A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Nature Genetics, 2012

781 citations recorded by the source corpus.

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Surgery

View Paper: https://www.semanticscholar.org/paper/ba6964681dfa3f6d58f98a587c4aa93a436451f8

Citations in the corpus

• Modulation of Genetic Associations with Serum Urate Levels by Body-Mass-Index in Humans (2015, background): “Other studies have reported modest (Pinter at best 10 ) interaction effect after testing for a joint effect of the main SNP effect and interaction term with the significant results driven by the main SNP effect [32,34].”
• Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, background): “Related concerns have also been reported in the context of physiological phenotypes [30, 31].”
• Partitioning heritability by functional annotation using genome-wide association summary statistics (2015, neutral): “• Fasting glucose summary statistics [30]: www.”
• Joint analysis of functional genomic data and genome-wide association studies of 18 human traits (2013, background): “These included studies of red blood cell traits 15 , platelet traits 32 , Crohn’s disease 33 , BMI 34 , lipid levels 27 , height 26 , bone mineral density 35 and fasting glucose levels 36 .”
• Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, neutral): “…53,991 (Zhu et al., 2018) Metabolic Trait Estimated glomerular filtration rate 111,666 (Li et al., 2017) Metabolic Trait Fasting insulin 51,750 (Manning et al., 2012) Metabolic Trait Fasting proinsulin 10,701 (Strawbridge et al., 2011) Metabolic Trait Glycated haemoglobin levels 123,665…”
• An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans (2017, neutral): “assessment for insulin resistance (HOMA-IR) (23); 2-hour glucose adjusted for BMI (24);”
• Beta Cell Dysfunction and Insulin Resistance (2013, background): “Further, as obesity is a critical determinant of insulin resistance, adiposity may modulate the genetic determinants of insulin resistance and contribute to the heterogeneity of type 2 diabetes (Manning et al., 2012).”