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Integrative analysis of genetic variation and transcription factor networks to elucidate mechanisms of mental health disorders

遗传变异和转录因子网络的综合分析以阐明精神健康障碍的机制

基本信息

批准号：
9886483
负责人：
Harmen J Bussemaker
金额：
$ 70.78万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2024-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9886483
关键词：
Affect Affinity Algorithms Alleles Animal Model Attention Binding Binding Sites Biology Bipolar Depression Bipolar Disorder CRISPR interference Catalogs Cells ChIP-seq Chromosome Mapping Clinical Complex Computing Methodologies DNA DNA Binding DNA-Protein Interaction Data Data Set Disease Environment Gene Expression Genes Genetic Genetic Determinism Genetic Diseases Genetic Polymorphism Genetic Risk Genetic Transcription Genetic Variation Genome Genomics Genotype-Tissue Expression Project Human Human Genetics In Vitro Individual Instruction Intervention Knowledge Light Linear Models Maps Mediator of activation protein Mental Health Mental disorders Methodology Methods Modeling Molecular Nuclear Pathway interactions Pharmacology Physiological Population Post-Translational Protein Processing Proteins Quantitative Genetics Quantitative Trait Loci Regulator Genes Regulatory Element Regulon Research Personnel Risk Role Schizophrenia Therapeutic Tissue Sample Tissues Trans-Omics for Precision Medicine Unipolar Depression Untranslated RNA Variant autism spectrum disorder base cell type cohort design disease phenotype disorder risk experimental study functional genomics genetic analysis genetic association genetic variant genome sequencing genome wide association study genome-wide human data human disease human tissue improved innovation inter-individual variation interest mRNA Expression promoter rare variant risk variant trait transcription factor transcriptome transcriptome sequencing whole genome

项目摘要

PROJECT SUMMARY In this project we will bridge the traditionally largely distinct fields of quantitative genetics and mechanistic biology to obtain a mechanistic understanding of regulatory effects of genetic variants in humans. Leveraging on large human data sets providing parallel whole genome and transcriptome sequencing data, we will extend proof-of-principle studies and computational approaches developed and validated in model organisms to achieve improved functional interpretation of GWAS loci associated to mental health disorders. We focus specifically on the role of transcription factors as both upstream regulators of genetic risk variants as well as mediators of downstream network-level effects. As Aim 1, we will develop extend methods to allow accurate modeling of transcription factor activity from transcriptome data from large cohorts of human tissue samples in GTEx, PsychENCODE, and TOPMed cohorts. These data will be used in Aim 2 to dissect the mechanisms underlying proximal genetic regulatory variants in cis. We hypothesize that dynamics of transcription factor activity and binding modifies the effect size of genetic regulatory variants across individuals, tissues, and cell types, and that by modeling this relationship we can detect TFs regulating specific regulatory variants and noncoding disease-associated loci. In parallel Aim 3, we will map network-level trans-acting genetic variants for inter-individual variation in TF activity. Going beyond treating TF activity as a tissue-specific parameter of the cellular environment, we will now consider it as a variable quantitative trait itself, and by GWAS/TWAS for inferred TF activity, we map specific polymorphisms that affect TF activity within each tissue. We anticipate that the trans-acting loci discovered in this analysis will be of major interest not only to basic biology of regulatory networks, but also for explaining GWAS associations to complex diseases, and to mental health in particular.

项目总结