Testing for candidate gene linkage disequilibrium using a dense array of single nucleotide polymorphisms in case-parents studies.

Abstract

The future of genetic studies of complex human diseases will rely more and more on the epidemiologic association paradigm, in particular the use of the transmission/disequilibrium test to detect linkage disequilibrium in a case-parents study. With the rapid progress in genomic studies, many single nucleotide polymorphisms will be identified and genotyped within a very short physical distance. Analyzing multiple single nucleotide polymorphisms within a candidate gene/region with Bonferroni correction for multiple transmission/disequilibrium tests will lead to a conservative test, and hence a power loss. I propose a new method, the "Adaptive PRIncipal COmponent Test" (APRICOT). The method has the following properties: (1) it does not need haplotype information; (2) it is nonparametric-it does not make specific assumptions about the population history or population structure; and (3) the calculation of the test statistic and the determination of its significance level are simple and straightforward. Monte-Carlo simulation reveals that adaptive principal component test maintains the nominal significance level under the null hypothesis of no linkage disequilibrium, even under complex situations of multiple ancestral haplotypes and structured populations. It provides a substantial power advantage over the conventional Bonferroni approach. The adaptive principal component test is a promising method for candidate gene testing using single nucleotide polymorphisms.