Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses.

Autor: Ojima T; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.; Graduate School of Medicine, Tohoku University, Sendai, Japan.; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.; Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan., Namba S; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.; Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan., Suzuki K; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan., Yamamoto K; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.; Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan.; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.; Laboratory of Children's Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Osaka, Japan., Sonehara K; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.; Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan., Narita A; Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan., Kamatani Y; Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan., Tamiya G; Graduate School of Medicine, Tohoku University, Sendai, Japan.; Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan.; Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan., Yamamoto M; Graduate School of Medicine, Tohoku University, Sendai, Japan.; Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan., Yamauchi T; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan., Kadowaki T; Toranomon Hospital, Tokyo, Japan., Okada Y; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. yuki-okada@m.u-tokyo.ac.jp.; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. yuki-okada@m.u-tokyo.ac.jp.; Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. yuki-okada@m.u-tokyo.ac.jp.; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. yuki-okada@m.u-tokyo.ac.jp.; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Osaka, Japan. yuki-okada@m.u-tokyo.ac.jp.
Jazyk: angličtina
Zdroj: Nature genetics [Nat Genet] 2024 Jun; Vol. 56 (6), pp. 1100-1109. Date of Electronic Publication: 2024 Jun 11.
DOI: 10.1038/s41588-024-01782-y
Abstrakt: Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (n T2D  = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R 2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.
(© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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