SynthesisBMC genomics2011
Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus.
Synthesis in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 14 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
93 citing papers in PubMed, 14 syntheses or guidelines pooled it, 193 citations in OpenAlex.
- Unravelling consensus genomic regions conferring stem-related traits in wheat via meta-QTL analysis.BMC plant biology · 2025Pooled it
- Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (International journal of molecular sciences · 2024Pooled it
- Meta-QTL s and haplotypes for efficient zinc biofortification of rice.The plant genome · 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
- Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification.Scientific reports · 2022Pooled it
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- Genome-wide meta-analysis of QTL for morphological related traits of flag leaf in bread wheat.PloS one · 2022Pooled it
- Meta-Analysis of the QTLome of Fusarium Head Blight Resistance in Bread Wheat: Refining the Current Puzzle.Frontiers in plant science · 2019Pooled it
- Meta-analysis of major QTL for abiotic stress tolerance in barley and implications for barley breeding.Planta · 2017Pooled it
- Genetic basis of qualitative and quantitative resistance to powdery mildew in wheat: from consensus regions to candidate genes.BMC genomics · 2013Pooled it
- QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Pooled it
- Quantitative Trait Locus (QTL) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (Lolium perenne L.).BMC genetics · 2012Pooled it
- QTL mapping of yield-associated traits in Brassica juncea: meta-analysis and epistatic interactions using two different crosses between east European and Indian gene pool lines.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2012Pooled it
- Genome-Wide Meta-QTL Analysis and InDel Markers for Salt Tolerance in Rice: Bridging the Gap from Genomics to Breeding.Rice (New York, N.Y.) · 2026Article
- Stress-tolerance genes in rice: bridging gene discovery, functional validation, and breeding applications.Frontiers in plant science · 2026Review
- Enhancing crop resilience through elite pre-breeding lines with key traits for dry direct-seeded rice.The plant genome · 2025Article
- An Integrated Meta-QTL and Transcriptome Analysis Provides Candidate Genes Associated with Drought Tolerance in Rice Seedlings.Plants (Basel, Switzerland) · 2025Article
- Genetic Architecture and Meta-QTL Identification of Yield Traits in Maize (Plants (Basel, Switzerland) · 2025Article
- CRISPR/Cas-mediated genome editing: playing a versatile role in mitigating the challenges of sustainable rice improvement.3 Biotech · 2025Review
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the last few years, efforts have been made to identify large effect QTL for grain yield under drought in rice. However, identification of most precise and consistent QTL across the environments and genetics backgrounds is essential for their successful use in Marker-assisted Selection. In this study, an attempt was made to locate consistent QTL regions associated with yield increase under drought by applying a genome-wide QTL meta-analysis approach.
resultsThe integration of 15 maps resulted in a consensus map with 531 markers and a total map length of 1821 cM. Fifty-three yield QTL reported in 15 studies were projected on a consensus map and meta-analysis was performed. Fourteen meta-QTL were obtained on seven chromosomes. MQTL1.2, MQTL1.3, MQTL1.4, and MQTL12.1 were around 700 kb and corresponded to a reasonably small genetic distance of 1.8 to 5 cM and they are suitable for use in marker-assisted selection (MAS). The meta-QTL for grain yield under drought coincided with at least one of the meta-QTL identified for root and leaf morphology traits under drought in earlier reports. Validation of major-effect QTL on a panel of random drought-tolerant lines revealed the presence of at least one major QTL in each line. DTY12.1 was present in 85% of the lines, followed by DTY4.1 in 79% and DTY1.1 in 64% of the lines. Comparative genomics of meta-QTL with other cereals revealed that the homologous regions of MQTL1.4 and MQTL3.2 had QTL for grain yield under drought in maize, wheat, and barley respectively. The genes in the meta-QTL regions were analyzed by a comparative genomics approach and candidate genes were deduced for grain yield under drought. Three groups of genes such as stress-inducible genes, growth and development-related genes, and sugar transport-related genes were found in clusters in most of the meta-QTL.
conclusionsMeta-QTL with small genetic and physical intervals could be useful in Marker-assisted selection individually and in combinations. Validation and comparative genomics of the major-effect QTL confirmed their consistency within and across the species. The shortlisted candidate genes can be cloned to unravel the molecular mechanism regulating grain yield under drought.
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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.