Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility

解决基因组学研究中的方法学挑战：因果关系、风险预测和再现性

• 批准号: 10260642
• 负责人: Xiaoquan Wen
• 金额: $ 33.61万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10260642
• 关键词: Biological; Biomedical Research; Communities; Complex; Computer software; Computing Methodologies; Data; Disease; Etiology; Genomics; Investigation; Knowledge; Lead; Medical; Methodology; Molecular; Phenotype; Preventive Medicine; Process; Reproducibility; Research; Scientist; Technology; base; computerized tools; experimental study; genomic data; improved; large scale data; novel; public health relevance; randomized trial; risk prediction; risk prediction model; treatment strategy; user-friendly

Project Summary With the availability of the high-through sequencing technology, the scientific community is now able to investigate complex phenotypes at both organismal and molecular levels. Nevertheless, it is still considerably difficult to perform controlled experiments and randomized trials to investigate the causal relationships between phenotypes at different levels. It is therefore critically important to perform causal inference based on the observational data. In this project, we will develop computational methods to facilitate systematic investigation of causal molecular mechanisms underlying complex disease process. Specifically, we will target three outstanding scientific issues: i) casual inference of molecular mechanisms of complex diseases; ii) analytic approaches for risk prediction utilizing genomic information and causal molecular mechanisms, and iii) statistical assessment of reproducibility in high-throughput genomic experiments. Finally, we will build user-friendly computational software packages and make them available to the broad community of biological and medical scientists.

项目摘要 随着高通量测序技术的可用性，科学界现在 能够在生物体和分子水平上研究复杂的表型。然而，尽管如此， 仍然很难进行对照实验和随机试验， 调查不同水平表型之间的因果关系。因此， 重要的是根据观察数据进行因果推理。在这个项目中，我们将 发展计算方法，以促进因果分子的系统研究 复杂疾病过程的潜在机制。具体来说，我们将针对三位杰出的 科学问题：i）复杂疾病的分子机制的偶然推断; ii）分析 利用基因组信息和因果分子机制进行风险预测的方法， 和iii）高通量基因组实验中再现性的统计评估。最后， 我们将建立用户友好的计算软件包， 生物和医学科学家的社区。