Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multi-trait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between non-pleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Causes of variability in estimates of mutational variance from mutation accumulation experiments

Characteristics of the new phenotypic variation introduced via mutation have broad implications in evolutionary and medical genetics. While many estimates of this mutational variance have been published, factors contributing to their range, spanning two orders of magnitude, remain poorly resolved. In this study, we first apply a meta-analytic approach to assess how previously implicated factors affect variability in estimates. While estimates of mutational variance are available from a range of taxa, and quantitative trait type over a range of timescales, these factors are confounded with one another, precluding a clear resolution of causes of observed variability. We call for further directly comparable empirical data to address this question. We then applied a modified mutation accumulation experimental design to generate independent repeated estimates of mutational variance for a single taxon (the vinegar fly, Drosophila serrata), and for a single trait type (wing morphology) under the same experimental conditions. Analyses revealed that, in this tightly controlled experiment, variability in among-line (mutational) variance was largely the consequence of sampling error. Micro-environmental variation in mutational effects was supported as a cause of low levels of variability in mutational variance for two of 11 traits analysed. Further, there was no evidence that variation in segregating mutations, as the realisation of the mutation-drift process, impacted estimates. This investigation demonstrates the utility of short-term repeated measures to be broadly applied to improve estimates of mutational variance, consequently expanding our understanding of the dynamics of mutations in natural populations.

Multi-morph eco-evolutionary dynamics in structured populations

Our understanding of the evolution of quantitative traits in nature is still limited by the challenge of including realistic trait distributions in the context of frequency-dependent selection and ecological feedbacks. We develop a theoretical framework to analyse the dynamics of populations composed of several morphs and structured into distinct classes (e.g. age, size, habitats, infection status, species...). Our approach extends to class-structured populations a recently introduced "oligomorphic approximation" which bridges the gap between adaptive dynamics and quantitative genetics approaches and allows for the joint description of the dynamics of ecological variables and of the moments of multimodal trait distributions. We also introduce a new approximation to simplify the eco-evolutionary dynamics using reproductive values. This effectively extends Lande's univariate theorem not only to frequency- and density-dependent selection but also to multimodal trait distributions. We illustrate the effectiveness of this approach by applying it to the important conceptual case of two-habitat migration-selection models. In particular, we use our approach to predict the equilibrium trait distributions in a local adaptation model with asymmetric migration and habitat-specific mutational variance. We discuss the theoretical and practical implications of our results and sketch perspectives for future work.

A broad mutational target explains a fast rate of phenotypic evolution

The rapid evolution of a trait in a clade of organisms can be explained by the sustained action of natural selection or by a high mutational variance, that is the propensity to change under spontaneous mutation. The causes for a high mutational variance are still elusive. In some cases, fast evolution depends on the high mutation rate of one or few loci with short tandem repeats. Here, we report on the fastest evolving cell fate among vulva precursor cells in Caenorhabditis nematodes, that of P3.p. We identify and validate causal mutations underlying P3.p's high mutational variance. We find that these positions do not present any characteristics of a high mutation rate, are scattered across the genome and the corresponding genes belong to distinct biological pathways. Our data indicate that a broad mutational target size is the cause of the high mutational variance and of the corresponding fast phenotypic evolutionary rate.

Effect of Salt Stress on Mutation and Genetic Architecture for Fitness Components in Saccharomyces cerevisiae

Mutations shape genetic architecture and thus influence the evolvability, adaptation and diversification of populations. Mutations may have different and even opposite effects on separate fitness components, and their rate of origin, distribution of effects and variance-covariance structure may depend on environmental quality. We performed an approximately 1,500-generation mutation-accumulation (MA) study in diploids of the yeast Saccharomyces cerevisiae in stressful (high-salt) and normal environments (50 lines each) to investigate the rate of input of mutational variation (Vm) as well as the mutation rate and distribution of effects on diploid and haploid fitness components, assayed in the normal environment. All four fitness components in both MA treatments exhibited statistically significant mutational variance and mutational heritability. Compared to normal-MA, salt stress increased the mutational variance in growth rate by more than sevenfold in haploids derived from the MA lines. This increase was not detected in diploid growth rate, suggesting masking of mutations in the heterozygous state. The genetic architecture arising from mutation (M-matrix) differed between normal and salt conditions. Salt stress also increased environmental variance in three fitness components, consistent with a reduction in canalization. Maximum-likelihood analysis indicated that stress increased the genomic mutation rate by approximately twofold for maximal growth rate and sporulation rate in diploids and for viability in haploids, and by tenfold for maximal growth rate in haploids, but large confidence intervals precluded distinguishing these values between MA environments. We discuss correlations between fitness components in diploids and haploids and compare the correlations between the two MA environmental treatments.

Fly wing evolution explained by a neutral model with mutational pleiotropy

ABSTRACTTo what extent the speed of mutational production of phenotypic variation determines the rate of long-term phenotypic evolution is a central question in evolutionary biology. In a recent study, Houle et al. addressed this question by studying the mutational variation, microevolution, and macroevolution of locations of vein intersections on fly wings, reporting very slow phenotypic evolution relative to the rates of mutational input, high phylogenetic signals of these traits, and a strong, linear correlation between the mutational variance of a trait and its rate of evolution. Houle et al. examined multiple models of phenotypic evolution but found none consistent with all these observations. Here we demonstrate that the purported linear correlation between mutational variance and evolutionary divergence is an artifact. More importantly, patterns of fly wing evolution are explainable by a simple model in which the wing traits are neutral or neutral within a range of phenotypic values but their evolutionary rates are reduced because most mutations affecting these traits are purged owing to their pleiotropic effects on other traits that are under stabilizing selection. We conclude that the evolutionary patterns of fly wing morphologies are explainable under the existing theoretical framework of phenotypic evolution.

A broad mutational target explains an evolutionary trend

ABSTRACTAn evolutionary trend, the rapid evolution of a trait in a group of organisms, can in some cases be explained by the mutational variance, the propensity of a phenotype to change under spontaneous mutation. However, the causes of high mutational variance are still elusive. For some morphological traits, fast evolution was shown to depend on the high mutation rate of one or few underlying loci with short tandem repeats. Here, we investigate the case of the fastest evolving cell fate among vulva precursor cells in Caenorhabditis nematodes, that of the cell called ‘P3.p’. For this, we combine mutation accumulation lines, whole-genome sequencing, genetic linkage analysis of the phenotype in recombinant lines, and candidate testing through mutant and CRISPR genome editing to identify causal mutations and the corresponding loci underlying the high mutational variance of P3.p. We identify and validate molecular lesions responsible for changes in this cell’s phenotype during a mutation accumulation experiment. We find that these loci do not present any characteristics of a high mutation rate, are scattered across the genome and belong to distinct biological pathways. Our data instead indicate that a broad mutational target size is the cause of the high mutational variance and of the corresponding evolutionary trend.