| 项目编号: | 1557151
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| 项目名称: | Elucidating and detecting adaptive introgression |
| 作者: | Emilia Huerta-Sanchez
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| 承担单位: | University of California - Merced
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| 批准年: | 2016
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| 开始日期: | 2016-06-01
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| 结束日期: | 2019-05-31
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| 资助金额: | 500000
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Biological Sciences - Environmental Biology
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| 英文关键词: | adaptive introgression
; selection
; beneficial mutation
; gene-flow
; population
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| 英文摘要: | Humans have adapted to many challenging environments during our evolution. For example, different temperatures, diets, pathogens and altitudes have led to local adaptations. Adaptations occur through the selection and increase of beneficial mutations in genes in a population, which can arise spontaneously by random mutation or can be acquired through admixture with another population. Analysis of human genomes has shown that some of our beneficial mutations have come from archaic human populations like the Neanderthals and Denisovans, facilitated by interbreeding between modern humans and those groups tens of thousands of years ago. This process is referred to as adaptive introgression. While there are a few examples of beneficial mutations in genes arising from adaptive introgression, in this project tools will be built to scan human genomes to identify more candidate genes for adaptive introgression and to fully characterize the importance of this process in many human populations. Importantly, the project will involve training of undergraduate and graduate students in computational science and genomics, and the tools created will be freely accessible for others to use. In addition, a new interdisciplinary course that bridges data analysis of human genetic variation, programming, statistics and biology will be offered to undergraduate students.
Detecting and characterizing adaptation has mostly been approached through two models: selection on de novo mutations (SDN) or selection on standing variation (SSV). Therefore it is assumed that a population either has to wait for a beneficial mutation to arise de novo or it harbors enough neutral standing variation that can become beneficial under a change in environment. However, most populations do not live in isolation and have exchanged genetic variants with other populations through admixture (gene-flow from a donor population to a recipient population). This process is an evolutionary force that may accelerate adaptation in the recipient population. The PI will model positive selection and gene-flow jointly to investigate the patterns of genetic variation under this model, to compare and contrast to the two other models of positive selection (SDN, SSV), to determine what summaries of the data accurately distinguishes adaptive gene flow from SDN and SSV, to develop novel statistics that accurately detect this type of selection and to develop statistical and computational tools to scan genomes to identify candidate regions. These tools will be applied to real data sets in humans and in other organisms. The broader impacts include training a postdoc, students, building a project-oriented course that integrates multiple disciplines (programming, statistics and modeling) that will teach students to visualize biological data sets for exploratory analyses, to test hypothesis and to fit models to the data, and bringing science to high school students through a series of lectures. Finally, all computational tools developed under this grant will be made freely available to the scientific community. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92259
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Emilia Huerta-Sanchez. Elucidating and detecting adaptive introgression. 2016-01-01.
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