CAREER: New Statistical Approaches for Studying Evolutionary Processes: Inference, Attribution and Computation

职业：研究进化过程的新统计方法：推理、归因和计算

• 批准号: 2143242
• 负责人: Julia Palacios
• 金额: $ 40万
• 依托单位: Stanford 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: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143242&HistoricalAwards=false
• 关键词: CAREER; New; Statistical; Approaches; Studying

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Statistical inference from a sample of molecular sequences such as DNA poses a series of fundamental challenges. These challenges include complex modeling of the sample's ancestry and past evolutionary history, large and noisy data. The ongoing large-scale increase of genetic data has led to a situation in which current methods are not applicable to the amount of data available and researchers are forced to down-sample available data or to infer parameters from insufficient summary statistics. This research project will address the need for optimally designed coalescent modeling for inference from modern molecular data. The coalescent is a probability model on genealogies, that is, the trees which represent the ancestry of the sample. Coalescent models are used for inferring parameters of scientific relevance such as effective population size, migration patterns and selection. The research goals of this project are to expand the class of coalescent models and to design novel efficient statistical algorithms, allowing us to address many practical problems that advance science. Furthermore, the outcomes of the projects will foster the development of new statistical theory and tractable methods that contribute to biological solutions. This project also outlines an active plan for a broad range of educational and outreach activities that will broaden participation in statistical sciences and will enhance more inclusive atmosphere in science. The undergraduate and graduate students involved into the project will be offered a unique opportunity for interdisciplinary hands-on research training at the interface of statistical sciences and biology, allowing them to contribute to progress in evolutionary biology, molecular biology, population genetics, phylogenetics, cancer genomics, probabilistic modeling, statistical inference, and related fields. The PI will actively participate in multiple outreach activities such as the Stanford undergraduate summer research program, which will allow for recruiting more diverse pool of future data scientists and for fostering more inclusive climate in science. The research findings of the project will serve as foundation for new program in statistical genetics and will be integrated into undergraduate and graduate courses. Concretely, this project will expand the class of coalescent models and provide a suite of new algorithmic and statistical approaches by exploiting a metric notion of genealogies, lumpability of Markov chains and divide-and-conquer strategies. The specific aims include (1) develop coalescent models to incorporate various sampling schemes and biological processes such as dynamic population structures, recombination and strong selection; (2) develop a metric framework for coalescent theory and applications; (3) develop scalable strategies for Bayesian inference of evolutionary parameters and (4) implement, validate and analyze molecular sequences of infectious disease such as SARS-CoV-2, ancient and modern human DNA samples and cancer single cell variation. Furthermore, the project will actively contribute to broadening participation in statistical sciences at multiple fronts, from team-based interdisciplinary research training to community outreach.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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。从分子序列（如DNA）的样本中进行统计推断，带来了一系列根本性的挑战。这些挑战包括对样本祖先和过去进化史的复杂建模，以及庞大而嘈杂的数据。遗传数据的持续大规模增加导致现有方法不适用于现有数据量的情况，研究人员被迫降低可用数据的样本或从不足的汇总统计中推断参数。该研究项目将解决从现代分子数据推断的最佳设计聚结模型的需求。聚结是一个关于家谱的概率模型，也就是代表样本祖先的树。聚结模型用于推断科学相关的参数，如有效人口规模、迁移模式和选择。该项目的研究目标是扩展聚结模型的类别，设计新颖有效的统计算法，使我们能够解决许多推动科学发展的实际问题。此外，这些项目的成果将促进有助于生物解决方案的新的统计理论和易于处理的方法的发展。该项目还概述了一项广泛的教育和推广活动的积极计划，这些活动将扩大对统计科学的参与，并将增强科学的包容性气氛。参与该项目的本科生和研究生将提供一个独特的机会，在统计科学和生物学的界面上进行跨学科的实践研究训练，使他们能够在进化生物学、分子生物学、群体遗传学、系统遗传学、癌症基因组学、概率建模、统计推断和相关领域做出贡献。PI将积极参与多种外展活动，如斯坦福大学本科生暑期研究项目，这将允许招募更多不同的未来数据科学家，并培养更具包容性的科学氛围。该项目的研究成果将作为统计遗传学新课程的基础，并将整合到本科和研究生课程中。具体而言，该项目将扩展聚结模型的类别，并通过利用系谱的度量概念、马尔可夫链的可集成性和分而治之策略，提供一套新的算法和统计方法。具体目标包括：(1)建立结合各种采样方案和生物过程（如动态种群结构、重组和强选择）的聚结模型；(2)建立凝聚理论和应用的度量框架；(3)开发可扩展的进化参数贝叶斯推断策略；(4)实施、验证和分析传染性疾病（如SARS-CoV-2）、古今人类DNA样本和癌症单细胞变异的分子序列。此外，该项目将积极促进在多个方面扩大对统计科学的参与，从以小组为基础的跨学科研究培训到社区外展。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。

项目成果

Statistical summaries of unlabelled evolutionary trees

• DOI: 10.1093/biomet/asad025
• 发表时间: 2023-06-23
• 期刊: BIOMETRIKA
• 影响因子: 2.7
• 作者: Samyak，Rajanala;Palacios，Julia A.
• 通讯作者: Palacios，Julia A.

CRP-Tree: a phylogenetic association test for binary traits

• DOI: 10.1093/jrsssc/qlad098
• 发表时间: 2024-03-11
• 期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
• 影响因子: 1.6
• 作者: Zhang，Julie;Preising，Gabriel A.;Palacios，Julia A.
• 通讯作者: Palacios，Julia A.