AF: Small: Computational Methods for Large-scale Inference of Population History

AF：小型：人口历史大规模推断的计算方法

• 批准号: 1718093
• 负责人: Yufeng Wu
• 金额: $ 40.5万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718093&HistoricalAwards=false
• 关键词: AF; Small; Computational; Methods; Large

Consider several unrelated human individuals from a population. It is a common sense that these individuals are descendants of some common ancestors if tracing backward in time long enough. Indeed, these sampled individuals share a genealogical history that specifies the ancestry of these individuals. This genealogy is very informative, since it can tell, e.g. which individuals are closely related. One potential application of the genealogy is understanding why some individuals are more susceptible to some phenotypic traits (such as diabetes or cancer) than others. It might be the case that individuals sharing a trait are more closely related to each other on the genealogy than the rest of the population. Genealogy, although useful, cannot be directly observed. The so-called genetic variation provides hints on the underlying genealogy of the sampled individuals. One common type of genetic variations is the single nucleotide polymorphism (SNP). A SNP refers to the genomic position where individuals in a population may have different nucleotides at the position. A moment of thoughts suggests that individuals with the same nucleotide at a SNP tend to be more closely related than individuals with different nucleotides. This may allow one to infer the plausible underlying genealogy from genetic data collected at multiple SNPs. Inference of genealogy from real SNP data is, however, much more complex than this. One main difficulty is caused by meiotic recombination. Without recombination, genealogy can be modeled as a tree (similar to the usual tree of life model that has been extensively studied in biology). Recombination allows one genome to have more than one ancestor and thus violates the basic property of this simple tree model. Recombination essentially breaks down the genome into many small segments, where each segment may originate from different ancestors. That is, genealogical history at different genomic positions may be different. Genealogy with recombination is thus much more complex than that with no recombination.This project aims to developing effective computational methods for analyzing large-scale population genetic data that has become available during the past several years. The main goals are first accurately inferring the genealogical history of sampled individuals from the genetic data, and then performing inference for several population genetic problems with the inferred genealogy. The successful completion of the proposed research will produce new computational tools and software that may allow population geneticists to better understand the implications of large-scale population genetic data. Potential applications of these tools include, for example, mapping the genomic positions that are associated with complex traits, inferring the population admixture history and finding regions of the genome that are under natural selection.The intellectual merits of this project are as follows. This project will develop efficient and accurate computational methods for inferring population history from haplotypes based on inferred gene genealogies. Gene genealogy refers to the evolutionary history of extant population haplotypes, and captures the underlying LD information. While gene genealogies are fundamental to population genetics, most existing inference methods don't use gene genealogies explicitly because genealogies are not directly observable. Inferring gene genealogies from haplotypes is just starting to become feasible, due to the latest development in genomic technologies and genealogy inference methods. This project aims to developing effective computational methods for the following two problems. First, new methods for inferring gene genealogies from haplotypes will be developed. Second, new methods for inferring population demographic history (e.g. population admixture) will be developed. Successful completion of the proposed research will produce new efficient and accurate algorithms that are implemented in practical software tools and allow population biologists to infer population history from genome-scale data.The broader impacts of this project include the following. Developed software tools will be made available freely to the multidisciplinary research community, and are expected to enable novel biological applications in complex population history inference. Research results will be integrated into classroom teaching. The project will ensure broad dissemination of the research results and teaching materials. The proposed educational and outreach activities include training of future researchers with unique interdisciplinary skills.

考虑一个种群中几个不相关的人类个体。如果追溯足够长的时间，这些个体是某些共同祖先的后代，这是一个常识。事实上，这些被采样的个体分享了一段确定这些个体祖先的家族史。这个家谱是非常有用的，因为它可以告诉我们，例如，哪些人是密切相关的。家谱的一个潜在应用是理解为什么一些人比其他人更容易患上某些表型特征(如糖尿病或癌症)。可能的情况是，拥有相同特征的个体在谱系上彼此之间的亲缘关系比群体中的其他人更密切。家谱虽然有用，但不能直接观察。所谓的遗传变异为样本个体的潜在家谱提供了线索。一种常见的遗传变异类型是单核苷酸多态(SNP)。SNP指的是一个群体中的个体在这个位置上可能有不同的核苷酸的基因组位置。片刻的思考表明，具有相同核苷酸的SNP个体往往比具有不同核苷酸的个体具有更密切的亲缘关系。这可能允许人们从在多个SNPs收集的遗传数据中推断出可信的潜在家系。然而，从真实的SNP数据推断家谱要复杂得多。一个主要的困难是减数分裂重组造成的。在没有重组的情况下，家谱可以被建模为一棵树(类似于生物学中广泛研究的常见的生命树模型)。重组允许一个基因组有一个以上的祖先，因此违反了这个简单树模型的基本性质。重组基本上将基因组分解成许多小片段，每个片段可能来自不同的祖先。也就是说，不同基因组位置的系谱历史可能是不同的。因此，有重组的家谱比没有重组的要复杂得多。这个项目的目的是开发有效的计算方法来分析在过去几年中可用的大规模群体遗传数据。其主要目的是首先从遗传数据中准确地推断样本个体的系谱历史，然后利用所推断的系谱对几个群体遗传问题进行推断。拟议研究的成功完成将产生新的计算工具和软件，使种群遗传学家能够更好地理解大规模种群遗传数据的含义。这些工具的潜在应用包括，例如，绘制与复杂性状相关的基因组位置图，推断种群混合历史，以及寻找基因组中处于自然选择下的区域。该项目的智力优势如下。这个项目将开发有效和准确的计算方法，根据推断的基因谱系，从单倍型推断种群历史。基因系谱是指现存种群单倍型的进化历史，并捕捉潜在的LD信息。虽然基因系谱是群体遗传学的基础，但大多数现有的推断方法并没有明确使用基因系谱，因为系谱是不可直接观察到的。由于基因组技术和系谱推断方法的最新发展，从单倍型推断基因系谱才刚刚开始成为可能。该项目旨在为以下两个问题开发有效的计算方法。首先，将开发从单倍型推断基因谱系的新方法。第二，将开发推断人口历史(例如人口混合)的新方法。拟议研究的成功完成将产生新的高效和准确的算法，这些算法可以在实用的软件工具中实现，并使种群生物学家能够从基因组规模的数据中推断种群历史。该项目的更广泛影响包括以下几个方面。开发的软件工具将免费提供给多学科研究社区，并有望在复杂的种群历史推断中实现新的生物学应用。研究成果将融入课堂教学。该项目将确保广泛传播研究成果和教材。拟议的教育和外联活动包括培训具有独特跨学科技能的未来研究人员。

项目成果

Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach

• DOI: 10.1093/bioinformatics/btaa465
• 发表时间: 2020-07-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Wu, Yufeng

Inferring the ancestry of parents and grandparents from genetic data

• DOI: 10.1371/journal.pcbi.1008065
• 发表时间: 2020-08-01
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Pei, Jingwen;Zhang, Yiming;Wu, Yufeng
• 通讯作者: Wu, Yufeng

CLADES: A classification-based machine learning method for species delimitation from population genetic data

CLADES：一种基于分类的机器学习方法，用于从种群遗传数据中进行物种界定

• DOI: 10.1111/1755-0998.12887
• 发表时间: 2018
• 期刊: Molecular Ecology Resources
• 影响因子: 7.7
• 作者: Pei, Jingwen;Chu, Chong;Li, Xin;Lu, Bin;Wu, Yufeng
• 通讯作者: Wu, Yufeng

Detecting circular RNA from high-throughput sequence data with de Bruijn graph

• DOI: 10.1186/s12864-019-6154-7
• 发表时间: 2018-04
• 期刊: BMC Genomics
• 影响因子: 4.4
• 作者: Xin Li;Yufeng Wu

HPV-EM: an accurate HPV detection and genotyping EM algorithm

• DOI: 10.1038/s41598-020-71300-7
• 发表时间: 2020-08-31
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Inkman, Matthew J.;Jayachandran, Kay;Zhang, Jin
• 通讯作者: Zhang, Jin