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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #346568
Title: Genome-enabled prediction models for yield related traits in chickpea

Author
item ROORKIWAL, MANISH - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item RATHORE, ABHISHEK - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item DAS, ROMA - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item SINGH, MUNEENDRA - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item JAIN, ANKIT - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item SRINIVASAN, SAMINENI - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item GAUR, POORAN - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item CHELLAPILLA, BHARADWAJ - Indian Agricultural Research Institute
item TRIPATHI, SHAILESH - Indian Agricultural Research Institute
item LI, YONGLE - University Of Adelaide
item HICKEY, JOHN - University Of Edinburgh
item LORENZ, AARON - University Of Nebraska
item SUTTON, TIM - University Of Adelaide
item CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT)
item Jannink, Jean-Luc
item VARSHNEY, RAJEEV - University Of Western Australia

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/30/2016
Publication Date: 11/22/2016
Citation: Roorkiwal, M., Rathore, A., Das, R., Singh, M., Jain, A., Srinivasan, S., Gaur, P., Chellapilla, B., Tripathi, S., Li, Y., Hickey, J., Lorenz, A., Sutton, T., Crossa, J., Jannink, J., Varshney, R. 2016. Genome-enabled prediction models for yield related traits in chickpea. Frontiers in Plant Science. 7:1666. doi:10.3389/fpls.2016.01666.

Interpretive Summary: Genomic selection (GS) 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 locations in India (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 the DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were analyzed with six statistical prediction models to estimate prediction accuracies for four yield related traits: seed yield, 100 seed weight, days to 50% flowering, and days to maturity. Prediction accuracy varied across traits from 0.138 (seed yield) to 0.912 (100 seed weight), whereas the statistical models all performed similarly. Genetic relatedness assessed by markers reaffirmed the existence of two different groups within the breeding lines, indicating population structure. There was not much effect of population structure on prediction accuracy. The present study established the necessary resources for deployment of GS in chickpea breeding.

Technical Abstract: 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.