Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
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DOI：
10.1016/j.ajhg.2017.03.004
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发表时间：
2017-04-06
影响因子：
9.8
通讯作者：
Kenny, Eimear E.
中科院分区：
文献类型：
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作者：
Martin, Alicia R.;Gignoux, Christopher R.;Kenny, Eimear E.
绝大多数全基因组关联研究（GWAS）都是在欧洲人中进行的，它们对其他人群的可转移性取决于许多因素（例如，连锁不平衡、等位基因频率、遗传结构）。随着医学基因组学研究变得越来越大和多样化，深入了解人口历史以及疾病风险测量的可转移性至关重要。在这里，我们解开最近的人口历史中广泛使用的1000个基因组计划的参考面板，强调人口在医学研究中代表不足。为了研究单祖先GWAS的可转移性，我们使用已发表的汇总统计数据来计算8种研究充分的表型的多基因风险评分。我们确定了所有分数的方向不一致性;例如，尽管有强有力的人类学证据表明西非人的平均身高与欧洲人一样高，但身高预计会随着与欧洲人的遗传距离而降低。为了更深入地定量了解GWAS可转移性，我们开发了一个复杂的基于性状聚结的模拟框架，考虑了多基因性、因果等位基因频率差异和遗传力的影响。正如预期的那样，真实风险和推断风险之间的相关性通常在汇总统计数据来源的人群中最高。我们证明，从欧洲GWAS推断的分数是有偏见的遗传漂变在其他人群中，即使选择相同的因果变异，在任何方向的偏见是可能的和不可预测的。这项工作警告说，总结大规模GWAS的发现可能对使用标准方法的其他人群具有有限的可移植性，并强调了对广义风险预测方法的需求，以及在医学基因组学中纳入更多样化的个体。
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.