CAREER: New Challenges in Statistical Genetics: Mendelian Randomization, Integrated Omics and General Methodology

职业：统计遗传学的新挑战：孟德尔随机化、综合组学和通用方法论

• 批准号: 2238656
• 负责人: Jingshu Wang
• 金额: $ 44.88万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238656&HistoricalAwards=false
• 关键词: CAREER; New; Challenges; Statistical; Genetics

With the rapid development of genetic technologies and the continuing collection of large-scale biobanks, scientists are provided with unprecedented opportunities to predict, prevent, and treat common diseases in a personalized and efficient way. In the meantime, analyzing such data presents many new challenges as 1) data come from multiple sources and can suffer from various biases and confounding; 2) scientific questions need an understanding of not only associations but also causal relationships among different risk factors and diseases. This project will address a range of statistical challenges in performing the integration of different omics data types to elucidate potential genetic changes that lead to disease development or relate to the discovery of treatment targets. The project will bridge statistics, machine learning, genetics, and medical research from an analytical perspective. In addition to helping young generations develop independent thinking, the educational activities will help them develop the ability to form objective opinions on social events and to analyze data to form an unbiased judgment on news stories. The PI will develop software and share research results on social media that can be useful to scientists and clinicians, doctors, and industrial professionals. This project also supports graduate students in the research. In the project, the PI plans to develop three aspects of research for modern statistical genetics. For Mendelian Randomization, which uses genetic mutations as natural experiments to understand risk factors for disease progression, the PI will focus specifically on adjusting for confounding and evaluating the temporal causal effects of clinical risk factors with new frameworks. For analyzing gene regulation with single-cell multi-omics data, the PI will work on a widespread regulation mechanism called alternative polyadenylation, building new statistical models to understand its functional roles with data from new technologies such as spatial transcriptomics and single-cell CRISPR screens. Furthermore, the PI will also investigate new statistical ideas motivated by recent methodological developments in genetics that can help to solve general problems in hypotheses testing and Bayesian inference.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.

随着基因技术的快速发展和大规模生物库的不断收集，为科学家提供了前所未有的机会，以个性化和有效的方式预测，预防和治疗常见疾病。与此同时，分析这些数据带来了许多新的挑战，因为1）数据来自多个来源，可能会受到各种偏见和混淆的影响; 2）科学问题不仅需要了解不同风险因素和疾病之间的关联，而且还需要了解它们之间的因果关系。该项目将解决一系列统计挑战，整合不同的组学数据类型，以阐明导致疾病发展或与发现治疗靶点有关的潜在遗传变化。该项目将从分析的角度连接统计学，机器学习，遗传学和医学研究。除了帮助年轻一代发展独立思考外，教育活动还将帮助他们培养对社会事件形成客观意见的能力，并分析数据以形成对新闻报道的公正判断。PI将开发软件，并在社交媒体上分享研究成果，这些成果对科学家、临床医生、医生和工业专业人士都很有用。该项目还支持研究生的研究。在该项目中，PI计划为现代统计遗传学开展三个方面的研究。对于孟德尔随机化，其使用基因突变作为自然实验来了解疾病进展的风险因素，PI将特别关注调整混杂因素，并使用新框架评价临床风险因素的时间因果效应。为了利用单细胞多组学数据分析基因调控，PI将研究一种称为替代多聚腺苷酸化的广泛调控机制，建立新的统计模型，利用空间转录组学和单细胞CRISPR等新技术的数据来了解其功能作用筛选。此外，PI还将研究遗传学最新方法学发展所激发的新的统计思想，这些思想有助于解决假设检验和贝叶斯推理中的一般问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。