ArticlePloS one2016
Genome Wide Single Locus Single Trait, Multi-Locus and Multi-Trait Association Mapping for Some Important Agronomic Traits in Common Wheat (T. aestivum L.).
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- A genome-wide association study reveals the genetic architecture of 19 agronomic traits in broomcorn millet (Panicum miliaceum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Mining of QTLs for Spring Bread Wheat Spike Productivity by Comparing Spring Wheat Cultivars Released in Different Decades of the Last Century.Plants (Basel, Switzerland) · 2024Article
- Uncovering the genomic regions underlying grain iron and zinc content using genome-wide association mapping in finger millet.3 Biotech · 2024Article
- Novel SNP markers for flowering and seed quality traits in faba bean (Frontiers in plant science · 2024Article
- Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat.Scientific reports · 2023Article
- Variations in phenological, physiological, plant architectural and yield-related traits, their associations with grain yield and genetic basis.Annals of botany · 2023Article
- GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat.Molecular breeding : new strategies in plant improvement · 2023Article
- GWAS for main effects and epistatic interactions for grain morphology traits in wheat.Physiology and molecular biology of plants : an international journal of functional plant biology · 2022Article
- GWAS Case Studies in Wheat.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genetics of spot blotch resistance in bread wheat (Frontiers in plant science · 2022Article
- Multi-locus genome-wide association mapping for spike-related traits in bread wheat (Triticum aestivum L.).BMC genomics · 2021Article
- Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat.Molecular breeding : new strategies in plant improvement · 2021Article
- Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models.Scientific reports · 2021Article
- Genome-wide association study identified candidate genes for seed size and seed composition improvement in M. truncatula.Scientific reports · 2021Article
- Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Frontiers in plant science · 2021Article
- Article
- Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods.BMC plant biology · 2020Article
- Article
- Multi-Locus Genome Wide Association Mapping for Yield and Its Contributing Traits in Hexaploid Wheat under Different Water Regimes.Scientific reports · 2019Article
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8 authors.
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Abstract
Genome wide association study (GWAS) was conducted for 14 agronomic traits in wheat following widely used single locus single trait (SLST) approach, and two recent approaches viz. multi locus mixed model (MLMM), and multi-trait mixed model (MTMM). Association panel consisted of 230 diverse Indian bread wheat cultivars (released during 1910-2006 for commercial cultivation in different agro-climatic regions in India). Three years phenotypic data for 14 traits and genotyping data for 250 SSR markers (distributed across all the 21 wheat chromosomes) was utilized for GWAS. Using SLST, as many as 213 MTAs (p ≤ 0.05, 129 SSRs) were identified for 14 traits, however, only 10 MTAs (~9%; 10 out of 123 MTAs) qualified FDR criteria; these MTAs did not show any linkage drag. Interestingly, these genomic regions were coincident with the genomic regions that were already known to harbor QTLs for same or related agronomic traits. Using MLMM and MTMM, many more QTLs and markers were identified; 22 MTAs (19 QTLs, 21 markers) using MLMM, and 58 MTAs (29 QTLs, 40 markers) using MTMM were identified. In addition, 63 epistatic QTLs were also identified for 13 of the 14 traits, flag leaf length (FLL) being the only exception. Clearly, the power of association mapping improved due to MLMM and MTMM analyses. The epistatic interactions detected during the present study also provided better insight into genetic architecture of the 14 traits that were examined during the present study. Following eight wheat genotypes carried desirable alleles of QTLs for one or more traits, WH542, NI345, NI170, Sharbati Sonora, A90, HW1085, HYB11, and DWR39 (Pragati). These genotypes and the markers associated with important QTLs for major traits can be used in wheat improvement programs either using marker-assisted recurrent selection (MARS) or pseudo-backcrossing method.
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