Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,186 bioRxiv papers from 306,470 authors.
Multi-ethnic genome-wide association study of decomposed cardioelectric phenotypes illustrates strategies to identify and characterize evidence of shared genetic effects for complex traits
Antoine R Baldassari,
Colleen M. Sitlani,
Heather M Highland,
Dan E. Arking,
Susan R. Heckbert,
Lucia A Hindorff,
Chani J Hodonsky,
Yii-Der Ida Chen,
Robert C. Kaplan,
Alex P Reiner,
Jerome I. Rotter,
Ralph V Shohet,
Amanda A. Seyerle,
Kent D Taylor,
Genevieve L. Wojcik,
Eimear E Kenny,
Henry J Lin,
Elsayed Z. Soliman,
Eric A Whitsel,
Kari E. North,
Christy L. Avery
Posted 31 May 2019
bioRxiv DOI: 10.1101/654012
Posted 31 May 2019
Background: Published genome-wide association studies (GWAS) are mainly European-centric, examine a narrow view of phenotypic variation, and infrequently interrogate genetic effects shared across traits. We therefore examined the extent to which a multi-ethnic, combined trait GWAS of phenotypes that map to well-defined biology can enable detection and characterization of complex trait loci. Methods: With 1000 Genomes Phase 3 imputed data in 34,668 participants (15% African American; 3% Chinese American; 51% European American; 30% Hispanic/Latino), we performed covariate-adjusted univariate GWAS of six contiguous electrocardiogram (ECG) traits that decomposed an average heartbeat and two commonly reported composite ECG traits that summed contiguous traits. Combined phenotype testing was performed using the adaptive sum of powered scores test (aSPU). Results: We identified six novel and 87 known ECG trait loci (aSPU p-value < 5E-9). Lead SNP rs3211938 at novel locus CD36 was common in African Americans (minor allele frequency=10%) and near-monomorphic in European Americans, with effect sizes for the composite trait, QT interval, among the largest reported. Only one novel locus was detected for the composite traits, due to opposite directions of effects across contiguous traits that summed to near-zero. Combined phenotype testing did not detect novel loci unapparent by univariate testing. However, this approach aided locus characterization, particularly when loci harbored multiple independent signals that differed by trait. Conclusions: Despite including one-third as few participants as the largest published GWAS of ECG traits, our study identifies multiple novel ECG genetic loci, emphasizing the importance of ancestral diversity and phenotype measurement in this era of ever-growing GWAS.
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