Zhao Sihai D, Cai T Tony, Li Hongzhe
Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820, U.S.A.
Biometrics. 2014 Dec;70(4):881-90. doi: 10.1111/biom.12206. Epub 2014 Jun 26.
Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.
整合基因组学为开展更强大的基因关联研究提供了一种很有前景的方法。人们希望将结果数据、基因型数据与其他类型的基因组信息相结合，能够实现更强大的单核苷酸多态性（SNP）检测。我们基于一个统计模型提出了一种新的关联测试，该模型明确假定基因变异通过干扰基因表达水平来影响结果。分析表明，与仅使用结果和基因型数据的测试相比，所提出的方法在检测通过转录调控与结果相关的SNP时具有更强的能力，并且模拟结果表明我们的方法对模型误设相对稳健。我们还提供了一种将我们的方法应用于高维基因组数据的策略。我们使用该策略鉴定出一个SNP与酵母细胞对天然产物番茄碱的反应之间潜在的新关联，而标准关联分析并未检测到这种关联。
