Dissecting genetic correlation through recombinant perturbations: the role of developmental bias

Abstract

AbstractDespite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. Organisms like angels might not emerge because of developmental biases that restrict how organisms can evolve, or because they have low fitness in any environment yet available on Earth. Given that both developmental bias and selection may create similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape inDrosophilawherein qualitative differences are rare within genera. We thus ask whether the absence of combinations of traits, indicated by genetic correlation, reflects developmental bias limiting the possibility of change. However, much confusion and controversy remain over definitions of developmental bias and quantifying it is challenging. We present a novel approach aiming to estimate developmental bias by leveraging a common but under-utilized type of data: recombinant genetic mapping populations. We reason that information rendered by such mild perturbations captures inherent interdependencies between traits – developmental bias. Through empirical analyses, we find that our developmental bias metric is a strong indicator of genetic correlation stability across conditions. Our framework presents a feasible way to quantify developmental bias between traits and opens up the possibility to dissect patterns of genetic correlation.Significance StatementGenetic correlation represents an important class of evolutionary constraint, which are themselves evolvable. Empirical studies have found mixed results on whether such evolutionary constraint changes rapidly or slowly. This uncertainty challenges our ability to predict the outcome of selection. Here, we propose a framework to dissect genetic correlation in a genetic mapping population and show that consistency of pleiotropic effects of loci across the genome, which we termed as developmental bias, is an indicator of genetic correlation stability. Our novel method empowers readily accessible QTL mapping data to understand complex genetic architecture underlying pleiotropy, mechanisms causing genetic correlation and, ultimately, long-term evolutionary divergence.