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Abstract
While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks1, a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted2. To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898; BioBank Japan (n = 179,066), UK Biobank (n = 361,194) and FinnGen (n = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02–1.04], Pmeta = 3.9 × 10−13) and parental lifespan (hazard ratio = 1.06[1.06–1.07], P = 2.0 × 10−86). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals (Pheterogeneity = 9.5 × 10−8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype–phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health.
Cross-biobank analysis reveals that polygenic risk scores (PRS) for hypertension and obesity are associated with shorter lifespan, serving as a proof-of-principle that PRS could pinpoint causal risk factors that affect long-term health outcomes.
Details
; Kanai Masahiro 2
; Karjalainen Juha 3 ; Akiyama Masato 4 ; Kurki Mitja 3 ; Matoba Nana 5
; Takahashi, Atsushi 6
; Hirata Makoto 7 ; Kubo Michiaki 8 ; Matsuda Koichi 9 ; Murakami Yoshinori 10 ; Daly, Mark J 3
; Kamatani Yoichiro 11
; Okada Yukinori 12
1 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255); Osaka University Graduate School of Medicine, Department of Statistical Genetics, Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971); The University of Tokyo, Department of Allergy and Rheumatology, Graduate School of Medicine, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
2 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255); Osaka University Graduate School of Medicine, Department of Statistical Genetics, Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971); Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA (GRID:grid.66859.34); Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, USA (GRID:grid.66859.34); Harvard Medical School, Department of Biomedical Informatics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland (GRID:grid.7737.4) (ISNI:0000 0004 0410 2071)
3 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA (GRID:grid.66859.34); Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, USA (GRID:grid.66859.34); Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland (GRID:grid.7737.4) (ISNI:0000 0004 0410 2071)
4 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255); Kyushu University, Fukuoka, Department of Ophthalmology, Graduate School of Medical Sciences, Fukuoka, Japan (GRID:grid.177174.3) (ISNI:0000 0001 2242 4849)
5 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255)
6 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255); National Cerebral and Cardiovascular Center, Department of Genomic Medicine, Research Institute, Suita, Japan (GRID:grid.410796.d) (ISNI:0000 0004 0378 8307)
7 The University of Tokyo, Laboratory of Genome Technology, Institute of Medical Science, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
8 RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.26999.3d)
9 The University of Tokyo, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
10 The University of Tokyo, Division of Molecular Pathology, Institute of Medical Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
11 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (GRID:grid.7597.c) (ISNI:0000000094465255); The University of Tokyo, Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
12 Osaka University Graduate School of Medicine, Department of Statistical Genetics, Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971); Osaka University, Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971); Osaka University, Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971)