Quantitative Genetic Analysis Indicates Natural Selection on Leaf Phenotypes Across Wild Tomato Species (Solanum sect. Lycopersicon; Solanaceae)

Adaptive evolution requires both raw genetic material and an accessible path of high fitness from one fitness peak to another. In this study, we used an introgression line (IL) population to map quantitative trait loci (QTL) for leaf traits thought to be associated with adaptation to precipitation in wild tomatoes (Solanum sect. Lycopersicon; Solanaceae). A QTL sign test showed that several traits likely evolved under directional natural selection. Leaf traits correlated across species do not share a common genetic basis, consistent with a scenario in which selection maintains trait covariation unconstrained by pleiotropy or linkage disequilibrium. Two large effect QTL for stomatal distribution colocalized with key genes in the stomatal development pathway, suggesting promising candidates for the molecular bases of adaptation in these species. Furthermore, macroevolutionary transitions between vastly different stomatal distributions may not be constrained when such large-effect mutations are available. Finally, genetic correlations between stomatal traits measured in this study and data on carbon isotope discrimination from the same ILs support a functional hypothesis that the distribution of stomata affects the resistance to CO₂ diffusion inside the leaf, a trait implicated in climatic adaptation in wild tomatoes. Along with evidence from previous comparative and experimental studies, this analysis indicates that leaf traits are an important component of climatic niche adaptation in wild tomatoes and demonstrates that some trait transitions between species could have involved few, large-effect genetic changes, allowing rapid responses to new environmental conditions.     

Understanding quantitative genetic variation

Until recently, it was impracticable to identify the genes that are responsible for variation in continuous traits, or to directly observe the effects of their different alleles. Now, the abundance of genetic markers has made it possible to identify quantitative trait loci (QTL)--the regions of a chromosome or, ideally, individual sequence variants that are responsible for trait variation. What kind of QTL do we expect to find and what can our observations of QTL tell us about how organisms evolve? The key to understanding the evolutionary significance of QTL is to understand the nature of inherited variation, not in the immediate mechanistic sense of how genes influence phenotype, but, rather, to know what evolutionary forces maintain genetic variability.     

Detection of QTL for flowering time in multiple families of elite maize

Flowering time is a fundamental quantitative trait in maize that has played a key role in the postdomestication process and the adaptation to a wide range of climatic conditions. Flowering time has been intensively studied and recent QTL mapping results based on diverse founders suggest that the genetic architecture underlying this trait is mainly based on numerous small-effect QTL. Here, we used a population of 684 progenies from five connected families to investigate the genetic architecture of flowering time in elite maize. We used a joint analysis and identified nine main effect QTL explaining approximately 50?% of the genotypic variation of the trait. The QTL effects were small compared with the observed phenotypic variation and showed strong differences between families. We detected no epistasis with the genetic background but four digenic epistatic interactions in a full 2-dimensional genome scan. Our results suggest that flowering time in elite maize is mainly controlled by main effect QTL with rather small effects but that epistasis may also contribute to the genetic architecture of the trait.     

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Bayesian analysis for genetic architecture of dynamic traits

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.     

Unifying Genetic Canalization,Genetic Constraint,and Genotype-by-Environment Interaction: QTL by Genomic Background by Environment Interaction of Flowering Time in Boechera stricta

Natural populations exhibit substantial variation in quantitative traits. A quantitative trait is typically defined by its mean and variance, and to date most genetic mapping studies focus on loci altering trait means but not (co)variances. For single traits, the control of trait variance across genetic backgrounds is referred to as genetic canalization. With multiple traits, the genetic covariance among different traits in the same environment indicates the magnitude of potential genetic constraint, while genotype-by-environment interaction (GxE) concerns the same trait across different environments. While some have suggested that these three attributes of quantitative traits are different views of similar concepts, it is not yet clear, however, whether they have the same underlying genetic mechanism. Here, we detect quantitative trait loci (QTL) influencing the (co)variance of phenological traits in six distinct environments in Boechera stricta, a close relative of Arabidopsis. We identified nFT as the QTL altering the magnitude of phenological trait canalization, genetic constraint, and GxE. Both the magnitude and direction of nFT''s canalization effects depend on the environment, and to our knowledge, this reversibility of canalization across environments has not been reported previously. nFT''s effects on trait covariance structure (genetic constraint and GxE) likely result from the variable and reversible canalization effects across different traits and environments, which can be explained by the interaction among nFT, genomic backgrounds, and environmental stimuli. This view is supported by experiments demonstrating significant nFT by genomic background epistatic interactions affecting phenological traits and expression of the candidate gene, FT. In contrast to the well-known canalization gene Hsp90, the case of nFT may exemplify an alternative mechanism: Our results suggest that (at least in traits with major signal integrators such as flowering time) genetic canalization, genetic constraint, and GxE may have related genetic mechanisms resulting from interactions among major QTL, genomic backgrounds, and environments.     

Genetic architecture of the dog: sexual size dimorphism and functional morphology

Purebred dogs are a valuable resource for genetic analysis of quantitative traits. Quantitative traits are complex, controlled by many genes that are contained within regions of the genome known as quantitative trait loci (QTL). The genetic architecture of quantitative traits is defined by the characteristics of these genes: their number, the magnitude of their effects, their positions in the genome and their interactions with each other. QTL analysis is a valuable tool for exploring genetic architecture, and highlighting regions of the genome that contribute to the variation of a trait within a population.     

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Within organisms, groups of traits with different functions are frequently modular, such that variation among modules is independent and variation within modules is tightly integrated, or correlated. Here, we investigated patterns of trait integration and modularity in Brassica rapa in response to three simulated seasonal temperature/photoperiod conditions. The goals of this research were to use trait correlations to understand patterns of trait integration and modularity within and among floral, vegetative and phenological traits of B. rapa in each of three treatments, to examine the QTL architecture underlying patterns of trait integration and modularity, and to quantify how variation in temperature and photoperiod affects the correlation structure and QTL architecture of traits. All floral organs of B. rapa were strongly correlated, and contrary to expectations, floral and vegetative traits were also correlated. Extensive QTL co-localization suggests that covariation of these traits is likely due to pleiotropy, although physically linked loci that independently affect individual traits cannot be ruled out. Across treatments, the structure of genotypic and QTL correlations was generally conserved. Any observed variation in genetic architecture arose from genotype × environment interactions (GEIs) and attendant QTL × E in response to temperature but not photoperiod.     

Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits.

In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S(0) and S(1); the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.     

Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative,teosinte

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte.     

Spatially and temporally varying selection on intrapopulation quantitative trait loci for a life history trade-off in Mimulus guttatus

Why do populations remain genetically variable despite strong continuous natural selection? Mutation reconstitutes variation eliminated by selection and genetic drift, but theoretical and experimental studies each suggest that mutation‐selection balance insufficient to explain extant genetic variation in most complex traits. The alternative hypothesis of balancing selection, wherein selection maintains genetic variation, is an aggregate of multiple mechanisms (spatial and temporal heterogeneity in selection, frequency‐dependent selection, antagonistic pleiotropy, etc.). Most of these mechanisms have been demonstrated for Mendelian traits, but there is little comparable data for loci affecting quantitative characters. Here, we report a 3‐year field study of selection on intrapopulation quantitative trait loci (QTL) of flower size, a highly polygenic trait in Mimulus guttatus. The QTL exhibit antagonistic pleiotropy: alleles that increase flower size, reduce viability, but increase fecundity. The magnitude and direction of selection fluctuates yearly and on a spatial scale of metres. This study provides direct evidence of balancing selection mechanisms on QTL of an ecologically relevant trait.     

Wild Mice As Bountiful Resources of Novel Genetic Variants for Quantitative Traits

Most traits of biological importance, including traits for human complex diseases (e.g., obesity and diabetes), are continuously distributed. These complex or quantitative traits are controlled by multiple genetic loci called QTLs (quantitative trait loci), environments and their interactions. The laboratory mouse has long been used as a pilot animal model for understanding the genetic architecture of quantitative traits. Next-generation sequencing analyses and genome-wide SNP (single nucleotide polymorphism) analyses of mouse genomes have revealed that classical inbred strains commonly used throughout the world are derived from a few fancy mice with limited and non-randomly distributed genetic diversity that occurs in nature and also indicated that their genomes are predominantly Mus musculus domesticus in origin. Many QTLs for a huge variety of traits have so far been discovered from a very limited gene pool of classical inbred strains. However, wild M. musculus mice consisting of five subspecies widely inhabit areas all over the world, and hence a number of novel QTLs may still lie undiscovered in gene pools of the wild mice. Some of the QTLs are expected to improve our understanding of human complex diseases. Using wild M. musculus subspecies in Asia as examples, this review illustrates that wild mice are untapped natural resources for valuable QTL discovery.     

QTL analysis of floral traits in Louisiana iris hybrids

The formation of hybrid zones between nascent species is a widespread phenomenon. The evolutionary consequences of hybridization are influenced by numerous factors, including the action of natural selection on quantitative trait variation. Here we examine how the genetic basis of floral traits of two species of Louisiana Irises affects the extent of quantitative trait variation in their hybrids. Quantitative trait locus (QTL) mapping was used to assess the size (magnitude) of phenotypic effects of individual QTL, the degree to which QTL for different floral traits are colocalized, and the occurrence of mixed QTL effects. These aspects of quantitative genetic variation would be expected to influence (1) the number of genetic steps (in terms of QTL substitutions) separating the parental species phenotypes; (2) trait correlations; and (3) the potential for transgressive segregation in hybrid populations. Results indicate that some Louisiana Iris floral trait QTL have large effects and QTL for different traits tend to colocalize. Transgressive variation was observed for six of nine traits, despite the fact that mixed QTL effects influence few traits. Overall, our QTL results imply that the genetic basis of floral morphology and color traits might facilitate the maintenance of phenotypic divergence between Iris fulva and Iris brevicaulis, although a great deal of phenotypic variation was observed among hybrids.     

Epistasis affecting litter size in mice

Litter size is an important reproductive trait as it makes a major contribution to fitness. Generally, traits closely related to fitness show low heritability perhaps because of the corrosive effects of directional natural selection on the additive genetic variance. Nonetheless, low heritability does not imply, necessarily, a complete absence of genetic variation because genetic interactions (epistasis and dominance) contribute to variation in traits displaying strong heterosis in crosses, such as litter size. In our study, we investigated the genetic architecture of litter size in 166 females from an F2 intercross of the SM/J and LG/J inbred mouse strains. Litter size had a low heritability (h2 = 12%) and a low repeatability (r = 33%). Using interval-mapping methods, we located two quantitative trait loci (QTL) affecting litter size at locations D7Mit21 + 0 cM and D12Mit6 + 8 cM, on chromosomes 7 and 12 respectively. These QTL accounted for 12.6% of the variance in litter size. In a two-way genome-wide epistasis scan we found eight QTL interacting epistatically involving chromosomes 2, 4, 5, 11, 14, 15 and 18. Taken together, the QTL and their interactions explain nearly 49% (39.5% adjusted multiple r2) of the phenotypic variation for litter size in this cross, an increase of 36% over the direct effects of the QTL. This indicates the importance of epistasis as a component of the genetic architecture of litter size and fitness in our intercross population.     

Mapping quantitative trait Loci interactions from the maternal and offspring genomes

The expression of most developmental or behavioral traits involves complex interactions between quantitative trait loci (QTL) from the maternal and offspring genomes. The maternal-offspring interactions play a pivotal role in shaping the direction and rate of evolution in terms of their substantial contribution to quantitative genetic (co)variation. To study the genetics and evolution of maternal-offspring interactions, a unifying statistical framework that embraces both the direct and indirect genetic effects of maternal and offspring QTL on any complex trait is developed. This model is derived for a simple backcross design within the maximum-likelihood context, implemented with the EM algorithm. Results from extensive simulations suggest that this model can provide reasonable estimation of additive and dominant effects of the QTL at different generations and their interaction effects derived from the maternal and offspring genomes. Although our model is framed to characterize the actions and interactions of maternal and offspring QTL affecting offspring traits, the idea can be readily extended to decipher the genetic machinery of maternal traits, such as maternal care. Our model provides a powerful means for studying the evolutionary significance of indirect genetic effects in any sexually reproductive organisms.     

Complex Genetic Effects on Early Vegetative Development Shape Resource Allocation Differences Between Arabidopsis lyrata Populations

Costs of reproduction due to resource allocation trade-offs have long been recognized as key forces in life history evolution, but little is known about their functional or genetic basis. Arabidopsis lyrata, a perennial relative of the annual model plant A. thaliana with a wide climatic distribution, has populations that are strongly diverged in resource allocation. In this study, we evaluated the genetic and functional basis for variation in resource allocation in a reciprocal transplant experiment, using four A. lyrata populations and F₂ progeny from a cross between North Carolina (NC) and Norway parents, which had the most divergent resource allocation patterns. Local alleles at quantitative trait loci (QTL) at a North Carolina field site increased reproductive output while reducing vegetative growth. These QTL had little overlap with flowering date QTL. Structural equation models incorporating QTL genotypes and traits indicated that resource allocation differences result primarily from QTL effects on early vegetative growth patterns, with cascading effects on later vegetative and reproductive development. At a Norway field site, North Carolina alleles at some of the same QTL regions reduced survival and reproductive output components, but these effects were not associated with resource allocation trade-offs in the Norway environment. Our results indicate that resource allocation in perennial plants may involve important adaptive mechanisms largely independent of flowering time. Moreover, the contributions of resource allocation QTL to local adaptation appear to result from their effects on developmental timing and its interaction with environmental constraints, and not from simple models of reproductive costs.     

Pleiotropic patterns of quantitative trait loci for 70 murine skeletal traits

Quantitative trait locus (QTL) studies of a skeletal trait or a few related skeletal components are becoming commonplace, but as yet there has been no investigation of pleiotropic patterns throughout the skeleton. We present a comprehensive survey of pleiotropic patterns affecting mouse skeletal morphology in an intercross of LG/J and SM/J inbred strains (N = 1040), using QTL analysis on 70 skeletal traits. We identify 798 single-trait QTL, coalescing to 105 loci that affect on average 7-8 traits each. The number of traits affected per locus ranges from only 1 trait to 30 traits. Individual traits average 11 QTL each, ranging from 4 to 20. Skeletal traits are affected by many, small-effect loci. Significant additive genotypic values average 0.23 standard deviation (SD) units. Fifty percent of loci show codominance with heterozygotes having intermediate phenotypic values. When dominance does occur, the LG/J allele tends to be dominant to the SM/J allele (30% vs. 8%). Over- and underdominance are relatively rare (12%). Approximately one-fifth of QTL are sex specific, including many for pelvic traits. Evaluating the pleiotropic relationships of skeletal traits is important in understanding the role of genetic variation in the growth and development of the skeleton.