On the potential for GWAS with phenotypic population means and allele-frequency data (popGWAS)

Abstract

This study explores the potential of a novel genome-wide association study (GWAS) approach for identifying loci underlying quantitative polygenic traits in natural populations. Extensive population genetic forward simulations demonstrate that the approach is generally effective for oligogenic and moderately polygenic traits and relatively insensitive to low heritability, but applicability is limited for highly polygenic architectures and pronounced population structure. The required sample size is moderate with very good results being obtained already for a few dozen populations scored. The method performs well in predicting population means even with a moderate false positive rate. When combined with machine learning for feature selection, this rate can be further reduced. The data efficiency of the method, particularly when using pooled sequencing, makes GWAS studies more accessible for research in biodiversity genomics. Overall, this study highlights the promise of this popGWAS approach for dissecting the genetic basis of complex traits in natural populations.