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，包括许多罕见和常见的基因变异。据报道，SNP 和 CNV 与精神障碍有关，但还有更多的信息有待发现。然而，尽管有大量高通量基因组学数据，但仍缺乏综合方法来系统地优先考虑赋予的变异。此外，尽管存在许多精神障碍的候选变异，但它们的致病机制仍然难以捉摸，因此无法设计适当的功能后续实验，总而言之，这些问题导致了大量的差异。关于遗传变异的数据以及我们对其对精神疾病的功能影响的理解，最终延迟了靶向治疗方法的开发。为了解决这些问题，我们建议开发一套新颖的生物信息学方法：（1）NeuroComplex，一种信息整合。接近那个利用表型信息和先前的生物学知识的多个来源，根据基因对特定表型表现的可能性进行排序（2）综合精神疾病基因组评分（iMEGES），它利用两层策略来预测变异的影响。第一层使用机器学习算法为编码、非编码和结构变异建立变异有害性评分；第二层整合变异评分和 NeuroComplex 来分配致病性评分。称为 CombOmics 的分层模型结合了跨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) 我们将开发用户友好的软件工具和网络。实施所有提到的生物信息学方法的应用程序，并不断发布和维护这些工具，以确保科学界的最大利益。