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Methods for linking GWAS peaks to function in psychiatric disease

将 GWAS 峰值与精神疾病功能联系起来的方法

基本信息

批准号：
8944830
负责人：
Benjamin Michael Neale
金额：
$ 74.6万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-01 至 2018-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8944830
关键词：
Accounting Area Autistic Disorder Biological Bipolar Disorder Brain region Cataloging Catalogs Categories Collaborations Communities Computer software Computing Methodologies DNA Data Data Set Deoxyribonucleases Development Disease Enhancers Ensure Explosion Genes Genetic Genetic Markers Genetic Variation Genome Genomics Genotype Heritability Hypersensitivity Joints Link Linkage Disequilibrium Maps Mental disorders Methods Molecular Polygenic Traits Probability Process Publications Quantitative Trait Loci RNA Sequence Analysis RNA Sequences RNA Splicing Relative (related person)Research Rest Role Sample Size Sampling Schizophrenia Site Source Statistical Methods Techniques Time Transcript Variant base brain tissue computerized tools disease phenotype empowered epigenomics genetic variant genome wide association study genome-wide genome-wide analysis improved indexing insight novel programs public health relevance response simulation statistics tool transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have been extremely successful in identifying variants associated to schizophrenia and other psychiatric disorders, but the molecular impact of these variants remains largely unknown. Meanwhile, an explosion of functional data has been generated by ENCODE, ROADMAP Epigenomics and other projects, making the development of new statistical methods to link associations in genetic data to biological interpretations from functional data a pressing priority Our specific aims are 1) To partition psychiatric disease heritability by functional category. We propose to understand at a global level the relative importance of different classes of variation i explaining heritability of mental illness. 2) To maximize biological inferences from expression data in brain tissues. We propose to extend current methods for the processing and analysis of RNA-sequencing data to improve identification of quantitative trait loci that influence expression levels and splicing diversity. 3) To use prioritized functional categories and QTLs to fine-map genome-wide significant loci. We will integrate the results of aims 1 and 2 to more thoroughly understand which genetic variants drive genome-wide significant associations to mental illness. We will guide our research using >200,000 samples from genome-wide associations studies of psychiatric disease. The methods we propose to develop will be implemented in software packages that we will make widely available to the community.