Human evolution and the genetic architecture of complex traits

University Of California, San Francisco, San Francisco CA

Investigators

Abstract

ABSTRACT Our research aims to uncover the ways in which evolutionary forces shape patterns of genetic variation and drive phenotypic variation across human populations. We focus on three broad areas of investigation. Firstly, we ex- plore the effects of recessive deleterious alleles and associative overdominance (AOD). While previous studies have mainly focused on additive fitness effects, we investigate the unique patterns of genetic variation caused by recessive deleterious alleles. We analyze additional summary statistics such as the site frequency spectrum (SFS), ancestral recombination graphs (ARGs), and haplotype structure to better understand the impact of AOD. We also examine the dynamics of genetic diversity in the context of demographic changes and investigate the likelihood of AOD in the MHC locus of the human genome. Secondly, we move beyond traditional racial and ethnic categorizations and develop a more refined approach based on genetic relatedness groups. We utilize ancestral recombination graphs (ARGs) to trace genetic lineages over time and identify relatedness clusters without relying on predefined reference populations. Our analysis focuses on different timescales, including con- tinental and sub-continental population structure, allowing us to capture the complexities and variabilities within human populations. Lastly, we build simulation tools that incorporate genetic and non-genetic interactions to study complex traits. By integrating statistical methods, population genetics theory, and flexible simulation tools, we aim to simulate multivariate traits with varying genetic architectures. We focus on both common and rare variants, developing power simulators for rare variant association tests (RVATs) to address the challenges of studying rare variants in complex populations. Overall, our research aims to provide novel insights into the evo- lutionary mechanisms driving phenotypic variation and enhance the realism and accuracy of simulations for studying complex traits in diverse human populations.