Family-based tests of association are widely used in the search for genes contributing to complex diseases such as Parkinson's disease and autism. However, these methods have focused on the analysis of autosomal loci and are not generally useful for analysis of X-linked genes. Family-based tests that have been suggested for analysis of markers on the X chromosome are designed as tests of linkage, and are not necessarily valid tests of allelic association in linked regions. Therefore these methods are not useful for fine-mapping regions of linkage through association analysis, and we are aware of no family-based tests of association that are valid for analysis of X-linked markers. Clearly, there is a need to develop statistical methods designed for identifying important genetic factors on the X chromosome underlying complex diseases and quantitative trait variation. Through this project, we propose to develop new methodology for family-based association tests of X-linked loci. These methods will allow for inclusion of families with or without parental genotype data and will allow testing of disease risk (affected/unaffected) or quantitative traits. These methods will be thoroughly evaluated in simulated data, as well as real data from studies of Parkinson's disease, autism and early-onset cardiovascular disease. Finally software to implement the novel methods for X-linked analysis will be developed and distributed. There is compelling evidence of involvement of X-linked genes in many complex diseases, and the lack of appropriate association tests to make use of family-data hinders progress in fine-mapping disease genes on the X chromosome. Thus, the statistical methods and software developed through this grant will have an immediate application in gene mapping studies, and will help researchers identify and localize these important X-linked genes.
