Unlocking the genetic control of spring wheat kernel traits under normal and heavy metals stress conditions

Abstract

Background and aims Pb and Sn concentration increase rapidly due to the industrial revolution and cause a significant reduction in wheat production and productivity. Understanding the genetic control of Pb and Sn tolerance is very important to produce wheat cultivars that are tolerant to such metals. Methods Extensive genetic analyses using genome-wide association study, functional annotation, and gene enrichment were investigated in a set of 103 highly diverse spring wheat genotypes. Kernel traits such as kernel length (KL), kernel diameter (KD), kernel width (KW), and 1000-kernel weight (TKW) were measured under each metal as well as under controlled conditions. Results The GWAS identified a total of 131, 126, and 115 markers that were associated with kernel traits under Ctrl, Pb, and Sn. Moreover, the stress tolerance index (STI) for Pb and Sn was calculated and GWAS revealed 153 and 105 significant markers, respectively. Remarkably, one SNP Ku_c269_2643 located within TraesCS2A02G080700 gene model was found to be associated with KL under the three conditions. The results of gene enrichment revealed three, three, and six gene networks that have an association with the processes involved in kernel formation. The target alleles of all significant markers detected by GWAS were investigated in the most tolerant wheat genotypes to truly select the candidate parents for crossing in future breeding programs. Conclusion This is the first study that unlocked the genetic control of kernel yield under controlled and heavy metals conditions. Understanding the genetic control of kernel traits under heavy metals will accelerate breeding programs to improve wheat tolerance to Pb and Sn.

Novel genomic regions on chromosome 5B controlling wheat powdery mildew seedling resistance under Egyptian conditions

“…In the current study, a highly diverse wheat panel collected from 22 different countries around the world was evaluated for their resistance to the recent Bgt Egyptian populations. This panel was reported to be genetically highly diverse and adapted to Egyptian field conditions ( Mourad et al., 2020 ; Abou-Zeid and Mourad, 2021 ; Hasseb et al., 2022 ; Mourad et al., 2022a ; Mourad et al., 2022b ). Such highly diverse plant materials will be very helpful in detecting the possible genetic resources for improving PM resistance in Egyptian wheat.…”

“…Two types of genetic markers (GBS-SNPs and 25K i-select SNP array) were used in conducting GWAS analysis. Both marker sets were reported to be effective and cover different parts of the wheat genome ( Liu et al., 2020 ; Mourad et al., 2022b ; Esmail et al, 2023 ). The GWAS models used in the current study were reported to be effective in correcting the effect of population structure and detecting the significant markers ( Turuspekov et al., 2016 ; Kaler et al., 2020 ; Muhammad et al., 2020 , Muhammad et al., 2021 ; Mourad et al., 2022b ; Esmail et al, 2023 ).…”

“…Using such a combined selection method will avoid human errors that could lead to misleading in the phenotypic selection process ( Sallam et al., 2019 ). Many previous studies used this approach in selecting promising genotypes for different abiotic and biotic stresses ( Abou-Zeid and Mourad, 2021 ; Eltaher et al., 2021 ; Mourad et al., 2021a ; Abo-elyousr et al., 2022 ; Eltaher et al., 2022 ; Mourad et al., 2022b ).…”

“…All the tested plant materials (198 genotypes) were genotyped using a 25K Infinium iSelect array (25K-SNPs). This marker set was generated by SGS Institute Fresenius GmbH TraitGenetics Section (Gatersleben, Germany) as described previously in ( Aleksandrov et al., 2021 ; Mourad et al., 2022b ). In addition, 103 genotypes from the tested materials were genotyped using genotyping-by-sequencing (GBS) as described in ( Mourad et al., 2020 ; Abou-Zeid and Mourad, 2021 ; Mourad et al., 2022a ).…”

“…Diverse populations are always helpful in detecting new sources of improving targeted traits ( Sallam et al., 2018 ; Mourad et al., 2021b ; Amro et al., 2022 ). In wheat, GWAS is used widely in detecting marker-trait associations (MTAs) for different biotic and abiotic traits ( Noubissié et al., 2017 ; Daba et al., 2018 ; Liu et al., 2018 ; Alqudaha et al., 2019 ; Ward et al., 2019 ; Kumar et al., 2020 ; Eltaher et al., 2022 ; Hasseb et al., 2022 ; Mourad et al., 2022b ). Due to the rapid development of state-of-art DNA sequencing techniques, candidate genes of the wheat genome have been identified.…”