Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score

Tsuyoshi Hachiya, Yoichiro Kamatani, Atsushi Takahashi, Jun Hata, Ryohei Furukawa, Yuh Shiwa, Taiki Yamaji, Megumi Hara, Kozo Tanno, Hideki Ohmomo, Kanako Ono, Naoyuki Takashima, Koichi Matsuda, Kenji Wakai, Norie Sawada, Motoki Iwasaki, Kazumasa Yamagishi, Tetsuro Ago, Toshiharu Ninomiya, Akimune FukushimaAtsushi Hozawa, Naoko Minegishi, Mamoru Satoh, Ryujin Endo, Makoto Sasaki, Kiyomi Sakata, Seiichiro Kobayashi, Kuniaki Ogasawara, Motoyuki Nakamura, Jiro Hitomi, Yoshikuni Kita, Keitaro Tanaka, Hiroyasu Iso, Takanari Kitazono, Michiaki Kubo, Hideo Tanaka, Shoichiro Tsugane, Yutaka Kiyohara, Masayuki Yamamoto, Kenji Sobue, Atsushi Shimizu

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)253-258
Number of pages6
JournalStroke
Volume48
Issue number2
DOIs
Publication statusPublished - 2017 Feb 1
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialised Nursing

Fingerprint Dive into the research topics of 'Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score'. Together they form a unique fingerprint.

Cite this