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Integrative methods for the identification of causal variants in mental disorder

识别精神障碍因果变异的综合方法

基本信息

批准号：
9262282
负责人：
Iuliana Ionita
金额：
$ 40.8万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-14 至 2019-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9262282
关键词：
Architecture Autistic Disorder Bioinformatics Biological Biological Assay Code Collaborations Communities Complex Computer software Confidence Intervals Data Data Set Development Disease Elements Generations Genes Genetic Genetic Variation Genomics Genotype Goals Gold Human In Vitro Individual Lead Maps Massive Parallel Sequencing Measures Mental disorders Methods Modeling Odds Ratio Pathogenesis Phenotype Play Population Proteins Psyche structure Public Health Reporter Research Research Personnel Resolution Role Schizophrenia Statistical Methods Technology Time Untranslated RNA Update Validation Variant Work autism spectrum disorder base cost efficient deep sequencing direct application epigenomics exome sequencing functional genomics genetic information genetic variant genome wide association study genome-wide high dimensionality interest neuropsychiatry novel public health relevance software development trait

项目摘要

DESCRIPTION (provided by applicant): The tremendous progress in massively parallel sequencing technologies enables investigators to obtain genetic information down to single base resolution on a genome-wide scale in a rapid and cost efficient manner. Despite this progress in data generation, it remains very challenging to analyze and interpret these data. The resulting datasets are high dimensional and very sparse, with millions of genetic variants, the vast majority of which are rare in the population. Identifying which of the many genetic variants in a region of interest are true causal variants is very difficult. Indeed, despite enormous progress in identifying robust associations in genome-wide association studies (GWAS) studies, the underlying causal variants for the vast majority of GWAS loci are unknown. The problem of identifying the underlying causal variants is of fundamental importance for understanding precise biological mechanisms. While experimental functional studies are the gold standard, they are expensive and difficult to implement for a large number of variants. Here we propose to develop state of the art and powerful statistical methods that integrate genome-wide functional annotation data with genetic data on a large number of individuals from whole-exome sequencing and GWAS studies of autism and schizophrenia to help us identify the true causal variants among the abundant natural variation that occurs at a particular locus of interest. The proposed statistical methods will be implemented into a publicly available software package. We believe that the proposed research is very timely and has the potential to be of great public health importance through direct application to autism and schizophrenia, and more broadly to other psychiatric diseases.