Genomic prediction models for morpho-phenological traits in durum wheat based on Vrn, Ppd, and Rht alleles

Area of Science:

• Plant genetics and breeding
• Agricultural science
• Genomics

Background:

• Durum wheat adaptability is significantly influenced by genetic variations in vernalization (Vrn), photoperiod (Ppd), and reduced plant height (Rht) genes.
• Systematic integration of these functional alleles into genomic prediction (GP) models has been limited, hindering breeding efficiency.

Purpose of the Study:

• To identify genetic markers associated with heading date (HD), flowering time (FT), and plant height (PH) in durum wheat.
• To evaluate the impact of incorporating functional alleles and genome-wide association study (GWAS)-derived markers into GP models on prediction accuracy (PA).

Main Methods:

• Conducted GWAS on 186 durum wheat genotypes across eight environments to identify major loci for HD, FT, and PH.
• Derived 19 allelic combinations (ACs) from Vrn-A1, Ppd-A1, Ppd-B1, and Rht-B1, noting selection trends in modern cultivars.
• Developed GP models incorporating ACs, markers (M), or both (AC-M) as fixed effects using 4399 SNPs under single (SE) and multi-environment (ME) scenarios.

Main Results:

• GWAS confirmed major loci at Rht-B1, Ppd-A1, Ppd-B1, and Vrn-A1, explaining trait variation.
• High prediction accuracies were achieved, particularly in ME models (PA up to 0.93 for all traits).
• Incorporating functional ACs with markers (AC-M) consistently improved prediction accuracy.

Conclusions:

• The study highlights the importance of Vrn, Ppd, and Rht genes in durum wheat.
• Targeted integration of functional alleles can moderately enhance GP accuracy, improving breeding efficiency under diverse environmental conditions.