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

随着基因技术的快速发展和大规模生物库的不断收集，科学家们以个性化和有效的方式预测、预防和治疗常见疾病提供了前所未有的机会。与此同时，分析这些数据也提出了许多新的挑战，因为1)数据来自多个来源，可能存在各种偏差和混淆；科学问题不仅需要了解不同危险因素和疾病之间的关联，而且需要了解它们之间的因果关系。该项目将解决在整合不同组学数据类型方面的一系列统计挑战，以阐明导致疾病发展或与发现治疗目标相关的潜在遗传变化。该项目将从分析的角度将统计学、机器学习、遗传学和医学研究结合起来。除了帮助年轻一代发展独立思考之外，这些教育活动还将帮助他们发展对社会事件形成客观意见的能力，以及分析数据以对新闻故事形成公正判断的能力。PI将开发软件，并在社交媒体上分享对科学家、临床医生、医生和工业专业人员有用的研究结果。该项目还支持研究生进行研究。在该项目中，PI计划开展现代统计遗传学的三个方面的研究。对于孟德尔随机化，它使用基因突变作为自然实验来理解疾病进展的风险因素，PI将特别关注在新的框架下调整混淆和评估临床风险因素的时间因果效应。为了用单细胞多组学数据分析基因调控，PI将研究一种被称为选择性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筛选等新技术的数据来理解其功能作用。此外，PI还将研究新的统计思想，这些思想是由遗传学中最近的方法发展所激发的，可以帮助解决假设检验和贝叶斯推理中的一般问题。该奖项反映了美国国家科学基金会的法定使命，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。

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.