Genome-enabled prediction models for yield related traits in chickpea

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...

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Detalles Bibliográficos
Autores: Roorkiwal, M., Abhishek Rathore, Das, R.R., Muneendra K. Singh, Ankit Jain, Samineni Srinivasan, Gaur, P., Chellapilla Bharadwaj, Tripathi, S., Yongle Li, Hickey, J., Lorenz, A.J., Sutton, T., Crossa, J., Jannink, J.L., Varshney, R.K.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:México
Institución:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositorio:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/21349
Acceso en línea:https://hdl.handle.net/10883/21349
Access Level:acceso abierto
Palabra clave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Genomic Prediction Accuracy
Genomic Selection
Training Population
Prediction Models
GENOMICS
GENETIC GAIN
MARKER-ASSISTED SELECTION
CHICKPEAS
POPULATION STRUCTURE
MODELS
Descripción
Sumario:Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011-12 and 2012-13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.

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