ArticleBMC genomics2022
Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 25 citations in OpenAlex.
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- GWAS and GS analysis revealed the selection and prediction efficiency for yield, plant morphological, and fiber quality in Gossypium barbadense.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale.International journal of molecular sciences · 2024Article
- Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection.Scientific reports · 2024Article
- Genome-wide association study of agronomic traits related to nitrogen use efficiency in Henan wheat.BMC genomics · 2024Article
- Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions.PloS one · 2024Article
- Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS.Scientific reports · 2023Article
- Genome-wide association mapping of genomic regions associated with drought stress tolerance at seedling and reproductive stages in bread wheat.Frontiers in plant science · 2023Article
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5 authors at 3 institutions in 1 country.
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Abstract
backgroundThe markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection.
resultsPopulation structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat.
conclusionWe illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.
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