Understanding the genetic basis of complex traits in admixed populations is challenging due to diverse genetic backgrounds, particularly for highly prevalent diseases. Misinterpreting admixture’s impact can have significant consequences for biomedical research. This study employs advanced computational biology to investigate how admixture shapes the genetic architecture of complex traits and influences GWAS outcomes. Using the forward-in-time population genetic simulator SLiM, we model trait evolution under admixture by varying the relationship between causal variant effect sizes and selection coefficients while accounting for population size changes and migration across five admixture scenarios.

Our findings reveal that GWAS power is influenced by genetic architecture and population demographic history. Traits with weak correlations between effect size and fitness (e.g., anthropometric traits) show higher GWAS power compared to traits with stronger correlations (e.g., genetic diseases). Populations with recent bottlenecks exhibit higher GWAS power, highlighting the role of rare variants. Surprisingly, fine- mapping ability remains consistent across traits. Empirical validation using data from diverse populations, including the All of Us database, supports our simulation predictions. For anthropometric traits like height, common variants exhibit moderate effects, while for traits like malignant neoplasms, rare variants show larger effects and common variants minimal effects.

By integrating population and complex trait genetics, this study provides insights into how population history shapes genetic architecture and heritability. Our findings improve understanding of genetic studies in diverse populations, enabling more accurate biomedical applications and personalized medicine strategies.


This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.