Identification of QTLs for wheat heading time across multiple-environments

Benaouda, Salma; Dadshani, Said; Koua, Patrice; Léon, Jens; Ballvora, Agim

doi:10.1007/s00122-022-04152-6

Cited by 12 publications

(21 citation statements)

References 83 publications

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“…For that, we applied a strategy to combine genetic analysis with comparative transcriptomics. The previously four uncovered loci involved in the regulation of flowering time detected in adapted German wheat cultivars [ 45 ] plus 17 other significant QTL were used for downstream selection of DEGs comparing “Kontrast” to “Basalt”. In total, 670 and 1075 genes were differentially expressed between the cultivars in SAM ( Additional file 7 ) and leaves ( Additional file 8 ) , respectively, and could be mapped to the 23 significant QTL intervals.…”

Section: Resultsmentioning

confidence: 99%

“…For the transcriptome study, two bred winter cultivars developed in Germany showing contrasting and stable flowering behavior in different environments (Additional file 1 ) were selected. The mean value of the heading date (HD) of both cultivars is based on the phenotyping data collected from six locations across Germany over 3 years [ 45 ]. “Kontrast” is the earliest flowering one in the adapted cultivars, which is released in 1990, and flowers 10 days earlier than the latest flowering cultivar “Basalt”, developed in 1980 [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…We employed feature Counts v1.6.4 [ 52 ] to obtain aggregate counts of aligned reads at exon-level and to construct a gene-level matrix of these counts comprising all samples. The transcripts have been mapped in the previous four identified QTL for heading [ 45 ]. The list has been extended to 23 QTL that are statistically significant to explore as much as possible the pathways and responses revealed by RNA-seq (Additional file 2 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this study, we joined QTL mapping provided by previous GWAS to transcriptome sequencing analysis for identifying candidate genes underpinning the detected QTL that underlay flowering time regulation in winter wheat [ 45 ]. For that, the contrasting genotypes identified in [ 45 ] are selected to perform comparative transcriptome analysis. The particular goals of the current study were to (1) assess the correlation between the observed flowering time trait in the field with microscopical phenotyping of trait-specific organ and stage, (2) to identify and map the genes differentially expressed in the early and late flowering cultivars in trait-specific organ and stage, (3) to explore the pathways and responses revealed by RNA-seq in QTL intervals and finally (4) to compare transcription levels of some selected genes mapped in significant QTL with relative gene expression via RT-PCR and identify polymorphisms in coding sequences and promoter regions of those genes.…”

Section: Introductionmentioning

confidence: 99%

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Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

et al. 2023

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Background The transition from vegetative to floral phase is the result of complex crosstalk of exogenous and endogenous floral integrators. This critical physiological event is the response to environmental interaction, which causes biochemical cascades of reactions at different internal tissues, organs, and releases signals that make the plant moves from vegetative status to a reproductive phase. This network controlling flowering time is not deciphered largely in bread wheat. In this study, a comparative transcriptome analysis at a transition time in combination with genetic mapping was used to identify responsible genes in a stage and tissue-specific manner. For this reason, two winter cultivars that have been bred in Germany showing contrasting and stable heading time in different environments were selected for the analysis. Results In total, 670 and 1075 differentially expressed genes in the shoot apical meristem and leaf tissue, respectively, could be identified in 23 QTL intervals for the heading date. In the transition apex, Histone methylation H3-K36 and regulation of circadian rhythm are both controlled by the same homoeolog genes mapped in QTL TaHd112, TaHd124, and TaHd137. TaAGL14 gene that identifies the floral meristem was mapped in TaHd054 in the double ridge. In the same stage, the homoeolog located on chromosome 7D of FLOWERING TIME LOCUS T mapped on chr 7B, which evolved an antagonist function and acts as a flowering repressor was uncovered. The wheat orthologue of transcription factor ASYMMETRIC LEAVES 1 (AS1) was identified in the late reproductive stage and was mapped in TaHd102, which is strongly associated with heading date. Deletion of eight nucleotides in the AS1 promoter could be identified in the binding site of the SUPPRESSOR OF CONSTANS OVEREXPRESSION 1 (SOC1) gene in the late flowering cultivar. Both proteins AS1 and SOC1 are inducing flowering time in response to gibberellin biosynthesis. Conclusion The global transcriptomic at the transition phase uncovered stage and tissue-specific genes mapped in QTL of heading date in winter wheat. In response to Gibberellin signaling, wheat orthologous transcription factor AS1 is expressed in the late reproductive phase of the floral transition. The locus harboring this gene is the strongest QTL associated with the heading date trait in the German cultivars. Consequently, we conclude that this is another indication of the Gibberellin biosynthesis as the mechanism behind the heading variation in wheat.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

et al. 2023

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Identifying genomic regions associated with key agro‐morphological traits in soft red winter wheat using genome‐wide association study

Subedi,

Bagwell,

Ghimire

et al. 2024

Crop Science

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Agro‐morphological traits play a significant role in the adaptation of wheat to diverse agroecosystems. Genetic understanding of these traits is crucial to develop cultivars adapted to specific environments and maximize their productivity. This is a comprehensive genome‐wide association study (GWAS) of 230 diverse lines of soft red winter wheat for identifying quantitative trait loci (QTLs) related to eight key agro‐morphological traits. The diversity panel was evaluated in two locations for three consecutive years (2020–2023). A total of 150 significant marker–trait associations were detected, including 65 for three flag leaf traits, 35 for peduncle length, 33 for plant height, 16 for heading date, and one for plant vigor using 27,466 single nucleotide polymorphism (SNP) markers. Eleven high‐confidence major‐effect QTLs explaining greater than 10% phenotypic variance were detected, of which seven were stable, and one showed an association with plant height and peduncle length. QTLs possibly allelic for important dwarfing (Rht23) and vernalization (Vrn‐B1) genes were identified. Six QTLs, QFlw.uga‐1A, QPdl.uga‐1A, QFlw.uga‐2B.2, QPdl.uga‐5A, QPdl.uga‐7A, and QPht.uga‐7B, are presumed to be novel, and nearby candidate gene(s) were identified for all except QPdl.uga‐1A. The pyramiding of favorable alleles from major‐effect QTLs was found to have significant improvement in peduncle length (shortened by 5 cm), flag leaf width (increased by 0.18 cm), and plant height (shortened by 11 cm). This study has improved our genetic understanding of important agro‐morphological traits. These results, upon further validation, can be used in breeding for desirable plant architecture to improve wheat yield potential.

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Predicting superior crosses in winter wheat using genomics: A retrospective study to assess accuracy

Ballén‐Taborda,

Lyerly,

Smith

et al. 2024

Crop Science

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In plant breeding, selecting cross‐combinations that are more likely to result in superior lines for cultivar development is critical. This step, however, is subjective with decisions being based on available genomic and phenotypic data for prospective parents. Genomic prediction (GP) provides new opportunities to accelerate genetic gain for a target trait by identifying superior crosses through simulation of progeny performance. In this context, this study deployed GP using the phenotype and genotype of potential parents to predict the progeny genetic variance (VG) and means of overall, inferior 10%, and superior 10% (μ, μip, and μsp, respectively). This retrospective experimental design investigated whether the crosses that produced superior soft red winter wheat breeding lines would have been made if progeny simulations had guided crossing decisions of breeding programs. Here, data from historical wheat breeding lines were used to train GP models and predict VG and means for yield, test weight, heading date, and plant height for all combinations of 217 parents. Predicted and observed data for 670 lines derived from biparental crosses were compared to assess the accuracy of progeny simulations, and low‐to‐moderate prediction accuracy was observed for the four traits (0.25–0.52). Of the pedigrees that produced lines that were selected and advanced into later stage nurseries, 76% were predicted to give rise to progeny with above‐average yield. The moderate correlation found between predicted progeny means and observed line per se performance justifies using cross‐combination prediction as a tool to reduce crossing number and focus on segregating populations that harbor future cultivars.

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Identification of QTLs for wheat heading time across multiple-environments

Cited by 12 publications

References 83 publications

Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

Identifying genomic regions associated with key agro‐morphological traits in soft red winter wheat using genome‐wide association study

Predicting superior crosses in winter wheat using genomics: A retrospective study to assess accuracy

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