Population differentiation of polygenic score predictions under stabilizing selection.

Area of Science:

• Population genetics
• Genomic medicine
• Evolutionary biology

Background:

• Genome-wide association studies (GWAS) identify many small-effect loci for complex traits.
• Polygenic scores, derived from GWAS, are crucial in genomic medicine and evolutionary studies.
• A key limitation is the poor portability of polygenic scores across different human populations.

Purpose of the Study:

• Investigate how stabilizing selection impacts polygenic score portability.
• Quantify the reduction in prediction accuracy due to stabilizing selection.
• Examine the overstatement of average genetic differences in phenotypes among populations by polygenic scores.

Main Methods:

• Developed theoretical models to quantify the effect of stabilizing selection.
• Analyzed allele frequency patterns and polygenic score differentiation.
• Utilized concepts from population genetics and evolutionary theory.

Main Results:

• Stabilizing selection accelerates the loss and fixation of trait-associated alleles within populations.
• This erosion of variance reduces polygenic score prediction accuracy in populations not included in GWAS.
• Polygenic scores can significantly overestimate average genetic differences in phenotypes between populations.

Conclusions:

• Stabilizing selection around a common optimum provides a null model for allele frequency and polygenic score differentiation.
• Understanding stabilizing selection is crucial for improving polygenic score portability and interpretation.
• The findings have implications for genomic medicine and the study of human adaptation.