{"title":"Genetic Risk Scores","authors":"Robert P. Igo Jr., Tyler G. Kinzy, Jessica N. Cooke Bailey","doi":"10.1002/cphg.95","DOIUrl":null,"url":null,"abstract":"<p>Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.</p>","PeriodicalId":40007,"journal":{"name":"Current Protocols in Human Genetics","volume":"104 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cphg.95","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Protocols in Human Genetics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cphg.95","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.
遗传风险评分
包含数百万个遗传标记的全基因组变异数据已经变得司空见惯。然而,解释和应用这些数据的潜力,以临床评估感兴趣的结果,并预测疾病风险,目前还没有完全实现。许多常见的复杂疾病现在有许多确定的风险位点,并且可能包含许多影响太小的遗传决定因素,无法在全基因组水平上检测到统计意义。将遗传数据转化为疾病易感性预测指标的一种简单而直观的方法是将这些基因座的影响汇总到一个单一的指标中,即遗传风险评分。在这里,我们描述了一些常见的方法和软件包计算遗传风险评分和多基因风险评分,重点研究常见的复杂疾病。我们回顾了所需的基本信息,以及构建遗传风险评分的重要考虑因素,包括对表型和遗传数据的具体要求,以及它们在应用中的局限性。©2019 by John Wiley &儿子,Inc。
本文章由计算机程序翻译,如有差异,请以英文原文为准。