EST-SNPs in bread wheat: discovery, validation, genotyping and haplotype structure

Rustgi, Sachin; Bandopadhyay, Rajib; Balyan, H. S.; Gupta, P. K.

doi:10.17221/16/2009-cjgpb

Cited by 11 publications

(7 citation statements)

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“…An alternative strategy is to shotgun-sequence cDNAs and then allocate each sequence to a genome. Both approaches have been used in polyploid wheat [ 13 - 15 ] although those studies were of limited scope and genome coverage [ 14 - 17 ] and none mapped the markers.…”

Section: Introductionmentioning

confidence: 99%

Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes

et al. 2010

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BackgroundA genome-wide assessment of nucleotide diversity in a polyploid species must minimize the inclusion of homoeologous sequences into diversity estimates and reliably allocate individual haplotypes into their respective genomes. The same requirements complicate the development and deployment of single nucleotide polymorphism (SNP) markers in polyploid species. We report here a strategy that satisfies these requirements and deploy it in the sequencing of genes in cultivated hexaploid wheat (Triticum aestivum, genomes AABBDD) and wild tetraploid wheat (Triticum turgidum ssp. dicoccoides, genomes AABB) from the putative site of wheat domestication in Turkey. Data are used to assess the distribution of diversity among and within wheat genomes and to develop a panel of SNP markers for polyploid wheat.ResultsNucleotide diversity was estimated in 2114 wheat genes and was similar between the A and B genomes and reduced in the D genome. Within a genome, diversity was diminished on some chromosomes. Low diversity was always accompanied by an excess of rare alleles. A total of 5,471 SNPs was discovered in 1791 wheat genes. Totals of 1,271, 1,218, and 2,203 SNPs were discovered in 488, 463, and 641 genes of wheat putative diploid ancestors, T. urartu, Aegilops speltoides, and Ae. tauschii, respectively. A public database containing genome-specific primers, SNPs, and other information was constructed. A total of 987 genes with nucleotide diversity estimated in one or more of the wheat genomes was placed on an Ae. tauschii genetic map, and the map was superimposed on wheat deletion-bin maps. The agreement between the maps was assessed.ConclusionsIn a young polyploid, exemplified by T. aestivum, ancestral species are the primary source of genetic diversity. Low effective recombination due to self-pollination and a genetic mechanism precluding homoeologous chromosome pairing during polyploid meiosis can lead to the loss of diversity from large chromosomal regions. The net effect of these factors in T. aestivum is large variation in diversity among genomes and chromosomes, which impacts the development of SNP markers and their practical utility. Accumulation of new mutations in older polyploid species, such as wild emmer, results in increased diversity and its more uniform distribution across the genome.

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Section: Introductionmentioning

confidence: 99%

Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes

et al. 2010

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“…Database mining has been combined with bioinformatic analysis to align wheat EST sequence into contigs and allow sequence variant discrimination (Somers et al. , 2003; Rustgi et al. , 2009).…”

Section: Status Of Snp Discovery In Allopolyploid Cropsmentioning

confidence: 99%

“…HSV incidence in the wheat genome based on EST‐derived data can vary from 1 in 24 bp (Somers et al. , 2003) to 1 in 136 bp (Rustgi et al. , 2009), while locus‐specific SNP frequency can vary between 1 in 274 bp (Rustgi et al.…”

Section: Status Of Snp Discovery In Allopolyploid Cropsmentioning

confidence: 99%

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Identification, characterization and interpretation of single‐nucleotide sequence variation in allopolyploid crop species

Kaur

Francki

Forster

2011

Plant Biotechnology Journal

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SummaryAn understanding of nature and extent of nucleotide sequence variation is required for programmes of discovery and characterization of single nucleotide polymorphisms (SNPs), which provide the most versatile class of molecular genetic marker. A majority of higher plant species are polyploids, and allopolyploidy, because of hybrid formation between closely related taxa, is very common. Mutational variation may arise both between allelic (homologous) sequences within individual subgenomes and between homoeologous sequences among subgenomes, in addition to paralogous variation between duplicated gene copies. Successful SNP validation in allopolyploids depends on differentiation of the sequence variation classes. A number of biological factors influence the feasibility of discrimination, including degree of gene family complexity, inbreeding or outbreeding reproductive habit, and the level of knowledge concerning progenitor diploid species. In addition, developments in high-throughput DNA sequencing and associated computational analysis provide general solutions for the genetic analysis of allopolyploids. These issues are explored in the context of experience from a range of allopolyploid species, representing grain (wheat and canola), forage (pasture legumes and grasses), and horticultural (strawberry) crop. Following SNP discovery, detection in routine genotyping applications also presents challenges for allopolyploids. Strategies based on either design of subgenome-specific SNP assays through homoeolocus-targeted polymerase chain reaction (PCR) amplification, or detection of incremental changes in nucleotide variant dosage, are described.

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“…For example, for analysis of a mere 48 contigs of bread wheat, 1260 PCR runs needs to be performed just for SNP detection (Rustgi et al, 2009). The validation and assembly then required resequencing, which occupied even more time.…”

Section: Applications In Biological Systemmentioning

confidence: 99%

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

Thakur¹,

Bandopadhyay²,

Chaudhary³

et al. 2012

Front. Gene.

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Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed “cloud computing”) has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.

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EST-SNPs in bread wheat: discovery, validation, genotyping and haplotype structure

Cited by 11 publications

References 50 publications

Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes

Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes

Identification, characterization and interpretation of single‐nucleotide sequence variation in allopolyploid crop species

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

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