Analysis of multiple SNPs in a candidate gene or region.
We consider the analysis of multiple single nucleotide polymorphisms (SNPs) within a gene or region. The simplest analysis of such data is based on a series of single SNP hypothesis tests, followed by correction for multiple testing, but it is intuitively plausible that a joint analysis of the SNPs will have higher power, particularly when the causal locus may not have been observed. However, standard tests, such as a likelihood ratio test based on an unrestricted alternative hypothesis, tend to have large numbers of degrees of freedom and hence low power. This has motivated a number of alternative test statistics. Here we compare several of the competing methods, including the multivariate score test (Hotelling's test) of Chapman et al. ([2003] Hum. Hered. 56:18-31), Fisher's method for combining P-values, the minimum P-value approach, a Fourier-transform-based approach recently suggested by Wang and Elston ([2007] Am. J. Human Genet. 80:353-360) and a Bayesian score statistic proposed for microarray data by Goeman et al. ([2005] J. R. Stat. Soc. B 68:477-493). Some relationships between these methods are pointed out, and simulation results given to show that the minimum P-value and the Goeman et al. ([2005] J. R. Stat. Soc. B 68:477-493) approaches work well over a range of scenarios. The Wang and Elston approach often performs poorly; we explain why, and show how its performance can be substantially improved.
Chapman
Juliet
J
London School of Hygiene and Tropical Medicine, London, United Kingdom. juliet.chapman@lshtm.ac.uk
Whittaker
John
J
D000483:Alleles; D001499:Bayes Theorem; D003198:Computer Simulation; D005583:Fourier Analysis; D005787:Gene Frequency; D005838:Genotype; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide