eprintid: 9797
rev_number: 14
eprint_status: archive
userid: 1305
dir: disk0/00/00/97/97
datestamp: 2016-11-29 06:38:01
lastmod: 2017-09-07 08:14:11
status_changed: 2016-11-29 06:38:01
type: article
metadata_visibility: show
creators_name: Roorkiwal, M
creators_name: Rathore, A
creators_name: Das, R R
creators_name: Singh, M K
creators_name: Jain, A
creators_name: Samineni, S
creators_name: Gaur, P M
creators_name: Chellapilla, B
creators_name: Tripathi, S
creators_name: Li, Y
creators_name: Hickey, J M
creators_name: Lorenz, A
creators_name: Sutton, T
creators_name: Crossa, J
creators_name: Jannink, J L
creators_name: Varshney, R K
icrisatcreators_name: Roorkiwal, M
icrisatcreators_name: Rathore, A
icrisatcreators_name: Das, R R
icrisatcreators_name: Singh, M K
icrisatcreators_name: Jain, A
icrisatcreators_name: Samineni, S
icrisatcreators_name: Gaur, P M
icrisatcreators_name: Varshney, R K
affiliation: ICRISAT (Patancheru)
affiliation: Division of Genetics, Indian Agricultural Research Institute (Delhi)
affiliation: Australian Centre for Plant Functional Genomics, University of Adelaide (Adelaide)
affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh (Easter Bush)
affiliation: Department of Agronomy and Horticulture, University of Nebraska (Lincoln)
affiliation: Crop Improvement, South Australian Research and Development Institute (Urrbrae)
affiliation: International Maize and Wheat Improvement Center (El Batan)
affiliation: School of Integrative Plant Science, Cornell University (Ithaca)
affiliation: School of Plant Biology and Institute of Agriculture, The University of Western Australia (Crawley)
country: India
country: Australia
country: UK
country: USA
country: Mexico
title: Genome-Enabled Prediction Models for Yield Related Traits in Chickpea
ispublished: pub
subjects: s1.1
subjects: s2.13
divisions: CRPS3
crps: crp1.5
full_text_status: public
keywords: Genomic prediction accuracy, Geneticgain, Genomic selection, Chickpea, Training population, Population structure, Prediction models, Chickpea population, Yield
note: This work has been undertaken as a part of Australia- India strategic research fund (AISRF) Project funded by Department of Science and Technology (DST) Government of India. This work was carried out as part of the CGIAR Research Program on Grain Legumes. ICRISAT is a member of the CGIAR consortium.
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.
date: 2016-11-22
date_type: published
publication: Frontiers in Plant Science
volume: 7
number: 1666
publisher: Frontiers Media
pagerange: 01-13
id_number: 10.3389/fpls.2016.01666
refereed: TRUE
issn: 1664-462X
official_url: http://dx.doi.org/10.3389/fpls.2016.01666
related_url_url: https://scholar.google.co.in/scholar?hl=en&q=Genome-Enabled+Prediction+Models+for+Yield+Related+Traits+in+Chickpea&btnG=
related_url_type: pub
funders: Department of Science and Technology (DST) Government of India
projects: Australia- India strategic research fund (AISRF) Project
citation:   Roorkiwal, M and Rathore, A and Das, R R and Singh, M K and Jain, A and Samineni, S and Gaur, P M and Chellapilla, B and Tripathi, S and Li, Y and Hickey, J M and Lorenz, A and Sutton, T and Crossa, J and Jannink, J L and Varshney, R K  (2016) Genome-Enabled Prediction Models for Yield Related Traits in Chickpea.  Frontiers in Plant Science, 7 (1666).  01-13.  ISSN 1664-462X     
document_url: http://oar.icrisat.org/9797/1/fpls-07-01666.pdf