Plant Genetic Resources for Food and Agriculture: The Role and Contribution of CREA (Italy) within the National Program RGV-FAO

Agronomy, Journal Year: 2024, Volume and Issue: 14(6), P. 1263 - 1263

Published: June 12, 2024

Conservation, characterization and exploitation of agrobiodiversity are key factors to guarantee food security face future challenges such as climate changes. These issues the subject a series international agreements, Convention Biological Diversity, with its Nagoya Protocol, International Treaty on Plant Genetic Resources for Food Agriculture (ITPGRFA) adopted in 2001 entered into force 2004. Italy ratified 2004 instituted long-lasting program, RGV-FAO, implement it. CREA is one three organizations involved RGV-FAO Program, together National Research Council (CNR) Reti Semi Rurali. maintains total 40,186 accessions including cereals, vegetables, fruits, forages, industrial crops, forest woody medicinal aromatic plants, their wild relatives. Accessions conserved using different ex situ conservation systems (seeds, vivo vegetative organs vitro plantlets), characterized genetic, morpho-phenological and/or biochemical methods. Herein, we will present program some examples use plant genetic resources breeding programs, molecular approaches. Some critical related access benefit sharing PGRFA, Protocol Digital Sequence Information, be discussed, highlighting potential impact advancement knowledge.

Language: Английский

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Targeted genotyping (90K-SPET) facilitates genome-wide association studies and the prediction of yield-related traits in faba bean (Vicia faba L.)

BMC Plant Biology, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 29, 2025

Establishing faba bean (Vicia L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These can be improved by exploiting extensive but largely uncharacterized germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per (SP), seed weight (SSW) 245 accessions (CGN panel) via Genome-Wide Association Study (GWAS). The were grown 2021 2022 Netherlands. Additionally, we developed genomic selection (GS) models predict merit within large germplasm collections for mentioned traits, well yield (YLD). CGN panel was an optimal performing high-resolution GWAS, showing phenotypic variation, high narrow-sense heritability all minimal relatedness among accessions. Population structure analysis revealed presence four groups. GWAS uncovered 33 SNP-trait 17 2022. We identified one stable QTL FT SSW over two years, representing key molecular markers testing breeding applications. Short linkage disequilibrium decay (~ 268 Kbp) facilitated identification several important candidate genes with interesting homologs other crops. Ten SNPs five predicted intra-genic missense variants, potentially altering function. Moreover, modeling SNP effect simultaneously Bayesian GS showed promising predictive ability (PA) prediction accuracy (ACC), supporting their potential application germplasm-improvement programs. Predictive ranged from 0.58 0.81 2021, 0.47 0.85 different traits. across-year predictions PA. QTLs use highlighted new genes. Interestingly, categorized 15 putatively affecting demonstrated first that has unlock untapped diversity genebank accelerate trait integration into

Language: Английский

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Genomic Selection for Pea Grain Yield and Protein Content in Italian Environments for Target and Non-Target Genetic Bases

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(7), P. 2991 - 2991

Published: March 25, 2025

Enhanced pea cultivation, which can increase the sustainability of European agriculture, requires better-performing cultivars. This study investigated genomic selection (GS) ability to predict grain yield, protein content, and yield on same or a different genetic base (target/non-target GB) relative that employed for model training. GS models were developed 276 lines from three Recombinant Inbred Line (RIL) populations evaluated in Italian autumn-sown environments using 5537 SNPs genotyping by sequencing. Validation two cropping years concerned 108 independent five RIL populations, belonged training set, shared one parent each with populations. A genome-wide association performed set 18,674 highlighted polygenic control content several environment-dependent QTLs yield. Intermediate/high predictive within across emerged all traits target GB (0.359-0.675), some variation depending population. Predictive non-target was modest/intermediate null/poor other traits. No inverse correlation between content. proved useful GB.

Language: Английский

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Genomic prediction for rust resistance in pea

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: July 23, 2024

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS predicting rust ( Uromyces pisi ) resistance pea Pisum sativum ), using a panel 320 accessions and set 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared prediction abilities different models explored impact incorporating marker × environment (M×E) interaction as covariate GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field controlled conditions. assessed predictive accuracies cross-validation strategies efficiency single traits versus multi-trait index, based on factor ideotype-design (FAI-BLUP), which combines model, when modified include M×E interactions, consistently outperformed other models, demonstrating its suitability affected by genotype-environment interactions (GEI). ability (0.635) was achieved FAI-BLUP approach within Bayesian Lasso (BL) inclusion significantly enhanced accuracy across diverse environments although it did not markedly improve predictions non-phenotyped lines. These findings underscore variability due GEI effectiveness approaches addressing Overall, our illustrates potential GS, especially employing index like accounting breeding programs focused resistance.

Language: Английский

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Genetic diversity of the Grain Gene Bank provided Pisum sativum (L.) accessions, including Chinese Core collection, in rainfall conditions in western Algeria using phenotypic markers

Published: Dec. 17, 2024

To achieve food security in the face of fast population expansion and climate change, it is vital to expand agriculture developing countries increase productivity while preserving natural resources. Using genetic resources, plant breeders can create new enhanced cultivars that possess advantageous traits (adaption, production, resistance). The current study aims characterize 95 Pisum sativum accessions grown rainfall conditions western Algeria use them breeding programs or upgrade existing material. pea provided by USDA Grains Gene Bank were installed conditions. Morphological, phenological, agronomic assessments carried out. statistical analysis revealed significant variation between for all studied parameters. Five clusters identified through classification process. Factor analyses mixed data 19 nine principal components with eigenvalues greater than 1 accounting 73.979% total variation. initial two dimensions explain 32.277% variance, first dimension contributing 18.426% second 13.85%. associate three negatively correlating groups. assessed parameters P. sativum-Eth (1), PI 193843, Ma Rou Wan, Tai Ba Cou, Bai Wan (3) best-performing accessions. L 1918, 1944, 1405 tardiest, however Liu Tang Ke JI 159, PIS 511 earlier. Numerous demonstrated desirable features, allowing be chosen used improvement and/or varietal creation programs.

Language: Английский

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