Cheng Lu, Hu Pingzhao, Sykes Jenna, Pintilie Melania, Liu Geoffrey, Xu Wei
Department of Biostatistics, Princess Margaret Hospital, 610 University Ave,, Toronto, ON M5G 2M9, Canada.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S85. doi: 10.1186/1753-6561-5-S9-S85.
How various genetic effects in combination affect susceptibility to certain disease states continues to be a major area of methodological research. Various rare variant models have been proposed, in response to a common failure to either identify or validate biologically driven causal genetic variants in genome-wide association studies. Adopting the idea that multiple rare variants may effectively produce a combined effect equal to a single common variant effect through common linkage with this variant, we construct a pathway-based genetic association analysis model using both common and rare variants. This genetic model is applied to the disease status of unrelated individuals in replication 1 from Genetic Analysis Workshop 17. In this simulated example, we were able to identify several pathways that were potentially associated with the disease status and found that common variants showed stronger genetic effect than rare variants.
多种基因效应如何共同影响对某些疾病状态的易感性,仍然是方法学研究的一个主要领域。由于在全基因组关联研究中普遍未能识别或验证具有生物学驱动作用的因果基因变异,人们提出了各种罕见变异模型。我们采用这样一种观点,即多个罕见变异可能通过与该变异的共同连锁有效地产生与单个常见变异效应相等的联合效应,利用常见变异和罕见变异构建了一个基于通路的基因关联分析模型。将这个遗传模型应用于遗传分析研讨会17的复制1中无关个体的疾病状态。在这个模拟例子中,我们能够识别出几条可能与疾病状态相关的通路,并发现常见变异比罕见变异表现出更强的基因效应。