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

• 批准号: 9304684
• 负责人: Heather Elizabeth Wheeler
• 金额: $ 42.9万
• 依托单位: LOYOLA UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9304684
• 关键词: Alzheimer' s Disease; Architecture; Atherosclerosis; Biological; Cholesterol; Cohort Studies; Complex; Computer software; DNA; DNA Sequence; Data; Data Set; Databases; Disease; European; Future; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic study; Genome; Genotype; Genotype-Tissue Expression Project; Height; Heritability; Individual; Knowledge; Lead; Machine Learning; Methods; Modeling; Names; Open Reading Frames; Pathway interactions; Performance; Phenotype; Play; Population; Population Genetics; Population Heterogeneity; Quantitative Trait Loci; Regulation; Role; Scanning; T-Lymphocyte; Testing; Tissues; Transcript; Triglycerides; Validation; Variant; Weight; base; data access; database of Genotypes and Phenotypes; drug development; experimental study; falls; follow-up; genetic association; genetic variant; genome wide association study; genome-wide; health disparity; improved; interest; lymphoblastoid cell line; malignant breast neoplasm; monocyte; novel; phenotypic data; precision medicine; predictive modeling; repository; response; statistics; trait; transcriptome

Project Summary A better understanding of the degree of transferability of genetic association results and implicated genes across populations has implications for precision medicine and can only be accomplished by studying the genetic architecture of complex traits in diverse populations. For many complex traits, gene regulation is likely to play a crucial mechanistic role given the consistent enrichment of regulatory variants among trait-associated variants. We have developed a gene-level association method called PrediXcan that harnesses the regulatory knowledge generated by expression quantitative trait loci (eQTL) studies to directly test for genes associated with complex traits. An advantage of this gene-based approach over other aggregate variant approaches is that the results are inherently mechanistic and provide directionality, guiding follow-up experiments and future drug development. The genetic contribution to population phenotypic differentiation is driven by differences in causal allele frequencies, effect sizes, and genetic architectures. We propose to broaden the scope of PrediXcan to include diverse populations by (1) optimizing predictors of gene expression within and across diverse populations in multiple tissues and (2) performing gene-level association studies and quantifying regulability on a range of phenotypes in non-European populations. We will use machine learning to optimize predictive models of gene expression in datasets with both genome-wide genotype and gene expression data. We will integrate prior results from larger European populations when appropriate. Based on preliminary results, we expect a range of predictive power (assessed by cross-validation R2) will be observed across genes dependent on the heritability of each gene expression trait and differences in allele frequencies and effect sizes among populations. We will compare populations by 1) calculating the correlation between heritability estimates and cross-validated prediction performance and by 2) by calculating trans-population genetic effect size correlations (allele frequency independent) and trans-population genetic impact correlations (allele frequency dependent). The optimal models will also inform the underlying genetic architectures (sparse vs. polygenic) of gene expression traits and how they vary across populations. As we have done for European populations, the predictive models and heritability estimates developed here will be added to an open access database for use in PrediXcan and other studies. We hypothesize that PrediXcan will increase power to identify genes and implicate mechanisms underlying complex traits and that we can quantify the overall effect of phenotypic variation explained by transcriptome regulation within and across populations. We will compare gene-level results across populations to determine if the same and/or unique genes and pathways are implicated for a particular phenotype. We will estimate the proportion of phenotypic variance explained collectively by all gene expression levels, which we name the regulability of a trait. All results, scripts, and software will be available in publicly accessible databases and repositories.

项目摘要 更好地理解遗传关联结果和牵连基因的可转移程度 对精准医学有影响，只能通过研究 复杂性状的遗传结构。对于许多复杂的性状，基因调控很可能是 发挥关键的机制作用，因为在性状相关的基因组中， 变体。我们开发了一种称为PrediXcan的基因水平关联方法， 表达数量性状基因座（eQTL）研究产生的知识，以直接测试相关基因 有复杂的特征这种基于基因的方法相对于其他聚合变体方法的优点是 这些结果具有内在的机械性，并提供了方向性，指导后续实验和未来 药物开发遗传对群体表型分化的贡献是由以下因素的差异驱动的： 因果等位基因频率、效应量和遗传结构。我们建议扩大 PrediXcan包括不同的人群，通过（1）优化基因表达的预测因子 在多个组织中的不同群体和（2）进行基因水平关联研究和定量 在非欧洲人群中的一系列表型的可调控性。我们将使用机器学习来优化 具有全基因组基因型和基因表达数据的数据集中的基因表达预测模型。 我们将在适当的时候整合来自更大欧洲人群的先前结果。根据初步 结果，我们预计将观察到一系列的预测能力（通过交叉验证R2评估）， 基因依赖于每个基因表达性状的遗传力和等位基因频率的差异， 人口的影响。我们将通过以下方式比较人口：1）计算 遗传力估计和交叉验证的预测性能和2）通过计算跨种群 遗传效应大小相关（等位基因频率无关）和跨群体遗传影响相关 （等位基因频率依赖性）。最佳模型还将告知潜在的遗传结构（稀疏 vs.多基因的）基因表达性状以及它们如何在人群中变化。正如我们为欧洲所做的那样， 在这里开发的预测模型和遗传力估计将被添加到开放获取中。 用于PrediXcan和其他研究的数据库。我们假设PrediXcan将增加功率， 确定基因和复杂性状的潜在机制，我们可以量化整体效果 通过群体内和群体间的转录组调节来解释表型变异。我们将比较 基因水平的结果，以确定是否相同和/或独特的基因和途径， 与特定的表型有关。我们将估计表型方差解释的比例 所有的基因表达水平，我们称之为性状的可调控性。所有结果、脚本和 软件将在可供公众查阅的数据库和储存库中提供。