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GenomicsCirculation Genomic and Precision Medicine
Peer-reviewed journal article

Multiplexed targeted resequencing identifies coding and regulatory variation underlying phenotypic extremes of HDL-cholesterol in humans

Sumeet A. KhetarpaliD, Paul L. BabbiD, Wei ZhaoiD, William F. Hancock-CeruttiiD, Christopher D. BrowniD, Daniel James RaderiD, Benjamin F. VoightiD

Abstract

Genome-wide association studies have uncovered common variants at many loci influencing human complex traits and diseases, such as high-density lipoprotein cholesterol (HDL-C). However, the contribution of the identified genes is difficult to ascertain from current efforts interrogating common variants with small effects. Thus, there is a pressing need for scalable, cost-effective strategies for uncovering causal variants, many of which may be rare and noncoding. Here, we used a multiplexed inversion probe (MIP) target capture approach to resequence both coding and regulatory regions at seven HDL-C associated loci in 797 individuals with extremely high HDL-C vs. 735 low-to-normal HDL-C controls. Our targets included protein-coding regions of GALNT2, APOA5, APOC3, SCARB1, CCDC92, ZNF664, CETP, and LIPG (>9 kb), and proximate noncoding regulatory features (>42 kb). Exome-wide genotyping in 1,114 of the 1,532 participants yielded a >90% genotyping concordance rate with MIP-identified variants in ~90% of participants. This approach rediscovered nearly all established GWAS associations in GALNT2, CETP, and LIPG loci with significant and concordant associations with HDL-C from our phenotypic-extremes design at 0.1% of the sample size of lipid GWAS studies. In addition, we identified a novel, rare, CETP noncoding variant enriched in the extreme high HDL-C group (P<0.01, Score Test). Our targeted resequencing of individuals at the HDL-C phenotypic extremes offers a novel, efficient, and cost-effective approach for identifying rare coding and noncoding variation differences in extreme phenotypes and supports the rationale for applying this methodology to uncover rare variation--particularly non-coding variation--underlying myriad complex traits.

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