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：
10594517
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/10594517
关键词：
Acute Address Adrenal Cortex Hormones 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 European ancestry 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 Regulatory Pathway Research Research Personnel Risk Risk Factors 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 biobank candidate identification 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 predictors 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 progression risk 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.

项目概要/摘要慢性阻塞性肺疾病 (COPD) 是全球呼吸系统死亡的主要原因15。在病程早期识别高易感个体并了解致病因素在肺功能发生不可逆转的丧失之前，遗传学起着至关重要的作用16,17。 40% 的慢性阻塞性肺病易感性18-20已发现多种变异。与 COPD21-23 相关的个体变异对于风险预测来说效果不佳，但总体而言遗传变异可以。汇集了数百万个 GWAS 变异，我创建了多基因风险评分 (PRS)。对于慢性阻塞性肺病，可以识别患有慢性阻塞性肺病高风险的个体，尽管在非慢性阻塞性肺疾病中表现不太理想欧洲人24. 由于遗传血统在临床实践中不易确定，因此需要多血统 PRS。此外，反映遗传和环境影响的基因表达提供了病理生物学信息 COPD 的易感性和异质性。 COPD 的转录风险评分 (TRS) 增加了预测价值。上述临床危险因素25尚待开发，使用组学数据进行风险分层的吸引力在于。这些数据可以帮助我们深入了解为什么某些慢性阻塞性肺病亚组进展风险较高。调节网络26已被用来揭示慢性阻塞性肺病异质性机制，而这些机制尚未被发现因此，我们发现多基因和转录风险评分将。显着改善识别慢性阻塞性肺病和相关表型高风险人群的临床因素，并可用于确定治疗干预的途径我们将使用 4,225 来训练多祖先 PRS。来自英国生物银行的非洲血统个体以及来自 CHARGE 的 8,429 名非裔美国人的现有分析，慢性阻塞性肺病 (COPDGene: n=10,198) 研究和肺组织研究中的遗传流行病学和测试联盟（LTRC：n=1,078）我们将使用全血创建多祖先转录风险评分（TRS）。训练中的 RNA 测序 (RNA-seq) 数据 (n=3,394) 并评估测试样本中的预测性能 (n=1,131) 我们将使用连接图 (CMap)8,27 来识别基于 COPDGene 的药物再利用候选者。我们将利用 LTRC 的肺部 RNA-seq 数据创建肺部 TRS，并在 COPDGene 中进行测试。我们将沿着现有 PRS 和肺 TRS 的轴对 COPDGene 参与者进行分类（例如， “高”PRS、“低”TRS），我们预计这将识别那些患有 COPD 相关表型的高风险人群，为了了解为什么某些个体处于慢性阻塞性肺病表型的高风险中，我们将利用基因监管网络来识别 PRS/TRS 分类之间不同的途径，并使用基因监管网络数据库 (GRAND)9 优先考虑药物再利用候选者这些目标将产生。未来研究的数据，重点是在现实世界中验证慢性阻塞性肺病组学风险评分和候选药物队列1，并使用机器学习来预测候选药物的网络效应。职业发展计划将训练我使用机器学习进行多组学集成和风险预测。