Toward Redesigning Hybrid Maize Breeding Through Genomics-Assisted Breeding

Kadam, Dnyaneshwar C.; Lorenz, Aaron J.

doi:10.1007/978-3-319-97427-9_21

Cited by 11 publications

(9 citation statements)

References 102 publications

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“…Next, selected individuals from each pool are used to develop inbred lines by doubled haploid (DH) production or selfing. During inbreeding of lines from two-parent crosses or upon availability of DH lines, lines are selected for per se performance, often for traits such as disease resistance ( Lee and Tracy, 2009 ; Kadam and Lorenz, 2018 ). Then, the selected lines are testcrossed, usually to a single tester, and selected by the performance of their hybrids in a few environments ( Lee and Tracy, 2009 ).…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…If the production of F 1 hybrid seed by cross-pollination is too expensive on a large scale with many hybrid combinations (as in self-pollinated crops), then F 1:2 individuals can be substituted into the training set with only modest reductions in prediction accuracies ( Technow, 2019 ). If entirely eliminating testcrossing is perceived as too risky, selections from testcrossing can be supplemented with predicted exceptional single crosses ( Kadam and Lorenz, 2018 ; Viana et al, 2018 ). Another cost-reducing alternative is to testcross a subset of several related lines, and predict combining abilities for their relatives ( Windhausen et al, 2012 ).…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…Another cost-reducing alternative is to testcross a subset of several related lines, and predict combining abilities for their relatives ( Windhausen et al, 2012 ). Once exceptional single crosses are identified, with or without testcrossing, their seed can be increased, advanced through preliminary and multi-environment yield trials, and eventually released as varieties for production ( Kadam and Lorenz, 2018 ).…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…The effectiveness of hybrid breeding can be improved by genomic selection, in which marker information partially replaces phenotypes in estimation of breeding values ( Heffner et al, 2009 ). Genomic selection can shorten the breeding cycle, reduce the costs of phenotyping, and improve selection accuracies ( Lorenz et al, 2011 ; Heslot et al, 2015 ; Zhao et al, 2015b ; Schulthess et al, 2017 ; Kadam and Lorenz, 2018 ). Genomic selection also opens new opportunities to establish hybrid breeding programs in crops which are widely cultivated as inbreds, such as wheat ( Triticum aestivum ; Zhao et al, 2015b ).…”

Section: Introductionmentioning

confidence: 99%

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Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review

2021

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Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.

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Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review

2021

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“…The main factors affecting genomic prediction accuracy include the size of TRN, the relationship between TRN and TST (testing population), the genetic architecture and the heritability of the target trait, the genotype by environment interaction, statistical models, etc. (Beyene et al 2015 ; Guo et al 2014 ; Kadam et al 2016 ; Kadam and Lorenz 2018 ). Brandariz and Bernardo ( 2019 ) showed that the relationship between TRN and TST was more important in improving prediction accuracy than the size of TRN.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhang

et al. 2020

Theor Appl Genet

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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 testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year’s data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

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