Atzmon, G. | bioRxiv Science

bioRxiv2018

Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes

BackgroundType 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.\n\nMethods and FindingsIn 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 GRSs 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.\n\nConclusionOur 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.

genetics↗

bioRxiv2017

High-depth whole genome sequencing of a large population-specific reference panel: Enhancing sensitivity, accuracy, and imputation

BackgroundWhile increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. In the present study, we sequenced at full-depth ([≥]30x) a moderately large (n=738) cohort of samples drawn from the Ashkenazi Jewish population across two platforms (Illumina X Ten and Complete Genomics, Inc.). We developed and refined a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population.\n\nResultsFor samples sequenced on the Illumina X Ten platform, quality thresholds were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. The Complete Genomics, Inc. platform was more conservative (fewer variants called) compared to the Illumina platform, but also demonstrated relatively greater numbers of false positives that needed to be filtered. Quality control procedures also permitted detection of novel genome reads that are not mapped to current reference or alternate assemblies. After stringent quality control, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.\n\nConclusionsOur primary results demonstrate enhanced accuracy of a population-specific imputation panel relative to cosmopolitan panels, especially in the range of infrequent (<5% non-reference allele frequency) and rare (<1% non-reference allele frequency) variants that may be most critical to further progress in mapping of complex phenotypes.

genomics↗