Integrative approaches with applications in eQTL analysis and randomized trials

综合方法在 eQTL 分析和随机试验中的应用

• 批准号: 2210860
• 负责人: Fei Xue
• 金额: $ 12.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210860&HistoricalAwards=false
• 关键词: Integrative; approaches; applications; eQTL; analysis

Multisource data frequently arise in many real data applications, where different data sources contain complementary information, but each has only limited samples. To make the best use of the limited data from different sources, there is a great need for integrative approaches to jointly analyze all the datasets instead of separately analyzing every single dataset. Motivated by this need, this project will develop new integrative statistical methods to improve parameter estimation accuracy and hypothesis testing power. The results of the project will empower researchers in scientific fields such as genetics, biology, and medicine that face multisource data problems. The project will provide a broad range of interdisciplinary training opportunities for undergraduate and graduate students, especially students from underrepresented groups. The technical goal of this project is to study the integration of multisource data in the following three aspects. First, the principal investigator (PI) plans to build an empirical Bayes regression model for genotype-expression association analysis integrating data from multiple tissues. Second, the project will improve the test of the genotype-expression association via borrowing shared information across genes. Third, the PI will develop a covariate-adjusted method for causal effects on high-dimensional outcomes. The developed methods and results in this project will deepen understanding of genetics and biology, yield a more powerful test of treatment or drug effects on patients, and hence foster significant biological and medical benefits.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多源数据在许多实际数据应用中经常出现，其中不同的数据源包含互补的信息，但每个数据源的样本都有限。为了充分利用来自不同来源的有限数据，非常需要综合方法来联合分析所有数据集，而不是单独分析每个数据集。受这一需求的推动，该项目将开发新的综合统计方法，以提高参数估计精度和假设检验能力。该项目的成果将为遗传学、生物学和医学等科学领域面临多源数据问题的研究人员提供帮助。该项目将为本科生和研究生，特别是来自弱势群体的学生提供广泛的跨学科培训机会。 本项目的技术目标是从以下三个方面研究多源数据的集成。首先，首席研究员 (PI) 计划建立一个经验贝叶斯回归模型，用于整合来自多个组织的数据进行基因型表达关联分析。其次，该项目将通过借用跨基因的共享信息来改进基因型表达关联的测试。第三，PI 将开发一种协变量调整方法，用于研究高维结果的因果影响。该项目开发的方法和结果将加深对遗传学和生物学的理解，对治疗或药物对患者的影响进行更强有力的测试，从而促进显着的生物学和医学效益。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。