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

预测基因表达：高功效、机制和作用方向

• 批准号: 9130902
• 负责人: Hae Kyung Im
• 金额: $ 43.06万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-21 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9130902
• 关键词: Address; Alzheimer' s Disease; Anorexia Nervosa; Architecture; Attention deficit hyperactivity disorder; Autistic Disorder; Bioinformatics; Biological; Biology; Bipolar Disorder; Brain region; Complex; Computer software; Computing Methodologies; Coupled; Data; Data Set; Databases; Disease; Disease Pathway; Etiology; Exons; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Genetic screening method; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Health; Heritability; Histocompatibility Testing; Human; Individual; Investments; Learning; Link; Machine Learning; Mental Depression; Mental disorders; Meta-Analysis; Methods; Methylation; MicroRNAs; Modeling; Molecular; National Institute of Mental Health; Obsessive-Compulsive Disorder; Performance; Phenotype; Population; Process; Quality Control; Regulation; Research; Sample Size; Schizophrenia; Source; Specificity; Testing; Tissue Model; Tissues; Training; Translations; Variant; Work; base; database of Genotypes and Phenotypes; disorder risk; drug development; gene function; genetic variant; genome wide association study; human tissue; improved; insight; interest; learning strategy; molecular phenotype; new therapeutic target; novel; novel strategies; statistics; tool; trait; transcriptome; user friendly software; web app; whole genome

 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.

 描述（由申请人提供）：尽管对精神障碍基因组研究的投资使得能够发现数千种与这些复杂疾病密切相关的变异，但由于缺乏对基因组变异与表型相关性的机制理解，这些发现转化为可操作的靶点受到阻碍。此外，已经广泛表明，包括精神障碍在内的复杂性状的遗传控制的很大一部分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仍然滞后。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-直接测试这种调节机制，并大大提高了相对于单变异测试和其他基于基因的测试的功率。PrediXcan具有内在的机制性，并提供方向性，突出了其在识别新的治疗靶点方面的潜在效用。该方法包括 预测全基因组对表达性状的影响，并将这种影响与疾病风险相关联，以鉴定新的疾病基因。此外，我们还提出了新的方法来研究表达性状的上下文特异性（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影响（可调控性）。可调节性类似于芯片遗传性的概念（总变异性由基因型变异共同解释）。首先，我们将开发跨组织，组织特异性（超过30种不同的人类组织类型）和大脑区域特异性表达特征。我们将使用统计机器学习方法为这些性状开发全基因组预测模型，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，我们将对7种精神障碍表型应用PrediXcan方法。更具体地说，我们将计算基因表达特征的遗传预测水平，并将其与疾病风险相关联，以确定参与疾病途径的基因。我们还将量化的集体效应， 预测转录组（可调控性）对多个组织中精神障碍风险的影响。最后，我们将扩展PrediXcan方法，并开发一种方法来推断PrediXcan的结果，使用汇总统计数据，而不是个人水平的数据。这将扩大该方法的适用性，所有汇总结果的荟萃分析财团和增加的权力，发现新的基因给定的更大的样本量。我们提出的研究是由一系列广泛的初步研究驱动的，并承诺在新的分析方法和公共访问结果数据库中提供实质性的成果。