High-throughput Genetic Clustering of Type 2 Diabetes Loci Reveals Heterogeneous Mechanistic Pathways of Metabolic Disease
AbstractAims/hypothesisType 2 diabetes (T2D) is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of T2D loci can be leveraged to identify such pathways, thus facilitating improved disease management.MethodsWe developed a high-throughput pipeline to enable clustering of T2D loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for T2D and related traits to generate a matrix of
... 24 variant x 64 trait associations and applied Bayesian Non-negative Factorization (bNMF) to identify genetic components of T2D. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.ResultsWe identified ten clusters, replicating the five from our prior analysis as well as novel clusters related to beta-cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein-A, and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance, and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta-cell clusters differentially enriched in functional and stressed pancreatic beta-cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple shared genetic pathways with T2D.Conclusions/interpretationOur approach stratifies T2D loci into physiologically meaningful genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.