Genomic selection for target traits in the Australian lentil breeding program.

Area of Science:

• Plant breeding
• Genetics
• Agricultural science

Background:

• Genomic selection (GS) leverages marker-phenotype associations for predicting breeding values.
• High-throughput genotyping enables GS implementation in plant breeding, including minor crops like pulses.
• GS reduces generation intervals, increasing genetic gain per unit time.

Purpose of the Study:

• To evaluate the effectiveness of GS in a lentil breeding program.
• To compare the prediction accuracy of GS with traditional BLUP-based selection.
• To assess the potential of GS for accelerating genetic gain in lentils.

Main Methods:

• Genomic prediction models were developed using 64,781 genome-wide SNPs and Bayesian methodology.
• Genomic estimated breeding values (GEBVs) were calculated for 2,081 lentil breeding lines.
• Forward cross-validation was used to assess prediction accuracy for traits including yield, disease resistance, and seed quality.

Main Results:

• GEBV prediction accuracy (0.34-0.83) was higher than BLUP EBVs (0.22-0.54).
• GS-led parental selection in early generations yielded greater genetic gain than BLUP in later generations.
• Narrow-sense heritabilities for target traits ranged from 0.24 to 0.66.

Conclusions:

• GS implementation in lentil breeding significantly enhances the rate of genetic gain.
• GS facilitates faster development of high-yielding lentils with improved stress tolerance and seed quality.
• GS is a powerful tool for accelerating cultivar development in minor crops.