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Rapport de sythese restitution des resultats WP1
Author(s): sghaier, amal; Rudiger, Udo
Date: 2024-11-01
Type: Internal Report
Status: Open access
The repport shows the different results obtained by the different partners of the initiative and the co-designed experiments.
Identification of the SNP markers for drought tolerance and related agronomic traits in chickpea under multi-environments using GWAS analysis
Author(s): Istanbuli, Tawffiq; Alsamman, Alsamman M.; Nasser, Ahmed; Tawkaz, Sawsan; Hamwieh, Aladdin (The International Conference on Legume Genetics and Genomics (ICLGG))
Date: 2024-10-23
Type: Poster
Status: Open access
Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought-tolerant varieties. Using genome-wide association study (GWAS) is a powerful approach to identifying the genetic factors underlying the intraspecific phenotypic variations. To address this, we cultivated 185 chickpea accessions in two distinct locations in Lebanon over two years, subjecting them to both irrigated and rain-fed environments. We measured 11 traits, including morphological, yield, yield components and tolerance score. SNP genotyping revealed 1344 variable SNP markers distributed across the chickpea genome. A genome-wide association study (GWAS) revealed several marker-trait associations (MTAs) associated with the traits evaluated. Within the rainfed conditions, 11 significant markers were identified, each associated with distinct chickpea traits. Another set of 11 markers exhibited associations in both rainfed and irrigated environments, reflecting shared genetic determinants across these conditions for the same trait. We identified 28 genetic regions containing SNPs significantly associated with several different drought traits, which was an indication of pleiotropic effects. Among the identified genes are CPN60-2, hsp70, GDSL(GELP), AHL16, NAT3, FAB1B, bZIP, and GL21. These genes collectively contribute to the multifaceted response of chickpea plants to drought stress.
Genomic selection for different agronomic traits in ICARDA chickpea breeding program
Author(s): Hamwieh, Aladdin; Jighly, Abdul-Qader; Kaur, Sukhjiwan; Istanbuli, Tawffiq (The International Conference on Legume Genetics and Genomics (ICLGG))
Date: 2024-10-23
Type: Poster
Status: Open access
Chickpea (Cicer arietinum) is a crucial legume crop for food security and agricultural sustainability. Genomic selection (GS), an emerging breeding approach, enables early selection by predicting the genetic value of individuals using genome-wide markers. This study investigated the prediction accuracy of key agronomic traits using ICARDA chickpea breeding germplasm. The training population for this study was comprised of 297 genotypes, where 48% were breeding lines, 22% recombinant inbred lines (RILs), 14% of varieties, landraces, and wild types. Approximately 8% of the training population represented materials imported From Australia and India. The germplasm was genotyped using 1,922 single nucleotide polymorphism (SNP) markers covering the eight chromosomes. Field evaluation was conducted over two years (2023 and 2024) at Terbol station, Lebanon, using a replicated Alpha Lattice design. Key agronomic traits assessed included 100 seed weight (100SW), flowering time (FLWR), maturity time (MAT), and plant height (PLHT). Grain and biological yield were also measured. The prediction accuracy was calculated with and without fitting the genotype by environment interaction in the prediction model with two cross-validation strategies (CV1: predicting new individuals; and CV2: predicting complete data across environments from unbalanced field trials). The results demonstrated moderate heritability values for 100SW, FLWR, MAT, and PLHT, and high prediction accuracy values ranging from 0.51 to 0.81 for CV1, and from 0.63 to 0.90 for CV2. However, yield and yield component traits exhibited relatively lower heritabilities and accuracies ranging from 0.38 to 0.48 for CV1, and from 0.37 to 0.51 for CV2. These findings underscore the potential of GS to enhance the efficiency and accuracy of chickpea breeding
programs to achieve better yield stability and adaptability in chickpea across diverse growing conditions.
Genomic Prediction in Faba bean for Heat and Herbicide Tolerance
Author(s): Abou-Khater, Lynn; Maalouf, Fouad; Hamwieh, Aladdin; Jighly, Abdul-Qader; Joukhadar, Reem; Alsamman, Alsamman M.; Ahmed, Zayed Babiker Mahgoub; Balech, Rind; Hu, Jinguo; Ma, Y.; Sanchez-Garcia, Miguel; Agrawal, Shiv Kumar (The International Conference on Legume Genetics and Genomics (ICLGG))
Date: 2024-10-23
Type: Poster
Status: Open access
Genomic selection (GS) offers significant potential to enhance genetic gain. The present study aimed to evaluate the accuracy and potential of GS in faba bean (Vicia faba L.), and to identify areas for further improvement and better implementation in practical breeding programs. 125 diverse faba bean accessions were phenotyped for different agronomic traits under herbicide and heat stresses
in 16 environments in Morocco, Lebanon, Sudan and the USA. These accessions were also genotyped. 170 SNPs highly associated with the target traits were identified. Subsequently, KASP markers were designed and validated across 4515 diverse breeding lines. Prediction accuracy (PA) was evaluated using the reproducing kernel Hilbert space model with and without considering genotype by environment interaction and considering two cross-validation strategies (CV1: predicting new lines; CV2: predicting complete records from unbalanced data). In addition, 75 KASP markers targeting heat tolerance traits were prioritized and used to estimate the PA of the models.
The findings indicated comparable PA between the two models. CV1 outperformed CV2, highlighting the challenge of predicting the performance of untested lines in tested environments compared to lines that were evaluated in some environments but not in others. Furthermore, the subset size and composition of SNPs significantly influenced PA, particularly under heat stress conditions. Notably, the highest accuracies were achieved for days to flowering and plant height, suggesting that these traits are suitable for use in training population selection. Optimizing the size and composition of the training population holds promise for successful application of GS in faba bean.
Expert feedback on weights survey results in the Decision Support Tool framework for Optimizing the Water-Energy-Food-Ecosystem Nexus within the Aral Sea Basin
Date: 2024-08-01
Type: Internal Report
Status: Timeless limited access
The present report was developed to describe the main futures of the validation workshop on the Decision Support Tool (DST) organized on the 1st of August in Tashkent, Uzbekistan. The introduction part of the report highlights the Nexus Gains initiative, the initiative's output - Decision Support Tool for optimizing the Water-Energy-Food-Ecosystem Nexus within the Aral Sea Basin, and the workshop aim and objectives, The main part of the report highlights workshop participants, conducted survey within WEFE sector stakeholders, ICARDA's DST instrument and discussions and recommendations during the workshop. The report ends with workshop agenda and list of participants.