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Coding-sequence variants are associated with blood lipid levels in 14,473 Chinese
Xiangfeng Lu1, Jun Li2, Huaixing Li3
1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Abstract:
Previously identified common variants explain only a small fraction of the trait heritability and at most loci the identities of the underlying causal genes and their functional variants still remain unknown. To identify the low-frequency and rare coding variants that influence lipid levels, we conducted a meta-analysis of exome-wide association studies in 14,473 Chinese subjects, followed by a joint analysis with 1000 genomes imputed data from 6,534 samples. We replicated 24 previously reported lipid loci with exome-wide significance (P < 3.3 × 10 - 7), including fourteen coding variants at ten confirmed lipid loci (P range from 1.44 × 10 - 7 to 1.64 × 10 - 45). Of these, six coding variants showed population-specific associations and were independent of previously identified associations in European populations, including four low-frequency (PCSK9 p.Arg93Cys, HMGCR p.Tyr311Ser, APOA5 p.Gly185Cys and CETP p.Asp399Gly) and two common (APOB p.Arg532Trp and APOA4 p.Ser147Asn) variants. Furthermore, we detected three new lead non-coding variants at LPA, LIPC and LDLR in Chinese. The independent variants at PCSK9, HMGCR, LPA, APOA5 and LDLR were also associated with increased risk of coronary artery disease in the expected direction. In gene-based tests, the burden of rare or low frequency variants in PCSK9, HMGCR and CEPT exhibited strong associations with blood lipid levels (P < 2.8 × 10 - 6). Our findings identify additional population-specific possible causal variants. Our data demonstrate that the inter-ethnic differences in allele frequencies of coding variants may lead to different association signals across ethnic groups, highlighting the importance of including diverse populations to uncover genetic variation associated with lipid levels.
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