Literature DB >> 17507673

Impact of interpopulation divergence on additive and dominance variance in hybrid populations.

J C Reif1, F-M Gumpert, S Fischer, A E Melchinger.   

Abstract

We present a theoretical proof that the ratio of the dominance vs. the additive variance decreases with increasing genetic divergence between two populations. While the dominance variance is the major component of the variance due to specific combining ability (sigma(SCA)(2)), the additive variance is the major component of the variance due to general combining ability (sigma(GCA)(2)). Therefore, we conclude that interpopulation improvement becomes more efficient with divergent than with genetically similar heterotic groups, because performance of superior hybrids can be predicted on the basis of general combining ability effects.

Mesh:

Year:  2007        PMID: 17507673      PMCID: PMC1931541          DOI: 10.1534/genetics.107.074146

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  4 in total

1.  [COVARIANCE BETWEEN RELATIVES IN A GENE-ORTHOGONAL POPULATION. I. GENERAL THEORY].

Authors:  F W SCHNELL
Journal:  Biom Z       Date:  1965

2.  Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL.

Authors:  T A Schrag; A E Melchinger; A P Sørensen; M Frisch
Journal:  Theor Appl Genet       Date:  2006-08-03       Impact factor: 5.699

3.  Optimum prediction of three-way crosses from single crosses in forage maize (Zea mays L.).

Authors:  A E Melchinger; H H Geiger; G Seitz; G A Schmidt
Journal:  Theor Appl Genet       Date:  1987-07       Impact factor: 5.699

Review 4.  Epistasis: too often neglected in complex trait studies?

Authors:  Orjan Carlborg; Chris S Haley
Journal:  Nat Rev Genet       Date:  2004-08       Impact factor: 53.242

  4 in total
  34 in total

1.  Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.

Authors:  Frank Technow; Christian Riedelsheimer; Tobias A Schrag; Albrecht E Melchinger
Journal:  Theor Appl Genet       Date:  2012-06-26       Impact factor: 5.699

2.  Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection.

Authors:  Pascal Schopp; Christian Riedelsheimer; H Friedrich Utz; Chris-Carolin Schön; Albrecht E Melchinger
Journal:  Theor Appl Genet       Date:  2015-08-01       Impact factor: 5.699

3.  Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize.

Authors:  Frank Technow; Tobias A Schrag; Wolfgang Schipprack; Eva Bauer; Henner Simianer; Albrecht E Melchinger
Journal:  Genetics       Date:  2014-05-21       Impact factor: 4.562

4.  Molecular marker assisted broadening of the Central European heterotic groups in rye with Eastern European germplasm.

Authors:  Sandra Fischer; A E Melchinger; V Korzun; P Wilde; B Schmiedchen; J Möhring; H-P Piepho; B S Dhillon; T Würschum; J C Reif
Journal:  Theor Appl Genet       Date:  2010-01       Impact factor: 5.699

Review 5.  Hybrid breeding in autogamous cereals.

Authors:  Carl Friedrich Horst Longin; Jonathan Mühleisen; Hans Peter Maurer; Hongliang Zhang; Manje Gowda; Jochen Christoph Reif
Journal:  Theor Appl Genet       Date:  2012-08-24       Impact factor: 5.699

6.  Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years.

Authors:  Theresa Albrecht; Hans-Jürgen Auinger; Valentin Wimmer; Joseph O Ogutu; Carsten Knaak; Milena Ouzunova; Hans-Peter Piepho; Chris-Carolin Schön
Journal:  Theor Appl Genet       Date:  2014-04-11       Impact factor: 5.699

7.  A unified framework for hybrid breeding and the establishment of heterotic groups in wheat.

Authors:  Philipp H G Boeven; C Friedrich H Longin; Tobias Würschum
Journal:  Theor Appl Genet       Date:  2016-03-08       Impact factor: 5.699

8.  Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

Authors:  Tobias A Schrag; Matthias Westhues; Wolfgang Schipprack; Felix Seifert; Alexander Thiemann; Stefan Scholten; Albrecht E Melchinger
Journal:  Genetics       Date:  2018-01-23       Impact factor: 4.562

9.  Identification of differentially expressed transcripts at critical developmental stages in sorghum [Sorghum bicolor (L.) Moench] in relation to grain yield heterosis.

Authors:  I Jaikishan; P Rajendrakumar; K Hariprasanna; D Balakrishna; B Venkatesh Bhat; Vilas A Tonapi
Journal:  3 Biotech       Date:  2019-05-29       Impact factor: 2.406

10.  Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs.

Authors:  Carl Friedrich Horst Longin; Manje Gowda; Jonathan Mühleisen; Erhard Ebmeyer; Ebrahim Kazman; Ralf Schachschneider; Johannes Schacht; Martin Kirchhoff; Yusheng Zhao; Jochen Christoph Reif
Journal:  Theor Appl Genet       Date:  2013-08-04       Impact factor: 5.699

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