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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。来自DNA等分子序列样本的统计推断会带来一系列基本挑战。这些挑战包括对样本的血统和过去进化历史的复杂建模，大而嘈杂的数据。正在进行的大规模增加遗传数据导致了当前方法不适用于可用数据量的情况，并且研究人员被迫下样本可用的数据或从不足的汇总统计数据中推断出参数。 该研究项目将满足对现代分子数据推断最佳合并建模的需求。结合是谱系的概率模型，即代表样本血统的树。合并模型用于推断科学相关性的参数，例如有效的人口规模，迁移模式和选择。该项目的研究目标是扩大合并模型的类别并设计新颖的有效统计算法，从而使我们能够解决许多推进科学的实际问题。此外，这些项目的结果将促进有助于生物解决方案的新统计理论和可拖动方法的发展。该项目还概述了广泛的教育和外展活动的积极计划，这些计划将扩大统计科学的参与，并将增强科学中更具包容性的氛围。参与该项目的本科生和研究生将获得一个独特的机会，用于在统计科学和生物学的界面上进行跨学科的动手研究培训，从而使他们能够为进化生物学，分子生物学，人群遗传学，癌症，门肠遗传学，癌症遗传学，癌症，门遗传学，癌症，癌症的进步做出贡献。基因组学，概率建模，统计推断和相关领域。 PI将积极参加多种外展活动，例如斯坦福大学夏季研究计划，这将允许招募更多多样化的未来数据科学家库，并培养科学中更具包容性的气候。 该项目的研究结果将成为统计遗传学新计划的基础，并将纳入本科和研究生课程。 具体而言，该项目将扩大合并模型的类别，并通过利用家谱的指标，马尔可夫链的块状以及分裂和构成策略来提供一系列新的算法和统计方法。具体目的包括（1）开发合并模型，以结合各种抽样方案和生物学过程，例如动态种群结构，重组和强烈的选择； （2）开发一个合并理论和应用的度量框架； （3）制定了贝叶斯推断进化参数的可扩展策略，（4）实施，验证和分析传染病的分子序列，例如SARS-COV-2，古代和现代的人类DNA样品以及癌症的单细胞变异。此外，该项目将积极地为扩大多个方面的统计科学的参与，从基于团队的跨学科研究培训到社区推广。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的评估来支持的。影响审查标准。

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.