UNDERSTANDING THE FUNCTIONAL IMPACTS OF GENETIC VARIANTS IN MENTAL DISORDERS

了解遗传变异对精神疾病的功能影响

• 批准号: 9389287
• 负责人: Kai Wang
• 金额: $ 38.15万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-11-19 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9389287
• 关键词: Address; Algorithms; Autistic Disorder; Bayesian Modeling; Benchmarking; Bioinformatics; Biological; Candidate Disease Gene; Clinical; Code; Communities; Computer software; Computing Methodologies; DNA; Data; Data Set; Development; Disease; Disease model; Ensure; Epigenetic Process; Family; Gene Expression; Gene Proteins; Generations; Genes; Genetic; Genetic Annotation; Genetic Variation; Genome; Genomics; Individual; Informatics; Internet; Knowledge; Life; Machine Learning; Malignant Neoplasms; Mental disorders; Methods; Modeling; Ontology; Outcome; Pathogenicity; Patient Selection; Patients; Performance; Phenotype; Predisposition; Procedures; Psyche structure; Psychiatry; Reporting; Research Methodology; Research Personnel; Role; Sampling; Schizophrenia; Societies; Software Tools; Source; Surveys; System; Techniques; Testing; Text; Uncertainty; Untranslated RNA; Validation; Variant; base; cohort; design; disease phenotype; disorder risk; experimental study; follow-up; genetic information; genetic variant; genome sequencing; genome wide association study; genomic data; high dimensionality; improved; individualized medicine; knowledge integration; novel; personalized therapeutic; prototype; public health relevance; response; skills; social; success; targeted treatment; tool; treatment strategy; user friendly software; user-friendly; web app

 DESCRIPTION (provided by applicant): In the US, 46% are afflicted with a mental illness at some point of life. The management and treatment of mental disorders pose significant economical and social burden to the society. Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders; additionally, despite the presence of many candidate variants for mental disorders, their disease-contributory mechanism remains elusive, so appropriate functional follow-up experiments cannot be designed. Altogether, these problems resulted in a large gap between the copious amount of data about genetic variation and our understanding of their functional impacts on mental diseases, which ultimately delays the development of targeted therapeutic approaches. To address these problems, we propose to develop a suite of novel bioinformatics approaches: (1) NeuroComplex, an information integration approach that leverages phenotype information and multiple sources of prior biological knowledge to rank genes by their likelihood of contributing to specific phenotypic presentations. (2) integrated MEntal-disorder GEnome Score (iMEGES), which leverages a two-layer strategy to predict the impacts of variants in personal genomes on mental disorders. The first layer uses machine learning algorithm to build variant deleteriousness scores for coding, non-coding and structural variants; the second layer integrates variant scores and NeuroComplex to assign pathogenicity scores. (3) a hierarchical model called CombOmics that incorporates cross-omics information to characterizes how variants confers disease risk, in order to help formulate hypothesis of pathogenicity and facilitate the design of functional follow-up experiments. (4) we will develop user-friendly software tools and web applications that implement all the aforementioned bioinformatics approaches, and continuously release and maintain these tools to ensure the maximum benefits to the scientific community.

 描述（由申请人提供）：在美国，46%的人在生命的某个阶段患有精神疾病。精神障碍的管理和治疗给社会带来了巨大的经济和社会负担。据报道，许多罕见和常见的遗传变异，包括SNP和CNV，与精神障碍有关，但仍有待发现。然而，尽管有大量的高通量基因组学数据，但缺乏综合方法来系统地优先考虑赋予精神障碍易感性的变异;此外，尽管存在许多精神障碍的候选变异，但其疾病贡献机制仍然难以捉摸，因此无法设计适当的功能性随访实验。总而言之，这些问题导致了大量关于遗传变异的数据与我们对其对精神疾病功能影响的理解之间存在巨大差距，最终推迟了靶向治疗方法的发展。为了解决这些问题，我们建议开发一套新的生物信息学方法：（1）NeuroComplex，一种信息整合方法，利用表型信息和多种来源的先验生物学知识，根据基因对特定表型表达的可能性对基因进行排序。(2)综合精神障碍基因组评分（iMEGES），它利用两层策略来预测个人基因组变异对精神障碍的影响。第一层使用机器学习算法来构建编码、非编码和结构变体的变体致病性评分;第二层集成变体评分和NeuroComplex来分配致病性评分。(3)一个名为CombOmics的分层模型，它结合了交叉组学信息来表征变异如何赋予疾病风险，以帮助制定致病性假设并促进功能性后续实验的设计。(4)我们将开发方便用户的软件工具和网络应用程序，以实施上述所有生物信息学方法，并不断发布和维护这些工具，以确保科学界获得最大惠益。