Genetic systems regulating flowering response in wheat

Stelmakh, A. F.

doi:10.1007/978-94-011-4896-2_64

Cited by 43 publications

(50 citation statements)

References 11 publications

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“…The frequencies of each of the dominant alleles in Argentina (Table 4) were very similar to those previously reported for a set of 101 varieties from Latin America (Stelmakh 1998). However, the frequencies of the dominant alleles in California (Table 4) were more similar to those in Argentina than to the ones reported for a collection of 45 varieties from Canada and the USA, or for a large worldwide collection (Stelmakh 1998).…”

Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 84%

“…However, the frequencies of the dominant alleles in California (Table 4) were more similar to those in Argentina than to the ones reported for a collection of 45 varieties from Canada and the USA, or for a large worldwide collection (Stelmakh 1998).…”

Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 71%

“…The frequencies of the different VRN-1 allele combinations in Argentina and California were similar to each other (Table 4, v 2 test P=0.24) but very different (v 2 , P<0.0001) from the ones reported for a large worldwide collection previously characterized for VRN-1 allele combinations (Stelmakh 1998).…”

Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 65%

“…2f) yielded PCR fragments of similar size, suggesting that all have the same deletion. This result was expected, because the dominant Vrn-D1 allele was introgressed into the Italian variety Mentana from the Japanese variety Akakomugi, and then transferred from Mentana into the Mexican semi-dwarf cultivars Lerma Rojo and Sonora 64, initiating a worldwide distribution of this allele (Stelmakh 1998). …”

Section: Comparisons Among Different Genomesmentioning

confidence: 95%

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Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat

et al. 2005

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The broad adaptability of wheat and barley is in part attributable to their flexible growth habit, in that spring forms have recurrently evolved from the ancestral winter growth habit. In diploid wheat and barley growth habit is determined by allelic variation at the VRN-1 and/or VRN-2 loci, whereas in the polyploid wheat species it is determined primarily by allelic variation at VRN-1. Dominant Vrn-A1 alleles for spring growth habit are frequently associated with mutations in the promoter region in diploid wheat and in the A genome of common wheat. However, several dominant Vrn-A1, Vrn-B1, Vrn-D1 (common wheat) and Vrn-H1 (barley) alleles show no polymorphisms in the promoter region relative to their respective recessive alleles. In this study, we sequenced the complete VRN-1 gene from these accessions and found that all of them have large deletions within the first intron, which overlap in a 4-kb region. Furthermore, a 2.8-kb segment within the 4-kb region showed high sequence conservation among the different recessive alleles. PCR markers for these deletions showed that similar deletions were present in all the accessions with known Vrn-B1 and Vrn-D1 alleles, and in 51 hexaploid spring wheat accessions previously shown to have no polymorphisms in the VRN-A1 promoter region. Twenty-four tetraploid wheat accessions had a similar deletion in VRN-A1 intron 1. We hypothesize that the 2.8-kb conserved region includes regulatory elements important for the vernalization requirement. Epistatic interactions between VRN-H2 and the VRN-H1 allele with the intron 1 deletion suggest that the deleted region may include a recognition site for the flowering repression mediated by the product of the VRN-H2 gene of barley.

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Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 84%

Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 71%

Section: Vrn-1 Allele Combinations In Wheat Varieties Grown In Argentsupporting

confidence: 65%

Section: Comparisons Among Different Genomesmentioning

confidence: 95%

See 2 more Smart Citations

Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat

et al. 2005

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“…Among modern wheat varieties, the strong Vrn-A1a allele for early flowering is predominant in regions of very cold winters, where spring wheats are planted in the spring. By contrast, the weaker Vrn-B1a and VrnD1a alleles are present at higher frequencies in the Mediterranean climates where spring wheats are planted during the fall (45,59,60). These results provide an example of the selective forces that could have also operated on VRN-D4.…”

Section: Discussionmentioning

confidence: 96%

Identification of the VERNALIZATION 4 gene reveals the origin of spring growth habit in ancient wheats from South Asia

Kippes

Debernardi

Vasquez-Gross

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

136

109

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Wheat varieties with a winter growth habit require long exposures to low temperatures (vernalization) to accelerate flowering. Natural variation in four vernalization genes regulating this requirement has favored wheat adaptation to different environments. The first three genes (VRN1-VRN3) have been cloned and characterized before. Here we show that the fourth gene, VRN-D4, originated by the insertion of a ∼290-kb region from chromosome arm 5AL into the proximal region of chromosome arm 5DS. The inserted 5AL region includes a copy of VRN-A1 that carries distinctive mutations in its coding and regulatory regions. Three lines of evidence confirmed that this gene is VRN-D4: it cosegregated with VRN-D4 in a high-density mapping population; it was expressed earlier than other VRN1 genes in the absence of vernalization; and induced mutations in this gene resulted in delayed flowering. VRN-D4 was found in most accessions of the ancient subspecies Triticum aestivum ssp. sphaerococcum from South Asia. This subspecies showed a significant reduction of genetic diversity and increased genetic differentiation in the centromeric region of chromosome 5D, suggesting that VRN-D4 likely contributed to local adaptation and was favored by positive selection. Three adjacent SNPs in a regulatory region of the VRN-D4 first intron disrupt the binding of GLYCINE-RICH RNA-BINDING PROTEIN 2 (TaGRP2), a known repressor of VRN1 expression. The same SNPs were identified in VRN-A1 alleles previously associated with reduced vernalization requirement. These alleles can be used to modulate vernalization requirements and to develop wheat varieties better adapted to different or changing environments.wheat | flowering | vernalization | VRN1 | Triticum aestivum ssp. sphaerococcum

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Phenotyping and predicting wheat spike characteristics using image analysis and machine learning

Hammers,

Winn,

Ben‐Hur

et al. 2023

The Plant Phenome Journal

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Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.

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Genetic systems regulating flowering response in wheat

Cited by 43 publications

References 11 publications

Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat

Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat

Identification of the VERNALIZATION 4 gene reveals the origin of spring growth habit in ancient wheats from South Asia

Phenotyping and predicting wheat spike characteristics using image analysis and machine learning

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