Molecular Characterization of the Lipid Genome-Wide Association Study Signal on Chromosome 18q11.2 Implicates HNF4A-Mediated Regulation of the TMEM241 Gene

Abstract

Objective We recently identified a locus on chromosome 18q11.2 for high serum triglycerides (TGs) in Mexicans. We hypothesize that the lead GWAS single nucleotide polymorphism (SNP) rs9949617, or its linkage disequilibrium (LD) proxies, regulate one of the 5 genes in the TG-associated region. Approach and Results We performed an LD analysis and found 9 additional variants in LD (r2>0.7) with the lead SNP. To prioritize the variants for functional analyses, we annotated the 10 variants using DNase I hypersensitive sites, transcription factor (TF) and chromatin states, and identified rs17259126 as the lead candidate variant for functional in-vitro validation. Using luciferase transcriptional reporter assay in liver HepG2 cells, we found that the G allele exhibits a significantly lower effect on transcription (p<0.05). The electrophoretic mobility shift and ChIPqPCR assays confirmed that the minor G allele of rs17259126 disrupts an HNF4A binding site. To find the regional candidate gene, we performed a local expression quantitative trait locus (cis-eQTL) analysis and found that rs17259126 and its LD proxies alter expression of the regional transmembrane protein 241 (TMEM241) gene in 795 adipose RNAs from the METSIM cohort (p=6.11x10−07–5.80x10−04). These results were replicated in expression profiles of TMEM241 from the MuTHER resource (n=856). Conclusions The Mexican GWAS signal for high serum TGs on chromosome 18q11.2 harbors a regulatory SNP, rs17259126, which disrupts normal HNF4A binding and decreases the expression of the regional TMEM241 gene. Our data suggest that decreased transcript levels of TMEM241 contribute to increased TG levels in Mexicans.

Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS

“…We processed the METSIM RNA-seq dataset similarly as described in Rodriguez et al 43 . Briefly, for the METSIM RNA-seq dataset, we isolated total RNA from abdominal subcutaneous adipose needle biopsy using the Qiagen miRNeasy kit.…”

“…We filtered genes for those with expression of FPKM > 0 in more than 90% of the samples. Additional details of this dataset have been previously described in Rodriguez et al 43 . We inverse-normal transformed the FPKMs and adjusted for hidden confounding factors using PEER 46 by removing 22 PEER factors based on a cis -eQTL analysis on chromosome 20 and choosing an optimal number of PEER factors without a loss of statistical power.…”

Functional genomics and assays of regulatory activity detect mechanisms at loci for lipid traits and coronary artery disease

“…Recently, individual or multiple regulatory variants have been shown to influence allelic differences in transcriptional activity at GWAS loci for lipid traits or coronary artery disease. At an association signal for triglycerides near TMEM241 , the rs17259126-A allele showed higher transcriptional activity than the rs17259126-G allele and also stronger HNF4A protein binding [25]. The authors proposed that the rs17259126-G allele is associated with lower TMEM241 expression, leading to higher triglyceride levels.…”

Recent Advances in the Genetics of Atherothrombotic Disease and Its Determinants

“…Rodríguez et al 16 analyzed a locus on chromosome 18 associated with high serum triglycerides in Mexicans. Using linkage disequilibrium analysis and a reporter assay, they found that the rs17259126-G allele alters expression of TMEM241 in the METSIM Cohort (Metabolic Syndrome in Men) and that reduced TMEM241 expression increases triglyceride levels.…”

“…Using linkage disequilibrium analysis and a reporter assay, they found that the rs17259126-G allele alters expression of TMEM241 in the METSIM Cohort (Metabolic Syndrome in Men) and that reduced TMEM241 expression increases triglyceride levels. 16 Other studies further explored CAD GWAS data by working to annotate replicated CAD loci. In one such study, Ghosh et al 17 integrated large-scale GWA data with a large pathway database.…”