TY - UNPB

T1 - Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

AU - Suzuki, Ken

AU - Hatzikotoulas, Konstantinos

AU - Southam, Lorraine

AU - Taylor, Henry J

AU - Yin, Xianyong

AU - Lorenz, Kim M

AU - Mandla, Ravi

AU - Huerta-Chagoya, Alicia

AU - Rayner, Nigel W

AU - Bocher, Ozvan

AU - Arruda, Ana Luiza de S V

AU - Sonehara, Kyuto

AU - Namba, Shinichi

AU - Lee, Simon S K

AU - Preuss, Michael H

AU - Petty, Lauren E

AU - Schroeder, Philip

AU - Vanderwerff, Brett

AU - Kals, Mart

AU - Bragg, Fiona

AU - Lin, Kuang

AU - Guo, Xiuqing

AU - Zhang, Weihua

AU - Yao, Jie

AU - Kim, Young Jin

AU - Graff, Mariaelisa

AU - Takeuchi, Fumihiko

AU - Nano, Jana

AU - Lamri, Amel

AU - Nakatochi, Masahiro

AU - Moon, Sanghoon

AU - Scott, Robert A

AU - Cook, James P

AU - Lee, Jung-Jin

AU - Pan, Ian

AU - Taliun, Daniel

AU - Parra, Esteban J

AU - Chai, Jin-Fang

AU - Bielak, Lawrence F

AU - Grarup, Niels

AU - Chen, Wei-Min

AU - Bork-Jensen, Jette

AU - Islam, Md Tariqul

AU - Jørgensen, Torben

AU - Linneberg, Allan

AU - Witte, Daniel R

AU - Lind, Lars

AU - Hansen, Torben

AU - Pedersen, Oluf

AU - Loos, Ruth J F

AU - VA Million Veteran Program

PY - 2023

Y1 - 2023

N2 - Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

AB - Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

U2 - 10.1101/2023.03.31.23287839

DO - 10.1101/2023.03.31.23287839

M3 - Preprint

C2 - 37034649

BT - Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

PB - medRxiv

ER -