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Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries

Huerta-Chagoya, Alicia
; 
Kim, Joohyun
; 
Mandla, Ravi
; 
Lu, Yingchang
; 
Suzuki, Ken
; 
Petty, Lauren E
; 
Ng, Hong Kiat
; 
Choi, Jaewon
; 
Lee, Simon
; 
Rout, Madhusmita
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Author
Huerta-Chagoya, Alicia
Kim, Joohyun
Mandla, Ravi
Lu, Yingchang
Suzuki, Ken
Petty, Lauren E
Ng, Hong Kiat
Choi, Jaewon
Lee, Simon
Rout, Madhusmita
Lin, Kuang
Taylor, Katherine
Consortium, ENSA Genomics
Team, Genes Health Research
Program, VA Million Veteran
Aguilar-Salinas, Carlos A
García-García, Lourdes
González-Villalpando, Clicerio
Haiman, Christopher A
Kim, Young Jin
Kwak, Soo Heon
Leong, Aaron
Loos, Ruth JF
Moreno-Estrada, Andres
Morris, Andrew P
Orozco, Lorena
Rotter, Jerome I
Sanghera, Dharambir
Tusie-Luna, Teresa
Voight, Benjamin F
Vujkovic, Marijana
Walters, Robin G
Ge, Tian
Manning, Alisa K
Loh, Marie
Below, Jennifer E
Sim, Xueling
Mercader, Josep M
Ng, Maggie CY
Consortium, D-PRISM
Adair, Linda S
Adeyemo, Adebowale
Ahsan, Habibul
Akiyama, Masato
An, Ping
Anand, Sonia S
Becker, Diane M
Bertoni, Alain G
Bian, Zheng
Bielak, Lawrence F
Blangero, John
Boehnke, Michael
Bottinger, Erwin P
Bowden, Donald W
Bragg, Fiona
Brody, Jennifer A
Buchanan, Thomas A
Cade, Brian E
Chai, Jin-Fang
Chambers, John C
Chandak, Giriraj R
Chang, Li-Ching
Chang, Kyong-Mi
Chee, Miao-Li
Chen, Chien-Hsiun
Chen, Yuan-Tsong
Chen, Zhengming
Chen, Yii-Der I
Chen, Ji
Chen, Guanjie
Chen, Shyh-Huei
Chen, Wei-Min
Cheng, Ching-Yu
Cho, Yoon Shin
Choi, Hyeok Sun
Chuang, Lee-Ming
Cruz, Miguel
Cushman, Mary
Das, Swapan K
DeFronzo, Ralph A
deSilva, H Janaka
Dimitrov, Latchezar
Doumatey, Ayo P
Du, Shufa
Duan, Qing
Duggirala, Ravindranath
Emery, Leslie S
Engert, James C
Evans, Daniel S
Evans, Michele K
Finer, Sarah
Florez, Jose C
Floyd, James S
Fornage, Myriam
Frankel, Elizabeth G
Freedman, Barry I
Genter, Pauline
Gerstein, Hertzel C
Goodarzi, Mark O
Gordon-Larsen, Penny
Graff, Mariaelisa
Gross, Myron
Guo, Yu
Guo, Xiuqing
Hai, Yang
Hanis, Craig L
Hayes, MGeoffrey
Horikoshi, Momoko
Howard, Annie-Green
Hsu, Sarah
Hsueh, Willa
Huang, Wei
Huang, Mengna
Hung, Yi-Jen
Hwang, Mi Yeong
Hwu, Chii-Min
Ichihara, Sahoko
Igase, Michiya
Ipp, Eli
Islam, Mohammad T
Isono, Masato
Jang, Hye-Mi
Jasmine, Farzana
Jonas, Jost B
Joo, Yoonjung Y
Kabagambe, Edmond
Kadowaki, Takashi
Kamatani, Yoichiro
Kandeel, Fouad R
Kardia, Sharon LR
Karlson, Elizabeth W
Kasturiratne, Anuradhani
Kato, Norihiro
Katsuya, Tomohiro
Kaur, Varinderpal
Kawaguchi, Takahisa
Keaton, Jacob M
Kho, Abel N
Khor, Chiea-Chuen
Kibriya, Muhammad
Kim, Bong-Jo
Koh, Woon-Puay
Kohara, Katsuhiko
Kooner, Jaspal S
Kooperberg, Charles
Kreienkamp, Raymond J
Lamri, Amel
Lange, Leslie A
Lee, Nanette R
Lee, Myung-Shik
Lee, Jung-Jin
Lehman, Donna M
Li, Liming
Li, Yun
Lim, Victor JY
Liu, Jianjun
Liu, Yongmei
Liu, Simin
Long, Jirong
Louie, Tin
Luo, Xi
Lv, Jun
Lynch, Julie A
Maeda, Shiro
Mahajan, Anubha
Maruthur, Nisa M
Matsuda, Fumihiko
McCarthy, Mark I
McKean-Cowdin, Roberta
Meigs, James B
Millwood, Iona Y
Mohlke, Karen L
Motala, Ayesha A
Nadkarni, Girish N
Nadler, Jerry L
Nakatochi, Masahiro
Nalls, Mike A
Nayak, Uma
Nicolas, Aude
North, Kari E
Nousome, Darryl
Okada, Yukinori
Paliwal, Sumit
Pan, Ian
Pankow, James S
Paré, Guillaume
Park, Jaehyun
Park, Kyong Soo
Parra, Esteban J
Patel, Sanjay R
Pereira, Mark A
Peyser, Patricia A
Pirie, Fraser J
Preuss, Michael
Province, Michael A
Psaty, Bruce M
PunyaSri, PSKDB
Raffel, Leslie J
Raffield, Laura M
Rasmussen-Torvik, Laura J
Redline, Susan
Reiner, Alexander P
Rich, Stephen S
Rohde, Rebecca
Roll, Kathryn
Roshani, Rashedeh
Rotimi, Charles N
Sabanayagam, Charumathi
Saleheen, Danish
Sandow, Kevin
Schurmann, Claudia
Shahriar, Mohammad
Shaw, Douglas M
Sheu, Wayne H-H
Shi, Jinxiu
Shu, Xiao-Ou
Shuey, Megan M
Siddiqui, Moneeza K
Smith, Jennifer A
Sofer, Tamar
Spracklen, Cassandra N
Stilp, Adrienne M
Sun, Meng
Tabara, Yasuharu
Tai, E-Shyong
Tajuddin, Salman M
Takahashi, Atsushi
Takeuchi, Fumihiko
Tan, Jingyi
Taylor, Kent D
Thameem, Farook
Tong, Lin
Tsai, Fuu-Jen
Tsao, Philip S
Udler, Miriam S
Valladares-Salgado, Adan
van Heel, David A
vanDam, Rob M
Varma, Rohit
Vora, Maheak
Wacher-Rodarte, Niels
Wang, Ya-Xing
Wheeler, Ellie
Whitsel, Eric A
Wickremasinghe, Ananda R
Wojcik, Genevieve L
Wong, Tien Y
Wu, Jer-Yuarn
Xiang, Yong-Bing
Xiang, Anny H
Yajnik, Chittaranjan S
Yamamoto, Ken
Yamauchi, Toshimasa
Yanek, Lisa R
Yao, Jie
Yokota, Mitsuhiro
Yu, Canqing
Yuan, Jian-Min
Yusuf, Salim
Zeggini, Eleftheria
Zhang, Liang
Zhang, Weihua
Zheng, Wei
Zonderman, Alan B
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Epidemiology
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Journal article
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http://creativecommons.org/licenses/by/4.0/
Language
English
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Abstract
BACKGROUND: Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes. METHODS: We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities. FINDINGS: Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In individuals with type 2 diabetes, the association between multi-ancestry PRSs and risk of microvascular complications and comorbidity was studied in populations of African, Admixed American, and European ancestries and was significant in all three ancestry groups for diabetic retinopathy (ORs per SD 1·28-1·57), diabetic nephropathy (1·25-1·58), proliferative diabetic retinopathy (1·39-2·08), and end-stage diabetic nephropathy (1·44-1·87); PRS was associated with coronary artery disease in the Admixed American ancestry group only (1·16 [95% CI 1·08-1·25]). INTERPRETATION: These validated, publicly available PRSs can improve risk stratification for type 2 diabetes onset and complications across diverse ancestries, supporting their further evaluation in clinical settings. FUNDING: The National Human Genome Research Institute of the US National Institutes of Health.
Citation
A. Huerta-Chagoya, J. Kim, R. Mandla, Y. Lu, K. Suzuki, L.E. Petty , et al., "Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries," The Lancet Diabetes & Endocrinology, 2026, https://doi.org/10.1016/s2213-8587(25)00405-x.
Source
The Lancet Diabetes & Endocrinology
Conference
Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 3205 Medical Biochemistry and Metabolomics
Subjects
Source
Publisher
Elsevier
DOI
10.1016/s2213-8587(25)00405-x
Additional links
https://www.sciencedirect.com/science/article/pii/S221385872500405X
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