Graduate Group in Computational Biology, UC Berkeley, Berkeley, CA 94703, USA.
Department of Statistics, University of Oxford, Oxford, UK.
Am J Hum Genet. 2021 Feb 4;108(2):219-239. doi: 10.1016/j.ajhg.2020.12.005. Epub 2021 Jan 12.
We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|r|=80%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5 × 10). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9 × 10 and 1.7 × 10, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5 × 10).
我们提出了一种全似然方法，通过 DNA 序列变异和 GWAS 汇总统计数据推断多基因适应，以量化复杂性状上近期短暂的定向选择。通过模拟多基因性状结构进化和 GWAS，我们表明该方法在当前方法的基础上大大提高了功效。我们在分层、边际效应的不确定性和偏差、因果 SNP 的不确定性、等位基因异质性、负选择和低 GWAS 样本量下检验了该方法的稳健性。该方法可以量化与相关性状相关的选择，即使在具有强遗传相关性（|r|=80%）的性状中，也可以通过控制多效性来控制选择，同时保持将选择归因于因果性状的高功效。当因果性状被排除在分析之外时，选择归因于其最接近的代理。我们讨论了该方法的局限性，警告不要对结果进行强烈的因果解释，以及无法检测基因-环境（GxE）相互作用的可能性。我们将该方法应用于 56 个人类多基因性状，揭示了在色素沉着、生命史、糖化血红蛋白（HbA1c）和其他性状上定向选择的信号。我们还对 137 对遗传相关性状进行了联合检验，揭示了这些性状上广泛存在的相关反应（富集 2.6 倍，p=1.5×10）。一些先前被报道为适应性的性状（例如，教育程度和头发颜色）的选择迹象在很大程度上归因于相关反应（p=2.9×10 和 1.7×10，分别）。最后，我们的联合检验表明，拮抗选择增加了 2 型糖尿病风险并降低了 HbA1c（p=1.5×10）。
