2020
DOI: 10.1007/s00122-020-03638-5
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Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

Abstract: Key message Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. Abstract With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage test… Show more

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Cited by 31 publications
(26 citation statements)
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References 28 publications
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“…CIMMYT Global Maize Program has evaluated various strategies to implement GS in maize breeding pipelines with promising results (Beyene et al, 2015 , 2019 ; Ceron-Rojas et al, 2015 ; Vivek et al, 2017 ; Wang et al, 2020 ). Crossa et al ( 2017 ) reported that GS is better than phenotypic selection to reduce breeding cycles and operational costs.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…CIMMYT Global Maize Program has evaluated various strategies to implement GS in maize breeding pipelines with promising results (Beyene et al, 2015 , 2019 ; Ceron-Rojas et al, 2015 ; Vivek et al, 2017 ; Wang et al, 2020 ). Crossa et al ( 2017 ) reported that GS is better than phenotypic selection to reduce breeding cycles and operational costs.…”
Section: Discussionmentioning
confidence: 99%
“…The effectiveness of GS is a product of the quality of the training population (TRN), with both genotypic and phenotypic data used to estimate the marker effects in the predicted population (TST). The strategy of test-half-and-predict-half based on marker data has been piloted in specific product profiles in eastern and southern Africa, as well as in Latin America, with highly encouraging results (Beyene et al, 2019 ; Santantonio et al, 2020 ; Wang et al, 2020 ; Atanda et al, 2021 ). The main objective of this study was to evaluate the potential of genomic prediction using breeding data from 1 year to predict the performance of phenotypically untested lines at an early testing stage to directly advance the best selection candidates to a second-year equivalent phenotypic trial, saving a year in the process of developing new elite hybrids and high potential breeding parents.…”
Section: Introductionmentioning
confidence: 99%
“…This level of testing corresponded to TC2 rather than TC1 trials (Cupka, 2013; Bernardo, 2020). The results suggested that, in effect, two‐cycle genomewide selection bypasses TC1 trials (Belamkar et al., 2018; Wang et al., 2020). By foregoing TC1 trials, two‐cycle genomewide selection may reduce the time required to release new maize cultivars.…”
Section: Discussionmentioning
confidence: 99%
“…These methods have the following four main goals: (i) to provide information on the genetic control of the trait under investigation; (ii) to generate populations to be used as a basis for the selection and development of cultivars; (iii) to provide estimates of genetic gain; (iv) to obtain information to evaluate the genitors used in the breeding program, based on the general and combination-specific capabilities (GCA and SCA), respectively. Although many articles have been published on GP in maize (Lorenzana and Bernardo, 2009 ; Windhausen et al, 2012 ; Lehermeier et al, 2014 ; Cooper et al, 2016 ; Zhang et al, 2017 ; Dias et al, 2018 ; Messina et al, 2018 ; Alves et al, 2019 ; Millet et al, 2019 ; Costa-Neto et al, 2020 ; Cui et al, 2020 ; Das et al, 2020 ; Wang et al, 2020 ; Rogers et al, 2021 ), no studies on the best genetic design to build the training population have yet been conducted. This population should maximize the accuracy and contemplate practical restrictions, such as the costs and logistics of crosses to be made.…”
Section: Design Of Training Populations For Genomic Predictionmentioning
confidence: 99%