权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Predicting gene regulation across populations to understand mechanisms underlying complex traits

预测人群中的基因调控，以了解复杂性状背后的机制

基本信息

批准号：
10652921
负责人：
Heather Elizabeth Wheeler
金额：
$ 43.65万
依托单位：
LOYOLA UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10652921
关键词：
African African American Alleles Authorship Biological Catalogs Chronic Disease Collaborations Complex DNA Data Data Sources Development Disease Disease Outcome Disease susceptibility East Asian Equilibrium Equity European European ancestry Filipino Gene Expression Gene Expression Regulation Gene Frequency Gene Proteins Genes Genetic Genetic study Genotype Goals Heterogeneity Hispanic Hispanic Populations Historical Demography Human Human Genetics Japanese Knowledge Linkage Disequilibrium Machine Learning Maps Mendelian randomization Methods Modeling Molecular Multi-Ethnic Study of Atherosclerosis Multiomic Data Pathway interactions Phenotype Population Population Heterogeneity Positioning Attribute Proteins Proteome Proteomics Publications Publishing Quantitative Trait Loci Rheumatoid Arthritis Risk Risk Assessment Sample Size Sleep Structure Testing Training Trans-Omics for Precision Medicine Transcript Ulcerative Colitis Variant Work biobank causal variant epidemiological model genetic architecture genetic predictors genetic variant genome sequencing genome wide association study genome-wide gut microbiome gut microbiota improved interest microbiome microbiome research model building novel polygenic risk score portability precision medicine predictive modeling risk prediction statistics trait transcriptome transcriptome sequencing undergraduate student whole genome

项目摘要

Project Summary Most chronic diseases are polygenic with hundreds to thousands of causal variants, and we are starting to predict disease susceptibility with risk scores derived from genome-wide association studies. However, 77% of training data for these risk scores come from European ancestries populations and thus do not include genetic variants uniquely or more predominantly found in non-European populations, which limits both discovery and precision medicine potential. Methods that better identify causal variants and implicated biological mechanisms across populations are essential for equitable precision medicine implementation and can only be accomplished by studying the genetic architectures of complex traits in diverse populations. Since this project began, we have characterized the genetic architecture of the transcriptome and proteome within and across diverse populations. We identified a subset of transcripts and proteins that are well-predicted in one population, but poorly predicted in another and showed these differences are due, in part, to allele frequency and linkage disequilibrium differences. When testing prediction accuracy, we have shown that we need to consider both similarity in training and test population ancestries and total training sample size to optimally predict gene expression or protein abundance. In this proposal, we seek to drive mechanistic understanding of complex traits in diverse populations by (1) improving omics-trait prediction models for maximum utility within and between diverse populations and (2) investigating causal relationships between omics traits and complex traits and disease in diverse populations. We will integrate multi-omics data from African, African American, East Asian, European, and Hispanic populations in this project, including genome-wide genotype, transcriptome, proteome, and microbiome data. Since allele frequencies and linkage disequilibrium structures differ between populations due to different demographic histories, genetic prediction models trained in one population do not perform as well in another and thus are currently of limited utility for risk prediction and mechanistic interpretation. We will use fine-mapping, machine learning, and multivariate adaptive shrinkage to improve genotypic prediction of gene expression and protein levels across populations. Predicting the transcriptome and proteome from genotype data allows inference of whether high or low transcript or protein levels are associated with traits of interest, but false positives often result from linkage disequilibrium. We will integrate Mendelian randomization and colocalization sensitivity analyses into our PrediXcan method to test for causal relationships of transcripts, proteins, gut microbiota, or other exposures on disease outcomes across diverse populations. Together, our proposed aims have the potential to identify likely causal genes and molecular pathways underlying complex diseases. Our aims work toward development of effective risk assessment and potential treatment targets in diverse populations. Our team is well positioned to perform novel PrediXcan-based analyses of omics traits in diverse populations and promises to maximize impact by making our scripts, models, and results publicly available.

项目摘要大多数慢性病都是多基因的，有数百到数千种致病变异，我们开始预测，疾病易感性与风险评分来自全基因组关联研究。77%的培训这些风险评分的数据来自欧洲血统人群，因此不包括遗传变异在非欧洲人群中唯一或更主要地发现，这限制了发现和精确度医学潜力更好地识别因果变异和涉及的生物学机制的方法人口对于公平的精准医疗实施至关重要，只能通过以下方式实现研究不同种群中复杂性状的遗传结构。自从这个项目开始以来，我们特征的转录组和蛋白质组内和跨不同人群的遗传结构。我们确定了一个转录本和蛋白质的子集，这些转录本和蛋白质在一个群体中预测良好，但预测不佳在另一项研究中，发现这些差异部分是由于等位基因频率和连锁不平衡造成的。差异当测试预测准确性时，我们已经表明，我们需要在训练中考虑两者的相似性。并测试群体祖先和总训练样本大小，以最佳地预测基因表达或蛋白质丰饶。在这个提议中，我们试图推动对不同人群中复杂性状的机械理解通过（1）改进组学-性状预测模型，以在不同群体内和群体之间实现最大效用， (2)调查不同人群中组学特征和复杂特征与疾病之间的因果关系。我们将整合来自非洲、非裔美国人、东亚、欧洲和西班牙裔的多组学数据本项目中的人群，包括全基因组基因型，转录组，蛋白质组和微生物组数据。由于等位基因频率和连锁不平衡结构在群体之间由于不同的遗传因素而不同，人口统计学历史，在一个群体中训练的遗传预测模型在另一个群体中表现不佳，因此目前对于风险预测和机械解释的实用性有限。我们将使用精细映射，机器学习和多变量自适应收缩，以改善基因表达的基因型预测，不同人群的蛋白质水平从基因型数据预测转录组和蛋白质组，高或低转录物或蛋白质水平与感兴趣的性状相关的推断，但错误阳性通常由连锁不平衡引起。我们将整合孟德尔随机化和共定位对我们的PrediXcan方法进行敏感性分析，以测试转录本、蛋白质、肠道微生物群或其他暴露对不同人群疾病结果的影响。我们共同提出的目标有潜力确定可能的致病基因和复杂疾病的分子途径。我们的目标致力于在不同人群中开发有效的风险评估和潜在的治疗目标。我们的团队有能力在不同人群中进行基于PrediXcan的组学特征分析并承诺通过公开我们的脚本、模型和结果来最大化影响。