Determining the driving factors shaping genetic architecture of complex traits in recently admixed populations

Abstract

Understanding the genetic architecture of complex traits in admixed populations remains challenging due to heterogeneous genetic backgrounds and demographic histories. Mischaracterizing admixture can bias genetic association estimates and limit the generalizability of biomedical findings. Here, we systematically evaluate how evolutionary forces--including admixture, natural selection, and demographic history--jointly shape complex trait architecture and influence genome-wide association study (GWAS) outcomes using a simulation-based framework complemented by empirical analyses. We model five human admixture scenarios and vary the correlation between causal variant effect sizes and selection coefficients to reflect different trait-fitness relationships. This framework enables simulation of complex trait phenotypes with environmental variance, allowing comprehensive assessment of GWAS power and fine-mapping precision across evolutionary contexts. We find that GWAS power is strongly modulated by both genetic architecture and demographic history. Traits with weak coupling between fitness and effect size, such as anthropometric traits, exhibit higher GWAS power than traits under stronger negative selection, including early-onset diseases. Because rare variants contribute substantially to heritability yet are poorly captured by GWAS, bottlenecked populations with fewer rare variants show enhanced power. Despite large differences in GWAS power, fine-mapping precision remains relatively consistent across traits and populations, improving primarily in regions of high recombination. Empirical analyses of diverse cohorts, the All of Us Research Program, support these patterns. Our findings highlight how evolutionary and demographic forces shape the genetic basis of complex traits in admixed populations and underscore the need for tailored study designs to improve GWAS accuracy and fine-mapping performance in diverse cohorts.