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

Edwards, Stefan McKinnon; Buntjer, J. B.; Jackson, Robert J.; Bentley, Alison R.; Lage, Jean-Luc Da; Byrne, Ed; Burt, Chris; Jack, Peter; Berry, Simon; Flatman, Edward; Poupard, Bruno; Smith, Stephen P.; Hayes, Charlotte; Gaynor, R. Chris; Gorjanc, Gregor; Howell, Phil; Ober, Eric S.; Mackay, Ian; Hickey, John M.

doi:10.1007/s00122-019-03327-y

Cited by 76 publications

(80 citation statements)

References 34 publications

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“…Except for the three traits in TP17 and DIS in TP18, the small sized population selected by stratified sampling also had higher predictive abilities than when the entire TP was used. Similar to our findings, several studies [23][24][25]43] have shown that a small TP with closely related lines had a greater predictive ability than a same-sized or larger TP with less-related lines.…”

Section: Small Population Sizesupporting

confidence: 92%

“…After 2017, our goal was to design an optimized training population (TP) that minimized the cost of phenotyping for FHB resistance while maximizing prediction accuracy for the untested lines. While simulation and empirical results have shown improved prediction accuracies when TP size is increased [17][18][19][20][21], other studies have achieved high accuracies with smaller TP sizes that were more closely related to the breeding population and lower accuracies as TP became more unrelated to the breeding population [22][23][24][25]. For example, Rincent et al [22] found that an optimized set of 100 lines achieved the same prediction accuracy as a set of 200 lines selected at random.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Training Population Size and Content to Improve Prediction Accuracy of FHB-Related Traits in Wheat

et al. 2020

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Genomic selection combines phenotypic and molecular marker data from a training population to predict the genotypic values of untested lines. It can improve breeding efficiency as large pools of untested lines can be evaluated for selection. Training population (TP) composition is one of the most important factors affecting the accuracy of genomic prediction. The University of Minnesota wheat breeding program implements genomic selection at the F5 stage for Fusarium head blight (FHB) resistance. This study used field data for FHB resistance in wheat (Triticum aestivum L.) to investigate the use of small-size TPs designed with and without stratified sampling for three FHB traits in three different F5 populations (TP17, TP18, and TP19). We also compared the accuracies of these two TP design methods with the accuracy obtained from a large size TP. Lastly, we evaluated the impact on trait predictions when the parents of F5 lines were included in the TP. We found that the small size TP selected randomly, without stratification, had the lowest predictive ability across the three F5 populations and across the three traits. This trend was statistically significant (p = 0.05) for all three traits in TP17 and two traits in TP18. Designing a small-size TP by stratified sampling led to a higher accuracy than a large-size TP in most traits across TP18 and TP19; this is because stratified sampling allowed the selection of a small set of closely related lines. We also observed that the addition of parental lines to the TP and evaluating the TP in two replications led to an increase in predictive abilities in most cases.

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Section: Small Population Sizesupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Optimizing Training Population Size and Content to Improve Prediction Accuracy of FHB-Related Traits in Wheat

et al. 2020

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“…These results were further supported by the highly significant (p>2.2e -16 ) positive correlation (R=0.92) between TS size and PAs. Similarly, positive correlations between the number of lines in the TS and the PAs, and plateau for the PAs were also reported by Edwards et al (111).…”

Section: Influence Of the Size And The Composition Of Ts And Bs On Passupporting

confidence: 81%

“…A net increase in PAs for maize resistance to FAW was realized when the size of the TS was increased from 37% (0.694 to 0.714) to 63% (0.833 to 0.838) similar to earlier reports on wheat yield (111). This increase was followed by a slight gain in predictability at 75% (0.837 to 0.843) and 85% (0.843 to 0.847), and thus, the PAs plateaued when TS sizes above 63% were considered in this study as reported earlier in other studies (21,64,(111)(112)(113). Thus, future GS programs for maize resistance to FAW could be designed around TS composed of a minimum of 60% of the entire breeding germplasm to achieve high genetic gains.…”

Section: Influence Of the Size And The Composition Of Ts And Bs On Passupporting

confidence: 67%

<strong>Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils</strong>

Badji¹,

Machida²,

Kwemoi³

et al. 2020

Preprint

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Genomic selection (GS) can accelerate variety improvement when training set (TS) size, and its relationship with the breeding set (BS) are optimized for prediction accuracies (PA) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW resistance traits, and, for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and, these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant since a positive correlation (R=0.92***) between TS size and PAs was observed for RBTS and, for the PBTS, it was negative (R=0.44**). This study pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.

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“…A positive correlation between prediction accuracy and TRN size was confirmed in several species [ 76 , 77 ]. However, the optimal TRN size seems to be highly influenced by the relatedness of TRS and TST [ 25 , 78 , 79 ]. The highest prediction accuracies were found using TRS with a strong relationship to the TST [ 28 , 80 , 81 ].…”

Section: Tomato Gs Schema Implementationmentioning

confidence: 99%

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Cappetta

Andolfo

Matteo

et al. 2020

Plants

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Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

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

Cited by 76 publications

References 34 publications

Optimizing Training Population Size and Content to Improve Prediction Accuracy of FHB-Related Traits in Wheat

Optimizing Training Population Size and Content to Improve Prediction Accuracy of FHB-Related Traits in Wheat

<strong>Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils</strong>

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

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