Large-Scale Gene-Centric Meta-analysis across 32 Studies Identifies Multiple Lipid Loci

Abstract

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.

Genetic meta-analysis of 15,901 African Americans identifies variation in EXOC3L1 is associated with HDL concentration

“…Greater than 150 lipid-associated loci have been discovered using genome-wide association studies (GWASs) mainly based on individuals of European ancestry ( 1,2 ). The number of variants discovered and the power to detect variants of lower allele frequency and effect size is directly proportional to the number of participants in the study, demonstrated by the success of gradually larger meta-analyses ( 1 ).…”

“…SNPs were specifi cally selected from African HapMap samples to be included on the array to ensure coverage of African populations ( 9 ). Results of genetic associations with total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), and TG concentrations in European-derived cohorts using the IBC array, as well as meta-analysis of multi-ethnic cohorts, have been published ( 2,6 ). Follow-up replication of signals with P < 10 Ϫ 5 from the discovery cohort in African…”

An interaction effect between glucokinase gene variation and carbohydrate intakes modulates the plasma triglyceride response to a fish oil supplementation

“…A recent meta-analysis has shown that the association between rs1799884 and type 2 diabetes varies according to ethnic populations and could be more important among Caucasians than among Asians (Fu et al 2013). In a large-scale gene-centric meta-analysis, the intronic SNP rs2070971 within GCK gene was associated with plasma TG concentrations (Asselbergs et al 2012). …”

“…For C/C homozygotes, low CHO intakes were associated with an increase in plasma TG concentrations, whereas when CHO intakes were high, they had the greatest decrease in their plasma TG concentrations following the fish oil supplementation of all genotype groups. The SNP rs741038 is an intronic SNP with no potential regulatory impact and is not in LD with rs1799884 or rs2070971 which have been associated previously with plasma TG levels, fasting glucose and/or type 2 diabetes risk (Asselbergs et al 2012;Holmkvist et al 2008;Sotos-Prieto et al 2013;Webster et al 2009;Hu et al 2010;Wang et al 2013). However, it is possible that rs741038 is in LD with other unknown functional SNPs.…”

“…At high glucose concentrations such as after a meal high in carbohydrates (CHO), fructose-1-phosphate binds with GCKR and releases GCK which enhances glucose metabolism leading to a greater glycolytic flux (Girard et al 1997b;Iynedjian 2009). A few single-nucleotide polymorphisms (SNPs) within GCK have been reported to have an impact on type 2 diabetes risk, glycemia, beta cell function and plasma TG concentrations (Hu et al 2010;Sotos-Prieto et al 2013;Wang et al 2013;Fu et al 2013;Asselbergs et al 2012).…”