Genomic Prediction of Barley Hybrid Performance
| Autor: | Sang He, Jochen C. Reif, Monika Spiller, Zuo Li, Guozheng Liu, Klaus Pillen, Gunther Stiewe, Norman Philipp, Yusheng Zhao |
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| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
0301 basic medicine Breeding program lcsh:QH426-470 Genotype Population Genomics Single-nucleotide polymorphism Plant Science Computational biology Biology Best linear unbiased prediction lcsh:Plant culture 01 natural sciences 03 medical and health sciences Genetics lcsh:SB1-1110 Plant breeding education Hybrid education.field_of_study Models Genetic Hordeum Regression lcsh:Genetics Plant Breeding 030104 developmental biology Phenotype Hybridization Genetic Agronomy and Crop Science Genome Plant 010606 plant biology & botany |
| Zdroj: | The Plant Genome, Vol 9, Iss 2 (2016) |
| ISSN: | 1940-3372 |
| Popis: | Hybrid breeding in barley ( L.) offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP) array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA) effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley. |
| Databáze: | OpenAIRE |
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