Polygenic Risk Scores

被引:15
作者
Osterman, Michael D. [1 ]
Kinzy, Tyler G. [1 ]
Bailey, Jessica N. Cooke [1 ]
机构
[1] Case Western Reserve Univ, Cleveland Inst Computat Biol, Dept Populat & Quantitat Hlth Sci, Cleveland, OH 44106 USA
来源
CURRENT PROTOCOLS | 2021年 / 1卷 / 05期
关键词
area under the curve; complex traits and diseases; disease prediction; genetic risk score; polygenic risk score; GENOME-WIDE ASSOCIATION; BREAST-CANCER RISK; PREDICTIVE ACCURACY; DIAGNOSTIC-TESTS; REGRESSION; MODEL; LOCI; COEFFICIENT;
D O I
10.1002/cpz1.126
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. (c) 2021 Wiley Periodicals LLC.
引用
收藏
页数:14
相关论文
共 78 条
[1]   Understanding diagnostic tests 3: receiver operating characteristic curves [J].
Akobeng, Anthony K. .
ACTA PAEDIATRICA, 2007, 96 (05) :644-647
[2]   A global reference for human genetic variation [J].
Altshuler, David M. ;
Durbin, Richard M. ;
Abecasis, Goncalo R. ;
Bentley, David R. ;
Chakravarti, Aravinda ;
Clark, Andrew G. ;
Donnelly, Peter ;
Eichler, Evan E. ;
Flicek, Paul ;
Gabriel, Stacey B. ;
Gibbs, Richard A. ;
Green, Eric D. ;
Hurles, Matthew E. ;
Knoppers, Bartha M. ;
Korbel, Jan O. ;
Lander, Eric S. ;
Lee, Charles ;
Lehrach, Hans ;
Mardis, Elaine R. ;
Marth, Gabor T. ;
McVean, Gil A. ;
Nickerson, Deborah A. ;
Wang, Jun ;
Wilson, Richard K. ;
Boerwinkle, Eric ;
Doddapaneni, Harsha ;
Han, Yi ;
Korchina, Viktoriya ;
Kovar, Christie ;
Lee, Sandra ;
Muzny, Donna ;
Reid, Jeffrey G. ;
Zhu, Yiming ;
Chang, Yuqi ;
Feng, Qiang ;
Fang, Xiaodong ;
Guo, Xiaosen ;
Jian, Min ;
Jiang, Hui ;
Jin, Xin ;
Lan, Tianming ;
Li, Guoqing ;
Li, Jingxiang ;
Li, Yingrui ;
Liu, Shengmao ;
Liu, Xiao ;
Lu, Yao ;
Ma, Xuedi ;
Tang, Meifang ;
Wang, Bo .
NATURE, 2015, 526 (7571) :68-+
[3]  
[Anonymous], 2011, R Foundation for Statistical Computing
[4]   Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma [J].
Bailey, Jessica N. Cooke ;
Loomis, Stephanie J. ;
Kang, Jae H. ;
Allingham, R. Rand ;
Gharahkhani, Puya ;
Khor, Chiea Chuen ;
Burdon, Kathryn P. ;
Aschard, Hugues ;
Chasman, Daniel I. ;
Igo, Robert P., Jr. ;
Hysi, Pirro G. ;
Glastonbury, Craig A. ;
Ashley-Koch, Allison ;
Brilliant, Murray ;
Brown, Andrew A. ;
Budenz, Donald L. ;
Buil, Alfonso ;
Cheng, Ching-Yu ;
Choi, Hyon ;
Christen, William G. ;
Curhan, Gary ;
De Vivo, Immaculata ;
Fingert, John H. ;
Foster, Paul J. ;
Fuchs, Charles ;
Gaasterland, Douglas ;
Gaasterland, Terry ;
Hewitt, Alex W. ;
Hu, Frank ;
Hunter, David J. ;
Khawaja, Anthony P. ;
Lee, Richard K. ;
Li, Zheng ;
Lichter, Paul R. ;
Mackey, David A. ;
McGuffin, Peter ;
Mitchell, Paul ;
Moroi, Sayoko E. ;
Perera, Shamira A. ;
Pepper, Keating W. ;
Qi, Qibin ;
Realini, Tony ;
Richards, Julia E. ;
Ridker, Paul M. ;
Rimm, Eric ;
Ritch, Robert ;
Ritchie, Marylyn ;
Schuman, Joel S. ;
Scott, William K. ;
Singh, Kuldev .
NATURE GENETICS, 2016, 48 (02) :189-194
[5]   Metrics for Evaluating Polygenic Risk Scores [J].
Baker, Stuart G. .
JNCI CANCER SPECTRUM, 2021, 5 (01)
[6]   Why the missing heritability might not be in the DNA [J].
Bourrat, Pierrick ;
Lu, Qiaoying ;
Jablonka, Eva .
BIOESSAYS, 2017, 39 (07)
[7]   PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores [J].
Chen, Lawrence M. ;
Yao, Nelson ;
Garg, Elika ;
Zhu, Yuecai ;
Nguyen, Thao T. T. ;
Pokhvisneva, Irina ;
Dass, Shantala A. Hari ;
Unternaehrer, Eva ;
Gaudreau, Helene ;
Forest, Marie ;
McEwen, Lisa M. ;
MacIsaac, Julia L. ;
Kobor, Michael S. ;
Greenwood, Celia M. T. ;
Silveira, Patricia P. ;
Meaney, Michael J. ;
O'Donnell, Kieran J. .
BMC BIOINFORMATICS, 2018, 19
[8]   Tutorial: a guide to performing polygenic risk score analyses [J].
Choi, Shing Wan ;
Mak, Timothy Shin-Heng ;
O'Reilly, Paul F. .
NATURE PROTOCOLS, 2020, 15 (09) :2759-2772
[9]   PRSice-2: Polygenic Risk Score software for biobank-scale data [J].
Choi, Shing Wan ;
O'Reilly, Paul F. .
GIGASCIENCE, 2019, 8 (07)
[10]   Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics [J].
Chun, Sung ;
Imakaev, Maxim ;
Hui, Daniel ;
Patsopoulos, Nikolaos A. ;
Neale, Benjamin M. ;
Kathiresan, Sekar ;
Stitziel, Nathan O. ;
Sunyaev, Shamil R. .
AMERICAN JOURNAL OF HUMAN GENETICS, 2020, 107 (01) :46-59