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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.

项目概要多组学数据对于理解 SNP 关联背后的潜在机制非常有价值。使用配对的基因组和转录组数据使研究人员能够确定非编码变异。然而，大多数此类数据主要针对欧洲血统人群。已经开发出线性模型，可以根据基因型数据估算基因表达——主要是从 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之上；他们的表现不太好非裔美国人 (AA) 群体。为了缓解知识和数据方面的这种差距，我们建议使用两者或拥有非裔美国人配对数据以及公共非裔美国人数据来创建线性和机器估算基因表达的学习模型。然后我们将评估这些模型在预测方面的效用我们的账户队列中静脉血栓栓塞的风险。通过建立我们目前的知识转录组插补，我们将推广这些方法来研究混合人群。