Wang Kai, Huang Jian
Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S60. doi: 10.1186/1753-6561-5-S9-S60.
Significance of genetic association to a marker has been traditionally evaluated through statistics that are standardized such that their null distributions conform to some known ones. Distributional assumptions are often required in this standardization procedure. Based on the observation that the phenotype remains the same regardless of the marker being investigated, we propose a simple statistic that does not need such standardization. We propose a resampling procedure to assess this statistic's genome-wide significance. This method has been applied to replicate 2 of the Genetic Analysis Workshop 17 simulated data on unrelated individuals in an attempt to map phenotype Q2. However, none of the selected SNPs are in genes that are disease-causing. This may be due to the weak effect that each genetic factor has on Q2.
传统上,基因与标记之间关联的显著性是通过标准化统计量来评估的,这些统计量的零分布符合某些已知分布。在这个标准化过程中通常需要分布假设。基于无论研究哪个标记,表型都保持不变这一观察结果,我们提出了一种无需此类标准化的简单统计量。我们提出了一种重采样程序来评估该统计量在全基因组范围内的显著性。此方法已应用于对遗传分析研讨会17中无关个体的模拟数据进行重复分析,以试图定位表型Q2。然而,所选的单核苷酸多态性(SNP)均不在致病基因中。这可能是由于每个遗传因素对Q2的影响较弱。