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OryzaSNP Consortium IDs Thousands of Variants in Rice Genome

NEW YORK (GenomeWeb News) – Members of the OryzaSNP Consortium reported this week that they have cataloged hundreds of thousands of SNPs in the rice genome — information that they are using to evaluate rice breeding history and to look for clues into further improving the crop plant.

An international team of investigators used microarray-based resequencing to look for SNPs in 100 million bases of non-repetitive DNA in the genomes of 20 different rice varieties and landraces. Using this approach, the researchers found about 160,000 high-quality SNPs, which they subsequently used to investigate rice breeding patterns as well as relationships between various rice varieties and sub-species. The research, part of the so-called "OryzaSNP project" appeared online last night in the Proceedings of the National Academy of Sciences.

"This comprehensive SNP data provides a foundation for deep exploration of rice diversity and gene-trait relationships and their use for future rice improvement," senior author Jan Leach, a plant biologist at Colorado State University, and colleagues wrote.

Domesticated rice, Oryza sativa, was the first crop plant to have its genome sequenced and assembled into a high quality reference genome. Now, by focusing on 20 rice varieties and landraces, Leach and her team have provided a foundation for future studies into genetic variation between different types of rice.

"These varieties, the OryzaSNPset collection, are genetically diverse and actively used in international breeding programs because of their wide range of agronomic attributes," the authors explained.

The team first designed six ultra-high-density tiling arrays, which were made by Affymetrix. The arrays were based on 100.1 million bases of non-repetitive sequence in the reference genome for Nipponbare, a variety belonging to the japonica sub-species. Although that genome includes about 390 million bases, the authors noted, the bases assessed in the new study represent roughly 80 percent of the non-repetitive genome.

Next, the researchers came up with a "gold-standard set of curated polymorphisms" by randomly sequencing 3.6 million bases of double-stranded DNA that was represented on the arrays. They then sifted potential SNPs out of their tiling array data using model-based and support vector machine learning approaches. Their search turned up 159, 879 SNPs that were detected by both of these computational methods.

Many of the SNPs were quite common. For example, the researchers detected about a third of the SNPs in between seven and 12 of the varieties tested. Coding region SNPs tended to be more common in the indica than in the japonica varieties, the team noted, probably because the reference genome used in the study belonged to a japonica variety.

By looking at the nature and frequency of SNPs present in the various rice varieties, the researchers were also able to get a snapshot of how genes have been mixed and matched between different types of rice — information that is helping them tease apart relationships between the varieties and trace past breeding events. For instance, some of the introgression patterns that they detected seem to reflect selection for important agricultural traits, such as grain quality and carbohydrate content.

And, the authors explained, such studies not only provide information about past breeding events, but may also highlight new avenues for improving rice crops. "Some introgressions correlate with known genomic regions responsible for traits transferred between varietal groups," they wrote, "whereas others represent candidates for additional events of potential significance for breeding."

The team argued that it should be relatively straightforward to apply the SNP information gleaned from this study to characterize genetic variation between many other rice varieties, although they touted the additional genetic insights that could be gained by sequencing other types of rice.

"Detailed knowledge of phenotypes, coupled with a deep genotype database, will create a powerful platform for association genetics and discovery of alleles that can be combined to achieve the much-needed increase in rice yield in the coming years," the researchers concluded.

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