Transcript‐specific, single‐nucleotide polymorphism discovery and linkage analysis in hexaploid bread wheat (<i>Triticum aestivum</i> L.)

Allen, Alexandra M.; Barker, G. L.; Berry, Simon; Coghill, Jane A.; Gwilliam, Rhian; Kirby, Susan; Robinson, Phil; Brenchley, Rachel; D'Amore, R.; McKenzie, Neil; Waite, Darren; Hall, Anthony; Bevan, Michael W.; Hall, Neil; Edwards, Keith J.

doi:10.1111/j.1467-7652.2011.00628.x

Cited by 222 publications

(215 citation statements)

References 38 publications

(48 reference statements)

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“…This lower number in the D genome was consistent with previous studies (Allen et al 2011(Allen et al , 2013Cavanagh et al 2013;Wu et al 2015). This might be due to the higher diversity of the A and B genomes compared to the D genome, which has lower diversity (Ling et al 2013;Jia et al 2013;Brenchley et al 2012).…”

Section: Deficiency Of Markers In the D Genomesupporting

confidence: 81%

“…As in previous reports (Allen et al 2011(Allen et al , 2013Wu et al 2015) the SNP clusters were integrated with SSR and AFLP markers. This clustering phenomenon might be related to the origins of the SNP, which were developed from the transcriptomes of only 26 hexaploid wheat accessions with most SNP being in gene regions (Cavanagh et al 2013).…”

Section: Discussionmentioning

confidence: 99%

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Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Shi

Azam

et al. 2017

Euphytica

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Wheat (Triticum aestivum L.) yield is directly proportional to physio-morphological traits. A high-density genetic map consisting of 2575 markers was used for mapping QTL controlling stay-green and agronomic traits in wheat grown under four diverse water regimes. A total of 108 additive QTL were identified in target traits. Among them, 28 QTL for chlorophyll content (CC) were detected on 11 chromosomes, 43 for normalized difference vegetation index (NDVI) on all chromosomes except 5B, 5D, and 7D, five for spikes per plant (NSP) on different chromosomes, nine for plant height (PH) on four chromosomes, and 23 for thousand-kernel weight (TKW) on 11 chromosomes. Considering all traits, the phenotypic variation explained (PVE) ranged from 3.61 to 41.62%. A major QTL, QNDVI.cgb-5A.7, for NDVI with a maximum PVE of 20.21%, was located on chromosome 5A. A stable and major PH QTL was observed on chromosome 4D with a PVE close to 40%. Most distances between QTL and corresponding flanking markers were less than 1 cM, and approximately one-third of the QTL coincided with markers. Each of 16 QTL clusters on 10 chromosomes controlled more than one trait and therefore could be regarded as pleiotropic regions in response to different water regimes. Forty-one epistatic QTL were identified for all traits having PVE of 6.00 to 25.07%. Validated QTL closely linked to flanking markers will be beneficial for marker-assisted selection in improving drought-tolerance in wheat.

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Section: Deficiency Of Markers In the D Genomesupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Shi

Azam

et al. 2017

Euphytica

View full text Add to dashboard Cite

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“…Recent advances in wheat genomics have led to the implementation of high-density SNP genotyping in wheat [72][73][74][75]. Gene-based SNP markers were developed for Lr34/Yr18/Pm38 locus that confers resistance to leaf rust, stripe rust, and powdery mildew diseases [76].…”

Section: Examples In Wheatmentioning

confidence: 99%

Maize (Zea mays L.) Genome Diversity as Revealed by RNA-Sequencing

2014

The Role of Bioinformatics in Agriculture

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The use of molecular markers has revolutionized the pace and precision of plant genetic analysis which in turn facilitated the implementation of molecular breeding of crops. The last three decades have seen tremendous advances in the evolution of marker systems and the respective detection platforms. Markers based on single nucleotide polymorphisms (SNPs) have rapidly gained the center stage of molecular genetics during the recent years due to their abundance in the genomes and their amenability for high-throughput detection formats and platforms. Computational approaches dominate SNP discovery methods due to the everincreasing sequence information in public databases; however, complex genomes pose special challenges in the identification of informative SNPs warranting alternative strategies in those crops. Many genotyping platforms and chemistries have become available making the use of SNPs even more attractive and efficient. This paper provides a review of historical and current efforts in the development, validation, and application of SNP markers in QTL/gene discovery and plant breeding by discussing key experimental strategies and cases exemplifying their impact.

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“…genetic diversity (Akhunov et al, 2010). Another study by showed that the number of SNP markers in a framework genetic map of hexaploid wheat were in asymmetrical proportions of 30:50:17 for the A, B and D genomes respectively (Allen et al, 2011) which is also supported by our observation of a greater SNP density in the B genome compared to the A and D genomes ( Figure 4-1).…”

Section: Construction Of a High Quality Pangenome-wide Snp Databasesupporting

confidence: 74%

The wheat pangenome: assembly and analysis

Cabrera¹,

Daniel²

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Wheat is one of the most important food crops in the world and its continued breeding is essential to achieve the production goals set by FAO in 2050. Breeding programs benefit from the development of genomic resources that reduce time and costs of selection of suitable varieties. Recent evidence has suggested that an important fraction of crop plant genomes exhibits presence-absence variation and cannot be exploited following the single reference paradigm. Pangenomic studies aim to fill this vacuum by creating a complete catalogue of genes in a species and characterizing them. With the release of the first wheat draft genome for cultivar Chinese Spring, we are able to explore the wheat pangenome. In the first chapter I performed a review of the current status of wheat genomics and pangenomic studies. In the second chapter the public wheat reference is assessed for its suitability as the basis of a pangenomic study. Extensive uncollapsed duplicated sequences and the absence of support for some gene models prompted us to reassemble the genome. Both assemblies were then compared and the new assembly was selected for further study. In the third chapter, eighteen wheat cultivars were used to extend the Chinese Spring reference. A metagenomics assembly approach was employed and 350 Mbp of additional sequence absent from the Chinese Spring reference were assembled. These sequences contained over 20,000 additional genes which were classified into core and variable genes and later characterized. The pangenome size was modelled as a function of the number of genomes and functional enrichment of the variable genes showed that these were enriched with genes involved in response to biotic and abiotic stress. In chapter 4, we use the new pangenome to identify over 34.6 million SNPs and further use these SNPs to characterize core and variable genes, to construct a high density genetic map and to assess the relatedness of the cultivars used in this study. We show that the variable genes have a higher SNP density particularly for non-synonymous SNPs. The results show that the synthetic cultivar W7984 is the most divergent accession alongside Chinese Spring. Finally, in chapter 5, the future of pangenomic studies is evaluated with a critique and suggestions to improve the current wheat pangenome.

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Transcript‐specific, single‐nucleotide polymorphism discovery and linkage analysis in hexaploid bread wheat (Triticum aestivum L.)

Cited by 222 publications

References 38 publications

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Maize (Zea mays L.) Genome Diversity as Revealed by RNA-Sequencing

The wheat pangenome: assembly and analysis

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