# Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to function > **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $415,512 ## Abstract PROJECT SUMMARY/ABSTRACT Insulin resistance is a physiological state in which normal levels of insulin fail to regulate blood glucose levels, and even in the absence of type 2 diabetes, there is strong evidence that insulin resistance dramatically increases risk for atherosclerosis and overt cardiovascular disease. In the past few years, we have identified 13 susceptibility loci for insulin resistance, but the causal gene and mechanisms are unknown for all but three of these loci, and the role of the ten remaining loci for development of insulin resistance has not been studied systematically. This represents a gold mine for in-depth physiological and mechanistic studies as increased understanding of the links between obesity, insulin resistance and cardiovascular disease may lead to new approaches to prevention and treatment that could have a huge public health impact. To establish and characterize genes associated with insulin resistance, we plan experiments in large human cohorts with functional follow-up using zebrafish and cell-based models. We will characterize suggested insulin resistance loci using detailed phenotypic information from large population-based samples (total N=13,811) assessed with dynamic measures of glucose and insulin metabolism, metabolomic, transcriptomic, epigenomic and proteomic profiling together with in silico data on gene regulation and transcription from public resources. Next, we will take 55 candidate genes forward to our pipeline for efficient characterization in zebrafish using high-throughput visualization techniques and biochemical measurements. We use CRISPR-Cas9 techniques to knockout the orthologous 55 genes from the 10 loci that are uncharacterized to date, and study the effect of perturbing these genes on insulin resistance. Finally, we will prioritize five candidate genes for mechanistic studies using gene knockdown in adipocytes and hepatocytes to study glucose, insulin and lipid metabolism, gene expression and metabolic pathways. By performing detailed follow-up analyses of loci hypothesized to be involved in insulin resistance, we expect to establish causal genes and mechanisms of action for several of these loci. The in-depth characterization using in vivo and in vitro models will provide further evidence towards causality and the mechanisms of action, as well as a first evaluation of which could be viable drug targets. Our approach of integrating comprehensive characterization in humans with experiments in functional model systems provides a translational framework, which by design is more likely to yield findings relevant for human biology and medicine. Importantly, we have access to unique study materials, state-of-the art methodology, and have a strong track record of successful collaborations in this field. Our work is anticipated to benefit the scientific community, to lead to new important insights into insulin resistance, cardiovascular disease and type 2 diabetes. ## Key facts - **NIH application ID:** 9959400 - **Project number:** 5R01DK106236-05 - **Recipient organization:** STANFORD UNIVERSITY - **Principal Investigator:** Joshua Wiley Knowles - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $415,512 - **Award type:** 5 - **Project period:** 2016-09-01 → 2021-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9959400 ## Citation > US National Institutes of Health, RePORTER application 9959400, Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to function (5R01DK106236-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9959400. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*