Multivariate genomic analysis and optimal contribution selection predicts high genetic gains in cooking time, iron, zinc and grain yield in common beans in East Africa
Date Issued
2022-01Language
enType
DatasetAccessibility
Open AccessUsage rights
CC-BY-4.0Metadata
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Mukankusi, C. (2022) "Multivariate genomic analysis and optimal contribution selection predicts high genetic gains in cooking time, iron, zinc and grain yield in common beans in East Africa", https://doi.org/10.7910/DVN/TSEZVG, Harvard Dataverse, V1, UNF:6:XGZWlNTH5nd5eCPjcbvwAw== [fileUNF]
Permanent link to cite or share this item: https://hdl.handle.net/10568/118434
Abstract/Description
Phenotypic and Genotypic data based on 358 genotypes used to estimate genomic estimated breeding values (GEBV’s) for cooking time (CKT) Seed iron content (SeedFe), Seed Zin content (SeedZn) and Grain yield (GY). The data was used to select parents for the Rapid bean cooking project (RCBP) supported by the ACIAR