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 journalArticle

25 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 - 01-02-2017

Fingerprint

Genetic Predisposition to Disease
Stroke
Confidence Intervals
Risk Management
Odds Ratio

All Science Journal Classification (ASJC) codes

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

Cite this

Hachiya, T., Kamatani, Y., Takahashi, A., Hata, J., Furukawa, R., Shiwa, Y., ... Shimizu, A. (2017). Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score. Stroke, 48(2), 253-258. https://doi.org/10.1161/STROKEAHA.116.014506
Hachiya, Tsuyoshi ; Kamatani, Yoichiro ; Takahashi, Atsushi ; Hata, Jun ; Furukawa, Ryohei ; Shiwa, Yuh ; Yamaji, Taiki ; Hara, Megumi ; Tanno, Kozo ; Ohmomo, Hideki ; Ono, Kanako ; Takashima, Naoyuki ; Matsuda, Koichi ; Wakai, Kenji ; Sawada, Norie ; Iwasaki, Motoki ; Yamagishi, Kazumasa ; Ago, Tetsuro ; Ninomiya, Toshiharu ; Fukushima, Akimune ; Hozawa, Atsushi ; Minegishi, Naoko ; Satoh, Mamoru ; Endo, Ryujin ; Sasaki, Makoto ; Sakata, Kiyomi ; Kobayashi, Seiichiro ; Ogasawara, Kuniaki ; Nakamura, Motoyuki ; Hitomi, Jiro ; Kita, Yoshikuni ; Tanaka, Keitaro ; Iso, Hiroyasu ; Kitazono, Takanari ; Kubo, Michiaki ; Tanaka, Hideo ; Tsugane, Shoichiro ; Kiyohara, Yutaka ; Yamamoto, Masayuki ; Sobue, Kenji ; Shimizu, Atsushi. / Genetic Predisposition to Ischemic Stroke : A Polygenic Risk Score. In: Stroke. 2017 ; Vol. 48, No. 2. pp. 253-258.
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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.",
author = "Tsuyoshi Hachiya and Yoichiro Kamatani and Atsushi Takahashi and Jun Hata and Ryohei Furukawa and Yuh Shiwa and Taiki Yamaji and Megumi Hara and Kozo Tanno and Hideki Ohmomo and Kanako Ono and Naoyuki Takashima and Koichi Matsuda and Kenji Wakai and Norie Sawada and Motoki Iwasaki and Kazumasa Yamagishi and Tetsuro Ago and Toshiharu Ninomiya and Akimune Fukushima and Atsushi Hozawa and Naoko Minegishi and Mamoru Satoh and Ryujin Endo and Makoto Sasaki and Kiyomi Sakata and Seiichiro Kobayashi and Kuniaki Ogasawara and Motoyuki Nakamura and Jiro Hitomi and Yoshikuni Kita and Keitaro Tanaka and Hiroyasu Iso and Takanari Kitazono and Michiaki Kubo and Hideo Tanaka and Shoichiro Tsugane and Yutaka Kiyohara and Masayuki Yamamoto and Kenji Sobue and Atsushi Shimizu",
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Hachiya, T, Kamatani, Y, Takahashi, A, Hata, J, Furukawa, R, Shiwa, Y, Yamaji, T, Hara, M, Tanno, K, Ohmomo, H, Ono, K, Takashima, N, Matsuda, K, Wakai, K, Sawada, N, Iwasaki, M, Yamagishi, K, Ago, T, Ninomiya, T, Fukushima, A, Hozawa, A, Minegishi, N, Satoh, M, Endo, R, Sasaki, M, Sakata, K, Kobayashi, S, Ogasawara, K, Nakamura, M, Hitomi, J, Kita, Y, Tanaka, K, Iso, H, Kitazono, T, Kubo, M, Tanaka, H, Tsugane, S, Kiyohara, Y, Yamamoto, M, Sobue, K & Shimizu, A 2017, 'Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score', Stroke, vol. 48, no. 2, pp. 253-258. https://doi.org/10.1161/STROKEAHA.116.014506

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

In: Stroke, Vol. 48, No. 2, 01.02.2017, p. 253-258.

Research output: Contribution to journalArticle

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

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.

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Hachiya T, Kamatani Y, Takahashi A, Hata J, Furukawa R, Shiwa Y et al. Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score. Stroke. 2017 Feb 1;48(2):253-258. https://doi.org/10.1161/STROKEAHA.116.014506