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Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci.

ABSTRACT: Type 1 diabetes (T1D) patients with low genetic risk scores (GRS) may be non-autoimmune or autoimmune mediated by other genetic loci. The T1D-GRS2 provides us an opportunity to look into the genetic architecture of these patients. A total of 18,949 European individuals were included in this study, including 6599 T1D cases and 12,323 controls. 957 (14.5%) T1D patients were identified with low GRS (GRS < 8.43). The genome-wide association study on these patients identified 41 unreported loci. Two loci with common variants and 39 loci with rare variants were identified in this study. This study identified common SNPs associated with both low GRS T1D and expression levels of the interferon-α-induced MNDA gene, indicating the role of viral infection in T1D. Interestingly, 16 of the 41 unreported loci have been linked to autism spectrum disorder (ASD) by previous studies, suggesting that genes residing at these loci may underlie both T1D and autism.

SUBMITTER: Qu HQ

PROVIDER: S-EPMC8302754 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci.

Qu Hui-Qi HQ Qu Jingchun J Bradfield Jonathan J Marchand Luc L Glessner Joseph J Chang Xiao X March Michael M Li Jin J Connolly John J JJ Roizen Jeffrey D JD Sleiman Patrick P Polychronakos Constantin C Hakonarson Hakon H

Communications biology 20210723 1

Type 1 diabetes (T1D) patients with low genetic risk scores (GRS) may be non-autoimmune or autoimmune mediated by other genetic loci. The T1D-GRS2 provides us an opportunity to look into the genetic architecture of these patients. A total of 18,949 European individuals were included in this study, including 6599 T1D cases and 12,323 controls. 957 (14.5%) T1D patients were identified with low GRS (GRS < 8.43). The genome-wide association study on these patients identified 41 unreported loci. Two ...[more]

PMID: 34302048

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Project description:BACKGROUND:Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification 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 separate subsets 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 (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants 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 versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), "lipodystrophy-like" fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry. CONCLUSION:Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

Dataset Information

Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci.

Publications

Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci.

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