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：
10550151
负责人：
Harmen J Bussemaker
金额：
$ 77.66万
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/10550151
关键词：
Affect Affinity Algorithms Alleles Attention Binding Binding Sites Biology Bipolar Disorder CRISPR interference Catalogs Cells ChIP-seq Chromosome Mapping Clinical Complex Computing Methodologies Cytoplasm DNA DNA Binding DNA-Protein Interaction Data Data Set Disease Environment 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 Linear Models Maps Mediator Mental Depression Mental Health Mental disorders Methodology Methods Modeling Molecular Nuclear Pathway interactions Physiological Population Post-Translational Protein Processing Proteins Quantitative Genetics Quantitative Trait Loci 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 cell type cohort disease phenotype disorder risk experimental study functional genomics gene expression variation gene regulatory network 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 model organism pharmacologic promoter rare variant rational design 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.

项目概要在这个项目中，我们将弥合传统上截然不同的定量遗传学和机械学领域生物学以获得对人类遗传变异的调节作用的机械理解。杠杆作用在提供并行全基因组和转录组测序数据的大型人类数据集上，我们将扩展在模型生物体中开发和验证原理验证研究和计算方法，以实现改进了与精神健康障碍相关的 GWAS 位点的功能解释。我们专注特别是转录因子作为遗传风险变异上游调节因子的作用以及下游网络水平效应的中介者。作为目标 1，我们将开发扩展方法以允许准确的根据大量人体组织样本的转录组数据对转录因子活性进行建模在 GTEx、PsychENCODE 和 TOPMed 队列中。这些数据将用于目标 2 来剖析机制顺式中潜在的近端遗传调控变异。我们假设转录因子的动力学活性和结合改变个体、组织和细胞中基因调控变异的效应大小类型，通过对这种关系进行建模，我们可以检测调节特定调节变体的转录因子，非编码疾病相关位点。在平行目标 3 中，我们将绘制网络级反式作用遗传变异图 TF 活性的个体间差异。超越将 TF 活性视为组织特异性参数的细胞环境，我们现在将其视为一个可变的数量性状本身，并通过 GWAS/TWAS 进行根据推断的 TF 活性，我们绘制了影响每个组织内 TF 活性的特定多态性。我们预计在该分析中发现的反式作用基因座不仅对调控的基础生物学具有重大意义网络，而且还用于解释 GWAS 与复杂疾病，特别是与心理健康的关联。