by Staff Writers
Shenzhen, China (SPX) Apr 01, 2013
The advanced online publication version of Nature presents two manuscripts that provide an unprecedented glimpse into the adaptation and domestication of wheat. These achievements are the results of joint efforts led by the Institute of Genetics and Developmental Biology (IGDB), Chinese Academy of Sciences, Chinese Academy of Agricultural Sciences (CAAS), and BGI.
The two projects sequenced and analyzed two ancestral wheat genomes of Triticum urartu and Aegilops tauschii, respectively, throwing light on the biology of the world's primary staple crop and providing valuable new resource for the genetic improvement of wheat.
Wheat is a globally important crop due to its enhanced adaptability to a wide range of climates and improved grain quality for the production of baker's flour. Major efforts are underway worldwide to increase its yield and quality by increasing genetic diversity and analyzing key traits related to its resistance to cold, drought and disease.
However, the extremely large size and polyploid complexity of the wheat genome has to date been a substantial barrier for researchers to gain insight into its biology and evolution.
The first manuscript, led by teams at IGDB and BGI, presents the genome of Bread wheat (T. aestivum, AABBDD), the progenitor of the Wheat A genome. Using a whole-genome shotgun strategy and Next-generation sequencing (NGS), researchers identified a large set of gene models (34,879) and abundant genetic markers with the potential to provide a valuable resource for accelerating deeper and more systematic genomic and breeding studies. For example, they found the T. urartu homolog of OsGASR7 might be a useful candidate for improving wheat yield.
The discovery of 2,989,540 SNPs (single nucleotide polymorphisms) is useful for the future development and characterization of genetic markers. The researchers also reported genomic evidence of the role of repeat expansion in the enlargement of genome size during the evolution of the Triticeae tribe of grasses.
Ae. tauschii (DD), also known as Tausch's goatgrass, is a diploid goat grass species which has contributed the D genome of common wheat. Around 8,000 years ago in the Fertile Crescent, it crossed with the tetraploid wheat T. turgidum (AABB) in rare hybridization events that resulted in the hexaploid wheat T. aestivum. However, the modern strategy of breeding for hybrid vigor has been accompanied by marked changes in patterns of gene expression.
The second manuscript, led by teams at CAAS and BGI, focuses on the genome sequencing and analysis of the wild diploid grass Ae. tauschii.
They found that more than 65.9% of the Ae. tauschii genome was comprised of 410 different transposable element (TE) families, and the expansion of the Ae. tauschii genome was relatively recent and coincided with the abrupt climate change that occurred during the Pliocene Epoch. They also found the expansion of the micro-RNA miR2275 family may contribute to Ae. Tauschii' s enhanced disease resistance.
Remarkably, a higher number of genes for the cytochrome P450 family were identified in Ae. tauschii (485) than sorghum (365), rice (333), Brachypodium (262) and maize (261). This family of genes has been found to be important for abiotic stress response, especially in biosynthetic and detoxification pathways.
Shancen Zhao, Project Manager of BGI, said, "Genetic improvement of crops is the key output of breeding research. The genomic data provides a valuable resource for botanists and breeders to comprehensively understand wheat's genetic diversity and evolutionary history. The two studies also represent a major step forward for improving this vital crop in the face of global climate change, growing human population, and bio-energy.
"Providing the global agricultural community with these resources new resources for crop improvement and in keeping with the scientific community's goals of making all data fully and freely available, the huge amounts of data (1.5 terabytes) are available in the GigaScience database, GigaDB, in a citable format and is available as raw reads in the NCBI SRA database.
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