Genome-enabled prediction models for yield related traits in chickpea

Manish Roorkiwal, Abhishek Rathore, Roma R. Das, Muneendra K. Singh, Ankit Jain, Srinivasan Samineni, Pooran M. Gaur, Bharadwaj Chellapilla, Shailesh Tripathi, Yongle Li, John Hickey, Aaron Lorenz, Tim Sutton, Jose Crossa, Jean-Luc Jannink, Rajeev K. Varshney

Research output: Contribution to journalArticlepeer-review

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 data on 3,000 polymorphic markers. Phenotypic and genotypic 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.
Original languageEnglish
JournalFrontiers in plant science
Volume7
Early online date24 Oct 2016
DOIs
Publication statusPublished - 22 Nov 2016

Keywords

  • Genomic prediction accuracy
  • Genetic gain
  • genomic selection
  • chickpea
  • training population
  • population structure
  • Prediction models

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