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染色体区域（位点），其中包含导致许多复杂人类特征和常见疾病的遗传变异，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转化为功能性基因产物的过程）来影响疾病的区域，这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，基因表达与复杂性状之间关系的研究仅限于小样本的调查。因此，在确定GWAS区域的致病基因和了解遗传变异影响疾病病理生理的生物学过程方面进展有限，从而阻碍了通过靶向药物开发将这些发现转化为临床。了解人类疾病的分子途径的一种越来越常用的方法是通过对遗传变异和大规模基于组织的分子谱分析计划的转录组数据资源进行综合分析。例如，基因型-组织表达项目已经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，并公开了这些数据。这些研究的一个主要发现是确定了表达数量性状位点（eQTL），该位点将遗传变异与不同组织中基因表达的调控联系起来。因此，开发了旨在检测复杂性状与基因表达关联的方法：(i)在这些分子谱资源中建立组织特异性多eqtl模型；（ii）使用这些模型预测（或“推算”）转录组到GWAS数据中（基于个体水平的基因型或关联汇总统计）。然而，现有的转录组归算方法通常：(i)单独考虑每种细胞类型，并且没有利用观察到的跨组织eqtl驱动的细胞类型之间基因表达的相关性；和/或（ii）没有考虑到eQTL模型的不确定性（即许多不同的遗传变异可能调节基因表达），导致假阳性结果的可能性。本提案的目的是开发新的统计方法，用于GWAS的转录组插入，以解决这些局限性：(i)利用多组织表达构建eQTL模型，比单独考虑每种细胞类型的模型更好地预测基因表达；（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，并将免费提供给更广泛的研究界。该方法和软件将用于创建一个输入多组织基因表达的存储库，该存储库来自英国生物银行（UK Biobank）的50万名参与者，他们已经可以获得GWAS数据。这些输入的转录组谱将被用于类风湿关节炎和其他肌肉骨骼疾病、心血管疾病、癌症和糖尿病的关联测试，揭示新的致病基因，提高对疾病生物学分子机制和相关细胞类型的理解。储存库也将返回英国生物银行存档和分发给批准的研究人员，以确定资源中任何感兴趣的特征的因果基因。通过确定与疾病风险相关的致病基因上调或下调的药物靶标，这些分析将增强翻译GWAS发现的潜力。

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.