ArticleBMC genomics2020
Genome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 16 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 16 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Unravelling consensus genomic regions conferring stem-related traits in wheat via meta-QTL analysis.BMC plant biology · 2025Pooled it
- Whole-genome meta-analysis coupled with haplotype analysis reveal new genes and functional haplotypes conferring pre-harvest sprouting in rice.BMC plant biology · 2025Pooled it
- Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.BMC genomics · 2024Pooled it
- Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.).Scientific reports · 2024Pooled it
- Meta-QTL s and haplotypes for efficient zinc biofortification of rice.The plant genome · 2023Pooled it
- Meta-analysis of seed weight QTLome using a consensus and highly dense genetic map in Brassica napus L.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Pooled it
- Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration.The plant genome · 2023Pooled it
- Meta-QTL analysis and in-silico transcriptome assessment for controlling chlorophyll traits in common wheat.The plant genome · 2023Pooled it
- Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis.G3 (Bethesda, Md.) · 2023Pooled it
- Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment.BMC genomics · 2023Pooled it
- Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.).BMC plant biology · 2022Pooled it
- Genome-wide meta-QTL analyses provide novel insight into disease resistance repertoires in common bean.BMC genomics · 2022Pooled it
- Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification.Scientific reports · 2022Pooled it
- Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Pooled it
- Genome-wide meta-analysis of QTL for morphological related traits of flag leaf in bread wheat.PloS one · 2022Pooled it
- Pinpointing genomic regions associated with root system architecture in rice through an integrative meta-analysis approach.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Pooled it
- Genomic dissection of iron toxicity tolerance in rice identifies key loci, candidate genes, and associated haplotypes.Scientific reports · 2026Article
- Unlocking the Genetic Blueprint of Erucic Acid Content in Mustard (Brassica napus): A Meta-QTL Exploration.TheScientificWorldJournal · 2026Article
- Meta-QTL analysis for mining key genes associated with seed oil content in maize.BMC plant biology · 2025Article
- Meta-QTL Analysis and Identification of Candidate Genes Associated with Stalk Lodging in Maize (Current issues in molecular biology · 2025Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImproving yield and yield-related traits is the crucial goal in breeding programmes of cereals. Meta-QTL (MQTL) analysis discovers the most stable QTLs regardless of populations genetic background and field trial conditions and effectively narrows down the confidence interval (CI) for identification of candidate genes (CG) and markers development.
resultsA comprehensive MQTL analysis was implemented on 1052 QTLs reported for yield (YLD), grain weight (GW), heading date (HD), plant height (PH) and tiller number (TN) in 122 rice populations evaluated under normal condition from 1996 to 2019. Consequently, these QTLs were confined into 114 MQTLs and the average CI was reduced up to 3.5 folds in compare to the mean CI of the original QTLs with an average of 4.85 cM CI in the resulted MQTLs. Among them, 27 MQTLs with at least five initial QTLs from independent studies were considered as the most stable QTLs over different field trials and genetic backgrounds. Furthermore, several known and novel CGs were detected in the high confident MQTLs intervals. The genomic distribution of MQTLs indicated the highest density at subtelomeric chromosomal regions. Using the advantage of synteny and comparative genomics analysis, 11 and 15 ortho-MQTLs were identified at co-linear regions between rice with barley and maize, respectively. In addition, comparing resulted MQTLs with GWAS studies led to identification of eighteen common significant chromosomal regions controlling the evaluated traits.
conclusionThis comprehensive analysis defines a genome wide landscape on the most stable loci associated with reliable genetic markers and CGs for yield and yield-related traits in rice. Our findings showed that some of these information are transferable to other cereals that lead to improvement of their breeding programs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.