Resampling Methods for Quantitative Genetic Analysis

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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.