Mengyun Wu and Shuangge Ma, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China and Yale School of Public Health, New Haven, CT 06520, USA.
Brief Bioinform. 2019 Mar 25;20(2):624-637. doi: 10.1093/bib/bby033.
For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.
对于许多复杂疾病的风险、进展和治疗反应,人们越来越认识到,遗传相互作用(包括基因-基因和基因-环境相互作用)除了主要的遗传和环境影响外,还起着重要作用。在实际的遗传相互作用分析中,响应变量和协变量中的模型误指定和异常值/污染并不罕见,需要稳健的分析方法。与非稳健的对应方法相比,稳健的遗传相互作用分析方法的应用并不广泛,但越来越受到关注。本文综述了稳健的遗传相互作用分析方法,包括边缘和联合分析的方法学和应用,以及针对响应变量和协变量中的模型误指定和异常值/污染的处理方法。