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W264: Achieving higher genetic gain by enhancing precision through genomic selection breeding in chickpea

Roorkiwal, M., Santantonio, N., Jarquin, D., Chellapilla, B., Singh, M., Gaur, P.M., Samineni, S., Howard, R., Gao, S.K., Jones, E., Crossa, J., Robbins, K. and Varshney, R.K.ORCID: 0000-0002-4562-9131 (2019) W264: Achieving higher genetic gain by enhancing precision through genomic selection breeding in chickpea. In: Plant & Animal Genome Conference XXVII, 12 - 16 January 2019, San Diego, CA


Genomic selection (GS) predicts breeding values of lines using genome-wide marker data and allows breeders to select lines prior to field phenotyping and shortening the breeding cycle. A set of 320 elite breeding lines were extensively phenotyped for different traits (e.g., plant height, days to maturity, and seed yield). These lines were genotyped using DArTseq (1.6K SNPs), Genotyping-by-Sequencing (GBS; 89K SNPs) and Axiom®CicerSNP Array (24K SNPs) platforms. Phenotypic data for eight traits for three seasons at two locations along with genotyping data were used to assess the impact of environment, lines, and other interactions for 13 different GS models. Three different cross-validation (CV) schemes that mimic real scenario that breeder encounters on field, were used to assess the prediction accuracies (CV2: incomplete field trials; CV1: newly developed lines; and CV0: new environments). These results suggested the potential of these GS models for chickpea cultivar improvement. In order to deploy the GS models in regular breeding program for selecting lines based on marker data, a pilot experiment was initiated as part of Genomic Open-source Breeding Informatics Initiative (GOBII) activities to deploy genomic information for crop improvement. GOBii focus on developing user friendly tools that have great potential in developing improved cultivars faster and more precisely by deploying modern breeding approaches. Chickpea breeding program from ICRISAT and IARI were targeted deploying the use of markers for selecting lines using GS. A total of 6000 F5 lines form 12 different crosses from ICRISAT and IARI breeding program were selected and genotyped using DArTseq platform. Based on data quality genotyping data for 4,923 lines were used for genomic predictions using 10-fold consolidation scheme cross-validation. We would like to highlight that, many lines that we targeted were not related with training population, therefore accuracies achieved for these lines is lower than the cross-validation accuracies. All the traits showed significant genotype by environment (i.e. ICRISAT and IARI) interactions, therefore, we produced predictions within each of the two breeding programs. Univariate (single trait) and multivariate (multi-trait) cross-validation produced equivalent prediction accuracies within the population, therefore we focused on univariate predictions. Based on these univariate prediction top 200 lines each for ICRISAT and IARI breeding programs were selected. In parallel, 200 lines each were selected by ICRISAT and IARI breeders based on visual score. These two sets (selected based on genomic prediction and based on visual score) evaluated in field condition to validate the genomic prediction results. Based on the initial results, we plan to expand the current training population by including founder parents from different chickpea breeding program in India and initiate deployment of GS. Preliminary results suggest that GS models hold potential for breeder’s applications on chickpea cultivar improvements.

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