Improving Association Studies and Genomic Predictions for Climbing Beans With Data From Bush Bean Populations

Area of Science:

• Plant genetics
• Agricultural science
• Genomics

Background:

• Common bean (Phaseolus vulgaris L.) exhibits significant diversity from Andean and Mesoamerican origins.
• Climbing growth habit is linked to desirable traits like increased flowering time, seed iron concentration (SdFe), nitrogen fixation, and yield.
• Breeding for climbing beans has historically lagged behind bush types.

Purpose of the Study:

• To advance climbing bean breeding through genome-wide association studies (GWAS) and genomic predictions.
• To investigate the genetic basis of traits including growth habit, days to flowering (DF), 100 seed weight, SdFe, and yield across diverse bean populations.
• To assess the impact of including bush bean lines on genomic prediction accuracy for climbing beans.

Main Methods:

• Conducted GWAS and genomic predictions on 1,869 common bean lines from five breeding panels, encompassing both gene pools and growth types.
• Utilized phenotypic data from 17 field trials and 16 published trials.
• Identified significant marker-trait associations and plausible candidate genes.

Main Results:

• Discovered 38 associations for growth habit, 14 for DF, 13 for 100 seed weight, three for SdFe, and one for yield.
• Found evidence for a common genetic basis for most traits across different bean panels and growth types, except for DF.
• Confirmed seven quantitative trait loci (QTL) for growth habits and identified four novel candidate genes for SdFe and 100 seed weight.
• Improved genomic prediction accuracy for SdFe and yield in climbing beans by up to 8.8% when bush bean lines were incorporated into the training population.

Conclusions:

• Large-scale genomic analyses across diverse common bean germplasm enhance the power to identify genetic associations.
• The study provides a robust germplasm base and genetic insights for improving common bean, particularly climbing varieties.
• Integrating diverse bean types in genomic prediction models can significantly boost accuracy for key agronomic traits.