GplusE: beyond genomic selection

Mackay, Ian; Ober, Eric S.; Hickey, John M.

doi:10.1002/fes3.52

Cited by 19 publications

(16 citation statements)

References 42 publications

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“…In our study, we observed that when the training population had at least one full‐sib from a cross, the pedigree‐based predictions performed similar to or gave a maximum increase in accuracy of 0.13 over the genomic prediction accuracies. This implies that although markers account for genomic relationships that occurred among the founders of the population, the narrow variance among full‐sibs and the insignificant contribution of more distant relationships further back in time (before the five generations of pedigree considered), as also observed by Luan et al (2012), might have contributed to high accuracies with the pedigree. In addition, the imperfect estimation of the identity‐by‐state relationships by the limited number of markers available for this study might have resulted in high pedigree‐based prediction accuracies.…”

Section: Discussionmentioning

confidence: 99%

Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat

et al. 2018

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Core Ideas Genomic prediction of grain yield across nurseries or years is challenging, because of genotype × environment interactions. Prediction accuracies can be improved by having at least one full‐sib in the training population. Genomic selection (GS) was less advantageous for within‐family selections in elite families with few full‐sibs and minimal Mendelian sampling variance. It is important to apply GS at the appropriate stage of the breeding cycle. Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat (Triticum aestivum L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across‐nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping‐by‐sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item‐based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree‐based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full‐sibs. However, GS was less advantageous for within‐family selections in elite families with few full‐sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early‐generation within‐family selections.

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Section: Discussionmentioning

confidence: 99%

Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat

et al. 2018

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“…Metabolomic methods would enable the sugar and starch profiles of tubers to be more closely examined, whereas high‐throughput imaging will allow plant biomass accumulation to be monitored under various biotic and abiotic stress conditions. The development of high‐throughput phenomic platforms will enable phenotype data to be captured at a rate to match the advances that have occurred with genomic selection (Cobb et al, 2013; Mackay et al, 2015), although there will be a requirement to optimize approaches for individual plant species. In this context, a focus on species‐specific traits for potato, particularly subterranean stolon and tuber characters, will be essential.…”

Section: Increasing the Suite Of Traits That Can Be Evaluated Or Selementioning

confidence: 99%

Improving Genetic Gain with Genomic Selection in Autotetraploid Potato

et al. 2016

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Potato (Solanum tuberosum L.) breeders consider a large number of traits during cultivar development and progress in conventional breeding can be slow. There is accumulating evidence that some of these traits, such as yield, are affected by a large number of genes with small individual effects. Recently, significant efforts have been applied to the development of genomic resources to improve potato breeding, culminating in a draft genome sequence and the identification of a large number of single nucleotide polymorphisms (SNPs). The availability of these genome-wide SNPs is a prerequisite for implementing genomic selection for improvement of polygenic traits such as yield. In this review, we investigate opportunities for the application of genomic selection to potato, including novel breeding program designs. We have considered a number of factors that will influence this process, including the autotetraploid and heterozygous genetic nature of potato, the rate of decay of linkage disequilibrium, the number of required markers, the design of a reference population, and trait heritability. Based on estimates of the effective population size derived from a potato breeding program, we have calculated the expected accuracy of genomic selection for four key traits of varying heritability and propose that it will be reasonably accurate. We compared the expected genetic gain from genomic selection with the expected gain from phenotypic and pedigree selection, and found that genetic gain can be substantially improved by using genomic selection.

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“…The objective of this study was to explore the effect of level of relatedness between 78 training set and validation set on genomic prediction accuracy using data from a large set of 79 field experiments. To do this, 44 bi-parental or three-way crosses were obtained from four 80 commercial wheat breeders in the United Kingdom, as described for the GplusE Project 81 (Mackay et al, 2015). The crosses had different degrees of relatedness among each other and 82 there were many shared parents.…”

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The effects of training population design on genomic prediction accuracy in wheat

Edwards

Buntjer

Jackson

et al. 2018

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17Genomic selection offers several routes for increasing genetic gain or efficiency of plant 18 breeding programs. In various species of livestock there is empirical evidence of increased 19 rates of genetic gain from the use of genomic selection to target different aspects of the 20 breeder's equation. Accurate predictions of genomic breeding value are central to this and the 21 design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, 22 this study emphasize the importance of the training set design in relation to the genetic 35 material to which the resulting prediction model is to be applied. 36

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GplusE: beyond genomic selection

Cited by 19 publications

References 42 publications

Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat

Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat

Improving Genetic Gain with Genomic Selection in Autotetraploid Potato

The effects of training population design on genomic prediction accuracy in wheat

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