Joint inferences of natural selection between sites and populations

地点和种群之间自然选择的联合推论

• 批准号: 10560525
• 负责人: Ryan Gutenkunst
• 金额: $ 29.43万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-12 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10560525
• 关键词: Architecture; Communicable Diseases; Daphnia; Drosophila melanogaster; Foundations; Future; Genes; Genetic Diseases; Genetic Variation; Genomics; Goals; Historical Demography; Human; Human Genetics; Individual; Joints; Link; Methodology; Methods; Modeling; Molecular; Mutation; Natural Selections; Outcome; Pattern; Play; Population; Population Genetics; Proteins; Public Health; Research; Resources; Site; Variant; combat; experimental study; fitness; gene function; genetic architecture; genetic selection; genomic locus; human disease; human pathogen; improved; innovation; insight; model organism; nonsynonymous mutation; novel; novel strategies; pathogen; statistics

Project Summary/Abstract There is a critical need for population genetic inference approaches to quantify natural selec- tion within and between populations. The PI's long-term goal is to develop comprehensive approaches for identifying selection in natural populations and understanding its functional consequences. The objectives of this application are to develop and apply novel approaches for inferring correlated selection between genomic sites and between natural populations. The rationale for the proposed research is that the approaches developed will be broadly applicable, providing a foundation understanding adaptation in pathogens and the genetic architecture of human disease. In Aim 1, the PI proposes to leverage his recently developed approach for calculating the statistics of pairs of linked genetic loci to quantify several aspects of natural selection in hu- mans and Drosophila melanogaster. He will first focus on individual known adaptive loci, quantifying the strength, timing, and mode of selection. He will then infer the distribution of fitness effects of new nonsynonymous mutations. Lastly, we will infer the joint distribution of fitness effects of nonsynonymous mutations within the same protein. In Aim 2, the PI proposes to quantify divergent natural selection between populations of hu- mans, D. melanogaster, and Daphnia pulex. To do so, he will develop an approach for inferring joint distributions of mutation fitness effects and apply it to genes sets of differing molecular function and populations of differing divergence. The proposed research is innovative both methodologically and conceptually. The methods to be developed are novel, as are the concepts of joint distributions of fitness effects be- tween sites and populations. The expected outcomes of the proposed research are new population genetic inference methods and inferences of natural selection in humans and two model organisms. These outcomes are expected to have important positive impact on the field of population genetics. The methods will be widely applicable and well-supported, and the inferences will feed into approaches for inferring the evolutionary past and predicting the evolutionary future. Project Summary/Abstract

项目总结/摘要 有一个关键的需要群体遗传推理的方法来量化自然选择， 人口内部和人口之间的关系。PI的长期目标是发展全面的 识别自然群体中的选择并理解其功能的方法 后果本申请的目的是开发和应用新的方法 用于推断基因组位点之间和自然种群之间的相关选择。 拟议研究的理由是，所开发的方法将广泛适用于 适用，提供了一个基础，了解适应病原体和遗传 人类疾病的结构。 在目标1中，PI建议利用他最近开发的方法来计算 对连锁遗传基因座的统计，以量化自然选择的几个方面， 人类和黑腹果蝇。他将首先关注单个已知的适应性位点， 量化选择的强度、时机和模式。然后他将推断出 新的非同义突变的适应性效应。最后，我们将推导出 同一蛋白质内非同义突变的适合性效应。 在目标2中，PI建议量化人类种群之间的趋异自然选择。 mans，D. melanogaster和蚤状水蚤（Daphnia pulex）。为了做到这一点，他将开发一种方法， 突变适合性效应的联合分布，并将其应用于不同分子的基因集 功能和种群的差异。 该研究在方法和概念上都具有创新性。的方法 要开发的是新颖的，因为是联合分布的拟合效果的概念是- 地点和人口之间。拟议研究的预期成果是新的 群体遗传推断方法和人类自然选择的推断， 模式生物预计这些成果将对促进可持续发展产生重要的积极影响。 群体遗传学。这些方法将得到广泛应用和支持， 这些推论将被用于推断进化的过去和预测未来的方法。 进化的未来 项目总结/摘要

项目成果

The genomic origins of the world's first farmers.

• DOI: 10.1016/j.cell.2022.04.008
• 发表时间: 2022-05-26
• 期刊: CELL
• 影响因子: 64.5
• 作者: Marchi, Nina;Winkelbach, Laura;Schulz, Ilektra;Brami, Maxime;Hofmanova, Zuzana;Bloecher, Jens;Reyna-Blanco, Carlos S.;Diekmann, Yoan;Thiery, Alexandre;Kapopoulou, Adamandia;Link, Vivian;Piuz, Valerie;Kreutzer, Susanne;Figarska, Sylwia M.;Ganiatsou, Elissavet;Pukaj, Albert;Struck, Travis J.;Gutenkunst, Ryan N.;Karul, Necmi;Gerritsen, Fokke;Pechtl, Joachim;Peters, Joris;Zeeb-Lanz, Andrea;Lenneis, Eva;Teschler-Nicola, Maria;Triantaphyllou, Sevasti;Stefanovic, Sofija;Papageorgopoulou, Christina;Wegmann, Daniel;Burger, Joachim;Excoffier, Laurent
• 通讯作者: Excoffier, Laurent

Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks.

• DOI: 10.1111/1755-0998.13355
• 发表时间: 2021-11
• 期刊: Molecular ecology resources
• 影响因子: 7.7
• 作者: Blischak PD;Barker MS;Gutenkunst RN
• 通讯作者: Gutenkunst RN

Shifts in Mutation Bias Promote Mutators by Altering the Distribution of Fitness Effects.

突变偏差的变化通过改变适应度效应的分布来促进突变。

• DOI: 10.1086/726010
• 发表时间: 2023
• 期刊: The American naturalist
• 作者: Tuffaha,MarwaZ;Varakunan,Saranya;Castellano,David;Gutenkunst,RyanN;Wahl,LindiM
• 通讯作者: Wahl,LindiM

Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations.

• DOI: 10.1093/molbev/msab162
• 发表时间: 2021-09-27
• 期刊: Molecular biology and evolution
• 影响因子: 10.7
• 作者: Huang X;Fortier AL;Coffman AJ;Struck TJ;Irby MN;James JE;León-Burguete JE;Ragsdale AP;Gutenkunst RN
• 通讯作者: Gutenkunst RN

dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units.

• DOI: 10.1093/molbev/msaa305
• 发表时间: 2021-05-04
• 期刊: Molecular biology and evolution
• 影响因子: 10.7
• 作者: Gutenkunst RN