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

Citation

Baldassari, Antoine R.; Sitlani, Colleen M.; Highland, Heather M.; Arking, Dan E.; Buyske, Steven G.; Darbar, Dawood; Gondalia, Rahul; Graff, Mariaelisa; Guo, Xiuqing; & Heckbert, Susan R., et al. (2020). Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits. Circulation: Genomic & Precision Medicine, 13(4), e002680. PMCID: PMC7520945

Abstract

Background: We examined how expanding electrocardiographic (ECG) trait genome-wide association studies (GWAS) 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-second, 12-lead ECGs from 34,668 multiethnic participants (15% African American; 30% Hispanic/Latino) into six contiguous, physiologically-distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and two composite, conventional (PR interval and QT interval) interval-scale traits and conducted multivariable-adjusted, trait-specific univariate GWAS using 1000-G imputed SNPs. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the six continuous ECG traits using the combined phenotype adaptive sum of powered scores test (aSPU).
Results: We identified six novel (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (aSPU p-value<5E-9). Lead SNP rs3211938 at CD36 was common in African Americans (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 five novel loci were observed when evaluating the contiguous, but not the composite ECG traits. Combined phenotype testing did not identify novel ECG 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 ECG trait GWAS, our study identified six novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing GWAS.

URL

http://dx.doi.org/10.1161/circgen.119.002680

Reference Type

Journal Article

Year Published

2020

Journal Title

Circulation: Genomic & Precision Medicine

Author(s)

Baldassari, Antoine R.
Sitlani, Colleen M.
Highland, Heather M.
Arking, Dan E.
Buyske, Steven G.
Darbar, Dawood
Gondalia, Rahul
Graff, Mariaelisa
Guo, Xiuqing
Heckbert, Susan R.
Hindorff, Lucia A.
Hodonsky, Chani J.
Chen, Yii-Der Ida
Kaplan, Robert C.
Peters, Ulrike
Post, Wendy
Reiner, Alexander P.
Rotter, Jerome I.
Shohet, Ralph V.
Seyerle, Amanda A.
Sotoodehnia, Nona
Tao, Ran
Taylor, Kent D.
Wojcik, Genevieve L.
Yao, Jie
Kenny, Eimear E.
Lin, Henry J.
Soliman, Elsayed Z.
Whitsel, Eric A.
North, Kari E.
Kooperberg, Charles L.
Avery, Christy L.

Article Type

Regular

PMCID

PMC7520945

Data Set/Study

Population Architecture using Genomics and Epidemiology (PAGE) Stud
Women’s Health Initiative (WHI) Clinical Trial
Atherosclerosis Risk in Communities (ARIC) Study
Hispanic Community Health Study/Study of Latinos (HCHS/SOL)
Multi-Ethnic Study of Atherosclerosis (MESA)

Continent/Country

United States of America

State

Nonspecific

Race/Ethnicity

African-American
Hispanic/Latinx
European American

ORCiD

Avery - 0000-0002-1044-8162
Highland - 000-0002-3583-8239
Graff - 0000-0001-6380-1735