Association test with the principal component analysis in case-parents studies.

Abstract

In this report, we proposed a statistic with the principal component analysis (PCA) to test association between multiple markers and the disease-susceptibility gene in case–parents data set. The proposed statistic is based on a difference vector calculated by comparing the genotypes of affected offspring with the hypothetical siblings who carry the parental genotypes not present in the affected offspring. The statistic here focuses on all the principal components and is asymptotically distributed as a χ2 distribution with one degree of freedom. Simulation studies showed that, when the number of markers is not very large, the proposed statistic has higher power than the APRICOT test which is based on the same method of the PCA. The rapid advancement of genotyping technologies and the availability of enormous quantities of genotype or haplotype data provide an unprecedented opportunity for identifying genes underlying complex traits. When multiple markers are available, haplotype-based methods and genotype-based methods are commonly used for conducting association between complex traits and a series of possibly linked markers. Owing to the information from individual markers as well as the linkage disequilibrium (LD) structure between the markers, the haplotype-based association study is considered to be a potentially superior strategy (Akey et al. 2001). However, haplotype-based methods are challenged by a large number of distinct haplotypes, which results in a large number of degrees of freedom, and some haplotypebased methods need estimating haplotype phases only when genotype data at multiple marker loci are collected. On the other hand, genotype-based methods have the advantage of not requiring phase information and in many situations they have higher power than haplotype-based methods (Chapman et al. 2003; Xu et al. 2006; Rakovski et al. 2007; Yu and Wang 2011).