Use of a Machine Learning Approach to Impute Gene Expression in African Americans

项目摘要 多组学数据在理解SNP关联背后的潜在机制方面非常宝贵。 使用配对的基因组和转录组数据允许研究人员确定组织特异性效应， 非编码变异然而，大多数这类数据主要存在于欧洲血统的人群中。 线性模型已经被开发出来，它可以从基因型数据中估算基因表达 从GTEx资源创建。该资源包含44个基因型和基因表达数据配对 人体组织不幸的是，这些模型主要是建立在欧洲的数据上;它们在欧洲的表现并不好。 非裔美国人（AA）队列。为了减少知识和数据的差距，我们建议使用 都或拥有非裔美国人配对数据以及公共非裔美国人数据，以创建线性和机器 学习模型来估算基因表达。然后，我们将评估这些模型在预测 ACCOuNT队列中静脉血栓栓塞的风险。通过建立我们现有的知识， 转录组插补，我们将推进这些方法来研究混合人群。

PROJECT SUMMARY Multi-omics data has been invaluable in understanding the potential mechanisms behind SNP associations. Using paired genomic and transcriptomic data allows investigators to determine the tissue specific effects of non-coding variation. However, most of this type of data exists for mostly European ancestry populations. Linear models have been developed which that can impute gene expression from genotype data  mostly created from the GTEx resource. This resource contains paired genotype and gene expression data on 44 human tissues. Unfortunately, these models are built mostly on European data; they do not perform as well on African American (AA) cohorts. To alleviate this disparity in both knowledge and data we are proposing to use both or own African American paired data as well as public African American data to create linear and machine learning models to impute gene expression. We will then assess the utility of these models in predicting the risk on venous thromboembolism in our ACCOuNT cohort. By building on our current knowledge of transcriptome imputation, we will be advancing these methods to understudies admixed populations.