In a recent study published in the journal Nature Genetics, researchers carried out a genome-wide association study (GWAS) of triglyceride (TG):high-density lipoprotein-cholesterol (HDL-C) ratios among 402,398 European people in the United Kingdom Biobank (UKBB).
Study: Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Image Credit: Victor Moussa / Shutterstock
Background on Insulin Resistance and Genetic Markers
Insulin resistance (IR), a significant risk factor for metabolic disease, is measured by the reference standard technique of glucose clamping. Simple techniques like the insulin sensitivity index (ISI) and the homeostatic model assessment for IR (HOMA-IR) have identified 130 loci associated with insulin resistance related to genes involved in glycogen metabolism, insulin receptor pathways, and adipogenesis. However, the genetic analyses have a limited scale compared to those from groups like the UKBB or the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, which collected data on non-fasting lipids.
Methodology of the Genome-Wide Association Study
In the present study, researchers performed a genetic study of TG: HDL-C levels, which indicate insulin resistance. The study focused on high-confidence insulin resistance-associated single-nucleotide polymorphisms (SNPs), attaining statistical significance values in external analyses of insulin resistance. The high-confidence-type loci were explored relative to insulin biology, analyzed for priorly undocumented associations with insulin resistance, and assessed for their contributions to illness in the external datasets.
The team obtained serological HDL-C and TG levels for the participants at enrolment. They calculated TG:HDL-C values and performed genome-wide association research by linear mixed modeling using the Scalable and Accurate Implementation of GEneralized mixed model (SAIGE). They applied conditional and joint multiple-SNP analysis (COJO) to extract independent SNPs. The study approach included data-based expression prioritization integration for complex traits (DEPICT) prioritization, proximity, tissue expression, and the expression quantitative trait loci (eQTLs) to select the most likely causative gene.
To identify TG:HDL-C genetic loci with priorly undocumented relationships with IR, the researchers performed conditional analyses including 130 variations from IR-related traits, as documented by the Meta-Analyses of Glucose and Insulin-related Traits Consortium (MAGIC) study researchers. They investigated whether TG:HDL-C included the 130 IR-associated loci previously identified in the MAGIC study. In total, 114 of the 369 independent variations of TG:HDL-C showed P values below 0.05 in at least one of the IR characteristics. 72 of the 114 high-confidence loci have no previous IR studies.
Subsequently, the researchers investigated the 114 SNPs with a Bonferroni-adjusted P value of <0.05 in other publicly available studies of metabolic characteristics. To determine the role of the high-confidence insulin resistance-associated SNPs in non-European ancestries, the team conducted a GWAS of TG:HDL-C values for South Asian, African, and Chinese individuals.
Results: Identifying Genetic Loci Associated with Insulin Resistance
The team identified 369 independent single-nucleotide polymorphisms, with 114 having p-values below 0.050 in other genome-level insulin resistance studies. These 114 genetic loci clustered into five groups on phenome-wide analysis and were enriched for candidate genes crucial to insulin signaling, protein metabolism, and adipocyte physiology. The team developed polygenic-risk scores using the high-confidence insulin resistance-related loci. They identified associations with hyperglyceridemia, diabetes, hypertension, ischemic heart disease, and non-alcoholic fatty liver disease.
GWAS identified 369 independent genetic loci for the TG:HDL-C biomarker, and 32,573 variants attained genome-wide statistical significance for the TG:HDL-C biomarker after excluding insertions and deletions, ambiguous or multiallelic single-nucleotide polymorphisms, and those unavailable in the Michigan Genomics Initiative (MGI). Of 369 independent SNPs, 318 had no prior reports for IR. In total, 322 of the 369 SNPs remained genome-wide significant. Of the 369 independent SNPs, 114 were high-confidence insulin resistance-associated loci.
Of 130 genetic loci, the team detected 127 in the UKBB. Of 127 priorly documented variants, 92 (72%) showed P values below 0.050 in summary statistical data of the study, and 57 (45%) out of 127 genetic variants attained genome-wide statistical significance. The phenome-wide association study identified distinct effects within metabolic traits. The team found that the subgroups had distinct impacts on insulin-related parameters such as the waist-hip ratio (WHR), body mass index (BMI), serum lipids, non-alcoholic fatty liver disease (NAFLD, evaluated as proton density fat fraction), and estimated glomerular filtration rate (eGFR).
All subgroups were associated with increased TGs and lowered HDL, the primary phenotype. High-confidence insulin resistance loci were enriched for insulin-related biology, with 114 loci showing robust enrichment in fatty tissues. Thirty-one high-confidence loci showed sex-specific effects. The study identified 24 SNPs with higher sex-specific effects in females, enriched for loci showing significant associations with weight gain (WHR) adjusted for body mass index. Further analysis of these sex-specific, high-confidence insulin resistance-related loci may help explain observed differences in metabolic phenotypes between men and women.
Conclusions and Implications for Understanding Insulin Resistance
The study findings showed that 369 SNPs are central to insulin resistance (IR) pathology, explaining 3.2% of TG: HDL-C levels and relating to IR-related traits. These loci are related to adipocyte biology, the endocrine system, growth and cancer pathways, hepatic genes, and those involved in the female reproductive system. These high-confidence insulin resistance-associated loci represent liver-related genes, including primary metabolic enzymes. Mutations in TM6SF2, a lipoprotein excretion regulator, may lead to fat retention and increased IR.
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