Xing Chuanhua, Satten Glen A, Allen Andrew S
Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1102 Hock Plaza, Box 2721, Durham, NC 27710, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S6. doi: 10.1186/1753-6561-5-S9-S6.
Currently there is a great deal of interest in developing methods for testing the role that rare variation plays in disease development. Here we propose a weighted association test that accumulates genetic variation across a signaling pathway. We evaluate our approach by analyzing simulated phenotype data from an exome sequencing study of 697 unrelated individuals from the Genetic Analysis Workshop 17 (GAW17) data set. Although our weighted approach identifies several interesting pathways associated with phenotype Q1, so does an alternative unweighted accumulation approach. Such a result is not unexpected because there is no systematic relationship between the allele frequency of a variant and its effect on phenotype in the GAW17 simulation model.
目前,人们对开发检测罕见变异在疾病发生中所起作用的方法极为关注。在此,我们提出一种加权关联检验方法,该方法可累积信号通路中的遗传变异。我们通过分析来自遗传分析研讨会17(GAW17)数据集的697名无关个体的外显子组测序研究中的模拟表型数据来评估我们的方法。尽管我们的加权方法识别出了几条与表型Q1相关的有趣通路,但另一种未加权的累积方法也能做到。这样的结果并不意外,因为在GAW17模拟模型中,变异的等位基因频率与其对表型的影响之间不存在系统关系。