Elucidating and detecting adaptive introgression

阐明和检测适应性基因渗入

• 批准号: 1557151
• 负责人: Emily Jane McTavish
• 金额: $ 50万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557151&HistoricalAwards=false
• 关键词: Elucidating; detecting; adaptive; introgression

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.

人类在进化过程中适应了许多具有挑战性的环境。例如，不同的温度、饮食、病原体和海拔导致了当地的适应。适应是通过选择和增加群体中基因中的有益突变而发生的，这些突变可以通过随机突变自发产生，也可以通过与另一个群体的混合而获得。对人类基因组的分析表明，我们的一些有益突变来自古老的人类群体，如尼安德特人和丹尼索瓦人，这是由现代人和数万年前的这些群体之间的杂交促成的。这个过程被称为适应性渐渗。虽然有几个例子表明基因中的有益突变是由适应性基因渗入引起的，但在本项目中，将建立工具来扫描人类基因组，以确定适应性基因渗入的更多候选基因，并充分表征这一过程在许多人群中的重要性。重要的是，该项目将涉及在计算科学和基因组学方面对本科生和研究生进行培训，所创建的工具将免费供其他人使用。此外，一个新的跨学科课程，桥梁人类遗传变异，编程，统计和生物学的数据分析将提供给本科生。 检测和表征适应主要通过两种模型进行：从头突变选择（SDN）或常设变异选择（SSV）。因此，假设一个种群要么必须等待一个有益的突变从头出现，要么它拥有足够的中性常设变异，可以在环境变化下变得有益。然而，大多数种群并不是孤立地生活，而是通过混合（从供体种群到受体种群的基因流动）与其他种群交换遗传变异。这个过程是一种进化的力量，可能会加速接受人口的适应。PI将对正选择和基因流进行联合建模，以研究该模型下的遗传变异模式，并与其他两种正选择模型进行比较和对比（SDN，SSV），以确定什么样的数据摘要准确地将自适应基因流与SDN和SSV区分开，开发新的统计学来准确地检测这种类型的选择，并开发统计和计算工具来扫描基因组以识别候选区域。这些工具将应用于人类和其他生物体的真实的数据集。更广泛的影响包括培训博士后，学生，建立一个以项目为导向的课程，整合多个学科（编程，统计和建模），将教学生可视化生物数据集进行探索性分析，测试假设和拟合模型的数据，并通过一系列讲座将科学带给高中学生。最后，所有在此资助下开发的计算工具将免费提供给科学界。