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Genome-wide SNP Discovery in Tetraploid Alfalfa Using 454 Sequencing and High Resolution Melting Analysis

Overview
Journal BMC Genomics
Publisher Biomed Central
Specialty Genetics
Date 2011 Jul 8
PMID 21733171
Citations 29
Authors
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Abstract

Background: Single nucleotide polymorphisms (SNPs) are the most common type of sequence variation among plants and are often functionally important. We describe the use of 454 technology and high resolution melting analysis (HRM) for high throughput SNP discovery in tetraploid alfalfa (Medicago sativa L.), a species with high economic value but limited genomic resources.

Results: The alfalfa genotypes selected from M. sativa subsp. sativa var. 'Chilean' and M. sativa subsp. falcata var. 'Wisfal', which differ in water stress sensitivity, were used to prepare cDNA from tissue of clonally-propagated plants grown under either well-watered or water-stressed conditions, and then pooled for 454 sequencing. Based on 125.2 Mb of raw sequence, a total of 54,216 unique sequences were obtained including 24,144 tentative consensus (TCs) sequences and 30,072 singletons, ranging from 100 bp to 6,662 bp in length, with an average length of 541 bp. We identified 40,661 candidate SNPs distributed throughout the genome. A sample of candidate SNPs were evaluated and validated using high resolution melting (HRM) analysis. A total of 3,491 TCs harboring 20,270 candidate SNPs were located on the M. truncatula (MT 3.5.1) chromosomes. Gene Ontology assignments indicate that sequences obtained cover a broad range of GO categories.

Conclusions: We describe an efficient method to identify thousands of SNPs distributed throughout the alfalfa genome covering a broad range of GO categories. Validated SNPs represent valuable molecular marker resources that can be used to enhance marker density in linkage maps, identify potential factors involved in heterosis and genetic variation, and as tools for association mapping and genomic selection in alfalfa.

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References
1.
Novaes E, Drost D, Farmerie W, Pappas Jr G, Grattapaglia D, Sederoff R . High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics. 2008; 9:312. PMC: 2483731. DOI: 10.1186/1471-2164-9-312. View

2.
Zhu H, Choi H, Cook D, Shoemaker R . Bridging model and crop legumes through comparative genomics. Plant Physiol. 2005; 137(4):1189-96. PMC: 1088312. DOI: 10.1104/pp.104.058891. View

3.
Cannon S, May G, Jackson S . Three sequenced legume genomes and many crop species: rich opportunities for translational genomics. Plant Physiol. 2009; 151(3):970-7. PMC: 2773077. DOI: 10.1104/pp.109.144659. View

4.
Choi H, Kim D, Uhm T, Limpens E, Lim H, Mun J . A sequence-based genetic map of Medicago truncatula and comparison of marker colinearity with M. sativa. Genetics. 2004; 166(3):1463-502. PMC: 1470769. DOI: 10.1534/genetics.166.3.1463. View

5.
Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S . TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics. 2003; 19(5):651-2. DOI: 10.1093/bioinformatics/btg034. View