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Autor: Johannes Martini
Editorial: Genomic selection: Lessons learned and perspectives
Johannes Martini Sarah Hearne Valentin Wimmer Fernando Henrique Toledo (2022)
Artículo
Genomic Selection Selection Gain Breeding Schemes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING PROGRAMMES MARKER-ASSISTED SELECTION GENOTYPE ENVIRONMENT INTERACTION PLANT BREEDING
Replication Data for: Approximate kernels for large data sets In genome-based prediction
Osval Antonio Montesinos-Lopez Johannes Martini Paulino Pérez-Rodríguez Jose Crossa (2020)
The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datasets provided here were used to support a comparative analysis of two genomic-enabled prediction models: the full genomic method single environment (FGSE) and the approximate kernel method for a single environment model (APSE). The data were also used to compare the full genomic method with genotype × environment model (FGGE) to the approximate kernel method with genotype × environment interaction (APGE). The results of the analyses are described in the related publication.
Dataset
Johannes Martini Fernando Henrique Toledo Carolina Sansaloni Jose Crossa Jaime Cuevas Sivakumar Sukumaran (2020)
The genetic diversity housed in germplasm banks may provide valuable contributions to breeding efforts. It is important to understand the best way to introduce this diversity into elite breeding materials. This files in this dataset provide phenotypic and genotypic data used to compare genomic prediction approaches and different cross-validation scenarios on a set of wheat families obtained from crosses between elite materials and diverse germplasm bank accessions. The linked top cross population (LTP) materials analyzed in the study were screened under yield potential, drought, and heat stress conditions.
Dataset
Replication Data for: A Bayesian Linear Phenotypic Selection Index to Predict the Net Genetic Merit
J. Jesús Cerón Rojas Sergio Pérez-Elizalde Jose Crossa Johannes Martini (2021)
In breeding, the plant net genetic merit may be predicted through the linear phenotypic selection index (LPSI). This paper associated with this dataset proposes a Bayesian LPSI (BLPSI). The supplemental files provided in this dataset include data that were used to compare the two indices as well as figures showing the results from these comparisons. The analysis revealed that the BLPSI is a good option when carrying out phenotypic selections in breeding programs.
Dataset
Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2023)
Disease resistance improvement efforts in plant breeding can help to reduce the negative impact of biotic stresses on crop production.Disease resistance can be assessed through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specially trained staff. Remote sensing (RS) tools can also be used to measure traits such as vegetation indices that can also be used to assess plant responses to diseases. This dataset contains phenotypic and genotypic data from a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH). Data from VS and RS methods for assessing common rust resistance were used in genome wide association study (GWAS) as well as genomic prediction (GP) analyses. A report on the comparison of the results of these analyses is provided in the accompanying article.
Dataset
Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize
Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024)
Artículo
Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING