Predicting gene regulation across populations to understand mechanisms underlying complex traits

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，我们已经 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。

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.