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.项目摘要/摘要 在通过全基因组关联研究努力了解复杂疾病的历史努力中 X染色体(X)通常被忽略或错误地分析，原因是分析 复杂性源于其独特的遗传模式和种群遗传模式。这一趋势已经 继续进行基于序列的关联研究，调控元件的全基因组研究，以及 基因表达的研究。除了占人类基因组的5%外，X很可能对性别有贡献-- 在大多数复杂疾病中观察到的特定流行率、症状或进展。其中包括许多 导致死亡和残疾的主要原因，如神经和精神障碍、心血管疾病、 自身免疫性疾病和癌症。该项目将支持申请者推进 寻找X连锁的复杂疾病基因，同时阐明进化史和自然选择 在X上形成独特的人类遗传变异。这项应用的目标是开发 分析GWAS和基于序列的关联研究中的X的方法和软件及其应用 发现了许多复杂疾病背后的风险基因。执行这项工作的理由是它 将揭示X在几种疾病的病因学中的作用，并推进对性二型性的探索 在疾病中。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 和X-wide关联研究的计算方法，表达数量性状基因座(EQTL) DNase-Seq实验的X研究和针对性别的X量身定制分析；2)促进准确的基因分型 序列数据中X的调用和处理，开发稀有变异关联的X优化测试 研究和身份-血统映射；3)发现、复制和解释X连锁关联，基于 对数百项研究的数据进行分析和荟萃分析，重点是常见的精神障碍， 冠状动脉疾病的量化危险因素和eQTL；4)开发开源的、免费的 实现来自目标1和目标2的所有方法以及现有方法的软件。建议数 研究是创新的，因为它将开发新的途径和方法来准确分析X， 率先将X纳入关联性研究和相关领域。它的贡献将是新的统计数据 以及专门为X量身定做的计算方法，以及对X在几种复杂疾病中的作用的洞察 和特点。由于提供了便于分析的软件，这一贡献将进一步增加 在数以千计的研究中，X基本上仍然是未被探索的。总体而言，拟议的研究是 重要的，与公共健康相关的，因为这将有助于揭示X在人类复杂疾病中的作用 病因学，并有助于推进特定性别疾病的诊断和治疗。

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.