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      Multilevel Selection 2: Estimating the Genetic Parameters Determining Inheritance and Response to Selection

      , , , ,
      Genetics
      Genetics Society of America

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          Abstract

          Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.

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          Recovery of inter-block information when block sizes are unequal

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            Estimating genetic parameters in natural populations using the "animal model".

            Estimating the genetic basis of quantitative traits can be tricky for wild populations in natural environments, as environmental variation frequently obscures the underlying evolutionary patterns. I review the recent application of restricted maximum-likelihood "animal models" to multigenerational data from natural populations, and show how the estimation of variance components and prediction of breeding values using these methods offer a powerful means of tackling the potentially confounding effects of environmental variation, as well as generating a wealth of new areas of investigation.
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              Evolutionary Quantitative Genetics

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                Author and article information

                Journal
                Genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                January 23 2007
                January 2007
                January 2007
                November 16 2006
                : 175
                : 1
                : 289-299
                Article
                10.1534/genetics.106.062729
                1775010
                17110493
                a7f3c462-83be-4444-a297-4f81fdc281ce
                © 2006
                History

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