Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of High-Density Lipoprotein Cholesterol in Humans

多重靶向重测序揭示人类高密度脂蛋白胆固醇表型极端情况背后的编码和调控变异

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Abstract

BACKGROUND: Genome-wide association studies have uncovered common variants at many loci influencing human complex traits, 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. METHODS: Here, we used a molecular inversion probe target capture approach to resequence both coding and regulatory regions at 7 HDL-C-associated loci in 797 individuals with extremely high HDL-C versus 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). RESULTS: Exome-wide genotyping in 1114 of the 1532 participants yielded a >90% genotyping concordance rate with molecular inversion probe-identified variants in ≈90% of participants. This approach rediscovered nearly all established genome-wide association studies 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 genome-wide association studies. In addition, we identified a novel, rare, CETP noncoding variant enriched in the extreme high HDL-C group (P<0.01, score test). CONCLUSIONS: 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 noncoding variation-underlying myriad complex traits.

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