Predicted Gene Expression: High Power, Mechanism, and Direction of Effect

 描述(申请人提供)：尽管对精神障碍的基因组研究的投资使数千个与这些复杂疾病密切相关的变异得以发现，但由于缺乏对基因组变异如何与表型相关的机械性理解，这些发现被阻碍了将这些发现转化为可操作的目标。此外，广泛的研究表明，包括精神障碍在内的复杂性状的很大一部分遗传控制是通过基因表达的调节来实现的。然而，充分利用这一机制的有效方法是滞后的。为了应对这些挑战，我们提出了一种新的基于基因的测试-PrediXcan-它直接测试这种调节机制，并大大提高了相对于单一变体测试和其他基于基因的测试的能力。PrediXcan本质上是机械性的，并提供方向性，突出了它在确定新的治疗靶点方面的潜在效用。该方法由以下部分组成 预测整个基因组对表达特征的影响，并将这种影响与疾病风险相关联，以识别新的疾病基因。此外，我们提出了新的方法来研究表达特征的上下文特异性(正交组织分解)，并量化受调控的转录组对感兴趣的表型的集体效应(可调节性)。可调节性类似于芯片遗传性的概念(通过基因分型变异解释的总体变异性)。首先，我们将开发跨组织、组织特定的(针对30多种不同的人类组织类型)和大脑区域特定的表达特征。我们将使用统计机器学习方法来开发这些特征的全基因组预测模型，并将这项工作扩展到其他分子表型。所有模型都将存储在开放获取数据库中。接下来，我们将把PrediXcan方法应用于7种精神障碍表型。更具体地说，我们将计算基因表达特征的遗传预测水平，并将它们与疾病风险相关联，以确定参与疾病途径的基因。我们还将量化以下项目的集体效应 跨多个组织的精神障碍风险的预测转录组(可调控性)。最后，我们将扩展PrediXcan方法，并开发一种使用汇总统计数据而不是单个级别数据来推断PrediXcan结果的方法。这将把该方法的适用性扩展到荟萃分析联盟产生的所有总结结果，并在样本量较大的情况下增加发现新基因的能力。我们提出的研究是由一系列广泛的初步研究推动的，并承诺在新的分析方法和公共访问结果数据库方面取得实质性成果。

 DESCRIPTION (provided by applicant): Although investments in genomic studies of mental disorders enabled the discovery of thousands of robustly associated variants with these complex diseases, the translation of these discoveries into actionable targets has been hampered by the lack of a mechanistic understanding on how genome variation relates to phenotype. Moreover, it has been widely shown that a substantial portion of the genetic control of complex traits, including mental disorders, is exerted through the regulation of gene expression. However, effective methods to fully harness this mechanism are lagging. To address these challenges, we propose a novel gene-based test -PrediXcan- that directly tests this regulatory mechanism and substantially improves power relative to single variant tests and other gene-based tests. PrediXcan is inherently mechanistic and provides directionality, highlighting its potential utility in identifying novel targets for therapy. The method consists of predicting the whole genome effect on expression traits and correlating this effect with disease risk to identify novel disease genes. In addition, we propose novel approaches to investigate the context-specificity of expression traits (Orthogonal Tissue Decomposition) and to quantify the collective effect of the regulated transcriptome on phenotypes of interest (Regulability). Regulability is similar to the concept of chip heritability (total variability explained collectivey by genotyped variants). First, we will develop cross-tissue, tissue-specific (for over 30 different human tissue types), and brain-region specific expression traits. We will use statistical machine learning methods to develop whole genome prediction models for these traits and extend this work to other molecular phenotypes. All models will be stored in open access databases. Next, we will apply the PrediXcan method to 7 mental disorder phenotypes. More specifically, we will compute genetically predicted levels of gene expression traits and correlate them with disease risk to identify genes involved in disease pathways. We will also quantify the collective effect of the predicted transcriptome (regulability) on mental disorder risk across multiple tissues. Finally we will extend PrediXcan method and develop a method to infer the results of PrediXcan using summary statistics data as opposed to individual level data. This will extend the applicability of the approach to all summary results generated by meta-analysis consortia and increase the power to discover novel genes given the larger sample sizes. The research we propose is driven by an extensive set of preliminary studies, and promises substantial deliverables in both new methods of analysis and public access results databases.