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: 10671641
• 负责人: Xiaoquan Wen
• 金额: $ 33.19万
• 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/10671641
• 关键词: Biological; Biomedical Research; Communities; Complex; Computer software; Computing Methodologies; Data; Disease; Etiology; Genomics; Investigation; Knowledge; Medical; Methodology; Molecular; Phenotype; Preventive Medicine; Process; Reproducibility; Research; Scientist; Technology; 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.

项目总结

项目成果

Psychosocial experiences modulate asthma-associated genes through gene-environment interactions.

• DOI: 10.7554/elife.63852
• 发表时间: 2021-06-18
• 期刊: eLife
• 影响因子: 7.7
• 作者: Resztak JA;Farrell AK;Mair-Meijers H;Alazizi A;Wen X;Wildman DE;Zilioli S;Slatcher RB;Pique-Regi R;Luca F
• 通讯作者: Luca F

JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies.

• DOI: 10.1093/bioinformatics/btad366
• 发表时间: 2023-06-01
• 期刊: Bioinformatics (Oxford, England)