Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease

被引:8
作者
King, Austin [1 ]
Wu, Lang [2 ]
Deng, Hong-Wen [3 ]
Shen, Hui [3 ]
Wu, Chong [4 ]
机构
[1] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA
[2] Univ Hawaii, Univ Hawaii Manoa, Canc Epidemiol Div, Populat Sci Pacific Program,Canc Ctr, Honolulu, HI USA
[3] Tulane Univ, Dept Global Biostat & Data Sci, Ctr Bioinformat & Genom, New Orleans, LA 70118 USA
[4] Univ Texas MD Anderson Canc Ctr, Dept Biostat, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
Pooled cohort equations; Integrated polygenic risk score; Genomic risk prediction; CARDIOVASCULAR-DISEASE; PREDICTIVE ACCURACY; RECLASSIFICATION; METAANALYSIS; VALIDATION; FRAMEWORK; COMMON;
D O I
10.1186/s12916-022-02583-y
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. Methods An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. Results In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and - 0.023 (95% CI, - 0.025 to - 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category. Conclusions Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.
引用
收藏
页数:14
相关论文
共 71 条
  • [41] Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers
    Mars, Nina
    Koskela, Jukka T.
    Ripatti, Pietari
    Kiiskinen, Tuomo T. J.
    Havulinna, Aki S.
    Lindbohm, Joni V.
    Ahola-Olli, Ari
    Kurki, Mitja
    Karjalainen, Juha
    Palta, Priit
    Neale, Benjamin M.
    Daly, Mark
    Salomaa, Veikko
    Palotie, Aarno
    Widen, Elisabeth
    Ripatti, Samuli
    [J]. NATURE MEDICINE, 2020, 26 (04) : 549 - +
  • [42] Clinical use of current polygenic risk scores may exacerbate health disparities
    Martin, Alicia R.
    Kanai, Masahiro
    Kamatani, Yoichiro
    Okada, Yukinori
    Neale, Benjamin M.
    Daly, Mark J.
    [J]. NATURE GENETICS, 2019, 51 (04) : 584 - 591
  • [43] Transethnic Meta-Analysis of Genome-Wide Association Studies Identifies Three New Loci and Characterizes Population-Specific Differences for Coronary Artery Disease
    Matsunaga, Hiroshi
    Ito, Kaoru
    Akiyama, Masato
    Takahashi, Atsushi
    Koyama, Satoshi
    Nomura, Seitaro
    Ieki, Hirotaka
    Ozaki, Kouichi
    Onouchi, Yoshihiro
    Sakaue, Saori
    Suna, Shinichiro
    Ogishima, Soichi
    Yamamoto, Masayuki
    Hozawa, Atsushi
    Satoh, Mamoru
    Sasaki, Makoto
    Yamaji, Taiki
    Sawada, Norie
    Iwasaki, Motoki
    Tsugane, Shoichiro
    Tanaka, Keitaro
    Arisawa, Kokichi
    Ikezaki, Hiroaki
    Takashima, Naoyuki
    Naito, Mariko
    Wakai, Kenji
    Tanaka, Hideo
    Sakata, Yasuhiko
    Morita, Hiroyuki
    Sakata, Yasushi
    Matsuda, Koichi
    Murakami, Yoshinori
    Akazawa, Hiroshi
    Kubo, Michiaki
    Kamatani, Yoichiro
    Komuro, Issei
    [J]. CIRCULATION-GENOMIC AND PRECISION MEDICINE, 2020, 13 (03): : 128 - 138
  • [44] Predictive Accuracy of a Polygenic Risk Score Compared With a Clinical Risk Score for Incident Coronary Heart Disease
    Mosley, Jonathan D.
    Gupta, Deepak K.
    Tan, Jingyi
    Yao, Jie
    Wells, Quinn S.
    Shaffer, Christian M.
    Kundu, Suman
    Robinson-Cohen, Cassianne
    Psaty, Bruce M.
    Rich, Stephen S.
    Post, Wendy S.
    Guo, Xiuqing
    Rotter, Jerome, I
    Roden, Dan M.
    Gerszten, Robert E.
    Wang, Thomas J.
    [J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 323 (07): : 627 - 635
  • [45] Genetics of Common, Complex Coronary Artery Disease
    Musunuru, Kiran
    Kathiresan, Sekar
    [J]. CELL, 2019, 177 (01) : 132 - 145
  • [46] Overview of the BioBank Japan Project: Study design and profile
    Nagai, Akiko
    Hirata, Makoto
    Kamatani, Yoichiro
    Muto, Kaori
    Matsuda, Koichi
    Kiyohara, Yutaka
    Ninomiya, Toshiharu
    Tamakoshi, Akiko
    Yamagata, Zentaro
    Mushiroda, Taisei
    Murakami, Yoshinori
    Yuji, Koichiro
    Furukawa, Yoichi
    Zembutsu, Hitoshi
    Tanaka, Toshihiro
    Ohnishi, Yozo
    Nakamura, Yusuke
    Kubo, Michiaki
    [J]. JOURNAL OF EPIDEMIOLOGY, 2017, 27 (03) : S2 - S8
  • [47] Parameters behind "nonparametric" statistics: Kendall's tau, Somers' D and median differences
    Newson, Roger
    [J]. STATA JOURNAL, 2002, 2 (01) : 45 - 64
  • [48] A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
    Nikpay, Majid
    Goel, Anuj
    Won, Hong-Hee
    Hall, Leanne M.
    Willenborg, Christina
    Kanoni, Stavroula
    Saleheen, Danish
    Kyriakou, Theodosios
    Nelson, Christopher P.
    Hopewell, Jemma C.
    Webb, Thomas R.
    Zeng, Lingyao
    Dehghan, Abbas
    Alver, Maris
    Armasu, Sebastian M.
    Auro, Kirsi
    Bjonnes, Andrew
    Chasman, Daniel I.
    Chen, Shufeng
    Ford, Ian
    Franceschini, Nora
    Gieger, Christian
    Grace, Christopher
    Gustafsson, Stefan
    Huang, Jie
    Hwang, Shih-Jen
    Kim, Yun Kyoung
    Kleber, Marcus E.
    Lau, King Wai
    Lu, Xiangfeng
    Lu, Yingchang
    Lyytikainen, Leo-Pekka
    Mihailov, Evelin
    Morrison, Alanna C.
    Pervjakova, Natalia
    Qu, Liming
    Rose, Lynda M.
    Salfati, Elias
    Saxena, Richa
    Scholz, Markus
    Smith, Albert V.
    Tikkanen, Emmi
    Uitterlinden, Andre
    Yang, Xueli
    Zhang, Weihua
    Zhao, Wei
    de Andrade, Mariza
    de Vries, Paul S.
    van Zuydam, Natalie R.
    Anand, Sonia S.
    [J]. NATURE GENETICS, 2015, 47 (10) : 1121 - +
  • [49] Statin therapy, LDL cholesterol, C-reactive protein, and coronary artery disease
    Nissen, SE
    Tuzcu, EM
    Schoenhagen, P
    Crowe, T
    Sasiela, WJ
    Tsai, J
    Orazem, J
    Magorien, RD
    O'Shaughnessy, C
    Ganz, P
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2005, 352 (01) : 29 - 38
  • [50] Net reclassification index at event rate: properties and relationships
    Pencina, Michael J.
    Steyerberg, Ewout W.
    D'Agostino, Ralph B., Sr.
    [J]. STATISTICS IN MEDICINE, 2017, 36 (28) : 4455 - 4467