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Testing the tests: a predictive framework to guide genome scans for locally adapted traits

测试测试：指导基因组扫描以寻找适应当地特征的预测框架

基本信息

批准号：
1655701
负责人：
Kathleen Lotterhos
金额：
$ 55.61万
依托单位：
Northeastern University
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：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1655701&HistoricalAwards=false
关键词：
Testing tests predictive framework guide

项目摘要

Today, biologists are able to obtain massive amounts of DNA sequence data from many species, including humans. These data have been used to analyze the genetic basis of species traits in thousands of diverse studies, with a particular focus on understanding traits that are adapted to the local environment. Current statistical methods for analyzing these data, known as genome scans, are limited because they are only designed to detect obvious patterns. However, mathematical models predict that more subtle, yet predictable, patterns will evolve for many traits that are common in nature. However, the genetic basis of these traits may not be detectable by widely used genome-scan methods. There are promising new approaches, however, that may be able to detect these more subtle patterns. This research project aims to "test the tests:" to evaluate genome scan methods in a common framework against simulated data. Results will provide new insights into how to implement tests and summarize results so researchers can more effectively study the genetic basis of species traits. Since genome scans have been widely applied in medicine, agriculture, and animal breeding, a better application of these tests can lead to measureable improvements in human lives. To engage persons at different levels of understanding in our research, we will develop a training and outreach program in Genomics, Evolution, Mathematical Modeling and Analysis (GEMMA).The project will develop a robust framework, grounded in quantitative genetic theory, to guide the creation of a novel set of simulated datasets spanning monogenic to highly polygenic architectures. In phase 1 of the project, researchers will ask how adding realism affects the evolution of genetic architecture and the extent to which populations are adapted to their local environment. Then, they will examine the extent to which the results from univariate and multivariate genome-scan approaches (differentiation outlier tests, association tests, and haplotype-based tests) agree and are accurate. More likely than not, no one method will be ideal for all architectures. Therefore, in phase 2 of the project, researchers will develop approaches for integrating signals from multiple tests to detect outliers in multivariate space, thereby leveraging the unique strengths of different methods. Because this study examines evolutionary processes with a particular focus on the design, implementation, and interpretation of genome scans for polygenic traits, results will allow more accurate characterization of the genetic variation responsible for locally adapted traits.

今天，生物学家能够从包括人类在内的许多物种中获得大量的DNA序列数据。这些数据已被用于分析数千项不同研究中物种性状的遗传基础，特别关注了解适应当地环境的性状。目前用于分析这些数据的统计方法，即基因组扫描，是有限的，因为它们只用于检测明显的模式。然而，数学模型预测，自然界中常见的许多特征将演变成更微妙但可预测的模式。然而，这些性状的遗传基础可能无法通过广泛使用的基因组扫描方法检测到。然而，有一些有希望的新方法可能能够检测到这些更微妙的模式。该研究项目旨在“测试测试”：在一个共同的框架内对模拟数据进行基因组扫描方法的评估。研究结果将为如何实施测试和总结结果提供新的见解，以便研究人员能够更有效地研究物种性状的遗传基础。由于基因组扫描已被广泛应用于医学，农业和动物育种，更好地应用这些测试可以导致人类生活的可衡量的改善。为了吸引不同层次的人参与我们的研究，我们将开发一个基因组学，进化，数学建模和分析（GEMMA）的培训和推广计划。该项目将开发一个强大的框架，以定量遗传理论为基础，指导创建一套新颖的模拟数据集，涵盖单基因到高度多基因的结构。在该项目的第一阶段，研究人员将询问增加现实主义如何影响遗传结构的进化以及种群适应当地环境的程度。然后，他们将检查单变量和多变量基因组扫描方法（差异离群值检验，关联检验和基于单倍型的检验）的结果一致和准确的程度。更有可能的是，没有一种方法对所有架构都是理想的。因此，在该项目的第二阶段，研究人员将开发整合来自多个测试的信号的方法，以检测多变量空间中的离群值，从而利用不同方法的独特优势。因为这项研究审查了进化过程，特别关注多基因性状的基因组扫描的设计，实施和解释，结果将允许更准确地表征负责本地适应性状的遗传变异。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

minotaur : A platform for the analysis and visualization of multivariate results from genome scans with R Shiny

minotaur ：使用 R Shiny 对基因组扫描的多变量结果进行分析和可视化的平台

DOI：
10.1111/1755-0998.12579
发表时间：
2017
期刊：
Molecular Ecology Resources
影响因子：
7.7
作者：
Verity, Robert;Collins, Caitlin;Card, Daren C.;Schaal, Sara M.;Wang, Liuyang;Lotterhos, Katie E.
通讯作者：
Lotterhos, Katie E.

Analysis validation has been neglected in the Age of Reproducibility