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Genomic-enabled prediction model with genotype × environment interaction in elite chickpea lines

OAR@ICRISAT

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Relation http://oar.icrisat.org/10287/
 
Title Genomic-enabled prediction model with genotype × environment interaction in elite
chickpea lines
 
Creator Roorkiwal, M
Jarquin, D
Jain, A
Garg, V
Kale, S M
Singh, M K
Samineni, S
Gaur, P M
Rathore, A
Chellapilla, B
Tripathi, S
Crossa, J
Varshney, R K
 
Subject Chickpea
Genetics and Genomics
 
Description 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
 
Type Conference or Workshop Item
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights —
 
Identifier http://oar.icrisat.org/10287/1/Abstract_Book_112.pdf
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.