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Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score.

Tsuyoshi Hachiya1, Yoichiro Kamatani2, Atsushi Takahashi2

  • 1From 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.

Stroke
|December 31, 2016
PubMed
Summary

A novel polygenic risk score (polyGRS) shows superior prediction of ischemic stroke (IS) risk compared to older methods. This genetic tool can improve identification of individuals at high risk for IS, aiding prevention strategies.

Keywords:
genome-wide association studygenotyperisk assessmentstroke

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Area of Science:

  • Genetics
  • Cardiovascular Disease Epidemiology
  • Biostatistics

Background:

  • Predicting genetic predispositions to ischemic stroke (IS) is crucial for early identification and prevention.
  • Previous genetic risk scores had limited predictive power for IS.
  • A newer polygenic risk score (polyGRS) approach considers multiple genetic signals collectively.

Purpose of the Study:

  • To evaluate the predictive ability of polygenic risk score (polyGRS) for ischemic stroke (IS).
  • To compare the performance of polyGRS against weighted multilocus genetic risk scores.
  • To assess the utility of polyGRS in improving IS risk prediction models.

Main Methods:

  • Genotyped 13,214 Japanese individuals with IS and 26,470 controls for derivation.
  • Developed both weighted multilocus genetic risk scores and polyGRS using derivation data.
  • Validated predictive abilities in two independent Japanese sample sets (KyushuU and JPJM).

Main Results:

  • Polygenic risk score (polyGRS) was significantly associated with IS in both validation sets; weighted multilocus scores were not.
  • Highest vs. lowest polyGRS quintiles showed odds ratios for IS of 1.75 (KyushuU) and 1.99 (JPJM).
  • In KyushuU samples, adding polyGRS to a non-genetic model significantly improved IS prediction (net reclassification improvement=0.151, P<0.001).

Conclusions:

  • Polygenic risk score (polyGRS) demonstrated superior predictive performance for IS compared to weighted multilocus genetic risk scores.
  • PolyGRS offers valuable information for individual IS risk assessment when combined with non-genetic factors.
  • This genetic tool can aid in the management of modifiable risk factors for ischemic stroke.