PT  - JOURNAL ARTICLE
AU  - Miriam S. Udler
AU  - Jaegil Kim
AU  - Marcin von Grotthuss
AU  - Sílvia Bonàs-Guarch
AU  - Josep M Mercader
AU  - Joanne B. Cole
AU  - Joshua Chiou
AU  - Christopher D Anderson
AU  - Michael Boehnke
AU  - Markku Laakso
AU  - Gil Atzmon
AU  - Benjamin Glaser
AU  - Kyle Gaulton
AU  - Jason Flannick
AU  - Gad Getz
AU  - Jose C. Florez
TI  - Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes
AID  - 10.1101/319509
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 319509
4099  - http://biorxiv.org/content/early/2018/05/10/319509.short
4100  - http://biorxiv.org/content/early/2018/05/10/319509.full
AB  - Background Type 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D.Methods and Findings In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), “lipodystrophy-like” fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRS’s were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile.Conclusion Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.