Format

Send to

Choose Destination
Stroke. 2017 Feb;48(2):253-258. doi: 10.1161/STROKEAHA.116.014506. Epub 2016 Dec 29.

Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score.

Author information

1
From the Division of Biomedical Information Analysis (T.H., R.F., Y.S., H.O., K. Ono, M. Satoh, A.S.), Division of Biobank and Data Management (T.H., Y.S., M. Satoh), Division of Clinical Research and Epidemiology (K. Tanno, K. Sakata), Division of Innovation and Education (A.F.), Division of Community Medical Supports and Health Record Informatics (M. Satoh), and Division of Public Relations and Planning (R.E.), Iwate Tohoku Medical Megabank Organization (M. Sasaki, S.K., K. Ogasawara, M.N., J. Hitomi, K. Sobue), Iwate Medical University, Japan; Laboratory for Statistical Analysis (Y. Kamatani, A.T.), RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan (M.K.); Laboratory for Omics Informatics, Omics Research Center, National Cerebral and Cardiovascular Center, Osaka, Japan (A.T.); Department of Environmental Medicine (J. Hata), Department of Medicine and Clinical Science (J. Hata, T.A., T.K.), and Center for Cohort Studies (J. Hata, T.N., T.K.), Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan (T.Y., N.S., M.I., S.T.); Department of Preventive Medicine, Faculty of Medicine, Saga University, Japan (M.H., K. Tanaka); Department of Public Health, Shiga University of Medical Science, Japan (N.T., Y. Kita); Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Japan (K.M.); Department of Preventive Medicine (K.W.) and Department of Epidemiology (H.T.), Nagoya University Graduate School of Medicine, Japan; Department of Public Health Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan (K.Y.); Department of Preventive Medicine and Epidemiology (A.H.), Department of Biobank (N.M.), and Department of Integrative Genomics (M.Y.), Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Faculty of Nursing Science, Tsuruga Nursing University, Fukui, Japan (Y. Kita); Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Japan (H.I.); Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan (H.T.); and Hisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan (Y. Kiyohara). thachiya@iwate-med.ac.jp ashimizu@iwate-med.ac.jp.
2
From the Division of Biomedical Information Analysis (T.H., R.F., Y.S., H.O., K. Ono, M. Satoh, A.S.), Division of Biobank and Data Management (T.H., Y.S., M. Satoh), Division of Clinical Research and Epidemiology (K. Tanno, K. Sakata), Division of Innovation and Education (A.F.), Division of Community Medical Supports and Health Record Informatics (M. Satoh), and Division of Public Relations and Planning (R.E.), Iwate Tohoku Medical Megabank Organization (M. Sasaki, S.K., K. Ogasawara, M.N., J. Hitomi, K. Sobue), Iwate Medical University, Japan; Laboratory for Statistical Analysis (Y. Kamatani, A.T.), RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan (M.K.); Laboratory for Omics Informatics, Omics Research Center, National Cerebral and Cardiovascular Center, Osaka, Japan (A.T.); Department of Environmental Medicine (J. Hata), Department of Medicine and Clinical Science (J. Hata, T.A., T.K.), and Center for Cohort Studies (J. Hata, T.N., T.K.), Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan (T.Y., N.S., M.I., S.T.); Department of Preventive Medicine, Faculty of Medicine, Saga University, Japan (M.H., K. Tanaka); Department of Public Health, Shiga University of Medical Science, Japan (N.T., Y. Kita); Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Japan (K.M.); Department of Preventive Medicine (K.W.) and Department of Epidemiology (H.T.), Nagoya University Graduate School of Medicine, Japan; Department of Public Health Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan (K.Y.); Department of Preventive Medicine and Epidemiology (A.H.), Department of Biobank (N.M.), and Department of Integrative Genomics (M.Y.), Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Faculty of Nursing Science, Tsuruga Nursing University, Fukui, Japan (Y. Kita); Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Japan (H.I.); Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan (H.T.); and Hisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan (Y. Kiyohara).

Abstract

BACKGROUND AND PURPOSE:

The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk.

METHODS:

We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets).

RESULTS:

In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33-2.31) and 1.99 (95% confidence interval, 1.19-3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001).

CONCLUSIONS:

The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors.

KEYWORDS:

genome-wide association study; genotype; risk assessment; stroke

PMID:
28034966
PMCID:
PMC5266416
DOI:
10.1161/STROKEAHA.116.014506
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Atypon Icon for PubMed Central
Loading ...
Support Center