Genome-wide prediction of three important traits in bread wheat

Charmet, G.; Storlie, Eric; Oury, François-Xavier; Laurent, V.; Béghin, D.; Chevarin, Laetitia; Lapierre, A.; Perretant, Marie-Reine; Rolland, Bernard; Heumez, Emmanuel; Duchalais, Laure; Goudemand, Ellen; Bordes, Jacques; Robert, Olivier

doi:10.1007/s11032-014-0143-y

Cited by 52 publications

(48 citation statements)

References 36 publications

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“…Accordingly, genomic selection is expected to give more accurate predictions if lines included in the training population are closely related to (Asoro et al 2011; Lehermeier et al 2014; Lorenz and Smith 2015) or even come from the same population as the selection candidates (Windhausen et al 2012; Charmet et al 2014). The underlying population structure can be readily deciphered when multiple large bi-parental populations (Heffner et al 2011a; Schulz-Streeck et al 2012; Riedelsheimer et al 2013; Lehermeier et al 2014) or larger heterotic groups (Technow et al 2013; Lehermeier et al 2014; Spindel et al 2015) are directly involved in the development of varietal candidates.…”

Section: Discussionmentioning

confidence: 99%

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Michel

Ametz²,

Güngör³

et al. 2016

Theor Appl Genet

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Key message Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. AbstractThe selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-016-2818-8) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Michel

Ametz²,

Güngör³

et al. 2016

Theor Appl Genet

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“…With the exception of FAY12 and FAY14, QTL detected in the combined analysis (i.e., stable QTL) were better able to explain phenotypic variation in GY across site-years, ranging from 11 to 39 %. Charmet et al (2014) reported genome-wide prediction accuracies for GY in wheat ranging from r = 0.32 to 0.70 within single RIL populations based on resampling of genotypes. Heffner et al (2011) found the prediction accuracies in a multi-family population of advanced breeding lines to be much lower (r = 0.180-0.223) but still advantageous over phenotypic selection or conventional MAS when predicting across years.…”

Section: Potential Marker Assisted Selection For Gymentioning

confidence: 99%

QTL and major genes influencing grain yield potential in soft red winter wheat adapted to the southern United States

et al. 2016

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The aim of this study was to identify quantitative trait locus (QTL) associated with grain yield (GY) in a recombinant inbred line (RIL) population from a cross between two elite soft red winter wheat (SRWW) cultivars ('Pioneer 26R61' and 'AGS2000'). The RIL population was grown from 2011 to 2014 in 12 site-year combinations throughout the southeastern US. Overall, AGS2000 was the higher yielding parental line, out-performing 26R61 in seven of the 12 environments. Mean GY for the RILs ranged from 3.39 to 7.16 t ha -1 with significant genotype, environment and genotype by environmental interaction effects. Nine stable QTL were detected for yield, explaining up to 53 % of the phenotypic variation when fit into a multiple-QTL model. The QTL with the largest effect was detected at the Vrn-B1 locus with the short vernalization winter allele from AGS2000 favorable for yield. In addition, vrn-B1 acted additively with a region on chromosome 2B near the Ppd-B1 locus, indicating that a shorter vernalization requirement combined with the Ppd-B1b allele for photoperiod sensitivity may play a key role in adaptation of SRWW to the southern US. Single nucleotide polymorphism markers linked to additional QTL on chromosomes 3A and 3B were in agreement with a previous genome-wide association study in spring wheat, confirming the importance of these regions for yield across environments and germplasm pools. Overall the stable QTL were more predictive of GY compared to individual site-year QTL, indicating that a targeted QTL approach can be utilized by breeding programs to enrich for favorable loci.Electronic supplementary material The online version of this article (

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“…Consequently, it is relevant to enable prediction in new progeny sets across years and predict across less related breeding sets. However, this issue is a major challenge in genomic prediction in plants, since prediction accuracy between genetically unrelated populations (or families) is often low [20,21]. Investigation of genomic prediction accurracy between families is done by cross-validation strategies, where less related groups are predicted.…”

Section: Introductionmentioning

confidence: 99%

“…Investigation of genomic prediction accurracy between families is done by cross-validation strategies, where less related groups are predicted. The authors of [21] performed cross-validation using three different wheat sets originated by different parents: two DH sets and one recombinant inbred set, and found low prediction accuracies when one set was used to predict another.…”

Section: Introductionmentioning

confidence: 99%

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

Nielsen¹,

Jahoor

Jensen³

et al. 2016

PLoS ONE

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Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

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Genome-wide prediction of three important traits in bread wheat

Cited by 52 publications

References 36 publications

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

QTL and major genes influencing grain yield potential in soft red winter wheat adapted to the southern United States

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

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