Testing the tests: a predictive framework to guide genome scans 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物种特征的遗传基础，在数千个不同的研究中，特别侧重于了解适应当地环境的特征。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，对于自然界中常见的许多特征，将会进化出更微妙但更可预测的模式。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，有一些很有希望的新方法，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”：根据模拟数据在通用框架中评估基因组扫描方法。结果将为如何实施测试和总结结果提供新的见解，以便研究人员可以更有效地研究物种特征的遗传基础。由于基因组扫描已广泛应用于医学、农业和动物育种，更好地应用这些测试可以显著改善人类生活。为了让不同层次的人参与我们的研究，我们将制定基因组学、进化、数学建模和分析（GEMMA）方面的培训和推广计划。该项目将开发一个强大的框架，以定量遗传理论为基础，指导创建一套新的模拟数据集，涵盖单基因到高多基因架构。在项目的第一阶段，研究人员将研究添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（分化异常值测试、关联测试和基于单倍型的测试）的结果在多大程度上一致和准确。更有可能的是，没有一种方法对所有体系结构都是理想的。因此，在该项目的第二阶段，研究人员将开发整合多个测试信号的方法，以检测多元空间中的异常值，从而利用不同方法的独特优势。由于本研究特别关注多基因性状的基因组扫描的设计、实施和解释，因此研究结果将允许更准确地描述导致局部适应性状的遗传变异。

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.