CAREER: Developing New Computational Methods to Address the Missing Data Problem in Population Genomics

职业：开发新的计算方法来解决群体基因组学中的缺失数据问题

• 批准号: 2042516
• 负责人: Arun Sethuraman
• 金额: $ 60.84万
• 依托单位: California State University San Marcos Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042516&HistoricalAwards=false
• 关键词: CAREER; Developing; New; Computational; Methods

Population genomic data are becoming increasingly affordable and accessible, causing a sudden data explosion in the field of evolutionary biology. With this increased degree of data generation comes another important issue - the missing data problem. This missing data problem could be due to data that is either unobserved (e.g., due to the sampling method), observed incorrectly (e.g., due to errors in method of observation), or can’t be observed (e.g., due to extinction). Missing data are often not accounted for and can cause incorrect conclusions in population genomics research. This project will build bioinformatics software to address these three missing data problems. The statistical framework and methods developed by this project will be utilized extensively by evolutionary biologists in a variety of fields. Additionally, this project will develop accessible software pipelines and curricular material for recruiting and retaining underrepresented groups into computer programming and bioinformatics at a variety of levels (K-12, Undergraduate, Graduate, post-graduate).Population genomic data are either considered to be missing due to (1) sequencing or genotyping errors, (2) systematic bias in the generation of genotyping libraries (e.g. from techniques such as restriction associated DNA sequencing (RADseq), or (3) the absence of genomic data from un-sampled, perhaps extinct “ghost” populations. This project will develop a series of tools to address all three missing data problems by accounting for missing data as an unobserved variable in statistical models for the estimation of population genetic parameters and evolutionary history from genomic data. Specifically, we will (1) build a parallelized statistical framework for estimating population genetic structure from multi-allelic, multi-locus genomic data that incorporates missing data into a maximum likelihood framework, (2) systematically explore RADseq data sets – using extensive simulations and a meta-analysis of published studies to both quantify and account for how missing data due to “lost” polymorphisms at restriction sites biases estimation of evolutionary history, and (3) develop a statistical model to classify genomic loci as those having introgressed from extant or from “ghost” populations based on their coalescent histories under the Isolation with Migration (IM) model. This work will form the basis of a set of robust tools that will be utilized by evolutionary biologists in a variety of fields to systematically both assess and account for the effects of missing data in their population genomic data sets. This CAREER grant will also strengthen University-public partnerships through (1) week-long summer bioinformatics workshops for high-school biology teachers in the Philadelphia and San Diego areas, (2) development of curricular material for The Galaxy Project, the Conservation Genomics Workshop at the University of Montana, and the California State University Program for Education and Research in Biotechnology (CSUPERB), (3) recruitment and retention of underrepresented student scholars into genomics research. All curricular material, software, and pipelines developed will be shared via the PI’s GitHub page: www.github.com/arunsethuraman.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.

人口基因组数据变得越来越便宜和容易获得，导致进化生物学领域的数据突然爆炸。随着数据生成程度的提高，另一个重要问题也随之而来--数据缺失问题。这种缺失数据问题可能是由于未观察到的数据（例如，由于采样方法），不正确地观察（例如，由于观察方法中的误差），或者不能被观察到（例如，由于灭绝）。缺失的数据往往不占，并可能导致人口基因组学研究中的错误结论。该项目将建立生物信息学软件来解决这三个数据缺失问题。该项目开发的统计框架和方法将被进化生物学家广泛用于各个领域。此外，该项目将开发可访问的软件管道和课程材料，以招募和保留代表性不足的群体进入各级计算机编程和生物信息学（K-12，本科生，研究生，研究生）。群体基因组数据被认为是缺失的，原因是（1）测序或基因分型错误，（2）在基因分型文库的产生中的系统偏差（例如来自诸如限制性相关DNA测序（RADseq）的技术），或（3）来自未取样的、可能灭绝的“幽灵”群体的基因组数据的缺失。该项目将开发一系列工具，解决所有三个缺失数据问题，方法是将缺失数据作为统计模型中的一个未观察到的变量，从基因组数据中估计群体遗传参数和进化历史。具体而言，我们将（1）建立一个并行统计框架，用于从多等位基因、多位点基因组数据中估计群体遗传结构，该框架将缺失数据纳入最大似然框架，（2）系统地探索RADseq数据集-使用广泛的模拟和对已发表研究的荟萃分析来量化和解释由于“丢失”而导致的数据缺失限制性位点的多态性使进化历史的估计产生偏差，以及（3）开发一个统计模型，根据迁移隔离（IM）模型下的结合历史，将基因组基因座分类为从现存群体或从“幽灵”群体渗入的基因座。这项工作将形成一套强大的工具的基础，这些工具将被进化生物学家在各个领域用来系统地评估和解释其人口基因组数据集中缺失数据的影响。这项职业资助还将通过以下方式加强大学与公众的合作关系：（1）为费城和圣地亚哥地区的高中生物学教师举办为期一周的暑期生物信息学研讨会，（2）为银河项目、蒙大拿大学的保护基因组学研讨会和加州州立大学生物技术教育和研究计划（CSUPERB）开发课程材料，（3）招募和保留代表性不足的学生学者从事基因组学研究。所有开发的课程材料，软件和管道将通过PI的GitHub页面共享：www.github.com/arunsethuraman.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。