Literature DB >> 26649866

Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection.

Malthe Schmidt1, Sonja Kollers1, Anja Maasberg-Prelle1, Jörg Großer1, Burkhard Schinkel1, Alexandra Tomerius1,2, Andreas Graner3, Viktor Korzun4.   

Abstract

KEY MESSAGE: Genomic prediction of malting quality traits in barley shows the potential of applying genomic selection to improve selection for malting quality and speed up the breeding process. ABSTRACT: Genomic selection has been applied to various plant species, mostly for yield or yield-related traits such as grain dry matter yield or thousand kernel weight, and improvement of resistances against diseases. Quality traits have not been the main scope of analysis for genomic selection, but have rather been addressed by marker-assisted selection. In this study, the potential to apply genomic selection to twelve malting quality traits in two commercial breeding programs of spring and winter barley (Hordeum vulgare L.) was assessed. Phenotypic means were calculated combining multilocational field trial data from 3 or 4 years, depending on the trait investigated. Three to five locations were available in each of these years. Heritabilities for malting traits ranged between 0.50 and 0.98. Predictive abilities (PA), as derived from cross validation, ranged between 0.14 to 0.58 for spring barley and 0.40-0.80 for winter barley. Small training sets were shown to be sufficient to obtain useful PAs, possibly due to the narrow genetic base in this breeding material. Deployment of genomic selection in malting barley breeding clearly has the potential to reduce cost intensive phenotyping for quality traits, increase selection intensity and to shorten breeding cycles.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 26649866     DOI: 10.1007/s00122-015-2639-1

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


  22 in total

1.  synbreed: a framework for the analysis of genomic prediction data using R.

Authors:  Valentin Wimmer; Theresa Albrecht; Hans-Jürgen Auinger; Chris-Carolin Schön
Journal:  Bioinformatics       Date:  2012-06-10       Impact factor: 6.937

Review 2.  Marker-assisted selection for disease resistance in wheat and barley breeding.

Authors:  Thomas Miedaner; Viktor Korzun
Journal:  Phytopathology       Date:  2012-06       Impact factor: 4.025

3.  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

4.  Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors:  Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal:  J Stat Softw       Date:  2010       Impact factor: 6.440

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.  Fine mapping of the Rrs1 resistance locus against scald in two large populations derived from Spanish barley landraces.

Authors:  Kerstin Hofmann; Cristina Silvar; Ana M Casas; Markus Herz; Bianca Büttner; M Pilar Gracia; Bruno Contreras-Moreira; Hugh Wallwork; Ernesto Igartua; Günther Schweizer
Journal:  Theor Appl Genet       Date:  2013-09-26       Impact factor: 5.699

7.  Recent history of artificial outcrossing facilitates whole-genome association mapping in elite inbred crop varieties.

Authors:  Nils Rostoks; Luke Ramsay; Katrin MacKenzie; Linda Cardle; Prasanna R Bhat; Mikeal L Roose; Jan T Svensson; Nils Stein; Rajeev K Varshney; David F Marshall; Andreas Graner; Timothy J Close; Robbie Waugh
Journal:  Proc Natl Acad Sci U S A       Date:  2006-11-03       Impact factor: 11.205

8.  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

9.  Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Authors:  Jennifer Spindel; Hasina Begum; Deniz Akdemir; Parminder Virk; Bertrand Collard; Edilberto Redoña; Gary Atlin; Jean-Luc Jannink; Susan R McCouch
Journal:  PLoS Genet       Date:  2015-02-17       Impact factor: 5.917

10.  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

View more
  18 in total

1.  A deep convolutional neural network approach for predicting phenotypes from genotypes.

Authors:  Wenlong Ma; Zhixu Qiu; Jie Song; Jiajia Li; Qian Cheng; Jingjing Zhai; Chuang Ma
Journal:  Planta       Date:  2018-08-12       Impact factor: 4.116

2.  When less can be better: How can we make genomic selection more cost-effective and accurate in barley?

Authors:  Amina Abed; Paulino Pérez-Rodríguez; José Crossa; François Belzile
Journal:  Theor Appl Genet       Date:  2018-06-01       Impact factor: 5.699

3.  Genomic prediction ability for yield-related traits in German winter barley elite material.

Authors:  Patrick Thorwarth; Jutta Ahlemeyer; Anne-Marie Bochard; Kerstin Krumnacker; Hubert Blümel; Eberhard Laubach; Nadine Knöchel; László Cselényi; Frank Ordon; Karl J Schmid
Journal:  Theor Appl Genet       Date:  2017-05-22       Impact factor: 5.699

4.  Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Authors:  Nanna Hellum Nielsen; Ahmed Jahoor; Jens Due Jensen; Jihad Orabi; Fabio Cericola; Vahid Edriss; Just Jensen
Journal:  PLoS One       Date:  2016-10-26       Impact factor: 3.240

5.  Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.

Authors:  Sebastian Michel; Christian Ametz; Huseyin Gungor; Batuhan Akgöl; Doru Epure; Heinrich Grausgruber; Franziska Löschenberger; Hermann Buerstmayr
Journal:  Theor Appl Genet       Date:  2016-11-08       Impact factor: 5.699

6.  A roadmap for breeding orphan leafy vegetable species: a case study of Gynandropsis gynandra (Cleomaceae).

Authors:  E O Deedi Sogbohossou; Enoch G Achigan-Dako; Patrick Maundu; Svein Solberg; Edgar M S Deguenon; Rita H Mumm; Iago Hale; Allen Van Deynze; M Eric Schranz
Journal:  Hortic Res       Date:  2018-01-10       Impact factor: 6.793

7.  Breeding progress, genotypic and environmental variation and correlation of quality traits in malting barley in German official variety trials between 1983 and 2015.

Authors:  Friedrich Laidig; Hans-Peter Piepho; Dirk Rentel; Thomas Drobek; Uwe Meyer
Journal:  Theor Appl Genet       Date:  2017-08-18       Impact factor: 5.699

Review 8.  Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding.

Authors:  Yong-Bi Fu; Mo-Hua Yang; Fangqin Zeng; Bill Biligetu
Journal:  Front Plant Sci       Date:  2017-07-06       Impact factor: 5.753

9.  Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program.

Authors:  Angela-Maria Bernal-Vasquez; Andres Gordillo; Malthe Schmidt; Hans-Peter Piepho
Journal:  BMC Genet       Date:  2017-05-31       Impact factor: 2.797

10.  Increased genomic prediction accuracy in wheat breeding using a large Australian panel.

Authors:  Adam Norman; Julian Taylor; Emi Tanaka; Paul Telfer; James Edwards; Jean-Pierre Martinant; Haydn Kuchel
Journal:  Theor Appl Genet       Date:  2017-09-08       Impact factor: 5.699

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.