8,581 to 8,590 of 10,613 Results
Mar 23, 2017
Hearne, Sarah; Chen, Charles; Buckler, Edward; Mitchell, Sharon; Romero Navarro, J. Alberto; Swarts, Kelly; Li, Huihui, 2014, "Imputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel: Imputation using Beagle v.4", https://hdl.handle.net/11529/10035, CIMMYT Research Data & Software Repository Network, V3
Obtain imputed SNP profiles using Beagle v.4 from genotyping-by-sequencing of the accession parents of the SeeD GWAS testcross panel. |
Mar 23, 2017 -
Imputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel: Imputation using Beagle v.4
Unknown - 261.2 KB -
MD5: f9a1886915ebbbec2d222f1ec0cd38f4
Links sample ID in genotypic results file to a Germplasm ID and a testcross ID at CIMMYT |
Mar 23, 2017 -
Unimputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel
Unknown - 261.2 KB -
MD5: f9a1886915ebbbec2d222f1ec0cd38f4
Links sample ID in genotypic results file to a Germplasm ID and a testcross ID at CIMMYT |
Mar 23, 2017
Hearne, Sarah; Buckler, Edward; Chen, Charles; Mitchell, Sharon; Swarts, Kelly; Li, Huihui; Romero Navarro, J. Alberto, 2014, "Imputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel: Imputation using FILLIN", https://hdl.handle.net/11529/10036, CIMMYT Research Data & Software Repository Network, V3
Obtain imputed SNP profiles from genotyping-by-sequencing of the accession parents of the SeeD GWAS testcross panel. |
Mar 23, 2017 -
Imputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel: Imputation using FILLIN
Unknown - 261.2 KB -
MD5: f9a1886915ebbbec2d222f1ec0cd38f4
Links sample ID in genotypic results file to a Germplasm ID and a testcross ID at CIMMYT |
Mar 9, 2017 -
A variational Bayes genomic-enabled prediction model with genotype × environment interaction
Unknown - 376.8 KB -
MD5: 0b0889757980aefa3f3bae20d3ecc318
Maize data |
Mar 9, 2017 -
A variational Bayes genomic-enabled prediction model with genotype × environment interaction
Unknown - 459.3 KB -
MD5: c59905b897fe95f9d600f78cd42b4fba
Wheat data |
Mar 9, 2017 -
A variational Bayes genomic-enabled prediction model with genotype × environment interaction
Unknown - 5.2 MB -
MD5: 4a3ec390c48988b3203c50904a8b78da
Wheat yield data |
Mar 2, 2017
Hearne, Sarah; Molnar, Terence; Garay, Carlos; Dreher, Kate; Ayala, Claudio, 2016, "KDSmart: Training Materials and Sample Files", https://hdl.handle.net/11529/10669, CIMMYT Research Data & Software Repository Network, V4
KDSmart is an app that can be used for data collection on any Android-based tablet or smartphone. It has many helpful features for capturing data in field and lab settings. This study contains documents and presentations that describe how KDSmart can be used and includes example... |
Feb 16, 2017
Bandeira e Sousa, Massaine; Cuevas, Jaime; Oliveira Couto, Evellyn Giselly de; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose, 2017, "Genomic-enabled prediction in maize using kernel models with genotype × environment interaction", https://hdl.handle.net/11529/10887, CIMMYT Research Data & Software Repository Network, V1
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