Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• Genomic selection (GS) offers a faster alternative to marker-assisted backcrossing (MABC) by enabling early selection of superior crop lines based on genome-wide marker data.
• Accelerating crop breeding cycles is crucial for developing improved varieties that meet growing global food demands.

Purpose of the Study:

• To evaluate the effectiveness of genomic selection (GS) models for predicting breeding values in elite chickpea lines.
• To establish the resources and methodologies required for implementing GS in chickpea breeding programs.

Main Methods:

• Phenotyped 320 elite chickpea breeding lines for yield and related traits across two locations and two crop seasons.
• Genotyped lines using DArTseq to generate data for 3000 polymorphic markers.
• Applied six statistical GS models to estimate prediction accuracies for seed yield, 100 seed weight, days to flowering, and days to maturity.

Main Results:

• Prediction accuracies varied across traits, ranging from 0.138 for seed yield to 0.912 for 100 seed weight.
• The six tested GS models showed similar performance in estimating prediction accuracy within each trait.
• Population structure analysis indicated two distinct groups within the selected lines, with minimal impact on prediction accuracy.

Conclusions:

• Genomic selection (GS) is a viable strategy for enhancing chickpea breeding efficiency.
• The study successfully generated essential data and validated models for future GS implementation in chickpea.
• GS has the potential to significantly shorten breeding cycles and accelerate the development of improved chickpea varieties.