3_166) 41.2 (C7_210) 5.0 (C7_312) 17.0 (C4_188) 10.0 (C7_461) 28.0 (C1_522) 2.0 (B5_535) 41.2 (C7_210) 3.0 (C7_312) 10.0 (C4_147) 19.0 (C4_188) 12.0 (C7_461) 0.0 (C1_594) 41.0 (C7_210) 15.0 (C7_310)  LOD score 1.7 14.1 2.8 7.5 32.7 21.8 5.8 1.1 16.2 12.4 45.5 14.7 26.0 10.0 2.1 17.6 2.14 50.0 17.8 29.3 14.3 16.3 18.0 59.6 24.3 16.8 34.8 19.1 3.6 3.4 1.6  Genetic effect 1, PVE 2  Cross: M. guttatus x M. micranthus Linkage Trait group 1 Corolla width 83 9 12 133 143 1 Corolla length 9 12 133 1 Pistil length 2  Name of QTL QTL1_1 QTL8_35 QTL9_19 QTL12_8 QTL13_5 QTL14_39 QTL1_19 QTL9_19 QTL12_8 QTL13_25 QTL1_1 QTL2_11  QTL map position cM (nearest marker) 1.0 (C1_479) 34.0 (B5_180) 19.0 (B5_070) 8.0 (C7_461) 0.0 (C7_245) 32.0 (B4_166) 13.0 (C1_200) 19.0 (B5_070) 8.4 (C7_461) 15.0 (C7_310) 1.0 (C1_479) 11.0 (C1_247)  LOD score 8.5 1.1 7.7 4.4 1.6 1.1 5.8 6.1 3.8 1.3 17.2 9.6  Genetic effect 1, PVE 2  126  0.2, 1.9% 2.1, 6.9% -0.9, 3.5% 1.7, 2.7% 0.8, 21.2% 2.7, 2.1% 3.9, 5.8% -0.3, 4.0% 2.7, 5.2% -3.7, 1.7% 0.9, 25.8% 3.3, 19.0% 3.4, 9.6% -0.3, 4.4% 1.0, 1.9% 3.0, 4.2% 0.9, 4.2% 0.9, 26.6% 4.0, 2.2% 4.0, 5.1% -0.4, 2.8% 3.4, 3.9% 4.7, 3.5% 0.8, 37.7% 1.3, 10.4% 5.0, 2.6% 5.0, 3.2% 0.1, 1.3% 0.8, 5.7% 0.1, 6.7% 0.3, 4.9%  2.2, 10.1% 0.2, 1.6% -0.8, 10.1% -0.8, 5.6% 0.5, 1.6% -1.5, 1.6% 0.8, 3.9% -0.7, 2.85 -0.9, 3.35 0.6, 1.9% 1.3, 6.6% -1.6, 3.3%  Stamen length  Stigma-anther separation  4 9 1 2 9 1 2 4 6 9 13  QTL4_0 QTL9_19 QTL1_1 QTL2_11 QTL9_19 QTL1_1 QTL2_11 QTL4_0 QTL6_43 QTL9_19 QTL13_18  0.0 (B5_535) 19.0 (B5_070) 1.0 (C1_479) 11.0 (C1_247) 19.0 (B5_070) 1.0 (C1_479) 11.0 (C1_247) 0.0 (B5_535) 43.0 (B5_052) 19.0 (B5_070) 15.0 (C7_310)  4.6 10.7 12.7 6.4 9.4 17.9 11.1 7.6 4.5 8.2 4.2  0.8, 4.7% -1.2, 6.9% 1.4, 2.1% -1.3, 2.1% -1.1, 4.5% 1.7, 1.2% -1.6, 7.0% 0.9, 2.1% -1.1, 1.3% -0.9, 2.8% 1.4, 1.2%  1. Joint estimate of additive and dominance genetic effect, all measure in mm 2. PVE, percentage of variance explained 3. The genetic locus only identified from pair-wise genome scan and tested significant in two-way ANOVA  127  Table 4-4. Significant interacting pairs of loci found by R/qtl pairwise genome scans. Interacting pairs were deemed significant if both the joint and interaction LOD exceeded the threshold of α = 0.05. The genome-wide LOD thresholds were generated by 1000 permutations. The nearest marker denotes the AFLP marker that appears closest to the map location of where identified the maximum LOD score. The percentage of variance explained by the pair of locus was estimated by the fit of a two-way ANOVA model  Locus 1 Floral Trait  Locus 2  Joint LOD In pairwise genome scans  LOD score in ANOVA  % of variance explained (PVE)  Chr (Map position)  Nearest marker  Chr (Map position)  Nearest marker  (1) (2) (3) (4)  4 (32.0) 3 (7.0) 9 (0.0) 10 (0.0)  C3_341 B5_186 C7_312 C1_295  8 (38.0) 8 (38.0) 12 (12.0) 10 (26.0)  C7_210 C7_210 C7_461 C7_224  69.2 69.5 45.9 46.6  2.9 2.3 2.1 3.2  1.37 * 2.30 * 1.01 * 1.53 *  (1) (2) (3) (4) (5) (6) (7)  2 (28.0) 7 (0.0) 12 (11.0) 8 (41.0) 8 (41.0) 8 (41.0) 8 (41.0)  C1_522 B3_166 C7_461 C7_210 C7_210 C7_210 C7_210  8 (41.0) 12 (5.0) 13 (25.0) 8 (0.0) 7 (0.0) 12 (5.0) 13 (25.0)  C7_210 C7_230 C7_310 C3_307 B3_166 C7_230 C7_310  49.0 18.7 13.9 59.7 27.6 39.8 37.7  7.7 2.6 3.2 3.8 2.4 3.5 2.6  4.66 *** 1.48 * 1.85 * 2.18 ** 2.39 ** 1.99 ** 1.48 *  (1) (2)  1 (1.0) 9 (19.0)  C1_479 B5_070  12 (8.0) 12 (8.0)  C7_461 C7_461  13.2 10.2  1.9 2.6  2.65 * 3.53 **  (1) (2) (3) (4) (5) (6) (7) (8)  1 (20.0) 8 (38.0) 4 (32.0) 4 (15.0) 4 (32.0) 8 (38.0) 10 (10.0) 10 (10.0)  C1_404 C7_210 C3_341 B3_485 C3_341 C7_210 C1_295 C1_295  8 (38.0) 11 (25.0) 11 (25.0) 13 (25.0) 8 (38.0) 8 (10.0) 10 (26.0) 13 (25.0)  C7_210 C4_188 C4_188 C7_310 C7_210 C3_307 C7_224 C7_310  78.6 89.4 75.1 52.1 89.3 89.3 63.1 50.4  2.7 2.8 2.9 3.2 5.9 5.9 2.9 2.8  1.05 * 1.09 * 1.16 * 1.28 * 1.29 * 2.39 *** 1.15 * 1.09 *  Trait: Corolla width Cross: M. guttatus X M. platycalyx  Cross: M. platycalyx X M. micranthus  Cross: M. guttatus X M. micranthus  Trait: Corolla length Cross: M. guttatus X M. platycalyx  128  Cross: M .platycalyx X M. micranthus  (1) (2) (3) (4) (5) (6) (7) (8)  2 (28.0) 6 (35.0) 8 (41.0) 8 (41.0) 9 (5.0) 9 (5.0) 11 (15.0) 11 (15.0)  C1_522 B5_399 C7_210 C7_210 C7_312 C7_312 C4_174 C4_174  8 (41.0) 11 (14.0) 9 (5.0) 11 (15.0) 11 (15.0) 12 (12.0) 12 (12.0) 13 (5.0)  C7_210 C4_174 C7_312 C4_174 C4_174 C7_461 C7_461 C7_245  67.7 35.3 60.1 64.5 39.8 24.1 37.5 38.8  6.4 2.9 4.7 4.8 3.3 5.0 4.6 3.1  2.74 *** 1.20 * 2.00 ** 2.04 ** 1.38 * 2.10 ** 1.91 ** 1.27 **  (1) (2) (3) (4) (5)  1 (20.0) 2 (12.0) 2 (12.0) 4 (32.0) 9 (15.0)  B4_106 C4_446 C4_446 C3_341 B5_070  8 (38.0) 8 (38.0) 10 (26.0) 8 (38.0) 12 (0.0)  C7_210 C7_210 C7_224 C7_210 C7_230  78.1 76.8 63.4 79.6 54.9  2.7 2.5 3.3 2.4 2.1  1.51 ** 2.47 * 3.32 * 2.40 * 2.10 *  (1) (2) (3) (4) (5) (6)  2 (30.0) 7 (0.0) 7 (0.0) 7 (0.0) 8 (41.0) 11 (17.0)  C1_343 B3_166 B3_166 B3_166 C7_210 C4_188  8 (41.0) 8 (41.0) 11 (17.0) 12 (10.0) 11 (17.0) 12 (10.0)  C7_210 C7_210 C4_188 C7_461 C4_188 C7_461  67.7 51.4 33.9 25.7 64.2 39.5  5.3 4.9 2.1 2.7 2.8 3.1  2.47 *** 2.29 ** 1.00 * 1.23 * 1.29 * 1.41 *  (1) (2)  1 (1.0) 4 (0.0)  C1_479 B5_535  4 (0.0) 9 (19.0)  B5_535 B5_070  18.6 16.0  2.4 2.5  2.67 * 2.57 *  (1) (2) (3) (4) (5) (6) (7)  2 (12.0) 4 (32.0) 4 (32.0) 4 (32.0) 8 (38.0) 8 (38.0) 10 (0.0)  C4_446 C3_341 C3_341 C3_341 C7_210 C7_210 C1_295  8 (38.0) 8 (38.0) 11 (24.0) 12 (10.0) 9 (5.0) 11 (24.0) 10 (26.0)  C7_210 C7_210 C4_188 C7_461 C7_312 C4_188 C7_224  62.4 65.5 53.8 34.5 68.0 67.5 37.5  4.3 2.4 2.6 2.3 2.3 2.5 2.7  2.50 ** 1.37 * 1.47 * 1.34 * 1.30 * 1.43 * 1.57 *  Trait: Pistil length Cross: M. guttatus X M. platycalyx  Cross: M. platycalyx X M. micranthus  Cross: M. guttatus X M. micranthus  Trait: Stamen length Cross: M. guttatus X M. platycalyx  129  Cross: M. platycalyx X M. micranthus  (1) (2) (3) (4) (5) (6)  2 (28.0) 2 (28.0) 4 (2.0) 8 (41.0) 8 (41.0) 11 (10.0)  C1_343 C1_343 B5_535 C7_210 C7_210 C4_147  8 (41.0) 11 (19.0) 10 (26.0) 9 (3.0) 11 (19.0) 11 (19.0)  C7_210 C4_188 C7_224 B5_070 C4_174 C4_174  75.7 60.9 32.0 68.7 71.5 44.3  6.8 3.5 2.7 10.6 3.5 4.4  2.47 ** 1.22 * 1.25 * 4.03 *** 1.21 * 1.55 **  (1)  8 (41.0)  C7_210  5 (15.0)  B4_341  21.7  2.1  2.19 *  (1)  8 (41.0)  C7_210  13 (15.0)  C7_310  6.5  1.8  3.41 *  Trait: Stigma-anther separation Cross: M. guttatus X M. platycalyx Cross: M. platycalyx X M. micranthus  130  Parent  Guttatus  F1 G x P  Intercross F1  Backcross  Mapping populations  Platycalyx  BC G x P  G  BC G x P  Micranthus  F1 P x M  P  Combined cross: Cross G-P  BC P x M  P  BC P x M  Guttatus  F1 G x M  M  Combined cross: Cross P-M  BC G x M  G  BC G x M  M  Combined cross: Cross G-M  Figure 4-1. The crossing scheme and mapping populations for QTL phylogenetic analysis. In the figure, G represents Mimulus guttatus, P represents M. platycalyx and M represents M. micranthus. Mapping populations were generated by combining two backcrosses from the same pair of parent species.  131  12  3 2 4 3  5 4  Figure 4-2. A central dissection of a Mimulus guttatus flower and the floral traits measured in this study. Trait 1, corolla width, shows the measure of the widest part of corolla width, trait 2, corolla length, takes the measure of the corolla tube length, trait 3 is the measure of pistil length and trait 4 is the measure of the average stamen length. Lastly, trait 5, stigma-anther separation was taken by the difference of trait 3 and trait 4.  132  (B) Corolla width, cross of M. platycalyx x M. micranthus  (A) Corolla width, cross of M. guttatus x M. platycalyx 12  40  (3)  11 10  60  (1)  15  (2)  11  3  30  te ra Jo cti in on t  40 20  In t  4  (4)  (1)  8  7  10  In  group Linkage Chromosome  er a Jo cti in on t  (2)  40  (6)  (5)  30  8  (3)  (7)  12  9  group Linkage Chromosome  13  (4)  20 2  10  2  5  20  10 1  1 0 1  2  3  4  8  9  10  11  0  0  1  12  2  7  8  11  12  13  Linkage group Chromosome  Linkage group Chromosome  (C) Corolla width, cross of M. guttatus x M. micranthus 14 3 13  (2)  (1)  10  te ra Jo cti in on t  9  8  2  In  group Linkage Chromosome  12  5  2 1  1  0 1  2  8  9  12  13  0  14  Linkage group Chromosome  Figure 4-3. The results in pairwise genome scan for the variation of corolla width in Mimulus crosses. The joint and interaction LOD scores were established through 1000 permutations using R/QTL program. (A) The joint and interaction LOD scores along linkage groups of M. guttatus x M. platycalyx that showed significance in individual QTL genetic effect as well as the interaction between loci. (B) The joint and interaction LOD scores along linkage groups of the cross of M. platycalyx x M. micranthus. (C) The joint and interaction LOD scores along linkage group of the cross of M. platycalyx x M. micranthus. The numbers showed in the figures correspond to the genetic interaction terms in Table 4-4. The vertical bars on the left are the LOD scores for interaction LOD (left side) and joint LOD (left side). 133  0  (B) Corolla length, cross of M. platycalyx x M. micranthus  (A) Corolla length, cross of M. guttatus x M. platycalyx (8)  (4)  13  50  13  (7)  (8)  80  (3)  20  60  (6)  12  40 20  (5) 15  (3)  9  (1)  8  te ra Jo cti in on t  (5)  30  (4)  40  In  8  (2)  11 60  group Linkage Chromosome  (1)  40  te ra Jo cti in on t  10  (2) (7) (6)  In  group Linkage Chromosome  11  10  4  20 6 20  5  10 1  2 0 1  4  8  10  11  0  0  2  13  6  8  9  11  12  13  Linkage group Chromosome  Linkage group Chromosome  (C) Corolla length, cross of M. guttatus x M. micranthus  13  2.5 8  12  ra Jo cti in on t  6 1.5  te  2  In  group Linkage Chromosome  2 9  4 1  1  0.5  0 1  2  9  12  2  0  13  Linkage group Chromosome  Figure 4-4. The results in pairwise genome scan for the variation of corolla length in Mimulus crosses. The joint and interaction LOD scores were established through 1000 permutations using R/QTL program. (A) The joint and interaction LOD scores along linkage groups of M. guttatus x M. platycalyx that showed significance in individual QTL genetic effect as well as the interaction between loci. (B) The joint and interaction LOD scores along linkage groups of the cross of M. platycalyx x M. micranthus. (C) The joint and interaction LOD scores along linkage group of the cross of M. platycalyx x M. micranthus. The numbers showed in the figures correspond to the genetic interaction terms in Table 4-4. The vertical bars on the left are the LOD scores for interaction LOD (left side) and joint LOD (left side). 134  0  (A) Pistil length, cross of M. guttatus x M. platycalyx  (B) Pistil length, cross of M. platycalyx x M. micranthus  50  12  12  (5)  11  (4)  80  (3)  40  4  20  2  8  15  (1)  te ra Jo cti in on t  (2)  group Linkage Chromosome  te ra Jo cti in on t  (1)  30  In  group Linkage Chromosome  8  9  60  (2)  40  In  (4)  9  10  20  7  10  20  5  1  2  0 1  2  4  8  9  10  11  0  0  12  2  7  8  9  11  12  Linkage group Chromosome  Linkage group Chromosome  (C) Pistil length, cross of M. guttatus x M. micranthus 6 13 20 12  (2) 4  te ra Jo cti in on t  9  (1)  In  group Linkage Chromosome  11  4  60  (5)  40  10  20  (6)  (3)  11  15  10  2  2 5  1  0 1  2  4  9  11  12  0  13  Linkage group Chromosome  Figure 4-5. The results in pairwise genome scan for the variation of pistil length in Mimulus crosses. The joint and interaction LOD scores were established through 1000 permutations using R/QTL program. (A) The joint and interaction LOD scores along linkage groups of M. guttatus x M. platycalyx that showed significance in individual QTL genetic effect as well as the interaction between loci. (B) The joint and interaction LOD scores along linkage groups of the cross of M. platycalyx x M. micranthus. (C) The joint and interaction LOD scores along linkage group of the cross of M. platycalyx x M. micranthus. The numbers showed in the figures correspond to the genetic interaction terms in Table 4-4. The vertical bars on the left are the LOD scores for interaction LOD (left side) and joint LOD (left side). 135  0  (A) Average stamen length, cross of M. guttatus and M. platycalyx  (B) Average stamen length, cross of M. platycalyx and M. micranthus  (4)  12  12  (3) 11  (6)  10  30  (7)  60  (5)  11  (6)  60  (5) 10  In  (1) (2)  4  10  2  20  (4)  te ra Jo cti in on t  8  40  (3)  9  8  40  In  te ra Jo cti in on t  20  group Linkage Chromosome  9  group Linkage Chromosome  30  (2)  (1)  10  20  20  4  1 2  0 1  2  4  8  9  10  11  0  12  0 2  4  8  9  10  11  12  Linkage group Chromosome  Linkage group Chromosome  (C) Average stamen length, cross of M. guttatus and M. micranthus 4  12  15  11  te ra Jo cti in on t  9  2  10 2  In  group Linkage Chromosome  3  1  5  1  0 1  2  9  11  0  12  Linkage group Chromosome  Figure 4-6. The results in pairwise genome scan for the variation of average stamen length in Mimulus crosses. The joint and interaction LOD scores were established through 1000 permutations using R/QTL program. (A) The joint and interaction LOD scores along linkage groups of M. guttatus x M. platycalyx that showed significance in individual QTL genetic effect as well as the interaction between loci. (B) The joint and interaction LOD scores along linkage groups of the cross of M. platycalyx x M. micranthus. (C) The joint and interaction LOD scores along linkage group of the cross of M. platycalyx x M. micranthus. The numbers showed in the figures correspond to the genetic interaction terms in Table 4-4. The vertical bars on the left are the LOD scores for interaction LOD (left side) and joint LOD (left side). 136  0  (A) Stigma/anther separation, cross of M. guttatus and M. platycalyx 11  (B) Stigma/anther separation, cross of M. platycalyx and M. micranthus (1)  6  8  20  (1)  1.5  6  13  3  te ra Jo cti in on t  10  8  2  2  1  4  0.5  2  0  0  In  4  15  group Linkage Chromosome  4  te ra Jo cti in on t  5  In  group Linkage Chromosome  6  5 2  1 0 1  2  3  4  5  6  8  0  11  2  Linkage group Chromosome  8  13  Linkage group Chromosome  (C) Stigma/anther separation, cross of M. guttatus and M. micranthus 13 20  12 11 9  te ra Jo cti in on t  4  In  group Linkage Chromosome  4  15  6  10  3 2 2  5  1 0 1  2  3  4  6  9  11  0  12 13  Linkage group Chromosome  Figure 4-7. The results in pairwise genome scan for the variation of stigma-anther separation in Mimulus crosses. The joint and interaction LOD scores were established through 1000 permutations using R/QTL program. (A) The joint and interaction LOD scores along linkage groups of M. guttatus x M. platycalyx that showed significance in individual QTL genetic effect as well as the interaction between loci. (B) The joint and interaction LOD scores along linkage groups of the cross of M. platycalyx x M. micranthus. (C) The joint and interaction LOD scores along linkage group of the cross of M. platycalyx x M. micranthus. The numbers showed in the figures correspond to the genetic interaction terms in Table 4-4. The vertical bars on the left are the LOD scores for interaction LOD (left side) and joint LOD (left side). 137  M. guttatus x M. micranthus M. guttatus x M. platycalyx  0.2  0.2  Percentage of variance explained by overall epistasis  M. platycalyx x M. micranthus  0.15  0.15  0.1  0.1  0.05  0.05  0  0 Corolla Width  Corolla Length  Pistil length  Stamen length  Stigma-anther separation  Genetic divergence  Figure 4-8. 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Even before the structure of DNA was discovered, the adaptational process was seen as a movement of a population towards the phenotype that best fits the present environment (Fisher 1930). With the development of advanced genotyping technologies and statistical methods, quantitative trait locus (QTL) mapping has become a powerful and prevailing tool to characterize the genetic architecture of adaptation (Mackay 2001). However, as stressed by (Orr 2001), results from QTL mapping studies vary greatly, the estimated number of genes ranging from few to many and the magnitude of genetic effect ranging from minor to large. There seems to be no obvious rules that would allow us to make a prediction about the genetic basis of a given trait between taxa (Barton and Keightley 2002). Such inconsistency of QTL numbers and the size of genetic effect raise the questions about the degree of genetic divergence between taxa, the strength of selection and the nature of standing variation. To further investigate these fundamental questions that surround genetic adaptation, this thesis is aimed to extend the classical QTL mapping approach to include the context of species evolutionary history, upon which I overly changes of QTL through evolutionary time among species lineages. Using the traditional biometric method where the effective number of genetic factors are estimated among Mimulus guttatus species complex, in this thesis I first  147  identified a wide range of effective number of genetic factors that distinguish related taxa; the number ranges from as little as 0.45 to as many as 13.96. Although the biometrical comparison was made with the some tenuous assumptions of additivity and uniformity of gene action, these findings only suggest the minimal mutational steps that allow populations to reach more distant fitness peaks. My results from both of the biometric method and the classical QTL mapping approach did not find the evidence supporting the correlation of gene numbers and species evolutionary divergence (Chapter 2 and Chapter 4). The divergence of traits driven by local adaptation could generate a covariance of alleles at the underlying QTL due to the response of all QTLs to selection. Also, the estimated numbers of QTL (or effective genetic factor) is undoubtedly smaller than the actual number, as QTL of smaller effects cannot be detected unless sample size is greatly increased. This would blur the correlation between gene number and evolutionary distance. The degree of dominance is known to play an important role in the evolution of mating system, especially in increasing the probability and the rate of evolution for selfing (Haldane 1927). With the availability of multiple species comparisons, directional partial dominance was found prevailing towards inbreeding characters, suggesting the accelerated rate of evolution of self fertilization from cross fertilization (Chapter 2; Fenster and Ritland 1994). More interestingly, the epistasis estimated between M. guttatus and M. micranthus was, in general, the least in comparison with other two intraspecific crosses (Chapter 4). This result suggests the evolution of selfing from outcrossing in M. micranthus was mainly governed by QTL with additive and dominant effects, with little epistasis.  148  Epistasis based on QTL evidence was considerable in progeny of Mimulus interspecific crosses; and notably, the percentage of variance explained by the epistasis term can exceed the amount explained by QTL loci individually. Not only was epistasis found between QTL loci identified, but also epistatic effects were uncovered between chromosome regions that showed no QTL individually (Chapter 4). These results indicate that floral traits are a co-adapted genetic complex where major functional genes as well as regulatory modifiers collectively interact. Epistasis in such a genetic complex may be a key element in population genetic differentiation and speciation, as recombination of unrelated alleles can lead to decreased fitness of hybrids, promoting genetic isolation. Moreover, epistasis was greater in crosses involving M. platycalyx (Chapter 4), and since this lineage is about twice as distant as the other two as estimated from AFLP markers (Chapter 3), this suggests that epistatic variance increases with evolutionary distance. Despite the strength of QTL analysis in providing fundamental information about numbers, locations, size of QTL as well as interactions between QTLs, many important questions remain unanswered (Orr 2001). These include: does adaptation mostly involve new mutations or standing genetic variation; does the adaptation process start with mutations of small effect (Fisher 1930) or mutations with of large effect (Gillespie 1984); can the distribution of phenotypic effects of beneficial mutations involved in the adaptation process be described; do the more complicated traits take more mutations to change? To gauge these evolutionary scenarios of QTLs, a comparative method that is capable of integrating the genetic changes on organism evolutionary history is required.  149  Studies involving comparative genomics might be a better method to infer adaptational processes, first through the identification of functional conserved DNA sequences/genes across a range of related taxa (Kellis et al. 2003; Eddy 2005). The core principle for identifying such conserved sequences and domains is that selection has constrained variation of the nucleotides in functionally important sequences relative to those sequences that are presumed to be non-functional (Boffelli et al. 2004). Using the rice (Oryza sativa sp. japonica) genome annotation, with genomic sequences and clustered transcript assemblies from other 184 plant species, 861 rice genes were identified that are evolutionarily conserved among six diverse species from Poaceae (Campbell et al. 2007). These findings can however only exhibit the presence of significant sequence similarity across the three separate Poaceae subfamilies, and the majority of conserved-Poaceae-specific sequences (86.6%) are found encoded with no putative function or functionally characterized protein domain. As a result, the functional connection between species genetic adaptation and genetic variation is missing in these comparisons. In this thesis, rather than inferring the genetic architecture of species divergence with the classical QTL mapping method, I went beyond this with a novel analysis for QTL, “lineage specific QTL mapping" (Chapter 3). This approach is a cross-fertilization between QTL mapping and phylogenetic analysis. The analysis of lineage specific QTL effect provides the appropriate evolutionary framework upon which the more incisive questions about genetic adaptation process can be tested. At the simplest, by adding just one additional taxon into the classical pairwise mapping routine, one can infer the QTL changes occurred along each of the lineages, specifically from the point of the most recent common ancestor to the end point of the branch. After partitioning the QTL 150  genetic effect on all phylogenetic branches, we can determine if QTL effects are homologous (arising in an ancestral lineage leading to two data) or arose independently in derived lineages. To illustrate the strength of the analysis of lineage specific QTL effect, I examined the evolutionary genetic basis that underlies variation of mating system in M. guttatus species complex (Chapter 3). For the evolution of inbreeding in two closely related M. platycalyx and M. micranthus, it appears that non-homologous major QTLs are predominant, while independently derived QTLs "fine tune" the trait. Second, selection was evident, as the directionality of QTL genetic effects as identified from lineage specific QTL mapping was consistent with the selective maintenance of intermediate outcrossing rate. Finally, I speculate that non-homologous QTLs, e.g., those being seen commonly between M. platycalyx and M. micranthus arisen via convergent evolution, are of larger effect as compared to those that occur later in derived lineages. This as well accords with theoretical expectations, as initial evolution towards selfing is more likely to occur with few loci, because associations more easily allow the development of associations between loci affecting inbreeding depression and loci controlling selfing (Holsinger 1991; Uyenoyama and Waller 1991); the evolution of selfing is more closely  coupled to the loss of inbreeding depression . To conclude, results from this thesis showed that the genetic basis for quantitative variation of mating system in the M. guttatus species complex is complex. The genetic architecture involved in the species adaptation process reflects a mixture of factors including the epistatic interaction between genetic loci, strength of selection, the nature of the standing genetic variation, and evolutionary separation between taxa. Although there  151  is a healthy body of theoretical work about adaptation, an analytical and testable framework for empirical research is still needed (Orr 2005). Finally, this thesis has developed the technique of detecting QTL in species lineages, which enables the distinction homology and homoplasy of QTL, and greater resolution of the pathway of QTL evolution. The challenge has now just begun. Aided by the revolution in genomic technology, we can hope that there will be much more progress towards understanding the genomic nature of QTL, though activities such as cloning of QTL, identification of the allelic variation at specific QTL and its association with gene-expression differences, and identification of the regulatory and structural changes involved with multiple allelic substitutions. With this thesis, I, hereby, once again emphasize the importance and the informativeness of phylogenetic comparisons in genomics.  152  REFERENCES  Barton, N. H., and P. D. Keightley. 2002. Understanding quantitative genetic variation. Nature Reviews Genetics 3:11-21. Boffelli, D., M. A. Nobrega, and E. M. Rubin. 2004. Comparative genomics at the vertebrate extremes. Nat Rev Genet 5:456-465. Campbell, M. A., W. Zhu, N. Jiang, H. Lin, S. Ouyang, K. L. Childs, B. J. Haas, J. P. Hamilton, and C. R. Buell. 2007. Identification and characterization of lineagespecific genes within the Poaceae. Plant Physiol. 145:1311-1322. Eddy, S. R. 2005. A model of the statistical power of comparative genome sequence analysis. Plos Biology 3:e10. Fenster, C. B., and K. Ritland. 1994. Quantitative genetics of mating system divergence in the yellow monkeyflower species complex. Heredity 73:422-435. Fisher, R. A. 1930. The genetical theory of nautral selection. Oxford University Press, Oxford. Gillespie, J. H. 1984. Molecular evolution over the mutational landscape. Evolution 38:1116-1129. Haldane, J. B. S. 1927. A mathematical theory of natural and artificial selection V. Selection and mutation. Proc. Cambridge Philos. Soc. 28:838-844. Holsinger, K. E. 1991. Inbreeding depression and the evolution of plant mating systems. Trends Ecol Evol 6:307-308. Kellis, M., N. Patterson, M. Endrizzi, B. Birren, and E. S. Lander. 2003. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423:241-254. Mackay, T. F. C. 2001. The genetic architecture of quantitative traits. Annu Rev Genet 35:303-339. Orr, H. A. 2001. The genetics of species differences. Trends Ecol Evol 16:343-350. Orr, H. A. 2005. The genetic theory of adaptation: a brief history. Nature Reviews Genetics 6:119-127. Risch, N. J. 2000. Searching for genetic determinants in the new millennium. Nature 405:847-856. 153  Uyenoyama, M. K., and D. M. Waller. 1991. Coevolution of self-fertilization and inbreeding depression I. Mutation-selection balance at one and two loci. Theoretical Population Biology 40:14-46.  154  

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