High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

Autor:	Kim H; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA., Westerman KE; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA., Smith K; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA., Chiou J; Department of Pediatrics, University of California San Diego, San Diego, CA, USA., Cole JB; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA.; Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.; Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA., Majarian T; Broad Institute of MIT and Harvard, Cambridge, MA, USA., von Grotthuss M; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Takeda Pharmaceuticals, Cambridge, MA, USA., Kwak SH; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Kim J; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; GlaxoSmithKline, Cambridge, MA, USA., Mercader JM; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA., Florez JC; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA., Gaulton K; Department of Pediatrics, University of California San Diego, San Diego, CA, USA., Manning AK; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA., Udler MS; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. mudler@mgh.harvard.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. mudler@mgh.harvard.edu.; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. mudler@mgh.harvard.edu.; Department of Medicine, Harvard Medical School, Boston, MA, USA. mudler@mgh.harvard.edu.
Jazyk:	angličtina
Zdroj:	Diabetologia [Diabetologia] 2023 Mar; Vol. 66 (3), pp. 495-507. Date of Electronic Publication: 2022 Dec 20.
DOI:	10.1007/s00125-022-05848-6
Abstrakt:	Aims/hypothesis: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. Methods: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. Results: We identified ten clusters of genetic loci, recapturing 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 overlapping clusters with type 2 diabetes. Conclusions/interpretation: Our approach stratifies type 2 diabetes loci into physiologically interpretable 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. (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze:	MEDLINE
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