Literature DB >> 24850820

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

Frank Technow1, Tobias A Schrag1, Wolfgang Schipprack1, Eva Bauer2, Henner Simianer3, Albrecht E Melchinger4.   

Abstract

Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent × Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill-Robertson effect and support the hypothesis of differential fixation of alleles due to pseudo-overdominance in these regions. In pericentromeric regions we also found indications for consistent marker-QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in test crosses.
Copyright © 2014 by the Genetics Society of America.

Entities:  

Keywords:  GenPred, shared data resources; genomic prediction; heterotic groups; hybrid breeding; linkage phases; training set design

Mesh:

Substances:

Year:  2014        PMID: 24850820      PMCID: PMC4125404          DOI: 10.1534/genetics.114.165860

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


  51 in total

1.  Efficient methods to compute genomic predictions.

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2.  Genome-based prediction of testcross values in maize.

Authors:  Theresa Albrecht; Valentin Wimmer; Hans-Jürgen Auinger; Malena Erbe; Carsten Knaak; Milena Ouzunova; Henner Simianer; Chris-Carolin Schön
Journal:  Theor Appl Genet       Date:  2011-04-20       Impact factor: 5.699

3.  The effect of linkage on limits to artificial selection.

Authors:  W G Hill; A Robertson
Journal:  Genet Res       Date:  1966-12       Impact factor: 1.588

4.  Common SNPs explain a large proportion of the heritability for human height.

Authors:  Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher
Journal:  Nat Genet       Date:  2010-06-20       Impact factor: 38.330

5.  Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study.

Authors:  Shengqiang Zhong; Jack C M Dekkers; Rohan L Fernando; Jean-Luc Jannink
Journal:  Genetics       Date:  2009-03-18       Impact factor: 4.562

6.  Extent of linkage disequilibrium in Holstein cattle in North America.

Authors:  M Sargolzaei; F S Schenkel; G B Jansen; L R Schaeffer
Journal:  J Dairy Sci       Date:  2008-05       Impact factor: 4.034

7.  Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

Authors:  Valentin Wimmer; Christina Lehermeier; Theresa Albrecht; Hans-Jürgen Auinger; Yu Wang; Chris-Carolin Schön
Journal:  Genetics       Date:  2013-08-09       Impact factor: 4.562

8.  A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome.

Authors:  Martin W Ganal; Gregor Durstewitz; Andreas Polley; Aurélie Bérard; Edward S Buckler; Alain Charcosset; Joseph D Clarke; Eva-Maria Graner; Mark Hansen; Johann Joets; Marie-Christine Le Paslier; Michael D McMullen; Pierre Montalent; Mark Rose; Chris-Carolin Schön; Qi Sun; Hildrun Walter; Olivier C Martin; Matthieu Falque
Journal:  PLoS One       Date:  2011-12-08       Impact factor: 3.240

9.  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

10.  Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups.

Authors:  Frank Technow; Anna Bürger; Albrecht E Melchinger
Journal:  G3 (Bethesda)       Date:  2013-02-01       Impact factor: 3.154

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  75 in total

1.  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

2.  Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding.

Authors:  Yusheng Zhao; Zuo Li; Guozheng Liu; Yong Jiang; Hans Peter Maurer; Tobias Würschum; Hans-Peter Mock; Andrea Matros; Erhard Ebmeyer; Ralf Schachschneider; Ebrahim Kazman; Johannes Schacht; Manje Gowda; C Friedrich H Longin; Jochen C Reif
Journal:  Proc Natl Acad Sci U S A       Date:  2015-12-09       Impact factor: 11.205

3.  Epistasis and covariance: how gene interaction translates into genomic relationship.

Authors:  Johannes W R Martini; Valentin Wimmer; Malena Erbe; Henner Simianer
Journal:  Theor Appl Genet       Date:  2016-02-16       Impact factor: 5.699

4.  Identification of key ancestors of modern germplasm in a breeding program of maize.

Authors:  F Technow; T A Schrag; W Schipprack; A E Melchinger
Journal:  Theor Appl Genet       Date:  2014-09-11       Impact factor: 5.699

5.  Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale.

Authors:  Jose J Marulanda; Xuefei Mi; Albrecht E Melchinger; Jian-Long Xu; T Würschum; C Friedrich H Longin
Journal:  Theor Appl Genet       Date:  2016-07-07       Impact factor: 5.699

6.  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

7.  Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.

Authors:  X Wang; L Li; Z Yang; X Zheng; S Yu; C Xu; Z Hu
Journal:  Heredity (Edinb)       Date:  2016-09-21       Impact factor: 3.821

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.  Efficiency of genomic prediction of non-assessed single crosses.

Authors:  José Marcelo Soriano Viana; Helcio Duarte Pereira; Gabriel Borges Mundim; Hans-Peter Piepho; Fabyano Fonseca E Silva
Journal:  Heredity (Edinb)       Date:  2017-11-28       Impact factor: 3.821

10.  Relevance of genetic relationship in GWAS and genomic prediction.

Authors:  Helcio Duarte Pereira; José Marcelo Soriano Viana; Andréa Carla Bastos Andrade; Fabyano Fonseca E Silva; Geísa Pinheiro Paes
Journal:  J Appl Genet       Date:  2017-11-30       Impact factor: 3.240

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