Different types of phenotypic traits consistently exhibit different levels of genetic variation in natural populations. There are two potential explanations: either mutation produces genetic variation at different rates, or natural selection removes or promotes genetic variation at different rates. Whether mutation or selection is of greater general importance is a longstanding unresolved question in evolutionary genetics. Where the input of genetic variation by mutation differs between traits, it is usually uncertain whether the difference is the result of different mutational effects (“mutational robustness”) or different numbers of underlying loci (“mutational target”), although conventional wisdom favors the latter. We report mutational variances (VM) for 19 traits related to the first mitotic cell division in C. elegans, and compare them to the standing genetic variances (VG) for the same suite of traits in a worldwide collection C. elegans. Two robust conclusions emerge. First, the mutational process is highly repeatable: the correlation between VM in two independent sets of mutation accumulation lines is ~0.9. Second, VM for a trait is a very good predictor of VG for that trait: the correlation between VM and VG is ~0.9. This result is predicted for a population at mutation-selection balance; it is not predicted if balancing selection plays a primary role in maintaining genetic variation. Goodness-of-fit of the data to two simple models of mutation suggest that differences in mutational effects may be a more important cause of differences in VM between traits than differences in the size of the mutational target.

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics F_st statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

In finite populations, mutation limitation and genetic drift can hinder evolutionary diversification. We consider the evolution of a quantitative trait in an asexual population whose size can vary and depends explicitly on the trait. Previous work showed that evolutionary branching is certain (“deterministic branching”) above a threshold population size, but uncertain (“stochastic branching”) below it. Using the stationary distribution of the population’s trait variance, we identify three qualitatively different sub-domains of “stochastic branching” and illustrate our results using a model of social evolution. We find that in very small populations, branching will almost never be observed; in intermediate populations, branching is intermittent, arising and disappearing over time; in larger populations, finally, branching is expected to occur and persist for substantial periods of time. Our study provides a clearer picture of the ecological conditions that facilitate the appearance and persistence of novel evolutionary lineages in the face of genetic drift.

A balance between phenotypic variability and robustness is crucial for populations to adapt to multiple selection pressures. The plasticity of genetic pathways underlies this balance. We investigated this plasticity by studying the regulation of phenotypic mean and variance in a biparental recombinant population of Saccharomyces cerevisiae grown in a variety of environments. We found that the growth of this population was well buffered in most environments, such that majority of alleles regulated the mean value of phenotype, and only a subset of these alleles regulated phenotypic variance. This latter class of alleles allowed the other genetic variants to express a range of phenotypic values around a shifted mean. This shift depends on the population and the environment, i.e. based on the evolutionary history of a strain, buffering can result in either a superior or an inferior phenotype in an environment but never both. Interestingly, intricate coupling of the genetic network regulating mean phenotype and robustness was observed in a few environments, which highlighted the importance of phenotypic buffering in layout of the genetic architecture. For loci regulating variance, show a higher tendency of genetic interactions, which not only establishes a genetic basis of release of variance, but also emphasizes the importance of mapping robustness in understanding the network topology of complex traits. Our study demonstrates differential robustness as one of the central mechanisms regulating variation in populations and underlines its role in identifying missing heritability in complex phenotypes and diseases.

The incorporation of ecological processes into models of trait evolution is important for understanding past drivers of evolutionary change. Species interactions have long been thought to be key drivers of trait evolution. However, models for comparative data that account for interactions between species are lacking. One of the challenges is that such models are intractable and difficult to express analytically. Here we present phylogenetic models of trait evolution that includes interspecific competition amongst species. Competition is modelled as a tendency of sympatric species to evolve towards distinct niches, producing trait overdispersion and high phylogenetic signal. The model predicts elevated trait variance across species and a slowdown in evolutionary rate both across the clade and within each branch. The model also predicts a reduction in correlation between otherwise correlated traits. We used an Approximate Bayesian Computation (ABC) approach to estimate model parameters. We tested the power of the model to detect deviations from Brownian trait evolution using simulations, finding reasonable power to detect competition in sufficiently large (20+ species) trees. We applied the model to examine the evolution of bill morphology of Darwin’s finches, and found evidence that competition affects the evolution of bill length.