| Title: | Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank |
| Journal: | Nature Genetics |
| Published: | 10 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38200128/ |
| DOI: | https://doi.org/10.1038/s41588-023-01625-2 |
| URL: | https://pmc.ncbi.nlm.nih.gov/articles/PMC10923176/pdf/nihms-1965249.pdf |
| Citations: | 13 (13 in last 2 years) as of 8 Aug 2024 |
Publication 8969
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Abstract
Insulin resistance (IR) is a well-established risk factor for metabolic disease. The ratio of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) is a surrogate marker of IR. We conducted a genome-wide association study of the TG:HDL-C ratio in 402,398 Europeans within the UK Biobank. We identified 369 independent SNPs, of which 114 had a false discovery rate-adjusted P value < 0.05 in other genome-wide studies of IR making them high-confidence IR-associated loci. Seventy-two of these 114 loci have not been previously associated with IR. These 114 loci cluster into five groups upon phenome-wide analysis and are enriched for candidate genes important in insulin signaling, adipocyte physiology and protein metabolism. We created a polygenic-risk score from the high-confidence IR-associated loci using 51,550 European individuals in the Michigan Genomics Initiative. We identified associations with diabetes, hyperglyceridemia, hypertension, nonalcoholic fatty liver disease and ischemic heart disease. Collectively, this study provides insight into the genes, pathways, tissues and subtypes critical in IR.</p>
9 Keywords
- Biological Specimen Banks
- Biomarkers
- Cholesterol, HDL
- Genome-Wide Association Study
- Humans
- Insulin
- Insulin Resistance
- Triglycerides
- UK Biobank
13 Authors
- Antonino Oliveri
- Ryan J Rebernick
- Annapurna Kuppa
- Asmita Pant
- Yanhua Chen
- Xiaomeng Du
- Kelly C. Cushing
- Hannah N Bell
- Chinmay Raut
- Ponnandy Prabhu
- Vincent L. Chen
- Brian D. Halligan
- Elizabeth K. Speliotes
1 Application
| Application ID | Title |
|---|---|
| 18120 | Identifying causes of obesity and liver disease using population genetics |
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