Resampling Methods for Quantitative Genetic Analysis

进化生物学和农业科学中许多问题的解决依赖于对所研究群体中存在的遗传变异和协变水平的准确估计。这些所谓的定量遗传参数的估计和检验传统上是基于 对 限制性假设和有限的 设置 的实验程序。这里提出的工作的目的是消除这些限制，利用最近开发的restaurant方法（主要是刀切法和自举法）作为一种手段，提供一个更一般的方法来检验定量遗传参数的统计假设。 将开发和测试用于简化参数估计和进行再循环分析的软件。 关于重新验证技术的使用，一个主要的问题是必须对使用它们的每个应用程序进行重新验证。 因此，大量时间将用于测试 的 新的统计性质 设计了恢复程序。将使用上述开发的恢复方法分析已知分布的数据集的蒙特卡罗模拟，并根据预期结果测试这些方法的误差估计和功效特性的准确性。 一旦开发和验证，该软件将大大简化定量遗传数据的分析，并为许多生物学家提供更大的信心，他们得出的结论。 尽管数量遗传学在农业系统中已经使用了几十年，但进化生物学家只是在最近15年或20年才开始使用数量遗传学方法。在此期间出现的一个中心问题是性状间遗传方差和协方差模式的进化变化的性质。这类问题通常是通过询问遗传方差-协方差矩阵是否随时间保持不变来发展的。本提案中开发的软件将允许提出关于遗传协方差结构进化的更广泛的问题。 通过利用 最近 事态发展 在 的 分析 在方差-协方差方法中，生物学家使用这里开发的方法将能够解决整个假设层次 关于 的 关系 之间 两个或多个群体的遗传方差-协方差矩阵。 一旦这些假设得到解决，导致两个种群分歧的净选择模式就可以重建和检验。 总之，一套连贯的方法和程序将被开发出来，使那些做定量遗传学的人从单调乏味的分析中解脱出来，并使他们能够追求许多新的和令人兴奋的研究途径。

The solution to many questions in evolutionary biology and agricultural science depend on the accurate estimation of the level of genetic variation and covariation present in the populations being studied. Estimation and testing of the these so-called quantitative genetic parameters has been traditionally based on restrictive assumptions and a limited set of experimental procedures. The work proposed here aims to eliminate many of these restrictions by utilizing recently developed resampling methods (primarily the jackknife and bootstrap) as a means of providing a more general approach to the statistical hypothesis testing of quantitative genetic parameters. Software for streamlining parameter estimation and for performing the resampling analysis will be developed and tested. A primary concern regarding the use of resampling techniques is that they must be revalidated for each application in which they are used. A great deal of time will therefore be devoted to testing the statistical properties of the newly devised resampling procedures. Monte carlo simulations of data sets of known distribution will be analyzed using the resampling methods developed above, and the error estimation and power properties of these methods tested for accuracy against the expected results. Once developed and validated, this software will greatly simplify the analysis of quantitative genetic data, and provide many biologists with greater confidence in the conclusions they reach. Although quantitative genetics has been used for many decades in agricultural systems, it is only in the last fifteen or twenty years that evolutionary biologists have begun to make use of quantitative genetic methodology. One of the central questions that has arisen during that time is nature of the evolutionary change in the pattern of genetic variance and covariance among traits. This line of questio ning is usually developed in terms of asking whether the genetic variance-covariance matrix remains unchanged through time. Software to be developed in this proposal will allow a much broader set of questions regarding the evolution of genetic covariance structure to be asked. By utilizing recent developments in the analysis of variance-covariance methods, biologists employing the methods developed here will be able to address an entire hierarchy of hypotheses regarding the relationship between genetic variance-covariance matrices from two or more populations. Once these hypotheses are addressed, the net pattern of selection that led to the divergence of the two populations can be reconstructed and tested. In all, a coherent set of methods and procedures will be developed that will free those doing quantitative genetics from the tedium of analysis and allow them to pursue many new and exciting avenues of research.