About Us | Help Videos | Contact Us | Subscriptions

Agronomy Journal Abstract - Biometry, Modeling & Statistics

Relevance of Pedigree, Historical Data, Dominance, and Data Unbalance for Selection Efficiency


This article in AJ

  1. Vol. 104 No. 3, p. 722-728
    Received: Nov 8, 2011

    * Corresponding author(s): jmsviana@ufv.br
Request Permissions

  1. José Marcelo Soriano Viana *a,
  2. Rodrigo Oliveira DeLimaa,
  3. Vinícius Ribeiro Fariaa,
  4. Gabriel Borges Mundima,
  5. Marcos Deon Vilela de Resendeb and
  6. Fabyano Fonseca e Silvac
  1. a Univ. Federal de Viçosa, Dep. de Biologia Geral, 36570-000, Viçosa, MG, Brazil
    b Embrapa Florestas/Univ. Federal de Viçosa, Dep. de Engenharia Florestal, 36570-000, Viçosa, MG, Brazil
    c Univ. Federal de Viçosa, Dep. de Estatística, 36570-000, Viçosa, MG, Brazil


The objective of this study was to assess the impact of pedigree, historical data, dominance, and data unbalance on the estimation and precision of genetic variances and breeding values and on the selection efficiency in annual crop breeding. Expansion volume and grain yield from 12 trials of inbred progeny and four tests of non-inbred families were used in the analyses. The S1 to S5 progeny trials were designed as incomplete blocks, the S6 progeny trials were designed as complete blocks, and the half- and full-sib family trials were designed as lattices. The half-sib, full-sib, and inbred family models were fitted in across-generation analyses. One complete and four reduced models were used to assess the relevance of pedigree, historical data, and dominance. Simulated plot losses of 30% in the half- and full-sib progeny trials were used to study the influence of data unbalance. All analyses were performed using ASReml. Ignoring pedigree information or ancestor data and simulating plot losses determined relevant biases in estimating the additive and dominance variances, marked reduction in the precision of the predicted breeding values, significant changes in the classification of the breeding values, and errors in identifying superior individuals, i.e., a significant reduction in the selection efficiency. In contrast, excluding dominance had no significant effect on either the ranking of breeding values or selection efficiency. Our results revealed that best linear unbiased prediction including pedigree and historical data, based on a model with dominance, is the ideal method for genetic evaluation by plant breeders even when lost records are considered.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © 2012. Copyright © 2012 by the American Society of Agronomy, Inc.