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Prediction of breeding values using genome wide markers for yield related traits in chickpea

Roorkiwal, M., Rathore, A., Das, R.R., Singh, M.K., Srinivasan, S., Gaur, P.M., Bharadwaj, C., Tripathi, S., Hickey, J.M., Lorenz, A., Jannink, J.L. and Varshney, R.K.ORCID: 0000-0002-4562-9131 (2014) Prediction of breeding values using genome wide markers for yield related traits in chickpea. In: 6th International Food Legume Research Conference/7th International Conference on Legume Genetics and Genomics, 7 - 11 July 2014, TCU Place Saskatoon, Saskatchewan, Canada.


Genomic selection (GS) is a modern breeding approach that predicts breeding value of lines and makes selection prior to phenotyping using genome-wide molecular marker profiling. GS can help to overcome the issues related to long selection cycles by accelerating breeding cycles so that the rate of annual genetic gain can be enhanced. In view of low productivity in chickpea, a collection of 320 elite breeding lines was selected as the “training population”. Training population was phenotyped for four yield and yield related traits at two locations for two seasons under rain-fed and irrigated conditions. Training population was also genotyped using KASPar assays (651) and DArT arrays (15,360). Genome-wide marker profiling data in combination with phenotypic data was used with six statistical methods to predict genomic estimated breeding values (GEBVs) for four yields and yield related traits. Correlation inside training (CIT) for the models tested varied from 0.138 to 0.912. Heat map analysis using genotyping data to understand the relationship within these lines suggested possibility of two different groups. As population structure can influence the accuracy in GS, analysis was re-performed by implementing population structure for calculation of GEBV. Population structure significantly affected the CIT that varied from 0.001 to 0.745 for desi group, and 0.004 to 0.727 for kabuli group. In general, Bayesian based model showed better prediction accuracy. The best prediction accuracy was obtained for 100 seed weight while prediction accuracy was low in case of seed yield.

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