eprintid: 10287
rev_number: 17
eprint_status: archive
userid: 1305
dir: disk0/00/01/02/87
datestamp: 2017-11-21 08:00:39
lastmod: 2018-08-06 08:24:45
status_changed: 2017-11-21 08:00:39
type: conference_item
metadata_visibility: show
creators_name: Roorkiwal, M
creators_name: Jarquin, D
creators_name: Jain, A
creators_name: Garg, V
creators_name: Kale, S M
creators_name: Singh, M K
creators_name: Samineni, S
creators_name: Gaur, P M
creators_name: Rathore, A
creators_name: Chellapilla, B
creators_name: Tripathi, S
creators_name: Crossa, J
creators_name: Varshney, R K
icrisatcreators_name: Roorkiwal, M
icrisatcreators_name: Jain, A
icrisatcreators_name: Garg, V
icrisatcreators_name: Kale, S M
icrisatcreators_name: Singh, M K
icrisatcreators_name: Samineni, S
icrisatcreators_name: Gaur, P M
icrisatcreators_name: Rathore, A
icrisatcreators_name: Varshney, R K
affiliation: ICRISAT(Patancheru)
affiliation: International Maize and Wheat Improvement Center (CIMMYT) (Mexico)
affiliation: Indian Agricultural Research Institute (Delhi)
affiliation: School of Plant Biology and the Institute of Agriculture, The University of Western Australia (UWA) (Crawley)
country: India
country: Mexico
country: Australia
title: Genomic-enabled prediction model with genotype × environment interaction in elite
chickpea lines
ispublished: pub
subjects: s1.1
subjects: s2.13
divisions: CRPS2
divisions: CRPS3
full_text_status: public
pres_type: paper
keywords: Genomic enabled prediction model, Genomics, Chickpea, Genomic Selection
abstract: Genomic selection (GS) allows safe phenotyping and reduces
cost and shortening selection cycles. Incorporating of genotype
× environment (G×E) interactions in genomic prediction models
improves the predictive ability of lines performance across environments
and in target environments. Phenotyping data on a set
of 320 elite chickpea breeding lines on different traits (e.g., plant
height, days to maturity, and seed yield), from three consecutive
years for two different treatments at two locations were recorded.
These lines were genotyped on DArTseq(1.6K) and Genotyping-
by-Sequencing (GBS; 89K SNPs) platforms. Five different
models were fitted, four of which included genomic information
as main effects (baseline model) and/or G×E interactions. Three
different cross-validation schemes that mimic real scenarios that
breeders might face on fields were considered to assess the predictive
ability of the models (CV2: incomplete field trials; CV1:
newly developed lines; and CV0: new previously untested environments).
Different prediction models gave different results for
the different traits; however, some interesting patterns were observed.
For CV1, analyzing yield seed interaction models improved
baseline counterparts on an average between 55 and 92% using
DArT and DArT combined with GBS data, respectively [between
9 and 112% for all traits]. While for CV2 these improvements varied
b tween 65 and 102% [between 8 and 130% remaining traits].
In CV0, no clear advantage was observed considering the interaction
term. These results suggest that GS models hold potential for
breeder’s applications on chickpea cultivar improvements.
date: 2017-02
date_type: published
pagerange: 99
event_title: InterDrought-V
event_location: Hyderabad, India
event_dates: February 21-25, 2017
event_type: conference
refereed: TRUE
related_url_url: http://idv.ceg.icrisat.org/wp-content/uploads/2017/02/Abstract_Book_Final.pdf
related_url_type: org
citation:   Roorkiwal, M and Jarquin, D and Jain, A and Garg, V and Kale, S M and Singh, M K and Samineni, S and Gaur, P M and Rathore, A and Chellapilla, B and Tripathi, S and Crossa, J and Varshney, R K  (2017) Genomic-enabled prediction model with genotype × environment interaction in elite chickpea lines.  In: InterDrought-V, February 21-25, 2017, Hyderabad, India.     
document_url: http://oar.icrisat.org/10287/1/Abstract_Book_112.pdf