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.

项目摘要在这个项目中，我们将桥接传统的定量遗传学和机械性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。利用在提供平行整个基因组和转录组测序数据的大型人类数据集中，我们将扩展原则证明研究和计算方法在模型生物中开发和验证以实现改善了与心理健康障碍相关的GWAS基因座的功能解释。我们集中精力具体而言，转录因子的作用是遗传风险变异的上游调节剂以及下游网络级效应的介体。作为目标1，我们将开发扩展方法以允许准确来自大量人类组织样品的转录组数据的转录因子活性的建模在GTEX，Psychencode和Topmed Colets中。这些数据将用于AIM 2用于剖析机制顺式中的基本遗传调节变体。我们假设转录因子的动力学活性和结合改变了个体，组织和细胞的遗传调节变异的效果大小类型，通过对这种关系进行建模，我们可以检测到调节特定调节变体的TFS和非编码疾病相关的基因座。在平行AIM 3中，我们将绘制网络级的反式遗传变异用于TF活性的个体间变化。超越将TF活性视为组织特异性参数在蜂窝环境中，我们现在将其视为可变定量性状本身，而GWAS/TWA则将其视为推断的TF活性，我们绘制影响每个组织内TF活性的特定多态性。我们预料到这一点在此分析中发现的跨作用基因座不仅对监管的基本生物学具有重要意义网络，但也用于解释GWAS与复杂疾病，尤其是对心理健康的关联。