Institute of Biostatistics, Leibniz University Hannover, D-30419 Hannover, Germany.
BMC Genet. 2012 Jul 18;13:59. doi: 10.1186/1471-2156-13-59.
Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs).
We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp.
We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.
在一个基因座的基因型组之间的性状方差，预计会在该基因座与另一个基因座或环境之间存在相互作用的情况下有所不同。因此，可以使用简单的方差异质性最大检验来识别可能存在相互作用的单核苷酸多态性（SNP）。
我们提出了一种用于方差异质性的多重对比检验方法，该方法将每个基因型组的 Levene 残差的平均值与其平均值进行比较，作为全局 Levene 检验的替代方法。我们将该检验应用于 Bogalusa 心脏研究数据集，以筛选整个基因组中影响多个数量性状的潜在相互作用 SNP。这种方法的用户友好型实现可在 R 统计软件包 multcomp 中使用。
我们表明，拟议的模型特异性方差异质性多重对比检验可用于检验 SNP 与未知等位基因、基因座或协变量之间的潜在相互作用，并与传统检验相比提供有价值的附加信息。尽管该检验在严重不平衡设计中具有统计学有效性，但在低频等位基因基因座处解释结果时需要谨慎。
