The X-factor of complex disease: Development, implementation, and extensive application of methods for analysis of the X chromosome in GWA, sequence-based association, and eQTL studies

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7. PROJECT SUMMARY/ABSTRACT In the historical endeavor striving to understand complex disease via genome-wide association studies (GWAS), the X chromosome (X) has typically been disregarded or incorrectly analyzed due to analytical complications stemming from its unique mode of inheritance and population genetic patterns. This trend has carried over into sequence-based association studies, genome-wide studies of regulatory elements, and studies of gene expression. Beyond comprising 5% of the human genome, X likely contributes to the sex- specific prevalence, symptoms or progression observed in most complex diseases. These include many leading causes of death and disability, such as neurological and psychiatric disorders, cardiovascular diseases, autoimmune diseases, and cancer. This project will support the applicant’s long-term goal of advancing the search for X-linked complex disease genes while elucidating how evolutionary history and natural selection uniquely shaped human genetic variation on X. The objectives of this application are the development of methods and software for analyzing X in GWAS and sequence-based association studies, and their application for discovering X many risk loci underlying complex diseases. The rationale for performing this work is that it will reveal the role of X in the etiology of several diseases, and advance the exploration of sexual dimorphism in disease. This will be achieved by pursuing the following specific aims: 1) Develop new X-specific statistical and computational methods for X-wide association studies (XWAS), expression quantitative trait loci (eQTL) studies of X, and sex-specific, X-tailored analysis of DNase-seq experiments; 2) Facilitate accurate genotype calling and processing of X in sequence data, and develop X-optimized tests for rare variant association studies and identity-by-descent mapping; 3) Discover, replicate, and interpret X-linked associations, based on analysis and meta-analysis of data from hundreds of studies, with a focus on common psychiatric disorders, quantitative risk factors of coronary artery disease, and eQTL; 4) Develop open source, freely available software that implements all methods from Aim 1 and Aim 2, together with existing methods. The proposed research is innovative in that it will develop new approaches and methodologies to accurately analyze X, pioneering the inclusion of X in association studies and related fields. Its contribution will be novel statistical and computational methods tailored specifically for X, and insight into the role of X in several complex diseases and traits. The contribution will be further increased by the availability of software that facilitates analysis by others of the thousands of studies where X remains essentially unexplored. Overall, the proposed research is significant, and relevant to public health, because it will help reveal the role of X in human complex disease etiology, and help advance sex-specific disease diagnosis and treatment.