Cole, J. B. | 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

Management of Mendelian Traits in Breeding Programs by Gene Editing: A Simulation Study

BackgroundGenotypes based on high-density single nucleotide polymorphisms have recently been used to identify a number of novel recessive mutations that adversely affect fertility in dairy cattle as well as to track conditions such as polledness. The use of sequential mate allocation strategies that account for increases in genomic inbreeding and the economic impact of affected matings may result in faster allele frequency changes than strategies that do not consider inbreeding and monetary losses. However, the effect of gene editing on selection programs also should be considered because gene editing has the potential to dramatically change allele frequencies in livestock populations.\n\nMethodsA simulation program developed to evaluate dairy cattle breeding schemes was extended to include the use of clustered regularly interspaced short palindromic repeat (CRISPR), transcription activator-like effector nuclease (TALEN), and zinc finger nuclease (ZFN) technologies for gene editing. A hypothetical technology with a perfect success rate was used to establish an upper limit on attainable progress, and a scenario with no editing served as a baseline for comparison.\n\nResultsThe technologies differed in the rate of success of gene editing as well as the success rate of embryo transfer based on literature estimates. The number of edited alleles was assumed to have no effect on success rate. The two scenarios evaluated considered only the horned locus or 12 recessive alleles that currently are segregating in the U.S. Holstein population. The top 1, 5, or 10% of bulls were edited each generation, and either no cows or the top 1% of cows were edited. Inefficient editing technologies produced less cumulative genetic gain and lower inbreeding than efficient ones. Gene editing was very effective at reducing the frequency of the horned haplotype (increasing the frequency of polled animals in the population), and allele frequencies of the 12 recessives segregating in the U.S. Holstein population decreased faster with editing than without.\n\nConclusionsGene editing can be an effective tool for reducing the rate of harmful alleles in a dairy cattle population even if only a small proportion of elite animals are modified.

genetics↗