Literature DB >> 30390129

Small ad hoc versus large general training populations for genomewide selection in maize biparental crosses.

Sofía P Brandariz1, Rex Bernardo2.   

Abstract

KEY MESSAGE: For genomewide selection in each biparental population, it is better to use a smaller ad hoc training population than a single, large training population. In genomewide selection, different types of training populations can be used for a biparental population made from homozygous parents (A and B). Our objective was to determine whether the response to selection (R) and predictive ability (rMP) in an A/B population are higher with a large training population that is used for all biparental crosses, or with a smaller ad hoc training population highly related to the A/B population. We studied 969 biparental maize (Zea mays L.) populations phenotyped at four to 12 environments. Parent-offspring marker imputation was done for 2911 single nucleotide polymorphism loci. For 27 A/B populations, training populations were constructed by pooling: (1) all prior populations with A as one parent (A/*, where * is a related inbred) and with B as one parent (*/B) [general combining ability (GCA) model]; (2) A/* or */B crosses only; (3) all */* crosses (same background model, SB); and (4) all */*, A/*, and */B crosses (SB + GCA model). The SB model training population was 450-6000% as large as the GCA model training populations, but the mean coefficient of coancestry between the training population and A/B population was lower for the SB model (0.44) than for the GCA model (0.71). The GCA model had the highest R and rMP for all traits. For yield, R was 0.22 Mg ha-1 with the GCA model and 0.15 Mg ha-1 with the SB model. We concluded that it is best to use an ad hoc training population for each A/B population.

Entities:  

Mesh:

Year:  2018        PMID: 30390129     DOI: 10.1007/s00122-018-3222-3

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


  7 in total

1.  The impact of genetic architecture on genome-wide evaluation methods.

Authors:  Hans D Daetwyler; Ricardo Pong-Wong; Beatriz Villanueva; John A Woolliams
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

2.  Estimation of relatedness by DNA fingerprinting.

Authors:  M Lynch
Journal:  Mol Biol Evol       Date:  1988-09       Impact factor: 16.240

3.  Genomic predictability of interconnected biparental maize populations.

Authors:  Christian Riedelsheimer; Jeffrey B Endelman; Michael Stange; Mark E Sorrells; Jean-Luc Jannink; Albrecht E Melchinger
Journal:  Genetics       Date:  2013-03-27       Impact factor: 4.562

4.  Estimation of coefficient of coancestry using molecular markers in maize.

Authors:  R Bernardo
Journal:  Theor Appl Genet       Date:  1993-02       Impact factor: 5.699

5.  Accuracy of genotypic value predictions for marker-based selection in biparental plant populations.

Authors:  Robenzon E Lorenzana; Rex Bernardo
Journal:  Theor Appl Genet       Date:  2009-10-17       Impact factor: 5.699

6.  Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines.

Authors:  Christian Riedelsheimer; Frank Technow; Albrecht E Melchinger
Journal:  BMC Genomics       Date:  2012-09-04       Impact factor: 3.969

7.  Accuracy of predicting the genetic risk of disease using a genome-wide approach.

Authors:  Hans D Daetwyler; Beatriz Villanueva; John A Woolliams
Journal:  PLoS One       Date:  2008-10-14       Impact factor: 3.240

  7 in total
  7 in total

Review 1.  Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

Authors:  C Anilkumar; N C Sunitha; Narayana Bhat Devate; S Ramesh
Journal:  Planta       Date:  2022-09-23       Impact factor: 4.540

2.  Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple.

Authors:  Morgane Roth; Hélène Muranty; Mario Di Guardo; Walter Guerra; Andrea Patocchi; Fabrizio Costa
Journal:  Hortic Res       Date:  2020-09-01       Impact factor: 6.793

3.  Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple.

Authors:  Xabi Cazenave; Bernard Petit; Marc Lateur; Hilde Nybom; Jiri Sedlak; Stefano Tartarini; François Laurens; Charles-Eric Durel; Hélène Muranty
Journal:  G3 (Bethesda)       Date:  2022-03-04       Impact factor: 3.542

4.  Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing.

Authors:  Nan Wang; Hui Wang; Ao Zhang; Yubo Liu; Diansi Yu; Zhuanfang Hao; Dan Ilut; Jeffrey C Glaubitz; Yanxin Gao; Elizabeth Jones; Michael Olsen; Xinhai Li; Felix San Vicente; Boddupalli M Prasanna; Jose Crossa; Paulino Pérez-Rodríguez; Xuecai Zhang
Journal:  Theor Appl Genet       Date:  2020-06-30       Impact factor: 5.699

5.  Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.

Authors:  Sikiru Adeniyi Atanda; Velu Govindan; Ravi Singh; Kelly R Robbins; Jose Crossa; Alison R Bentley
Journal:  Theor Appl Genet       Date:  2022-03-28       Impact factor: 5.574

6.  Origin Specific Genomic Selection: A Simple Process To Optimize the Favorable Contribution of Parents to Progeny.

Authors:  Chin Jian Yang; Rajiv Sharma; Gregor Gorjanc; Sarah Hearne; Wayne Powell; Ian Mackay
Journal:  G3 (Bethesda)       Date:  2020-07-07       Impact factor: 3.154

7.  Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize.

Authors:  Xiaogang Liu; Hongwu Wang; Xiaojiao Hu; Kun Li; Zhifang Liu; Yujin Wu; Changling Huang
Journal:  Front Plant Sci       Date:  2019-09-18       Impact factor: 5.753

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.