Igo Robert P, Kinzy Tyler G, Cooke Bailey Jessica N
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio.
Curr Protoc Hum Genet. 2019 Dec;104(1):e95. doi: 10.1002/cphg.95.
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.
包含数百万个遗传标记的全基因组变异数据已变得很常见。然而,目前这些数据在用于对感兴趣的结果进行临床评估以及疾病风险预测的解读和应用潜力尚未得到充分实现。现在,许多常见的复杂疾病都有众多已确立的风险位点,并且可能存在许多遗传决定因素,其效应过小,在全基因组水平的统计学显著性下无法检测到。一种将遗传数据转化为疾病易感性预测指标的简单直观方法是将这些位点的效应汇总为一个单一指标,即遗传风险评分。在此,我们描述一些用于计算遗传风险评分和多基因风险评分的常见方法和软件包,重点是常见复杂疾病的研究。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应用中的局限性。© 2019 约翰威立父子公司版权所有