Open AccessMeta‐analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems
Author(s) -
Wood Zachary T.,
Wiegardt Andrew K.,
Barton Kayla L.,
Clark Jonathan D.,
Homola Jared J.,
Olsen Brian J.,
King Benjamin L.,
Kovach Adrienne I.,
Kinnison Michael T.
Publication year - 2021
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.13264
Subject(s) - biology , natural selection , phenotype , evolutionary biology , genetics , context (archaeology) , phenotypic trait , population , selection (genetic algorithm) , gene , paleontology , demography , artificial intelligence , sociology , computer science
Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta‐analysis of studies examining population‐level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population‐level comparisons) we examined, there was a moderate ( R 2 = 0.39) relationship between genomic differentiation ( F ST ) and phenotypic differentiation ( P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross‐system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes “strong” differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.