Mooney Michael A, Wilmot Beth
Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon.
OHSU Knight Cancer Institute, Portland, Oregon.
Am J Med Genet B Neuropsychiatr Genet. 2015 Oct;168(7):517-27. doi: 10.1002/ajmg.b.32328. Epub 2015 Jun 8.
To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.
为了最大限度地发挥全基因组关联研究的潜力，许多研究人员正在进行二次分析，以识别与感兴趣的性状共同相关的基因集。尽管基因集分析（GSA）方法，也称为通路分析，已经存在了十多年，但该领域仍在不断发展。有许多算法可用于测试多个单核苷酸多态性（SNP）的累积效应，但在如何进行基因集分析的最佳方法上，该领域尚未达成真正的共识。本文概述了可能影响基因集分析结果的因素、从过去研究中吸取的教训，以及如何针对不同类型的数据做出最合适分析选择的建议。© 2015威利期刊公司
