Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles

Kristensen, Peter Skov; Jahoor, Ahm ed; Andersen, Jeppe Reitan; Orabi, Jihad; Janss, Luc; Jensen, Just

doi:10.20900/cbgg20190010

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…The predictive abilities decreased the most in the case of GPC (0.5 and 0.2 for LOO and LFO, respectively) when increasing the genetic distance between populations, while the smallest impact of increased genetic distance was recorded in the case of Zeleny sedimentation (0.79 and 0.68 for LOO and LFO, respectively). Similar results were reported for FY and alveograph traits, where the decrease of GS accuracy in a range of 24% to 35% was observed when comparing LOO and LFO cross-validation methods [65], and for Zeleny sedimentation, GPC, TKW, and test weight (TW) [64], suggesting that genetic composition of TP is crucial for achieving accurate genomic predictions.…”

Section: Relatedness Of Training and Validation Populationsupporting

confidence: 81%

“…Further research studies showed that using different GS indices in simultaneous selection for yield and wheat quality traits still does not outperform single-trait prediction for GPC, PY, and the dough rheological traits, but suggested that simultaneous improvement of yield and wheat quality should target protein quality, rather than GPC [66]. A significant gain of multitrait approach is expected only for low heritable traits that are incorporated with high heritable traits, between which high genetic correlation exists [64]. Data for traits incorporating together in a multitrait analysis must be already available or easy to obtain on a large number of samples in a short period of time [67].…”

Section: Multitrait Genomic Selectionmentioning

confidence: 99%

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An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Plavšin

Gunjača

Šatović

et al. 2021

Plants

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Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

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Section: Relatedness Of Training and Validation Populationsupporting

confidence: 81%

Section: Multitrait Genomic Selectionmentioning

confidence: 99%

An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Plavšin

Gunjača

Šatović

et al. 2021

Plants

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“…As shown in various studies, this new approach has the potential for wheat biofortification [96][97][98]. GS has the potential to quickly improve complex traits with low heritability, like grain protein content, etc., as well as to significantly reduce the time and cost of wheat breeding.…”

Section: -8 Years 10mentioning

confidence: 99%

“…The forward prediction accuracies for the studied parameters ranged from 0.32 to 0.62, and the expected genetic gain was 1.4 to 2.7 times higher than PS for all traits. The predictive abilities of the genomic predictions (GPs) can be improved using genetically correlated traits in multi-trait models [97,98]. Michel et al [97] phenotyped more than 400 genotyped wheat lines for protein content and baking quality traits, in multi-environment trials from 2009 to 2016, and applied GS to select the best individuals in terms of their good protein content and baking quality traits, as well as grain yield.…”

Section: Different Prospects Of Gs For Wheat Biofortification With Proteinmentioning

confidence: 99%

“…They observed nearly twice the selection response, compared to the indirect selection, by protein content for the baking quality-related traits, using GS, which was applied 2-3 years earlier than direct phenotypic selection. Recently, in 2019, Kristensen et al [98] studied multi-trait and trait-assisted genomic prediction models, dealing with two or four traits, including GPC, phenotyped in 1152 advanced winter wheat lines, and compared the GPs of these models with single-trait models. They observed increased predictive abilities for GPC with trait-assisted models.…”

Section: Different Prospects Of Gs For Wheat Biofortification With Proteinmentioning

confidence: 99%

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Advances in Genomic Interventions for Wheat Biofortification: A Review

2020

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Wheat is an essential constituent of cereal-based diets, and one of the most significant sources of calories. However, modern wheat varieties are low in proteins and minerals. Biofortification is a method for increasing the availability of essential elements in the edible portions of crops through agronomic or genetic and genomic interventions. Wheat biofortification, as a research topic, has become increasingly prevalent. Recent accomplishments in genomic biofortification could potentially be helpful for the development of biofortified wheat grains, as a sustainable solution to the issue of “hidden hunger”. Genomic interventions mainly include quantitative trait loci (QTL) mapping, marker-assisted selection (MAS), and genomic selection (GS). Developments in the identification of QTL and in the understanding of the physiological and molecular bases of the QTLs controlling the biofortification traits in wheat have revealed new horizons for the improvement of modern wheat varieties. Markers linked with the QTLs of desirable traits can be identified through QTL mapping, which can be employed for MAS. Besides MAS, a powerful tool, GS, also has great potential for crop improvement. We have compiled information from QTL mapping studies on wheat, carried out for the identification of the QTLs associated with biofortification traits, and have discussed the present status of MAS and different prospects of GS for wheat biofortification. Accelerated mapping studies, as well as MAS and GS schemes, are expected to improve wheat breeding efficiency further.

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Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis

et al. 2022

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Key message Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Abstract Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) “I-model” (baseline), (ii) “I + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.

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Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles

Cited by 5 publications

References 22 publications

An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Advances in Genomic Interventions for Wheat Biofortification: A Review

Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis

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