%0 Journal Article
%T Sex chromosomes and genetic association studies
%A David G Clayton
%J Genome Medicine
%D 2009
%I BioMed Central
%R 10.1186/gm110
%X The statistical problem of testing for association between phenotype and genetic markers on the sex chromosomes has received less attention than tests for autosomal markers. The advent of genome-wide association studies has hugely increased the number of studies of associations with the sex chromosomes and, in this context, it has recently been recognized that the X chromosome, in particular, poses special problems [1].Firstly, in population-based case-control studies involving both male and female subjects, associations can be confounded by differences in sex ratio between cases and controls even when, as is usually the case, allele frequencies do not differ between the sexes. Conventional epidemiological approaches to deal with this confounding can be very inefficient.Secondly, the phenomenon of X inactivation, which affects most loci on the X chromosome in females, means that the risk attributable to a single allele would generally be expected to be less in females than in males. An efficient statistical test would allow for this.This review describes approaches to statistical testing for association with loci on the sex chromosomes, largely in the context of case-control studies of binary phenotypes. The X chromosome will be the focus of most of the review. Later sections will briefly discuss family-based association studies, quantitative phenotypes and methods for the Y chromosome.Before turning to the special problems presented by the X chromosome, we shall review simple methods of analysis for autosomal loci in case-control studies.Many early analyses of association between a binary phenotype and a genetic marker used simple tests for association in contingency tables in which cell entries were counts of chromosomes rather than people. Thus, for an autosomal locus, the total cell count is twice the number of subjects studied, and associations were tested simply by comparing allele frequencies between cases and controls. In the diallelic case, this reduces to
%U http://genomemedicine.com/content/1/11/110