CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying cardiovascular disease

Abstract

BackgroundCardiovascular disease is the leading cause of death in the United States. Consequently, individuals who are genetically predisposed for high risk of cardiovascular disease would benefit most from prevention and early intervention approaches. Among common health risk factors affecting adult populations, we evaluated 23 cardiovascular disease-related traits, including BMI, glucose levels and lipid profiling to determine their associations with low-frequency recurrent copy number variations (CNV) (population frequency < 5%). ResultsWe examined 10,619 unrelated subjects of European ancestry from the Electronic Medical Records and Genomics (eMERGE) Network who were genotyped with 657,366 markers genome-wide on the Illumina Infinium Quad 660 array. We performed CNV calling based on array marker intensity and evaluated data quality, ancestry stratification, and relatedness to ensure unbiased association discovery. Using a segment-based scoring approach, we assessed the association of all CNVs with each trait. In this large genome-wide analysis of low-frequency CNVs, we observed 11 novel genome-wide significant associations of low-frequency CNVs with major cardiovascular disease traits. ConclusionIn one of the largest genome-wide studies for low-frequency recurrent CNVs, we identified 11 loci associated with cardiovascular disease and related traits at the genome-wide significance level that may serve as biomarkers for prevention and early intervention studies in subjects who are at elevated risk. Our study further supports the role of low-frequency recurrent CNVs in the pathogenesis of common complex disease traits.