Data from: Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies

We present a full-likelihood method to estimate and quantify polygenic
adaptation from contemporary DNA sequence data. The method combines
population genetic DNA sequence  data and GWAS summary statistics
from up to thousands of nucleotide sites in a joint likelihood function to
estimate the strength of transient directional selection acting on a
polygenic trait. Through population genetic simulations of polygenic trait
architectures and GWAS, we show that the method substantially improves
power over current methods. We examine the robustness of the method under
uncorrected GWAS stratification, uncertainty and ascertainment bias in the
GWAS estimates of SNP effects, uncertainty in the identification of causal
SNPs, allelic heterogeneity, negative selection, and low GWAS sample size.
The method can quantify selection acting on correlated traits, fully
controlling for pleiotropy even among traits with strong genetic
correlation (|rg|  = 80%; c.f. schizophrenia and bipolar
disorder) while retaining high power to attribute selection to the causal
trait. We apply the method to study 56 human polygenic traits for signs of
recent adaptation. We find signals of directional selection on
pigmentation (tanning, sunburn, hair, P=5.5e-15, 1.1e-11, 2.2e-6,
respectively), life history traits (age at first birth, EduYears,
P=2.5e-4, 2.6e-4, respectively), glycated hemoglobin (HbA1c, P=1.2e-3),
bone mineral density (P=1.1e-3), and neuroticism (P=5.5e-3). We also
conduct joint testing of 137 pairs of genetically correlated traits. We
find evidence of widespread correlated response acting on these traits
(2.6-fold enrichment over the null expectation, P=1.5e-7). We find that
for several traits previously reported as adaptive, such as educational
attainment and hair color, a significant proportion of the signal of
selection on these traits can be attributed to correlated response, vs
direct selection (P=2.9e-6, 1.7e-4, respectively). Lastly, our joint test
uncovers antagonistic selection that has acted to increase type 2 diabetes
(T2D) risk and decrease HbA1c (P=1.5e-5).