Literature DB >> 29445844

Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Roberto Fritsche-Neto1, Deniz Akdemir2, Jean-Luc Jannink3.   

Abstract

KEY MESSAGE: Testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Even though many papers have been published about genomic prediction (GP) in maize, the best mating design to build the training population has not been defined yet. Such design must maximize the accuracy given constraints on costs and on the logistics of the crosses to be made. Hence, the aims of this work were: (1) empirically evaluate the effect of the mating designs, used as training set, on genomic selection to predict maize single-crosses obtained through full diallel and North Carolina design II, (2) and identify the possibility of reducing the number of crosses and parents to compose these training sets. Our results suggest that testcross is the worst mating design to use as a training set to predict maize single-crosses that would be obtained through full diallel or North Carolina design II. Moreover, North Carolina design II is the best training set to predict hybrids taken from full diallel. However, hybrids from full diallel and North Carolina design II can be well predicted using optimized training sets, which also allow reducing the total number of crosses to be made. Nevertheless, the number of parents and the crosses per parent in the training sets should be maximized.

Entities:  

Mesh:

Year:  2018        PMID: 29445844     DOI: 10.1007/s00122-018-3068-8

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


  14 in total

1.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

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.  A stage-wise approach for the analysis of multi-environment trials.

Authors:  Hans-Peter Piepho; Jens Möhring; Torben Schulz-Streeck; Joseph O Ogutu
Journal:  Biom J       Date:  2012-09-25       Impact factor: 2.207

4.  The impact of population structure on genomic prediction in stratified populations.

Authors:  Zhigang Guo; Dominic M Tucker; Christopher J Basten; Harish Gandhi; Elhan Ersoz; Baohong Guo; Zhanyou Xu; Daolong Wang; Gilles Gay
Journal:  Theor Appl Genet       Date:  2014-01-24       Impact factor: 5.699

5.  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

6.  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

7.  Optimization of genomic selection training populations with a genetic algorithm.

Authors:  Deniz Akdemir; Julio I Sanchez; Jean-Luc Jannink
Journal:  Genet Sel Evol       Date:  2015-05-06       Impact factor: 4.297

8.  A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array.

Authors:  Sandra Unterseer; Eva Bauer; Georg Haberer; Michael Seidel; Carsten Knaak; Milena Ouzunova; Thomas Meitinger; Tim M Strom; Ruedi Fries; Hubert Pausch; Christofer Bertani; Alessandro Davassi; Klaus Fx Mayer; Chris-Carolin Schön
Journal:  BMC Genomics       Date:  2014-09-29       Impact factor: 3.969

9.  Training set optimization under population structure in genomic selection.

Authors:  Julio Isidro; Jean-Luc Jannink; Deniz Akdemir; Jesse Poland; Nicolas Heslot; Mark E Sorrells
Journal:  Theor Appl Genet       Date:  2014-11-01       Impact factor: 5.699

10.  Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline.

Authors:  Dnyaneshwar C Kadam; Sarah M Potts; Martin O Bohn; Alexander E Lipka; Aaron J Lorenz
Journal:  G3 (Bethesda)       Date:  2016-11-08       Impact factor: 3.154

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  15 in total

1.  Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage.

Authors:  Alizarine Lorenzi; Cyril Bauland; Tristan Mary-Huard; Sophie Pin; Carine Palaffre; Colin Guillaume; Christina Lehermeier; Alain Charcosset; Laurence Moreau
Journal:  Theor Appl Genet       Date:  2022-08-02       Impact factor: 5.574

2.  Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.

Authors:  Prabina Kumar Meher; Sachin Rustgi; Anuj Kumar
Journal:  Heredity (Edinb)       Date:  2022-05-04       Impact factor: 3.832

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.  Modeling copy number variation in the genomic prediction of maize hybrids.

Authors:  Danilo Hottis Lyra; Giovanni Galli; Filipe Couto Alves; Ítalo Stefanine Correia Granato; Miriam Suzane Vidotti; Massaine Bandeira E Sousa; Júlia Silva Morosini; José Crossa; Roberto Fritsche-Neto
Journal:  Theor Appl Genet       Date:  2018-10-31       Impact factor: 5.699

5.  Genomic prediction of hybrid crops allows disentangling dominance and epistasis.

Authors:  David González-Diéguez; Andrés Legarra; Alain Charcosset; Laurence Moreau; Christina Lehermeier; Simon Teyssèdre; Zulma G Vitezica
Journal:  Genetics       Date:  2021-05-17       Impact factor: 4.562

6.  Genomic prediction applied to multiple traits and environments in second season maize hybrids.

Authors:  Amanda Avelar de Oliveira; Marcio F R Resende; Luís Felipe Ventorim Ferrão; Rodrigo Rampazo Amadeu; Lauro José Moreira Guimarães; Claudia Teixeira Guimarães; Maria Marta Pastina; Gabriel Rodrigues Alves Margarido
Journal:  Heredity (Edinb)       Date:  2020-05-29       Impact factor: 3.821

7.  Automated Machine Learning: A Case Study of Genomic "Image-Based" Prediction in Maize Hybrids.

Authors:  Giovanni Galli; Felipe Sabadin; Rafael Massahiro Yassue; Cassia Galves; Humberto Fanelli Carvalho; Jose Crossa; Osval Antonio Montesinos-López; Roberto Fritsche-Neto
Journal:  Front Plant Sci       Date:  2022-03-07       Impact factor: 5.753

8.  Use of F2 Bulks in Training Sets for Genomic Prediction of Combining Ability and Hybrid Performance.

Authors:  Frank Technow
Journal:  G3 (Bethesda)       Date:  2019-05-07       Impact factor: 3.154

9.  Design of training populations for selective phenotyping in genomic prediction.

Authors:  Deniz Akdemir; Julio Isidro-Sánchez
Journal:  Sci Rep       Date:  2019-02-05       Impact factor: 4.379

10.  Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.

Authors:  Roberto Fritsche-Neto; Giovanni Galli; Karina Lima Reis Borges; Germano Costa-Neto; Filipe Couto Alves; Felipe Sabadin; Danilo Hottis Lyra; Pedro Patric Pinho Morais; Luciano Rogério Braatz de Andrade; Italo Granato; Jose Crossa
Journal:  Front Plant Sci       Date:  2021-07-01       Impact factor: 5.753

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