Department of Human Genetics, Emory University, Atlanta, GA, USA.
Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.
Epigenetics. 2020 Jan-Feb;15(1-2):1-11. doi: 10.1080/15592294.2019.1644879. Epub 2019 Jul 22.
Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
多基因方法通常比单变体方法能够更多地获取复杂性状的变异。对于基因型数据，遗传风险评分（GRS）广泛用于风险预测以及关联和交互研究。最近，人们对将 GRS 方法转移到 DNA 甲基化数据（甲基化风险评分，MRS）越来越感兴趣，这些数据可用于 1）作为环境暴露的生物标志物，2）在单个 CpG 位点未达到显著性的关联分析中，3）作为交互和中介分析中的维度减少方法，以及 4）预测疾病或治疗成功的个体风险。大多数 GRS 方法可以直接转移到甲基化数据。然而，由于甲基化数据对混杂因素（例如年龄和组织）更敏感，因此找到合适的外部权重更加复杂。在这篇综述中，我们将概述当前 GRS 方法对甲基化数据的适应，并强调出现的挑战。
