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

使用机器学习方法估算非裔美国人的基因表达

基本信息

批准号：
10199406
负责人：
Minoli A Perera
金额：
$ 23.93万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-10 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10199406
关键词：
Address African African American Alleles Benchmarking Clinical Clinical assessments Complex Data Data Set European Evaluation Funding Gene Expression Gene Expression Profile Gene Frequency Genes Genetic Variation Genome Genomic medicine Genomics Genotype Genotype-Tissue Expression Project Hepatocyte Hispanics Image Individual Inheritance Patterns Inherited Knowledge Linear Models Linkage Disequilibrium Liver Machine Learning Methodology Methods Modeling Mosaicism Multiomic Data Neural Network Simulation Participant Patients Pharmaceutical Preparations Population Population Heterogeneity Research Personnel Resources Risk Statistical Methods Testing Tissues Untranslated RNA Variant Work base cohort convolutional neural network deep learning disease phenotype genomic data human model human tissue improved innovation learning strategy monocyte novel novel strategies recruit response tool transcriptome transcriptomics venous thromboembolism whole genome

项目摘要

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.

项目摘要多词数据在理解SNP关联背后的潜在机制方面非常宝贵。使用成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化。但是，大多数此类数据都存在于大多数欧洲血统人群中。已经开发了线性模型，该模型可以从基因型数据中估算基因表达主要是由GTEX资源创建。该资源包含44的配对基因型和基因表达数据人体组织。不幸的是，这些模型主要建立在欧洲数据上。他们的表现不佳非裔美国人（AA）同伙。为了减轻我们建议使用的知识和数据的这种差异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以估算基因表达。然后，我们将评估这些模型的实用性在我们的帐户队列中，静脉血栓栓塞的风险。通过我们当前的知识来建立转录组插补，我们将推进这些方法来理解混杂的人群。