Population Genetics Methods for Understanding Complex Trait Evolution

点击翻译按钮获取中文摘要

Abstract Understanding the genetic basis of complex phenotypes is a critical problem in medical and evolutionary genetics. As the influx of rich genomic and phenotypic data accelerates via biobank-level resources and reveals even greater fine scale genetic information, modern population genetic methods will be critical to examine the influence of evolutionary processes on the distribution of complex traits. My research in this area has focused on characterizing the effects of recent population history on the distribution of deleterious variation in individuals and on the design and application of statistical methods for the inference of positive selection in populations. Specifically, my work demonstrates that certain population processes create concentrations of deleterious homozygotes in genomes and that the strength of concentration depends on recent population history. This work reveals an important mechanism by which population history can influence the genomics of complex traits. I have also contributed to methodological advances for the identification of genomic regions undergoing positive selection, including designing novel haplotype summary statistics, novel likelihood statistics that account for spatial autocorrelation in genomes, and efficient software implementing this work which has been cited hundreds of times. I have applied these and other methods to human and non-human genomic data, uncovering the genomic basis of adaptation to pathogen exposure in different human populations and the polygenic nature of adaptation to high altitude in rhesus macaques. During the next five years, and beyond, my research will focus on identifying how evolutionary forces shape the phenotypic landscape of modern humans and on developing uses for our knowledge of human history to learn the genetic basis of complex traits. To this end, I will develop novel methods for the inference of natural selection in genomes, I will develop theoretical models that connect evolutionary history to variation of complex traits via its effect on the distribution of non-neutral genetic variation, and I will develop novel statistical methods that leverage evolutionary information to identify genomic variation associated with traits. This work will incorporate varying models of dominance, of genetic architecture, and of genetic overlap among two or more traits. I will apply these models and methods to human whole-genome sequencing data sets paired with biomedical phenotype data from diverse human populations with their own distinct histories, using biobank resources such as the Trans-Omics for Precision Medicine program and the Hispanic Community Health Study / Study of Latinos. My work in these areas will disentangle the relative influences of various evolutionary processes that contribute to differences in complex traits, including disease risk, among populations and will expand our understanding of the genetic basis of these traits in understudied populations.