Record Details

Genetic architecture and genomic prediction of cooking time in common bean (Phaseolus vulgaris L.)

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Genetic architecture and genomic prediction of cooking time in common bean (Phaseolus vulgaris L.)
 
Identifier https://doi.org/10.7910/DVN/B3YLRF
 
Creator Diaz, Santiago
Ariza-Suarez, Daniel
Ramdeen, Raisa
Aparicio Arce, Johan Steven
Arunachalam, Nirmala
Hernandez Lira, Juan Carlos
Diaz, Harold
Ruiz Guzman, Henry Alonso
Piepho, Hans-Peter
Raatz, Bodo
 
Publisher Harvard Dataverse
 
Description These datasets contain phenotypic and genotypic data of a MAGIC population, DOR364 x G19833 biparental population, VEF panel and MIP panel. The main goal for these populations is to be used for genetic analysis and applications in breeding and breeding tool development, as well as information for basic research questions aiming to uncover the genetic basis of seed quality traits. The raw phenotypic data come from trials carried out in Palmira (Colombia). The trials were laid out in the field with an alpha-lattice experimental design in 2011, 2013, 2017 and 2019. Three trails were assessed: Cooking time (CKT), Water absorption capacity (WAC) and seed weight (SdW). The agronomic performance of the population was modeled using linear mixed models. From these models, best linear unbiased predictors were obtained (BLUPs). The genotypic datasets include a variant call format (VCF) file from MAGIC population, VEF panel and MIP panel.
 
Subject Agricultural Sciences
Earth and Environmental Sciences
COOKING QUALITY
QUANTITATIVE TRAIT LOCI
WATER BIDING CAPACITY
GENETIC CONTROL
GENETIC IMPROVENMENT
Latin America and the Caribbean
Crops for Nutrition and Health
 
Language English
 
Date 2020-11-13
 
Contributor Alliance Data Management
 
Relation https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JR4X4C
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XCD67U
 
Type Experimental Data
Trial Data
Quantitative Data
Breeding Data
Phenomic Data
Genomic Data