Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

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.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…Population structure did not impact the prediction accuracy, and modelling genotype by environment by management (G×E×M) indicated improved prediction efficiency in chickpea . Further, Roorkiwal et al (2016) used statistical models such as RR-BLUP, Kinship GAUSS, Bayes Cπ, Bayes B, Baysian LASSO, and random forest regression and reported high prediction accuracies for days to maturity, days to flowering, and seed dry weight. The development and deployment of improved and more precise statistical models will eventually enhance further the prediction accuracy leading to enhanced GS efficiency in legume crops.…”

Genetic relatedness and the ratio of subpopulation‐common alleles are related in genomic prediction across structured subpopulations in maize

“…In maize, 384 inbred lines were used as the training population to predict total root length of 2,431 inbred lines, then the lines with the predicted longest or shortest total root length were validated (Pace, Yu, & Lubberstedt, ). GP was also used to predict important agronomic and economic traits in other plant species including wheat, apple and chickpea (Crossa et al., ; Kumar et al, ; Roorkiwal et al, ). These reports demonstrated that GP is feasible in practical breeding activities and crop researches.…”

Legume endosymbionts: Drought stress tolerance in second‐generation chickpea (Cicer arietinum) seeds

“…The current chickpea production is less than 1 t/ha À1 (11.6 Mt/12.3 Mha) worldwide (FAO 2012;Roorkiwal et al, 2014), which is much lower than its potential yield of 6 t/ha under optimum growing conditions (Singh, 1987). Hence, there is a wide gap between current and optimum production of chickpea, thus offering a range of innovation opportunities to meet the demand of the growing world population (Roorkiwal et al, 2016).…”