Adaptation in response to selection on polygenic phenotypes may occur via
subtle allele frequencies shifts at many loci. Current population genomic
techniques are not well posed to identify such signals. In the past decade,
detailed knowledge about the specific loci underlying polygenic traits has
begun to emerge from genome-wide association studies (GWAS). Here we combine
this knowledge from GWAS with robust population genetic modeling to identify
traits that may have been influenced by local adaptation. We exploit the fact
that GWAS provide an estimate of the additive effect size of many loci to
estimate the mean additive genetic value for a given phenotype across many
populations as simple weighted sums of allele frequencies. We first describe a
general model of neutral genetic value drift for an arbitrary number of
populations with an arbitrary relatedness structure. Based on this model we
develop methods for detecting unusually strong correlations between genetic
values and specific environmental variables, as well as a generalization of
QST/FST comparisons to test for over-dispersion of genetic values among
populations. Finally we lay out a framework to identify the individual
populations or groups of populations that contribute to the signal of
overdispersion. These tests have considerably greater power than their single
locus equivalents due to the fact that they look for positive covariance
between like effect alleles, and also significantly outperform methods that do
not account for population structure. We apply our tests to the Human Genome
Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation,
type 2 diabetes, body mass index, and two inflammatory bowel disease datasets.
This analysis uncovers a number of putative signals of local adaptation, and we
discuss the biological interpretation and caveats of these results.Comment: 42 pages including 8 figures and 3 tables; supplementary figures and
tables not included on this upload, but are mostly unchanged from v