23 research outputs found
Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies
Full text linkGenetic 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 multitrait 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 nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.Peer reviewe
The relative impact of evolving pleiotropy and mutational correlation on trait divergence
Full text linkBoth pleiotropic connectivity and mutational correlations can restrict the decoupling of traits under divergent selection, but it is unknown which is more important in trait evolution. To address this question, we create a model that permits within-population variation in both pleiotropic connectivity and mutational correlation, and compare their relative importance to trait evolution. Specifically, we developed an individual-based stochastic model where mutations can affect whether a locus affects a trait and the extent of mutational correlations in a population. We find that traits can decouple whether there is evolution in pleiotropic connectivity or mutational correlation, but when both can evolve, then evolution in pleiotropic connectivity is more likely to allow for decoupling to occur. The most common genotype found in this case is characterized by having one locus that maintains connectivity to all traits and another that loses connectivity to the traits under stabilizing selection (subfunctionalization). This genotype is favored because it allows the subfunctionalized locus to accumulate greater effect size alleles, contributing to increasingly divergent trait values in the traits under divergent selection without changing the trait values of the other traits (genetic modularization). These results provide evidence that partial subfunctionalization of pleiotropic loci may be a common mechanism of trait decoupling under regimes of corridor selection.Peer reviewe
Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies.
No full text<p>Data for "Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies". Results from simulations with four different genetic architectures to compare how pleiotropy and linkage differentially affect the genetic correlation between traits. Three different sets of genetic architecture had varying distances between 120 pairs of additive loci affecting two quantitative traits. Each pair of loci was located on its own chromosome (i.e., unlinked to other pairs) and the recombination distance between each pair on a chromosome was either 0cM, 0.1cM, or 1cM apart for a particular genetic architecture representing no recombination between linked loci, as well as an average and an extreme value of recombination at ``hotspots'' in the human genome, respectively. A fourth genetic architecture consisted of 120 unlinked, additive, pleiotropic loci that affected both quantitative traits. Each simulation was run with 5,000 initially monomorphic (variation is gradually introduced through mutations), diploid individuals for 10,000 generations achieving mutation-selection(-migration) balance in order to observe general patterns of genetic correlation in the near-absence of drift. Individuals were hermaphrodites mating at random within a population, with non-overlapping generations. Phenotypes were calculated for each of the two traits modeled by summing the allelic values of all loci affecting one trait. Gaussian stabilizing selection was applied and determined the survival probability of juveniles. To examine the effects of the strength of stabilizing selection on each trait and strength of correlational selection between traits, different sets of simulations were run with selection strength of 50 or 100 and correlational selection of 0.5 and 0.9. To examine the effects of mutational input on genetic correlation between traits, different sets of simulations were run with mutation rates of 0.001, 0.0001, or 0.00001, and mutational effect sizes of 0.1, 0.01, or 0.001. </p>
<p>To examine the effects of migration from a source population on genetic correlation between traits, additional sets of simulations were run with uni-directional migration from a second population (as in an island-mainland model with each population consisting of 5000 individuals) with backward migration rates of 0.1, 0.01, and 0.001. The backward migration rate represents the average proportion of new individuals in the focal population whose parent is from the source population. The local optimum values for the two traits in the source population were set at 10 units distance from the focal population's local optimum. Both focal and source populations had weak stabilizing selection with a strength of 100, the focal population had no correlational selection between the two traits and the source population had a correlational selection of 0 or 0.9. Fifty replicate simulations were run for each set of parameter values and statistics were averaged over replicates.</p>
What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation
Full text linkPhenotypic traits do not always respond to selection independently from each other and often show correlated responses to selection. The structure of a genotype-phenotype map (GP map) determines trait covariation, which involves variation in the degree and strength of the pleiotropic effects of the underlying genes. It is still unclear, and debated, how much of that structure can be deduced from variational properties of quantitative traits that are inferred from their genetic (co)variance matrix (G-matrix). Here we aim to clarify how the extent of pleiotropy and the correlation among the pleiotropic effects of mutations differentially affect the structure of a G-matrix and our ability to detect genetic constraints from its eigen decomposition. We show that the eigenvectors of a G-matrix can be predictive of evolutionary constraints when they map to underlying pleiotropic modules with correlated mutational effects. Without mutational correlation, evolutionary constraints caused by the fitness costs associated with increased pleiotropy are harder to infer from evolutionary metrics based on a G-matrix's geometric properties because uncorrelated pleiotropic effects do not affect traits' genetic correlations. Correlational selection induces much weaker modular partitioning of traits' genetic correlations in absence then in presence of underlying modular pleiotropy. This article is protected by copyright. All rights reserved
