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

基本信息

批准号：
10805689
负责人：
Milton Pividori
金额：
$ 24.9万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-12 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10805689
关键词：
Acceleration Affect Algorithms Area Asthma Biological Clinic Clinical Colorado Complex Computing Methodologies Databases Detection Dimensions Disease Disparity Drug Targeting Environment Environmental Exposure Environmental Risk Factor Etiology FDA approved Gene Combinations Gene Expression Profile Generations Genes Genetic Genetic Heterogeneity Genetic Transcription Genetic study Genome Goals Grouping Heritability Heterogeneity Human Individual 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 Probability Prognosis Property Reduce health disparities Regulation Research Research Personnel Resources Role Systems Biology Technology Testing Therapeutic Tissues Training Transcription Process Translating Universities Validation Variant Work biobank career cell type clinical care common treatment design disease diagnosis disease phenotype disorder risk drug candidate drug discovery effective therapy flexibility genetic risk factor 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 pleiotropism polygenic risk score portability precision medicine profiles in patients recruit risk prediction risk stratification trait transcriptome translational medicine

项目摘要

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，环境和多摩变数据能够产生以前所未有的规模，为患者提供了个性化疾病诊断，进展 NOSIS和治疗。但是，精确的医学方法仍然是在大多数多工件中实现其潜力其高度多基因性质以及表型和遗传异质性的疾病使该疾病变得复杂鉴定与疾病相关的细胞类型特异性转录机制。更好地表征异质性和对处于高疾病风险的个体的可解释性预测是交付的关键步骤精确医学的承诺。在这种情况下，多基因风险评分（PRS）可能在疾病风险预测的精密医学。但是，有人认为PR可能会突出不同非欧洲祖先之间的关系，在个人级别的预测方面的稳定性较低，可能是由于更大的疾病病因的基本复杂性，并未以单个分数捕获。目前为缓解健康的努力差异涉及招募来自不同人口祖先的个人。但是，如果基本的生物学疾病病因的复杂性仍然没有被指责，风险地层方法将继续受到限制。该项目的目的是开发机器学习方法，以提高精确的关键计算方面药品。在第一个目标中，将在大量遗传研究中应用一种无监督的方法检测与多个人类特征相关的基因集，这也将确定环境风险因素。在第二个目的，将开发新的计算方法来学习针对优化的基因共表达模式更好地理解与复杂性状及其治疗方式相关的转录机制。这将检测具有复杂的基因模块（即具有相同表达蛋白的基因）基因关系，该方法将通过预测已知的FDA批准的药物疾病链接来验证。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信息疾病风险的地层将在不同的人口祖先范围内移植。尽管这些方法将最初是将其应用于哮喘，显然也可以扩展到其他常见疾病。对于该项目的K99阶段，Mentalship团队的专业知识涵盖了精密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，药理学和反式理性医学。导师和顾问直接参与精确医学计划，以增强两者科学发现及其在临床护理中的实施。对于R00阶段及以后的所有概念和以前学到的方法论专业知识将为申请人做好准备的独立研究职业在计算方法开发中应用于精密医学。佩雷尔曼医学院宾夕法尼亚大学始终被排名最高的研究医学院，代表了理想这个高度协作项目的环境。