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bioRxiv2023

QTL Mapping of Seed Fe Concentration in an Interspecific RIL Population Derived from Lens culinaris x Lens ervoides

Biofortification of lentil (Lens culinaris Medik.) was investigated to potentially increase bioavailable iron (Fe) in the human diet. This study assessed the genetic variation for seed Fe concentration (SFeC) and identified the genomic regions associated with SFeC in an interspecific mapping population derived from crossing between L. culinaris cv. Eston and L. ervoides accession IG 72815. A total of 134 RILs were evaluated in three environments. The SFeC data for individual environments and best linear unbiased prediction (BLUP) of the SFeC across environments were used for QTL analysis. The seeds of the RILs exhibited variation for SFeC from 47.0 to 102.9 mg kg-1 and several RILs showed transgressive segregation for SFeC. QTL analysis identified two QTLs on chromosomes 2 and 6 that accounted for 11.9-14.0% and 12.5-20.5%, respectively, of the total phenotypic variation for SFeC. The SNP markers linked to the identified QTLs may prove useful for increasing SFeC via marker-assisted selection. RILs with high SFeC can be incorporated into the lentil breeding program to broaden the genetic base of the breeding pool and/or used for the development of genetic resources for future genomic studies.

genomics↗

bioRxiv2022

Dissection of genotype-by-environment interaction and simultaneous selection for grain yield and stability in faba bean (Vicia faba L.)

Increasing faba bean production is indispensable to supply the growing demand for plant-based protein on the global scale. A thorough understanding of genotype (G) x environment (E) interaction (GEI) patterns is critical to developing high-yielding varieties with wider adaptation. Thirteen faba bean genotypes were evaluated in 15 environments during 2019-2020 in western Canada to estimate their yield stability using different stability statistics. The combined analysis of variance and additive main effects and multiplicative interaction (AMMI) analysis revealed that G, E, and GEI effects were highly significant (P<0.001), indicating differential responses of the genotypes across the environments, enabling the stability analysis. The result of the model comparison found the best linear unbiased prediction (BLUP) to outperform AMMI models. The BLUP-based biplot of the weighted average of absolute scores (WAASB) stability and mean grain yield identified AO1155 (Navi), 1089-1-2, 1310-5, DL Tesoro, and 1239-1 as high-yielding and stable genotypes. The correlation analysis revealed that most of the stability parameters had a strong association with grain yield and with each other, indicating that they should be used in combination with one another to select genotypes with high yield. Overall, the WAASB superiority index (WAASBY) and the average sum of ranks of all stability statistics identified the same genotypes in terms of high yielding and stability, and genotype AO1155 is considered the most stable and highest yielding among the tested genotypes. Genotypes with stable yields across environments would be beneficial for faba bean genetic improvement programs globally. Core IdeasO_LIStability analysis was estimated using 13 faba bean genotypes over 15 site-years. C_LIO_LIThe different stability methods described genotypic performance in different ways. C_LIO_LIThe majority of stability models showed a strong rank correlation with grain yield. C_LIO_LIAMMI and BLUP analyses revealed a highly significant GxE interaction, with BLUP outperforming AMMI. C_LIO_LIOverall, the employed stability statistics identified AO1155 as the highest yielding and most stable genotype. C_LI

plant biology↗