Literature DB >> 24263919

The use of the relationship matrix to account for genetic drift variance in the analysis of genetic experiments.

D A Sorensen1, B W Kennedy.   

Abstract

Selection experiments can provide information on genetic parameters such as realized heritability and response to selection. Often, due to lack of adequate replication, empirical sampling variances of estimated response cannot be computed and therefore use must be made of theoretical formulae. Most of the variance between a conceptually large number of selected lines drawn from the same base population is contributed by genetic drift, which depends on the population structure and can therefore be predicted before the experiment is carried out. The theory of variation of response to selection has been developed mainly by Hill, who produced formulae to adjust the variance of estimators to take account of genetic drift. In this paper, we draw attention to properties of the additive genetic relationship matrix that lead to well established results in population genetics theory. We show how inclusion of the additive genetic relationship matrix among the observations leads to sampling variances of estimators of genetic means that account for the variance due to genetic drift.

Year:  1983        PMID: 24263919     DOI: 10.1007/BF00251147

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  3 in total

1.  Variability in genetic parameters among small populations.

Authors:  P J Avery; W G Hill
Journal:  Genet Res       Date:  1977-06       Impact factor: 1.588

2.  Effective size of populations with overlapping generations.

Authors:  W G Hill
Journal:  Theor Popul Biol       Date:  1972-09       Impact factor: 1.570

3.  Design and efficiency of selection experiments for estimating genetic parameters.

Authors:  W G Hill
Journal:  Biometrics       Date:  1971-06       Impact factor: 2.571

  3 in total
  4 in total

1.  Genomic-assisted prediction of genetic value with semiparametric procedures.

Authors:  Daniel Gianola; Rohan L Fernando; Alessandra Stella
Journal:  Genetics       Date:  2006-04-28       Impact factor: 4.562

2.  Expected early genetic gain from selection for milk yield in dairy cattle.

Authors:  M R Dentine; B T McDaniel
Journal:  Theor Appl Genet       Date:  1987-10       Impact factor: 5.699

3.  Response to selection for litter size in Danish Landrace pigs: a Bayesian analysis.

Authors:  C S Wang; D Gianola; D A Sorensen; J Jensen; A Christensen; J J Rutledge
Journal:  Theor Appl Genet       Date:  1994-05       Impact factor: 5.699

4.  Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

Authors:  John B Holmes; Ken G Dodds; Michael A Lee
Journal:  Genet Sel Evol       Date:  2017-03-02       Impact factor: 4.297

  4 in total

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