Computational approaches to characterize heterogeneity and improve risk stratification in complex disease phenotypes

表征复杂疾病表型异质性并改善风险分层的计算方法

• 批准号: 10448966
• 负责人: Milton Pividori
• 金额: $ 12.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-18 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10448966
• 关键词: Affect; Algorithms; Area; Asthma; Biological; Cells; Clinic; Clinical; Colorado; Complex; Computing Methodologies; Databases; Detection; Dimensions; Disease; Drug Targeting; Environment; Environmental Exposure; Environmental Risk Factor; Etiology; FDA approved; Gene Combinations; Gene Expression Profile; Generations; Genes; Genetic; Genetic Heterogeneity; Genetic Risk; Genetic Transcription; Genetic study; Genome; Goals; Grouping; Heritability; Heterogeneity; Human; Individual; Lead; Learning; Link; Maps; Medical Research; Medicine; Mentors; Mentorship; Methodology; Methods; Modality; Modeling; Molecular; Multiomic Data; Nature; Ontology; Outcome; Pathway interactions; Pattern; Pennsylvania; Performance; Peripheral; Pharmaceutical Preparations; Pharmacology; Phase; Phenotype; Play; Population; Precision Medicine Initiative; Prognosis; Property; Reduce health disparities; Regulation; Research; Research Personnel; Role; Systems Biology; Testing; Therapeutic; Tissues; Training; Transcription Process; Translating; Universities; Validation; Variant; base; biobank; career; cell type; clinical care; common treatment; design; disease diagnosis; disease phenotype; disorder risk; drug candidate; effective therapy; flexibility; genetic variant; genome wide association study; high risk; human disease; improved; individualized medicine; latent gene expression; machine learning method; medical schools; method development; personalized medicine; polygenic risk score; portability; precision medicine; profiles in patients; recruit; risk prediction; risk stratification; trait; transcriptome; translational medicine

PROJECT SUMMARY/ABSTRACT Recent technological breakthroughs have enabled the generation of clinical, environmental, and multi-omics data at an unprecedented scale, providing a complete profile of the patient for individualized disease diagnosis, prog- nosis, and treatment. However, the precision medicine approach is yet to realize its potential in most multi-factorial diseases, for which their highly polygenic nature, as well as phenotypic and genetic heterogeneity, complicate the identification of disease-associated cell type-specific transcriptional mechanisms. A better characterization of this heterogeneity and an interpretable prediction of individuals at high risk of disease are crucial steps to deliver the promises of precision medicine. In this context, polygenic risk scores (PRS) are likely to play a crucial role in precision medicine for disease-risk prediction. However, it has been argued that PRS might accentuate dispari- ties among non-European ancestries and have low stability at individual-level predictions, probably due to greater underlying complexity in disease etiology that is not captured in a single score. Current efforts to mitigate health disparities involve recruiting individuals from different population ancestries. However, if the underlying biological complexity of disease etiology remains unaccounted, risk stratification methods will continue to be limited. The goal of this project is to develop machine learning methods to advance key computational aspects of precision medicine. In the first aim, an unsupervised method will be applied across large amounts of genetic studies to detect gene sets associated with multiple human traits, which will also identify environmental risk factors. In the second aim, new computational approaches will be developed to learn gene co-expression patterns optimized for a better understanding of transcriptional mechanisms linked to complex traits and their therapeutical modalities. This will detect gene modules (i.e., genes with similar expression profiles across the same cell types) with complex gene relationships, and the approach will be validated by predicting known FDA-approved drug-disease links. Finally, the outcomes of these aims will inform a gene module-based polygenic risk score for accurate and robust disease-risk stratification that will be portable across different population ancestries. Although the methods will be initially applied to asthma, they are clearly extendable to other common diseases as well. For the K99 phase of this project, the mentorship team's expertise covers all key areas of precision medicine, including computational genetics, systems biology, environmental exposure studies, pharmacology, and trans- lational medicine. Mentors and advisors are directly involved in precision medicine initiatives to enhance both scientific discovery and its implementation in clinical care. For the R00 phase and beyond, all the conceptual and methodological expertise previously learned will prepare the applicant for an independent research career in computational methods development applied to precision medicine. The Perelman School of Medicine at the University of Pennsylvania, consistently ranked among the top research medical schools, represents the ideal environment for this highly collaborative project.

项目摘要/摘要 最近的技术突破使临床、环境和多组学数据的产生成为可能 以前所未有的规模，为患者提供完整的fiLE，以实现个性化的疾病诊断、编程和 诺斯，和治疗。然而，精准医学方法在大多数多因素中还没有发挥其潜力。 疾病，其高度多基因的性质，以及表型和遗传的异质性，使 识别疾病相关细胞类型的fi阳离子--特定fic转录机制。更好地描述这一点 异质性和对疾病高危个体的可解释性预测是实现以下目标的关键步骤 对精准医疗的承诺。在这种情况下，多基因风险分数(PR)很可能在 疾病风险预测的精确医学。然而，有人认为，PR可能会突出不同的- 非欧洲祖先之间的联系，在个人层面的预测稳定性较低，可能是由于 疾病病因学的潜在复杂性，不是在一个分数中就能捕捉到的。目前为缓解健康问题所做的努力 差异涉及招募来自不同种群祖先的个人。然而，如果潜在的生物学 疾病病因的复杂性仍未解决，风险分层方法将继续受到限制。 这个项目的目标是开发机器学习方法，以提高精度的关键计算方面 医药。在fi的第一个目标中，一种无监督的方法将应用于大量的遗传研究，以 检测与多种人类特征相关的基因组，这也将识别环境风险因素。在 第二个目标，将开发新的计算方法来学习优化的基因共表达模式 更好地了解与复杂性状及其治疗模式相关的转录机制。 这将检测具有复合体的基因模块(即，在相同细胞类型中具有相似表达的fiLes的基因 基因关系，该方法将通过预测已知的FDA批准的药物与疾病的联系来验证。 最后，这些目标的结果将为基于基因模块的多基因风险评分提供准确和稳健的信息 疾病风险策略fi阳离子将可在不同的种群祖先之间移植。尽管这些方法将 最初应用于哮喘，显然也可推广到其他常见疾病。 对于这个项目的K99阶段，导师团队的专业知识涵盖了精准医学的所有关键领域， 包括计算遗传学、系统生物学、环境暴露研究、药理学和转基因技术。 职业医学。导师和顾问直接参与精准医疗计划，以提高两者 科学fic的发现及其在临床护理中的应用。对于R00阶段及以后，所有概念性的 和以前学到的方法学专业知识将为申请人的独立研究生涯做好准备。 在计算方法方面，发展应用于精确医学。佩雷尔曼医学院 宾夕法尼亚大学一直位居顶级研究型医学院之列，代表着理想的 为这个高度协作的项目提供了一个良好的环境。