Literature DB >> 24212855

Correlation between testcross performance of lines at early and late selfing generations.

R Bernardo1.   

Abstract

In hybrid breeding programs, testcross evaluation of lines can be done during the early stages of selfing (early testing) or delayed until the lines are near-homozygous. To evaluate the usefulness of early testing, the expected genetic and phenotypic correlations between testcross performance at different selfing generations were examined. The genetic correlation (r GnGn' ) between testcross performance of S n and S n' , (n'>n) individuals or lines is equal to the square root of the ratio of their testcross genetic variances, and it is a function of the inbreeding coefficients (F) at the two selfing generations, i.e., r GnGn'=[(1+F n )/(1+F n )](0.5). The genetic correlation between testcross performance of lines and their directly descended homozygous (n'=∞) lines is 0.71 for S1; 0.87 for S2, 0.93 for S3, 0.97 for S4, 0.98 for S5, and 0.99 for S5 lines. The effectiveness of early testing is limited mainly by nongenetic effects. The square root of testcross heritability at generation n sets the upper limit on the correlation between phenotypic value at generation n and genotypic value at homozygosity. The probabilities of correctly retaining S n individuals or lines that have superior testcross performance at homozygosity (n'=∞) indicate that early testing should be effective in identifying lines with above- and below-average combining ability. However, the risk of losing lines with superior combining ability is high if strong (best 10%) selection pressure is applied during early testing. If only a small proportion of lines is retained based on testcross performance and/or if the heritability of the trait is low, selfing for two or three generations prior to testcrossing may be desirable to increase the likelihood of retaining lines that perform well at homozygosity. The theoretical results in this study support the testcross evaluation procedures for grain yield used by most maize (Zea mays L.) breeders.

Entities:  

Year:  1991        PMID: 24212855     DOI: 10.1007/BF00231272

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


  1 in total

1.  Isolating Better Foundation Inbreds for Use in Corn Hybrids.

Authors:  F D Richey
Journal:  Genetics       Date:  1945-09       Impact factor: 4.562

  1 in total
  6 in total

1.  Covariation between line and testcross performance for reduced mycotoxin concentrations in European maize after silk channel inoculation of two Fusarium species.

Authors:  Martin Löffler; Bettina Kessel; Milena Ouzunova; Thomas Miedaner
Journal:  Theor Appl Genet       Date:  2010-12-14       Impact factor: 5.699

2.  Hybrid maize breeding with doubled haploids: III. Efficiency of early testing prior to doubled haploid production in two-stage selection for testcross performance.

Authors:  C Friedrich H Longin; H Friedrich Utz; Jochen C Reif; Thilo Wegenast; Wolfgang Schipprack; Albrecht E Melchinger
Journal:  Theor Appl Genet       Date:  2007-06-29       Impact factor: 5.699

3.  Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.

Authors:  K O G Dias; H P Piepho; L J M Guimarães; P E O Guimarães; S N Parentoni; M O Pinto; R W Noda; J V Magalhães; C T Guimarães; A A F Garcia; M M Pastina
Journal:  Theor Appl Genet       Date:  2019-11-22       Impact factor: 5.699

4.  Inheritance of nitrogen use efficiency in inbred progenies of tropical maize based on multivariate diallel analysis.

Authors:  Fernando Lisboa Guedes; Rafael Parreira Diniz; Marcio Balestre; Camila Bastos Ribeiro; Renato Barbosa Camargos; João Cândido Souza
Journal:  ScientificWorldJournal       Date:  2014-12-21

5.  On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids.

Authors:  Giovanni Galli; Filipe Couto Alves; Júlia Silva Morosini; Roberto Fritsche-Neto
Journal:  PLoS One       Date:  2020-02-07       Impact factor: 3.240

6.  Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

Authors:  Vanessa S Windhausen; Gary N Atlin; John M Hickey; Jose Crossa; Jean-Luc Jannink; Mark E Sorrells; Babu Raman; Jill E Cairns; Amsal Tarekegne; Kassa Semagn; Yoseph Beyene; Pichet Grudloyma; Frank Technow; Christian Riedelsheimer; Albrecht E Melchinger
Journal:  G3 (Bethesda)       Date:  2012-11-01       Impact factor: 3.154

  6 in total

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