Integrative Approaches to Mapping Susceptibility Genes of Complex Neuropsychiatric Disorders

绘制复杂神经精神疾病易感基因的综合方法

• 批准号: 9311685
• 负责人: Xin He
• 金额: $ 57.77万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-17 至 2020-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311685
• 关键词: Affect; Algorithms; Amino Acid Sequence; Autistic Disorder; Biological; Case-Control Studies; Child; Chromosome Mapping; Collaborations; Communities; Complex; Computer software; Copy Number Polymorphism; Cost of Illness; DNA; Data; Disease; Epilepsy; Etiology; Event; Family; Floods; Frequencies; Gene Expression; Gene Frequency; Genes; Genetic; Genetic Variation; Genetic study; Genomics; Genotype; Goals; Hereditary Disease; Heritability; Histones; Inherited; Joints; Knowledge; Lead; Link; Maps; Mental disorders; Methods; Missense Mutation; Molecular; Mutation; Neurodevelopmental Disorder; Nucleotides; Onset of illness; Parents; Patients; Phenotype; Play; Population; Quantitative Trait Loci; Regulatory Element; Research; Resources; Role; Schizophrenia; Scientist; Statistical Methods; Susceptibility Gene; Testing; Time; Tissues; Translating; Untranslated RNA; Variant; computerized tools; data to knowledge; design; developmental disease; early onset; epigenomics; exome sequencing; gene discovery; genetic analysis; genetic variant; genome sequencing; genome wide association study; genome-wide analysis; improved; insight; neuropsychiatric disorder; novel; open source; psychogenetics; risk variant; statistics; success; therapeutic target; trait; user friendly software; whole genome

Project Summary Identifying the susceptibility genes and variants of neurodevelopmental and psychiatric diseases will not only contribute to our understanding of these diseases, but also point to potential therapeutic targets. Genome-wide association studies (GWAS) are commonly used to study complex diseases, including neuro-psychiatric diseases. Nevertheless, GWAS focus on common variants, and have not been successful in studying early- onset diseases, including many developmental disorders, whose risk alleles are generally kept at very low frequencies in population. Additionally, the results of GWAS often cannot be directly translated into knowledge of risk genes and disease mechanisms. The goal of this project is to develop comprehensive statistical methods for analyzing genetic data of neuropsychiatric diseases to map their susceptibility genes and gain insights of the disease genetics. (1) We propose methods to analyze exome and genome sequencing data from patient families. Unlike existing methods for genetic studies which often focus on type of data per time, our methods will integrate a broad spectrum of genetic variations at the level of genes, including non-synonymous and regulatory non-coding mutations, both de novo and inherited from parents in origin. This leads to a higher power of detecting risk genes. (2) Copy number variants (CNVs) make substantial contribution to neurodevelopmental disorders. But CNVs often overlap multiple genes and it is difficult to identify risk genes within disease-related CNVs. A new algorithm is proposed to extract gene-level information from CNVs. This allows us to combine CNV data and nucleotide variation data from sequencing, to better detect disease genes. (3) Importance of non-coding variants to complex disease has now been firmly established and expression QTL (eQTL) is a promising strategy to map non-coding variants that have functional effects on gene expression levels. We propose a novel statistical approach to joint analysis of eQTL and GWAS data. The method is unique in that it uses information of all eQTL of a gene to test its role in disease, including both cis- and trans-eQTL, across the entire range of effect sizes. (4) A key component of our effort is the integration of the methods we develop into user-friendly software that could benefit the broad psychiatric genetic community.

项目概要 识别神经发育和精神疾病的易感基因和变异不仅可以 有助于我们了解这些疾病，同时也指出了潜在的治疗靶点。全基因组 关联研究（GWAS）通常用于研究复杂疾病，包括神经精神疾病 疾病。然而，GWAS 专注于常见变异，并没有成功地研究早期- 发病疾病，包括许多发育障碍，其风险等位基因通常保持在非常低的水平 人口中的频率。此外，GWAS的结果往往不能直接转化为知识 风险基因和疾病机制。 该项目的目标是开发用于分析遗传数据的综合统计方法 神经精神疾病，绘制其易感基因图谱并深入了解疾病遗传学。 (1) 我们 提出分析患者家属的外显子组和基因组测序数据的方法。与现有的不同 遗传研究方法通常侧重于每次数据类型，我们的方法将整合广泛的 基因水平的遗传变异谱，包括非同义变异和监管非编码变异 突变，包括从头开始的突变和从父母遗传的突变。这导致检测风险的能力更高 基因。 (2) 拷贝数变异 (CNV) 对神经发育障碍有重大影响。但 CNV 通常与多个基因重叠，因此很难识别疾病相关 CNV 中的风险基因。一个新的 提出了从CNV中提取基因级信息的算法。这使我们能够将 CNV 数据与 来自测序的核苷酸变异数据，以更好地检测疾病基因。 (3)非编码的重要性 复杂疾病的变异现已牢固确立，表达 QTL (eQTL) 是一种有前途的方法 绘制对基因表达水平具有功能性影响的非编码变体的策略。我们提出一个 eQTL 和 GWAS 数据联合分析的新颖统计方法。该方法的独特之处在于它使用 基因的所有 eQTL 信息，以测试其在疾病中的作用，包括顺式和反式 eQTL。 整个范围的效应大小。 (4) 我们努力的一个关键组成部分是将我们开发的方法整合到 用户友好的软件可以使广泛的精神病遗传界受益。