STATISTICAL GENETICS APPROACHES FOR IDENTIFYING POLYGENIC ADAPTATION IN HUMAN POPULATIONS

Abstract

Background: Polygenic adaptation, through small changes in allele frequencies at many loci, is the driving force behind the evolution of complex human traits. Advances in statistical genetics and large-scale genomic datasets have improved the detection of adaptive genetic signatures in a wide variety of human populations.

Objective: The present study aimed to investigate statistical genetics methods to detect polygenic adaptation based on genome-wide association studies (GWAS), polygenic risk score analysis and population differentiation statistics.
Methodology: Human genomic data from African, European and Asian populations was used to analyze allele frequency variation and adaptive trait enrichment. The statistical analyses involved principal component analysis (PCA), fixation index (FST) estimation, linkage disequilibrium analysis and Bayesian polygenic adaptation modelling. The identified adaptive loci for metabolic, immune-related and environmental traits were compared between the populations.

Findings: It detected significant adaptive divergence, with 185 metabolic trait-associated loci enriched in European populations and 171 environmental adaptation loci identified in Asian populations. The Asian populations also showed the highest mean FST value (0.21) indicating strong signals of population differentiation and adaptive selection.

Conclusion: Statistical genetics methods are powerful tools to detect signatures of polygenic adaptation and to gain insight into evolutionary processes and the genetic architecture of complex traits in human populations.