Relative Efficiency of Genomewide Selection for Testcross Performance of Doubled Haploid Lines in a Maize Breeding Program

Krchov, Lisa Marie; Bernardo, Rex

doi:10.2135/cropsci2015.01.0064

Cited by 28 publications

(33 citation statements)

References 13 publications

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“…Pedigree-based analyses have been widely used to evaluate field experiments, estimate genetic parameters, and predict breeding values (Piepho et al 2008). However, due to the decreasing genotyping costs of thousands or millions of markers, and to the increasing phenotyping costs (Krchov and Bernardo 2015), genomic selection (GS; Meuwissen et al 2001) is emerging as an alternative genome-wide marker-based method to predict yet-to-be seen genetic responses. Appropriate GS methods provide accurate predictions even for untested genotypes, resulting in a considerable progress for breeding programs, reducing the number of field-tested genotypes, with a consequent reduction in the phenotyping costs (Krchov and Bernardo 2015).…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the decreasing genotyping costs of thousands or millions of markers, and to the increasing phenotyping costs (Krchov and Bernardo 2015), genomic selection (GS; Meuwissen et al 2001) is emerging as an alternative genome-wide marker-based method to predict yet-to-be seen genetic responses. Appropriate GS methods provide accurate predictions even for untested genotypes, resulting in a considerable progress for breeding programs, reducing the number of field-tested genotypes, with a consequent reduction in the phenotyping costs (Krchov and Bernardo 2015). The benefits of GS are more evident when traits are difficult, time-consuming, and/or expensive to measure, or when several environments need to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

et al. 2018

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Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

et al. 2018

View full text Add to dashboard Cite

show abstract

“…However, due to the decreasing costs of genotyping thousands or millions of markers and the 89 increasing costs of phenotyping (Krchov and Bernardo, 2015), GS is arising as an alternative 90 genome-wide marker-based method to predict future genetic responses. 91…”

Section: Several Genomic Prediction Models Incorporating Genotype X Ementioning

confidence: 99%

“…Appropriate GS methods provide accurate predictions even for untested genotypes, allowing 92 considerable progress in breeding programs by reducing the number of field-tested genotypes 93 and, consequently, the costs of phenotyping (Krchov and Bernardo, 2015). The benefits of GS 94 are more evident when traits are difficult, time-consuming, expensive to measure, and several 95 environments need to be evaluated.…”

Section: Several Genomic Prediction Models Incorporating Genotype X Ementioning

confidence: 99%

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G × E

Souza

Francisco

Gonçalves

et al. 2019

Preprint

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19Genomic Selection in Rubber Tree 2 ranging from 0.59 to 0.75 for both traits. Applying the model (BSG, SM, MM, MDs, and MDe) 33 and kernel method (GBLUP and GK) combinations to rubber tree data showed that models with 34 the nonlinear GK and linear GBLUP kernel had similar PAs. Multi-environment models were 35 superior to single-environment genomic models regardless the kernel (GBLUP or GK), 36suggesting that introducing interactions between markers and environmental conditions increases 37 the proportion of variance explained by the model and, more importantly, the PA. In the best 38 scenario (well-watered (WW / GK), an increase of 6.7 and 8.7 fold of genetic gain can be 39 obtained for AP and DAP, respectively, with multi-environment GS (MM, MDe and MDS) than 40by conventional genetic breeding model (CBM). Furthermore, GS resulted in a more balanced 41 selection response in DAP and AP and if used in conjunction with traditional genetic breeding 42 programs will contribute to a reduction in selection time. With the rapid advances in and 43 declining costs of genotyping methods, balanced against the overall costs of managing large 44 progeny trials and potential increased gains per unit time, we are hopeful that GS can be 45implemented in rubber tree breeding programs. 46

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“…Including information on the performance of both parents in other earlier crosses improves the prediction accuracy further (Technow et al, 2014). Genomic selection was especially more effective than phenotypic selection when the correlation between marker‐predicted values and phenotypic values exceeded 0.50 (Krchov and Bernardo, 2015). However, apart from gain per unit cost, GS still proved advantageous at lower values in cases, where lines do not produce enough seed for actual testcrossing, and when field testing is reduced due to resource constraints.…”

Section: Hybrid‐enabled Line Profiling (Help)mentioning

confidence: 99%

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops

Ginkel¹,

Ortíz

2018

Crop Science

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P lant breeding, certainly in the era before genomics and marker-assisted selection, was often referred to as a "numbers game." The more crosses, the more likely it is to find improved genetic combinations. We propose a newly integrated breeding strategy for self-fertilizing crops that dramatically reduces the number of crosses being promoted while increasing the likelihood of obtaining superior new cultivars. This strategy emerges from experiments in the Bread Wheat Program of the International Center for the Improvement of Maize and Wheat (CIMMYT).In the late 1990s we noticed that ~8% of the elite new lines in the International Bread Wheat Screening Nursery (IBWSN) for international distribution were derived from <20 crosses. The remaining 92% of elite lines came from 5000 to 10,000 crosses. These few original crosses, and the lines derived from them, represented a strategy that we now describe as hybrid-enabled line profiling (HELP). This strategy offers breeding programs for selfing crops enormous benefits through improved performance. It Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing CropsMaarten van Ginkel* and Rodomiro Ortiz ABSTRACT Hybrid-enabled line profiling (HELP) is a new integrated breeding strategy for self-fertilizing crops that combines existing and recently identified elements, resulting in a strategy that synergistically exceeds existing breeding concepts. Heterosis in selfing crops is often driven by additive and additive ´ additive gene action, the molecular basis of which is increasingly being revealed. Unlike nonadditive heterosis, additive forms can be relatively easily fixed in homozygous lines, meaning that their seed can simply be resown to express the same "heterosis." Crossing diverse, complementary "selfing" parents to create the desired trait or allele line profile requires strict male sterility of the female; this can now be achieved relatively easily through present and emerging chemical, environmental, or genetic techniques. Fairly small amounts of hybrid seed are needed, with no need to scale up seed production, as it is not the hybrid that will be commercialized. After multilocation testing, homozygous lines from only the most superior hybrids, driven mainly by additive effects and additive ´ additive gene action, are rapidly derived using techniques such as doubled haploids. Multilocation testing and molecular confirmation of target line profiles then identify superior lines for release to farmers. The HELP strategy integrates modern high-throughput versions of existing and new concepts and methodologies into a breeding system strategy that focuses on the most superior crosses, <10% of all crosses. This focus results in significant increases in efficiency and can reverse the edible yield plateauing seen or feared in some of our major selfing food crops.M. van Ginkel,

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Relative Efficiency of Genomewide Selection for Testcross Performance of Doubled Haploid Lines in a Maize Breeding Program

Cited by 28 publications

References 13 publications

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G × E

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops

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