Dataset Information

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

ABSTRACT:

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.

SUBMITTER: Baldassari AR

PROVIDER: S-EPMC7520945 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

Baldassari Antoine R AR Sitlani Colleen M CM Highland Heather M HM Arking Dan E DE Buyske Steve S Darbar Dawood D Gondalia Rahul R Graff Misa M Guo Xiuqing X Heckbert Susan R SR Hindorff Lucia A LA Hodonsky Chani J CJ Ida Chen Yii-Der YD Kaplan Robert C RC Peters Ulrike U Post Wendy W Reiner Alex P AP Rotter Jerome I JI Shohet Ralph V RV Seyerle Amanda A AA Sotoodehnia Nona N Tao Ran R Taylor Kent D KD Wojcik Genevieve L GL Yao Jie J Kenny Eimear E EE Lin Henry J HJ Soliman Elsayed Z EZ Whitsel Eric A EA North Kari E KE Kooperberg Charles C Avery Christy L CL

Circulation. Genomic and precision medicine 20200630 4

<h4>Background</h4>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.<h4>Methods</h4>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, QR ...[more]

PMID: 32602732

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Dataset Information

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

Background

Methods

Results

Conclusions

Publications

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

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