Yang Xinlan, Wang Shuaichen, Zhang Shuanglin, Sha Qiuying
Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
BioStat Solutions, Inc, Frederick, MD, USA.
Genet Epidemiol. 2017 Apr;41(3):233-243. doi: 10.1002/gepi.22034. Epub 2017 Feb 8.
Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.
尽管已广泛发现与疾病相关的常见变异，但复杂性状的许多遗传贡献仍无法解释。罕见变异可能解释额外的疾病风险或性状变异性。虽然测序技术为研究罕见变异在复杂疾病中的作用提供了绝佳机会，但在基于测序的关联研究中检测这些变异面临重大挑战。在本文中，我们提出了新的统计检验方法，以基于交叉验证预测误差（PE）来检验基因组区域中罕见和常见变异与感兴趣的复杂性状之间的关联。我们首先提出一种基于岭回归的PE方法。基于PE，我们还通过使用两种不同加权方案测试变异的加权组合，提出了另外两种检验方法PE-WS和PE-TOW。PE-WS是基于加权和统计量（WS）的检验的PE版本，PE-TOW是基于变异的最优加权组合（TOW）的检验的PE版本。通过广泛的模拟研究，我们能够表明：（1）PE-TOW和PE-WS分别始终比TOW和WS更具功效；（2）当因果变异同时包含常见和罕见变异时，PE是最具功效的检验方法。
