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Sanchez-Villeda, H.

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Sanchez-Villeda
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Sanchez-Villeda, H.

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    IBFIELDBOOK, an integrated breeding field book for plant breeding
    (Sociedad Mexicana de Fitogenética, 2013) Lugo Espinosa, O.; Sanchez-Gutierrez, T.M.; Camarena-Sagredo, J.G.; Vargas Hernández, M.; Alvarado Beltrán, G.; Jarquin, D.; Burgueño, J.; Crossa, J.; Sanchez-Villeda, H.
    The development of an integrated breeding field book (IBFieldbook) for different crops involves the generation, handling and analysis of large amounts of data. Managing the integration of environmental, pedigree, and phenotypic information for plant breeding data analyses requires appropriate and successful software that facilitates breeders, technicians, and researchers management of the vast collected field information in an easy, efficient and interactive way. Users may also need methods to exchange information with different devices used to record information in the field. Additionally, collected information needs to be analyzed inside or outside the application, and then generate reports for germplasm improvement.
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    Genomic selection in wheat breeding using genotyping-by-sequencing
    (Crop Science Society of America, 2012) Poland, J.; Endelman, J.; Dawson, J.; Rutkoski, J.; Shuangye Wu; Manes, Y.; Dreisigacker, S.; Crossa, J.; Sanchez-Villeda, H.; Sorrells, M.E.; Jannink, J.L.
    Genomic selection (GS) uses genome-wide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat genome. With GBS we discovered 41K SNPs in a set of 254 advanced breeding lines from CIMMYT?s semi-arid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate-normal expectation maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, though no significant differences were observed in the accuracy of genomic-estimated breeding values (GEBVs). GEBV prediction accuracies with GBS were 0.28 ? 0.45 for grain yield, an improvement of 0.1-0.2 over an established marker platform for wheat. GBS combines marker discovery and genotyping of large populations making it an excellent marker platform for breeding applications even in the absence of reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low-cost of GBS make this an ideal approach for genomics-assisted breeding.
    Publication