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

7.项目总结/摘要 在通过全基因组关联研究努力了解复杂疾病的历史奋进中， （GWAS）中，X染色体（X）通常由于分析缺陷而被忽视或被错误地分析。 复杂性源于其独特的遗传模式和群体遗传模式。这一趋势 转入基于序列的关联研究，调控元件的全基因组研究， 基因表达的研究。除了占人类基因组的5%之外，X还可能对性别产生影响- 在大多数复杂疾病中观察到的特定患病率、症状或进展。其中包括许多 死亡和残疾的主要原因，如神经和精神疾病，心血管疾病， 自身免疫性疾病和癌症。该项目将支持申请人的长期目标， 寻找X连锁的复杂疾病基因，同时阐明进化历史和自然选择 X染色体上独一无二的人类基因变异本申请的目的是开发 在GWAS和基于序列的关联研究中分析X的方法和软件及其应用 发现了许多复杂疾病的潜在危险基因。执行这项工作的理由是， 将揭示X在几种疾病病因学中的作用，并推进对两性异形的探索 疾病。这将通过追求以下具体目标来实现：1）开发新的X特定统计 X-wide association studies（XWAS），expression quantitative trait loci（eQTL） X研究，性别特异性，X定制的DNase-seq分析实验; 2）促进准确的基因型 调用和处理序列数据中的X，并开发针对罕见变异关联的X优化测试 研究和身份的血统映射; 3）发现，复制，并解释X连锁协会，基于 对来自数百项研究的数据进行分析和荟萃分析，重点关注常见的精神疾病， 冠状动脉疾病的定量风险因素和eQTL; 4）开发开源，免费提供 实现目标1和目标2中所有方法以及现有方法的软件。拟议 研究是创新的，因为它将开发新的方法和方法来准确地分析X， 开创性地将X纳入关联研究和相关领域。它的贡献将是新颖的统计 和专门为X量身定制的计算方法，并深入了解X在几种复杂疾病中的作用 和特质如果有软件， 在成千上万的研究中，X仍然基本上未被探索。总的来说，拟议的研究是 这是一项意义重大的研究，与公共卫生有关，因为它将有助于揭示X在人类复杂疾病中的作用。 病因学，并帮助推进性别特异性疾病的诊断和治疗。

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.