Statistical Models for Dissecting Human Population Admixture and its Role in Evolution and Disease

解剖人口混合的统计模型及其在进化和疾病中的作用

• 批准号: 10239056
• 负责人: Sriram Sankararaman
• 金额: $ 33.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10239056
• 关键词: Admixture; Alleles; Chromosome Mapping; Collection; Complex; Computing Methodologies; DNA; Data Set; Disease; Ensure; European; Event; Evolution; Genetic; Genome; Genomics; Human; Lead; Measurement; Methods; Modeling; Modernization; Partner in relationship; Pattern; Phenotype; Population; Population Group; Process; Recording of previous events; Recurrence; Risk; Role; Series; Source; Statistical Methods; Statistical Models; Structure; Technology; Time; Work; analytical tool; genetic architecture; genetic evolution; insight; novel; reference genome; statistical and machine learning; structural genomics; tool

Project Summary Over the past decade, it has become clear that mixture between diverged populations (admixture) has been a recurrent feature in human evolution. It has also become evident that a detailed understanding of admixture is essential for effective disease gene mapping as well as evolutionary inference. Nevertheless, adequate analytical tools to dissect admixture and its impact on phenotype are lacking. As a result, disease gene mapping or evolutionary studies have either excluded admixed populations or relied on simplified models at the risk of inaccurate inferences. This proposal proposes to develop computational methods to infer the genomic structure and history of admixed populations across a range of evolutionary time scales and to leverage this structure to obtain a comprehensive understanding of the genetic architecture and evolution of complex phenotypes. The proposed methods will integrate powerful sources of information from ancient DNA with genomes from present-day human populations. These methods will enable populations with a history of admixture to be studied just as effectively as homogeneous populations. The first step in obtaining a thorough understanding of admixture is a principled and scalable statistical framework to infer fine-scale genomic structure (local ancestry) and evolutionary relationships. This proposal leverages recent advances in statistical machine learning to develop effective tools for the increasingly common and challenging problem of local ancestry inference where reference genomes for ancestral populations are unavailable (de-novo local ancestry). Further, the proposal intends to develop models to infer complex evolutionary histories as well as realistic mating patterns in admixed populations. These inferences will form the starting point to systematically understand how admixture has shaped phenotypes. For example, it is becoming clear that admixture between modern humans and archaic humans (Neanderthals and Denisovans) could have had a major impact on human phenotypes. This question will be explored by applying novel statistical methods to large genetic datasets with phenotypic measurements to assess the adaptive as well as phenotypic impact of Neanderthal alleles. Finally, large collections of genomes from extinct populations that are now becoming available due to advances in ancient DNA technologies can lead to vastly more powerful methods for evolutionary inference that overcome the limitation of methods that rely only on extant genomes. Statistical models that use ancient genome time-series to efficiently infer admixture histories, local ancestry and selection will be developed.

项目总结

项目成果

STENSL: Microbial Source Tracking with ENvironment SeLection.

• DOI: 10.1128/msystems.00995-21
• 发表时间: 2022-10-26
• 期刊: mSystems
• 影响因子: 6.4

Advancing admixture graph estimation via maximum likelihood network orientation.

• DOI: 10.1093/bioinformatics/btab267
• 发表时间: 2021-07-12
• 期刊: Bioinformatics (Oxford, England)
• 作者: Molloy EK;Durvasula A;Sankararaman S
• 通讯作者: Sankararaman S

An efficient linear mixed model framework for meta-analytic association studies across multiple contexts.

用于跨多个上下文的元分析关联研究的有效线性混合模型框架。

• 发表时间: 2016
• 期刊: LIPIcs : Leibniz international proceedings in informatics
• 作者: Jew,Brandon;Li,Jiajin;Sankararaman,Sriram;Sul,JaeHoon
• 通讯作者: Sul,JaeHoon

A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies

全基因组关联研究中汇总统计数据的统一框架

• DOI: 10.1089/cmb.2019.0449
• 发表时间: 2020
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Wu, Yue;Eskin, Eleazar;Sankararaman, Sriram
• 通讯作者: Sankararaman, Sriram

Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.

• DOI: 10.1038/s41588-023-01558-w
• 发表时间: 2023-12
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: An, Ulzee;Pazokitoroudi, Ali;Alvarez, Marcus;Huang, Lianyun;Bacanu, Silviu;Schork, Andrew J.;Kendler, Kenneth;Pajukanta, Paeivi;Flint, Jonathan;Zaitlen, Noah;Cai, Na;Dahl, Andy;Sankararaman, Sriram
• 通讯作者: Sankararaman, Sriram