TY - JOUR
T1 - Genetic Predisposition to Ischemic Stroke
T2 - A Polygenic Risk Score
AU - Hachiya, Tsuyoshi
AU - Kamatani, Yoichiro
AU - Takahashi, Atsushi
AU - Hata, Jun
AU - Furukawa, Ryohei
AU - Shiwa, Yuh
AU - Yamaji, Taiki
AU - Hara, Megumi
AU - Tanno, Kozo
AU - Ohmomo, Hideki
AU - Ono, Kanako
AU - Takashima, Naoyuki
AU - Matsuda, Koichi
AU - Wakai, Kenji
AU - Sawada, Norie
AU - Iwasaki, Motoki
AU - Yamagishi, Kazumasa
AU - Ago, Tetsuro
AU - Ninomiya, Toshiharu
AU - Fukushima, Akimune
AU - Hozawa, Atsushi
AU - Minegishi, Naoko
AU - Satoh, Mamoru
AU - Endo, Ryujin
AU - Sasaki, Makoto
AU - Sakata, Kiyomi
AU - Kobayashi, Seiichiro
AU - Ogasawara, Kuniaki
AU - Nakamura, Motoyuki
AU - Hitomi, Jiro
AU - Kita, Yoshikuni
AU - Tanaka, Keitaro
AU - Iso, Hiroyasu
AU - Kitazono, Takanari
AU - Kubo, Michiaki
AU - Tanaka, Hideo
AU - Tsugane, Shoichiro
AU - Kiyohara, Yutaka
AU - Yamamoto, Masayuki
AU - Sobue, Kenji
AU - Shimizu, Atsushi
N1 - Funding Information:
Sources of Funding The BioBank Japan Project was supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of the Japanese government. The Japan Multi-Institutional Collaborative Cohort (J-MICC) study was supported by the Grants-in-Aid for Scientific Research (B), grant numbers 17390186, 20390184, 24390165, priority area grant number 17015018, and innovative area grant number 221S0001 of the MEXT and the Japan Society for the Promotion of Science. The Japan Public Health Center (JPHC)- based Prospective study was supported by National Cancer Center Research and Development Fund (23-A-31[toku] and 26-A-2; since 2011) and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan (from 1989 to 2010). This work was supported by a grant of the Tohoku Medical Megabank Project from the MEXT of Japan; Ministry of Health, Labour and Welfare of Japan; Japan Agency for Medical Research and Development.
Publisher Copyright:
© 2017 American Heart Association, Inc.
PY - 2017/2/1
Y1 - 2017/2/1
N2 - 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.
AB - 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.
KW - genome-wide association study
KW - genotype
KW - risk assessment
KW - stroke
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UR - http://www.scopus.com/inward/citedby.url?scp=85010843480&partnerID=8YFLogxK
U2 - 10.1161/STROKEAHA.116.014506
DO - 10.1161/STROKEAHA.116.014506
M3 - Article
C2 - 28034966
AN - SCOPUS:85010843480
SN - 0039-2499
VL - 48
SP - 253
EP - 258
JO - Stroke
JF - Stroke
IS - 2
ER -