Wilson Ian J, Howey Richard Aj, Houniet Darren T, Santibanez-Koref Mauro
Institute of Genetic Medicine, Newcastle University, Newcastle NE3 1NB, UK.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S98. doi: 10.1186/1753-6561-5-S9-S98.
We present a new statistical method to identify genes in which one or more variants influence quantitative traits. We use the Genetic Analysis Workshop 17 (GAW17) data set of unrelated individuals as a test of the method on the raw GAW17 phenotypes and on residuals after fitting linear models to individual-based covariates. By performing appropriate randomization tests, we found many significant results for a proportion of the genes that contain variants that directly contribute to disease but that have an increased type I error for analyses of raw phenotypes. Power calculations show that our methods have the ability to reliably identify a subset of the loci contributing to disease. When we applied our method to derived phenotypes, we removed many false positives, giving appropriate type I error rates at little cost to power. The correlation between genome-wide heterozygosity and the value of the trait Q1 appears to drive much of the type I error in this data set.
我们提出了一种新的统计方法，用于识别其中一个或多个变体影响数量性状的基因。我们使用无关个体的遗传分析研讨会17（GAW17）数据集，对该方法在原始GAW17表型以及对基于个体的协变量拟合线性模型后的残差上进行测试。通过进行适当的随机化检验，我们发现对于一部分包含直接导致疾病的变体的基因，有许多显著结果，但对原始表型进行分析时I型错误增加。功效计算表明，我们的方法有能力可靠地识别出导致疾病的一部分基因座。当我们将我们的方法应用于衍生表型时，我们去除了许多假阳性，在对功效几乎没有损失的情况下给出了适当的I型错误率。全基因组杂合性与性状Q1的值之间的相关性似乎是该数据集中大部分I型错误的驱动因素。
