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Multi-omic Risk Prediction of Chronic Obstructive Pulmonary Disease in European- and African-Ancestry Populations

欧洲和非洲血统人群慢性阻塞性肺疾病的多组学风险预测

基本信息

批准号：
10445739
负责人：
Matthew R Moll
金额：
$ 16.85万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10445739
关键词：
Address African American population African ancestry Age Binding Blood Blood specimen Chronic Obstructive Pulmonary Disease Classification Clinical Data Databases Detection Development Development Plans Disease Disease susceptibility Early identification European Future Gene Expression Genes Genetic Genetic Risk Genetic Transcription Genomic Segment Glass Heterogeneity Individual Inhalation Intervention Learning Lung Machine Learning Maps Measures Odds Ratio Outcome Participant Pathogenesis Pathogenicity Pathway interactions Patients Performance Pharmaceutical Preparations Phenotype Population Predictive Value Predisposition Pulmonary Function Test/Forced Expiratory Volume 1 Regulator Genes Regulatory Pathway Research Research Personnel Risk Sample Size Sampling Smoking Smoking History Spirometry Structure of parenchyma of lung Subgroup Techniques Testing Therapeutic Therapeutic Intervention Tissue Sample Training Transcript Variant Whole Blood base biobank career development cigarette smoking clinical practice clinical risk cohort disease heterogeneity disease phenotype disorder risk disorder subtype drug candidate drug repurposing gene regulatory network genetic architecture genetic association genetic epidemiology genetic variant genome wide association study high risk improved insight learning network lung basal segment machine learning method mortality multiple omics personalized intervention polygenic risk score predictive modeling pulmonary function research and development respiratory risk prediction risk stratification tool transcription factor transcriptome transcriptome sequencing transcriptomics

项目摘要

PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is a leading cause of respiratory mortality worldwide15. Identifying highly susceptible individuals early in their disease course and understanding pathogenic mechanisms, before irreversible loss of lung function, is of utmost importance16,17. Genetics account for about 40% of COPD susceptibility18–20. Genome-wide association studies (GWASs) have identified multiple variants associated with COPD21–23. Individual variants are poor for risk prediction, but in aggregate genetic variants can account for a substantial portion of risk. Pooling millions of GWAS variants, I created a polygenic risk score (PRS) for COPD that can identify individuals at high risk for COPD, though performance was less optimal in non- Europeans24. Multi-ancestry PRSs are needed as genetic ancestry is not readily determined in clinical practice. Further, gene expression, reflecting genetic and environmental influences, provides pathobiologic information for COPD susceptibility and heterogeneity. A transcriptional risk score (TRS) for COPD that adds predictive value above clinical risk factors25 has yet to be developed. The appeal of using -Omics data for risk stratification is that these data can lend insight into why certain COPD subgroups are at elevated risk of progression. Gene regulatory networks26 have been used to uncover mechanisms of COPD heterogeneity that were not found by traditional gene-based approaches. Therefore, we hypothesize that polygenic and transcriptional risk scores will substantially improve upon clinical factors in identifying those at higher risk for COPD and related phenotypes, and can be used to identify pathways for therapeutic intervention. We will train multi-ancestry PRSs using 4,225 African ancestry individuals from UK Biobank and existing analyses of 8,429 African-Americans from CHARGE, and test in the Genetic Epidemiology of COPD (COPDGene: n=10,198) study and Lung Tissue Research Consortium (LTRC: n=1,078). We will create a multi-ancestry transcriptional risk score (TRS) using whole blood RNA-sequencing (RNA-seq) data in training (n=3,394) and evaluate predictive performance in testing samples (n=1,131) of COPDGene. We will use Connectivity Map (CMap)8,27 to identify drug repurposing candidates based on TRS transcripts. We will leverage lung RNA-seq data from LTRC to create a lung TRS, and test in COPDGene blood samples. We will classify COPDGene participants along the axes of the existing PRS and lung TRS (e.g. “High” PRS, “Low” TRS), which we expect will identify those at high risk for COPD-related phenotypes and progression. To understand why certain individuals are at high risk for COPD phenotypes, we will utilize gene regulatory networks to identify pathways differing between PRS/TRS classifications, and use the Gene RegulAtory Network Database (GRAND)9 to prioritize drug repurposing candidates. These aims will generate data for future studies, which will focus on validating COPD -Omics risk scores and drug candidates in real-world cohorts1, and using machine learning to predict the network effects of drug candidates. The proposed research and career development plan will train me to use machine learning for multi-omic integration and risk prediction.

项目总结/文摘