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Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank

Nature Genetics volume 56, pages 212–221 (2024)Cite this article

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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.

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Fig. 1: Study design: TG:HDL-C in the UK Biobank.
Fig. 2: Overlap of the 114 high-confidence IR-associated loci with insulin-related traits.
Fig. 3: Effects of 114 high-confidence IR-associated loci with IR-related traits.
Fig. 4: Tissue, cell type, and physiological system enrichment of the 114 high-confidence IR-associated loci.
Fig. 5: Gene-set enrichment of the 114 high-confidence IR-associated loci.
Fig. 6: PRS analysis in the MGI using the 114 high-confidence IR-associated loci.

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Data availability

GWAS results from this study are available at the GWAS Catalog (study accessions GCST90295949–GCST90295954, all intervening numbers). UKBB genomic and phenotypic data supporting this publication are available upon application (https://ukbiobank.ac.uk). MGI individual-level data are not currently available to the public due to patient privacy requirements. Otherwise, all data used to generate figures can be found in supplementary tables, source data or in the above publicly available datasets. Source data are provided with this paper.

Code availability

The code to assign the gene labels is publicly available at https://doi.org/10.5281/zenodo.10182519 (ref. 89) and https://github.com/oliveriantonino/annotation_TGHDL.

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Acknowledgements

The authors would like to acknowledge the MGI participants, Precision Health at the University of Michigan, the University of Michigan Medical School Central Biorepository and the University of Michigan Advanced Genomics Core for providing data and specimen storage, management, processing and distribution services, as well as the Center for Statistical Genetics in the Department of Biostatistics at the School of Public Health for genotype data curation, imputation and management in support of the research reported in this publication. A.O., A.K., A.P., X.D., Y.C., K.C.C., C.R., P.P., V.L.C., B.D.H. and E.K.S. supported in part by R01 DK106621 (to E.K.S.), R01 DK107904 (to E.K.S.), R01 DK128871 (to E.K.S.), R01 DK131787 (to E.K.S.) and/or the University of Michigan Department of Internal Medicine and/or The University of Michigan MBioFAR Award. R.J.R. is supported by F30 CA275039-02. H.B. is supported by F30 CA257292. Analyses in the UKBB were conducted under approved project 18120 (to E.K.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. These authors contributed equally: Antonino Oliveri, Ryan Rebernick.

Authors and Affiliations

  1. Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA

    Antonino Oliveri, Ryan J Rebernick, Annapurna Kuppa, Asmita Pant, Yanhua Chen, Xiaomeng Du, Kelly C. Cushing, Chinmay Raut, Ponnandy Prabhu, Vincent L. Chen, Brian D. Halligan & Elizabeth K. Speliotes

  2. Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA

    Hannah N Bell

  3. Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

    Elizabeth K. Speliotes

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Contributions

E.K.S. contributed to the concept development, study design, data analysis and interpretation, writing of the manuscript and critical revision of the manuscript. A.O. and R.R. contributed to the study design, data analysis and interpretation, statistical analysis, drafting of the manuscript and critical review of the manuscript. X.D., Y.C. and C.R. contributed to data analysis and interpretation, statistical analysis and critical review of the manuscript. A.K., K.C.C., V.L.C., P.P., A.P., H.B. and B.H. contributed to data analysis and interpretation, drafting of the manuscript and critical review of the manuscript. All authors have critically reviewed the paper and approved the final version of this paper.

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Correspondence to Elizabeth K. Speliotes.

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Competing interests

V.L.C. received grant funding from KOWA and AstraZeneca. The Regents of the University of Michigan and E.K.S. have a pending patent on the use of systems and methods for analysis of samples associated with IR and related conditions. The rest of the authors do not have any conflicts of interest.

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Supplementary Note (abbreviations) and Supplementary Fig. 1.

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Supplementary Tables 1–25.

Source data

Source Data Fig. 2

Overlap of the 114 high-confidence IR-associated loci with insulin traits, novelty, gene annotation and expression of the gene in tissues.

Source Data Fig. 3

Heatmap and clusters of the 114 high-confidence IR-associated loci, and cluster PRSs and association in the UKBB.

Source Data Fig. 4

Results of the tissue, cell and system enrichment analysis.

Source Data Fig. 5

Results of the gene-sets enrichment analysis.

Source Data Fig. 6

Results of the PheWAS PRS analysis.

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Oliveri, A., Rebernick, R.J., Kuppa, A. et al. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat Genet 56, 212–221 (2024). https://doi.org/10.1038/s41588-023-01625-2

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  • DOI: https://doi.org/10.1038/s41588-023-01625-2

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