Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL

Schrag, Tobias; Melchinger, Albrecht E.; Sørensen, A.; Frisch, Matthias

doi:10.1007/s00122-006-0363-6

Cited by 73 publications

(66 citation statements)

References 15 publications

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“…Therefore, the results of our quantitative genetic investigations show that in the absence of epistasis divergent heterotic groups lead to a predominance of s 2 GCA over s 2 SCA . These theoretical findings are in accordance with experimental data of genetically divergent heterotic groups in various crops such as maize (Schrag et al 2006), rye (Miedaner and Geiger 1996), and sunflower (Kaya 2005) and explain the high prediction accuracy of hybrid performance based on GCA effects. A* refer to the dominance and additive variance for the strategy of sampling the heterotic groups out of one synthetic established using both heterotic groups.…”

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confidence: 87%

Impact of Interpopulation Divergence on Additive and Dominance Variance in Hybrid Populations

et al. 2007

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We present a theoretical proof that the ratio of the dominance vs. the additive variance decreases with increasing genetic divergence between two populations. While the dominance variance is the major component of the variance due to specific combining ability (s 2 SCA ), the additive variance is the major component of the variance due to general combining ability (s 2 GCA ). Therefore, we conclude that interpopulation improvement becomes more efficient with divergent than with genetically similar heterotic groups, because performance of superior hybrids can be predicted on the basis of general combining ability effects.

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confidence: 87%

Impact of Interpopulation Divergence on Additive and Dominance Variance in Hybrid Populations

et al. 2007

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“…The most likely explanation for the high prediction accuracies generally observed is that both H 2 and the realized relationships among parental lines tend to be very high in commercial maize breeding programs. For example, our high estimates of H 2 were for both traits in close agreement to those of Schrag et al (2006) and Massman et al (2013), the latter of which analyzed data from a U.S. corn-belt breeding program. Massman et al (2013) also found similarly high pairwise realized relationships to those in our study (details not shown).…”

Section: Prediction Accuracy Of T2 T1 and T0 Hybridssupporting

confidence: 64%

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

et al. 2014

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Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent 3 Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill-Robertson effect and support the hypothesis of differential fixation of alleles due to pseudooverdominance in these regions. In pericentromeric regions we also found indications for consistent marker-QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in testcrosses.H YBRID breeding was pioneered in maize (Shull 1908) and plays an ever increasing role in other globally important field (Duvick 1999) and vegetable crops (Silva Dias 2010). Maize has also served as a model species for research in heterosis, the phenomenon behind the success of hybrid varieties, for which the genetic mechanisms have been elusive (Duvick 1999;Lippman and Zamir 2006). In recent years, evidence emerged for the importance of (pseudo-)overdominance in the manifestation of heterosis in maize (Lippman and Zamir 2006;Schön et al. 2010) and the particular role of the centromeres in this process (Gore et al. 2009;McMullen et al. 2009). Today, the availability of high-density marker data and whole-genome regression methods developed in the context of genomic prediction (Meuwissen et al. 2001) allows us to revisit this hypothesis by studying key genome properties such as allele frequencies and linkage phases.Consistency of linkage phases between quantitative trait loci (QTL) and markers is a key prerequisite for pooling of diverse breeds and germplams to increase sample size for genetic studies and transferability of their results to different populations (De Roos et al. 2008). Weber et al. (2012) used whole-genome estimates of marker effects of several cattle breeds to investigate across-breed marker-QTL linkage phase consistency. Such a study is still missing for maize and other important crops. For o...

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“…This kind of simulation could reflect real situations of random unbalance obtained in field crosses. Nonetheless, some recent papers have proposed using more predictor parentals in the cross-validation process in order to increase the prediction efficiency (Vuylsteke et al, 2000;Schrag et al, 2006). Although this procedure increases the prediction efficiency, it does not resolve the breeder's need to predict non-evaluated crosses even when their genitors are not among the set of appraised hybrids.…”

Section: Discussionmentioning

confidence: 99%

“…In all these studies, a relatively good correlation between phenotypic means and predicted means was observed, demonstrating acceptable accuracy. The prediction accuracy is dependent on the heritability of traits in the tested hybrids, adequate estimates of variance components, accurate approximation of relationship coefficients by molecular markers (Balestre et al, 2009), and number of predictor parentals in the analysis (Vuylsteke et al, 2000;Schrag et al, 2006). Some authors suggest the use of molecular marker information associated with similarity-by-state coefficients for genotypic value prediction (Bauer et al, 2006;Balestre et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Prediction of maize double-cross hybrids using the best linear unbiased prediction with microsatellite marker information

Balestre¹,

Rg²,

Jc³

2011

Genet. Mol. Res.

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ABSTRACT. We examined the usefulness of the best linear unbiased prediction associated with molecular markers for prediction of untested maize double-cross hybrids. Ten single-cross hybrids from different commercial backgrounds were crossed using a complete diallel design. These 10 single-cross hybrids were genotyped with 20 microsatellite markers. The best linear unbiased prediction associated with microsatellite information gave relatively good prediction ability of the double-cross hybrid performance, with correlations between observed phenotypic values and genotypic prediction values varying from 0.27 to 0.54. Taking into account the predictions of specific combing ability, the correlation between observed and predicted specific combining ability varied from 0.50 to 0.88. Based on these results, we infer that it is feasible to predict maize doublecross hybrids with different unbalance degrees without including any prior information about parental inbreed lines or single-cross hybrid performance.

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Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL

Cited by 73 publications

References 15 publications

Impact of Interpopulation Divergence on Additive and Dominance Variance in Hybrid Populations

Impact of Interpopulation Divergence on Additive and Dominance Variance in Hybrid Populations

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

Prediction of maize double-cross hybrids using the best linear unbiased prediction with microsatellite marker information

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