Friday, April 19, 2024
4/19/2024
PROJECT SUMMARY (DESCRIPTION) Deleterious mutations are ubiquitous in genomes. However, the manner in which they impact evolution and complex traits remains unclear. My laboratory has focused on understanding the role of deleterious mutations in evolution by combining polymorphism data from multiple species with population genetic models. During the previous funding period, we have developed tools to infer the distribution of fitness effects (DFE) and domi- nance coefficients from polymorphism data in natural populations and made several discoveries as to how pu- rifying selection acts in different species. In particular, we have learned from our recent work that the DFE and selection coefficients at individual mutations differ across species, many deleterious mutations are recessive, the fate of deleterious mutations in populations and their effects on genome variation heavily depend on specif- ic demographic and biological parameters, strongly deleterious recessive mutations determine the fitness of a population on short timescales more than weakly deleterious mutations do, and deleterious mutations contrib- ute to poor transferability of genetic risk prediction between populations. Despite this progress, critical gaps in knowledge remain. Much of the existing work on deleterious variation has focused on single nucleotide muta- tions in coding regions in a limited subset of species. Further, there has been limited work on testing the ex- planatory power of inferred model parameters. Here we propose to expand our knowledge of deleterious varia- tion across genomes by addressing five new questions. First, we will combine large-scale functional genomic data with polymorphism data to infer a DFE for noncoding regulatory mutations, which is vital for understanding complex traits as the vast majority of disease-associated mutations are non-coding variants. Second, we will develop new computational approaches to infer a DFE for complex mutations, such as short tandem repeats and copy number variants. These new methods and inferences will enable direct quantitative comparison of the fitness effects of different types of mutations in different parts of the genome. Third, by combining polymor- phism data from multiple species with different population sizes, complexity, and lifespan, we will test how the- se factors influence the DFE over evolutionary time. Fourth, we will use both human polymorphism data as well as yeast functional genomic data to test our previously developed model for the existence of dominance. Final- ly, we will use detailed forward simulations to establish whether state-of-the-art population genetic models of multiple evolutionary forces occurring simultaneously can explain genetic variation across genomes. Success- ful completion of this research will paint a more complete picture of how evolutionary processes influence ge- netic variation and the causes and consequences of deleterious variation.
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