Genomewide selection for rapid introgression of exotic germplasm in maize.

By R. Bernardo. 2009

130 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/23dc27041b1cd5cc67bb44cbed0759ac034dfaf2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. M. Sorrells
2. Jean-Luc Jannink
3. Nicolas Heslot
4. K. Semagn
5. Yunbi Xu
6. R. Bernardo
7. Å. Bjornstad
8. E. Heffner

In Plant Science

1. M. Sorrells
2. Jean-Luc Jannink
3. K. Semagn
4. Nicolas Heslot
5. Yunbi Xu
6. Å. Bjornstad
7. E. Heffner
8. R. Bernardo

Citations in the corpus

• Perspectives for Genomic Selection Applications and Research in Plants (2015, background): “With the advent of GS, efforts have been devoted to make those recurrent selection schemes work without identification of QTLs (Bernardo and Yu, 2007; Bernardo, 2009).”
• Genomic selection in forest tree breeding (2011, background): “In plants, scenarios for the application of GS have been presented for inbred annual crops such as maize where doubled haploid lines are used (Bernardo and Yu 2007; Mayor and Bernardo 2009) or where the rapid introgression of exotic germplasm is the objective (Bernardo 2009).”
• Genomewide Prediction Accuracy within 969 Maize Biparental Populations (2014, background): “Association between rMG and Individual Factors As has been found in previous studies (Combs and Bernardo, 2013a; Crossa et al., 2013; Lorenzana and Bernardo, 2009), increases in the individual factors N, h2, and NM generally led to increases in rMG, particularly when it was possible to keep other…”
• Genomic selection for durable stem rust resistance in wheat (2011, background): “In a recent simulation investigating the utility of GS for introgressing exotic germplasm in maize, Bernardo (2009) compared population improvement based on BC2 ,B C 1 and F2 populations derived from exotic and elite parents.”
• Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation (2014, background): “When the completely unrelated material, such as individuals from exotic landraces for the purposes of introgression (e.g., Bernardo, 2009), is added to the breeding pool phenotypic data pertaining to this material needs to be collected to update the prediction equation.”
• The genetic dissection of quantitative traits in crops (2010, methodology): “…and genotype the population with markers spaced about 10 to 15 cM apart, then an analysis of the phenotypic and marker data with an appropriate statistical method will almost always lead to the identification of at least a few markers associated with each trait of interest (Bernardo, 2008).”
• Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “It was concluded that the F2 population should be large (~300), testcross performance should be evaluated in replicated trials over locations, and F2 generation is superior to BC1 and BC2 generations for the initiation of recurrent selection (Bernardo 2009).”
• Genomic Selection for Crop Improvement (2017, unknown): “021 Bernardo R (2010) Breeding for quantitative traits in plants, 2nd edn. Stemma Press, Woodbury, MN Bernardo R, Yu J (2007) Prospects for Genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090. doi:10.2135/cropsci2006.11.0690 Bos I (1983) The optimum number of replications wh”
• REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Will genomic selection be a practical method for plant breeding? (2012, methodology): “Moreover, simulation studies using maize were performed to determine the advantages of using DHLs compared with F2 populations in GS and MARS (Mayor and Bernardo, 2009), and to develop a methodology for the rapid introgression of exotic germplasms in an adapted line of maize via GS (Bernardo, 2009).”