GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Terminal drought significantly limits pea (Pisum sativum L.) grain yield in Mediterranean climates.
• Genomic selection (GS) shows promise for improving crop resilience to abiotic stresses.

Purpose of the Study:

• To assess the predictive ability of GS for pea grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data.
• To evaluate different GBS data quality filters, GS models, and compare intrapopulation with interpopulation GS predictive ability.
• To conduct genome-wide association studies (GWAS) for yield and flowering time.

Main Methods:

• Phenotypic and genotypic data were collected from 315 recombinant inbred lines (RILs) across three populations under controlled drought conditions.
• Genomic selection models including Bayesian Lasso (BL), ridge regression best linear unbiased prediction (rrBLUP), and support vector regression (SVR) were tested.
• An adjusted yield metric was defined to represent intrinsic drought tolerance.

Main Results:

• GS predictive ability exceeded 0.5 for yield and flowering time using Bayesian Lasso or rrBLUP with 400-500 markers.
• Intrapopulation GS predictive ability for adjusted yield approached 0.4 in a high-variation population.
• GWAS identified genomic regions associated with high yield and early flowering, suggesting potential targets for breeding.

Conclusions:

• Genomic selection is a powerful tool for predicting pea grain yield under terminal drought, outperforming phenotypic selection for predicted gains.
• Optimized GBS data quality and appropriate GS models are crucial for accurate predictions.
• Identified genomic regions provide valuable insights for marker-assisted selection in breeding programs.