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

转录组插补的新统计方法可增强对人类疾病因果机制的理解

• 批准号: MR/V020749/1
• 负责人: Andrew Morris
• 金额: $ 57.45万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV020749%2F1
• 关键词: Novel; statistical; methods; transcriptomic; imputation

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.

全基因组关联研究 (GWAS) 已成功识别出包含遗传变异的染色体区域（位点），这些变异导致许多复杂的人类特征和常见疾病，包括那些造成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集中于通过调节基因表达（即 DNA 转化为功能性基因产物的过程）影响疾病的区域，这些区域可能因组织和细胞类型（称为转录组）而异。然而，由于成本和相关组织的可用性，基因表达与复杂性状之间关系的研究仅限于小样本的研究。因此，在识别 GWAS 区域的致病基因以及了解遗传变异影响疾病病理生理学的生物过程方面进展有限，从而阻碍了通过靶向药物开发将这些发现转化为临床。一种越来越多地使用的了解人类疾病分子途径的方法是通过对来自大规模组织分子的遗传变异和转录组数据资源进行综合分析。 概况分析举措。例如，基因型组织表达项目已经在多种组织中生成了高密度全基因组基因分型和基因表达，并将这些数据公开。这些研究的一个主要发现是鉴定了表达数量性状位点（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，已经开发出一些方法，旨在通过以下方式检测复杂性状与基因表达的关联：(i) 在这些分子分析资源中构建组织特异性多 eQTL 模型； (ii) 使用这些模型将转录组预测（或“估算”）到 GWAS 数据中（基于个体水平的基因型或关联汇总统计）。然而，现有的转录组插补方法通常：（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基因表达的相关性；和/或 (ii) 不考虑 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），从而导致出现假阳性结果的可能性。本提案的目的是开发新的转录组插补到 GWAS 的统计方法，以通过以下方式解决这些局限性：(i) 利用多组织表达来构建 eQTL 模型，该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，该软件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存储库，其中包含来自英国生物银行的 500,000 名参与者的多组织基因表达估算，这些参与者的 GWAS 数据已经可用。这些估算的转录组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、癌症和糖尿病的关联，揭示新的致病基因并增进对疾病生物学基础的分子机制和相关细胞类型的理解。该存储库还将返回英国生物银行进行存档并分发给经批准的研究人员，以识别资源中可用的任何感兴趣特征的因果基因。这些分析将通过确定上调或下调致病基因的药物靶点来增强 GWAS 研究结果的转化潜力，这些基因的表达与疾病风险相关。