Novel statistical methods for transcriptomic imputation to enhance understanding of causal mechanisms underlying human diseases

全基因组关联研究已经成功地确定了包含导致许多复杂人类特征和常见疾病的遗传变异的染色体区域(基因座)，包括那些具有重大公共卫生负担的疾病，如癌症、糖尿病和关节炎。许多复杂性状的关联信号主要定位于通过调节基因表达(即DNA转化为功能基因产物的过程)影响疾病的区域，基因表达可能因组织和细胞类型(称为转录组)而异。然而，由于相关组织的成本和可获得性，对基因表达和复杂性状之间关系的研究一直局限于小样本调查。因此，在确定GWA区的致病基因和了解遗传变异影响疾病病理生理学的生物学过程方面进展有限，从而阻碍了通过有针对性的药物开发将这些发现转化到临床上。一种越来越多地被使用的理解人类疾病潜在分子途径的方法是通过整合分析遗传变异和大规模基于组织的分子图谱倡议的转录数据资源。例如，基因类型-组织表达项目在广泛的组织中产生了高密度的全基因组基因分型和基因表达，并将这些数据公之于众。这些研究的一个主要发现是确定了表达数量性状基因座(EQTL)，它将遗传变异与不同组织中基因表达的调节联系起来。因此，已经开发了旨在通过以下方式来检测复杂性状与基因表达的关联的方法：(I)在这些分子图谱资源中建立特定于组织的多eQTL模型；以及(Ii)使用这些模型来预测(或将转录组“归因于”)到GWAs数据中(基于个体水平的基因类型或关联汇总统计)。然而，现有的转录组分配方法通常：(I)单独考虑每种细胞类型，并且没有利用观察到的由跨组织eQTL驱动的细胞类型之间的基因表达的相关性；和/或(Ii)没有考虑eQTL模型的不确定性(即，许多不同的基因变体可能调节基因表达)，从而导致可能的假阳性发现。本建议的目的是开发新的统计方法，用于将转录分配到GWA中，以解决这些局限性：(I)利用多组织表达来建立eQTL模型，该模型比那些单独考虑每种细胞类型的模型更好地预测基因表达；以及(Ii)使用计算效率高的贝叶斯统计方法，适当考虑eQTL模型中的不确定性，减少“过度拟合”的可能性。该方法将在用户友好的软件中实施，该软件将向更广泛的研究界免费提供。该方法和软件将被用于创建一个输入到来自英国生物库的500,000名参与者的多组织基因表达的储存库，这些参与者的Gwas数据已经可用。这些推测的转录图谱将被测试与类风湿性关节炎和其他肌肉骨骼疾病、心血管疾病、癌症和糖尿病的相关性，揭示新的致病基因，并提高对疾病生物学基础的分子机制和相关细胞类型的理解。该储存库还将被送回英国生物库存档并分发给经批准的研究人员，以确定资源中任何感兴趣的特征的因果基因。这些分析将通过确定药物靶点来上调或下调与疾病风险相关的因果基因的表达，从而增强GWAS研究结果的转化潜力。

Genome-wide association studies (GWAS) have been successful in identifying chromosomal regions (loci) that contain genetic variants that contribute to many complex human traits and common diseases, including those that have major public health burden, such as cancers, diabetes and arthritis. Association signals for many complex traits predominantly localise to regions that influence disease by modulating gene expression (i.e. the process by which DNA is converted into a functional gene product), which may vary across tissues and cell types (referred to as the transcriptome). However, studies of the relationships between gene expression and complex traits have been restricted to investigations in small samples because of cost and availability of relevant tissues. Consequently, there has been limited progress in identifying the causal genes in GWAS regions and in understanding of the biological processes through which genetic variants impact on disease pathophysiology, thereby hindering the translation of these findings into the clinic through targeted drug development.One increasingly utilised approach to understand molecular pathways underlying human disease is through integrated analysis of genetic variation and transcriptomic data resources from large-scale tissue-based molecular profiling initiatives. For example, the Genotype-Tissue Expression Project has generated high-density genome-wide genotyping and gene expression across a wide range of tissues, and has made these data publicly available. One primary finding of these investigations has been the identification of expression quantitative trait loci (eQTL) that link genetic variation to the regulation of gene expression in diverse tissues. Methods have thus been developed that aim to detect association of complex traits with gene expression by: (i) building tissue-specific multi-eQTL models in these molecular profiling resources; and (ii) using these models to predict (or "impute") the transcriptome into GWAS data (based on individual-level genotypes or association summary statistics). However, existing transcriptome imputation methods typically: (i) consider each cell type separately, and do not take advantage of the observed correlations in gene expression between cell types driven by cross-tissue eQTLs; and/or (ii) do not account for eQTL model uncertainty (i.e. many different genetic variants may regulate gene expression), resulting in potential for false positive findings.The aim of this proposal is to develop novel statistical methods for transcriptomic imputation into GWAS to address these limitations by: (i) harnessing multi-tissue expression to build eQTL models that better predict gene expression than those that consider each cell type separately; and (ii) use computationally efficient Bayesian statistical methods that appropriately allow for uncertainty in the eQTL model, reducing the potential for "over-fitting". The methodology will be implemented in user friendly software that will be made freely available to the wider research community. The methodology and software will be utilised to create a repository of imputed multi-tissue gene expression into 500,000 participants from the UK Biobank for whom GWAS data are already available. These imputed transcriptomic profiles will be tested for association with rheumatoid arthritis and other musculoskeletal diseases, cardiovascular disease, cancer and diabetes, revealing novel causal genes and improving understanding of molecular mechanisms and relevant cell types underlying disease biology. The repository will also be returned to UK Biobank for archiving and distribution to approved researchers to identify causal genes for any trait of interest available in the resource. These analyses will have enhanced potential for translation of GWAS findings by identifying drug targets for up- or down-regulation of causal genes for which expression is associated with risk of disease.