TY  - JOUR
AV  - public
A1  - Roorkiwal, M
A1  - Rathore, A
A1  - Das, R R
A1  - Singh, M K
A1  - Jain, A
A1  - Samineni, S
A1  - Gaur, P M
A1  - Chellapilla, B
A1  - Tripathi, S
A1  - Li, Y
A1  - Hickey, J M
A1  - Lorenz, A
A1  - Sutton, T
A1  - Crossa, J
A1  - Jannink, J L
A1  - Varshney, R K
TI  - Genome-Enabled Prediction Models for Yield Related Traits in Chickpea
UR  - http://dx.doi.org/10.3389/fpls.2016.01666
JF  - Frontiers in Plant Science
SN  - 1664-462X
PB  - Frontiers Media
N1  - 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.
N2  - 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.
KW  - Genomic prediction accuracy
KW  -  Geneticgain
KW  -  Genomic selection
KW  -  Chickpea
KW  -  Training population
KW  -  Population structure
KW  -  Prediction models
KW  -  Chickpea population
KW  -  Yield
Y1  - 2016/11/22/
SP  - 01
ID  - icrisat9797
EP  - 13
VL  - 7
IS  - 1666
ER  -