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• W2897069170 abstract "We describe the development and application of the Sorghum QTL Atlas, a high-resolution, open-access research platform to facilitate candidate gene identification across three cereal species, sorghum, maize and rice. The mechanisms governing the genetic control of many quantitative traits are only poorly understood and have yet to be fully exploited. Over the last two decades, over a thousand QTL and GWAS studies have been published in the major cereal crops including sorghum, maize and rice. A large body of information has been generated on the genetic basis of quantitative traits, their genomic location, allelic effects and epistatic interactions. However, such QTL information has not been widely applied by cereal improvement programs and genetic researchers worldwide. In part this is due to the heterogeneous nature of QTL studies which leads QTL reliability variation from study to study. Using approaches to adjust the QTL confidence interval, this platform provides access to the most updated sorghum QTL information than any database available, spanning 23 years of research since 1995. The QTL database provides information on the predicted gene models underlying the QTL CI, across all sorghum genome assembly gene sets and maize and rice genome assemblies and also provides information on the diversity of the underlying genes and information on signatures of selection in sorghum. The resulting high-resolution, open-access research platform facilitates candidate gene identification across 3 cereal species, sorghum, maize and rice. Using a number of trait examples, we demonstrate the power and resolution of the resource to facilitate comparative genomics approaches to provide a bridge between genomics and applied breeding." @default.
• W2897069170 created "2018-10-26" @default.
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• W2897069170 date "2018-10-20" @default.
• W2897069170 modified "2023-10-11" @default.
• W2897069170 title "The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement" @default.
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• W2897069170 doi "https://doi.org/10.1007/s00122-018-3212-5" @default.
• W2897069170 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30343386" @default.
• W2897069170 hasPublicationYear "2018" @default.
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