Background:We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci.Methods:We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test.Results:We identified 6 novels (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (adaptive sum of powered score test P<5×10⁻⁹). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci.Conclusions:Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

中文翻译：

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分解心电表型的多种族全基因组关联研究阐明了识别和表征复杂性状的共享遗传效应证据的策略。


背景：我们研究了如何扩大心电图特征全基因组关联研究以包括祖先多样化的人群，优先考虑更精确的表型测量，并评估共享遗传效应的证据，从而实现基因座的检测和表征。方法：我们分解了 10 秒、12 导联来自 34 668 名多种族参与者（15% 黑人；30% 西班牙裔/拉丁裔）的心电图分为 6 个连续的、生理上不同的（P 波、PR 段、QRS 间期、ST 段、T 波和 TP 段）和 2 个复合的、传统的（PR 间期和 QT 间期）区间尺度性状，并使用 1000-G 估算的单核苷酸多态性进行多变量调整、性状特异性单变量全基因组关联研究。通过使用组合表型适应性幂分数总和测试来汇总 6 个连续心电图特征的荟萃分析单变量结果，评估共享遗传效应的证据。结果：我们确定了 6 部小说（ CD36、PITX2、EMB、ZNF592、YPEL2和BC043580） ）和87个已知基因座（幂分数测试的自适应总和P <5×10 ^-9 ）。 CD36处的先导单核苷酸多态性 rs3211938 在黑人中很常见（次要等位基因频率 = 10%），在欧洲裔美国人中接近单态性，并且对 QT 间期和 TP 段有影响，在迄今为止报道的常见变异中排名最大。在评估连续心电图特征但未评估复合心电图特征时，观察到了其他 5 个新位点。组合表型测试并未识别出使用传统单变量方法不明显的新心电图基因座，尽管这种方法确实有助于已知基因座的表征。结论：尽管包括已发表的心电图性状全基因组关联研究参与者数量的三分之一，我们的研究还是确定了 6 个新基因座，强调了祖先多样性和表型解析在这个不断增长的全基因组关联研究时代的重要性。