Predicting gene regulation across populations to understand mechanisms underlying complex traits

项目摘要 更好地理解遗传关联结果和相关基因的可转移性 跨人群对精准医学有影响，只能通过研究 不同群体中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 发挥关键的机械性作用，因为在与性状相关的 变种。我们已经开发了一种名为PrediXcan的基因水平关联方法，它利用 通过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 有着复杂的特征。与其他聚合变体方法相比，这种基于基因的方法的优势在于 结果本质上是机械性的，并提供方向性，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 因果等位基因频率、效应大小和遗传结构。我们建议扩大以下范围： PrediXcan将通过(1)优化内部和跨区域的基因表达预测因子来包括不同的人群 多个组织中的不同群体以及(2)进行基因水平的关联研究和量化 在非欧洲人群中对一系列表型的可调性。我们将使用机器学习来优化 具有全基因组基因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多欧洲人口的先前结果。基于初步的 结果，我们预计将观察到一系列的预测力(通过交叉验证R2评估) 基因取决于每个基因表达性状的遗传力和等位基因频率的差异 在人群中的影响大小。我们将通过1)计算两者之间的相关性来比较种群 遗传力估计和交叉验证的预测性能以及2)通过计算跨种群 遗传效应大小相关(等位基因频率无关)和跨种群遗传影响相关 (等位基因频率相关)。最优模型还将告知底层遗传结构(稀疏 与多基因相比)，以及基因表达特征在不同种群之间的差异。就像我们为欧洲人所做的那样 这里开发的种群、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 确定基因并揭示复杂性状背后的机制，我们可以量化总体影响 由种群内和种群间的转录组调节解释的表型变异。我们会比较一下 跨种群的基因水平结果，以确定相同和/或唯一的基因和途径是否 与特定的表型有关。我们将估计解释的表型差异的比例 统称为所有基因表达水平，我们称之为性状的可调性。所有结果、脚本和 软件将在公众可访问的数据库和储存库中提供。

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.