ArticleBMC plant biology2014
Prioritization of candidate genes in QTL regions based on associations between traits and biological processes.
Article in BMC plant biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Whole-Genome Resequencing Reveals Selection Signatures Associated With Lambskin Patterns in Hu Sheep.Animal genetics · 2026Article
- Mitochondria-Related Genome-Wide Mendelian Randomization Identifies Putatively Genes for Chronic Fatigue.Molecular neurobiology · 2026Article
- Molecular polymorphisms that underlie trait variation in crops: Lessons learned from soybean.The plant genome · 2026Review
- Adaptive immune response to West Nile virus infection in the Collaborative Cross mouse model: A database of cellular phenotypes and Quantitative Trait Loci.Scientific data · 2025Article
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- Comparative transcriptome analysis reveals candidate gene for flowering time QTL HvHeading in barley.BMC plant biology · 2025Article
- Analysis of coat texture characteristics of bread wheat grains obtained from digital images.Frontiers in plant science · 2025Article
- Exploration and Enrichment Analysis of the QTLome for Important Traits in Livestock Species.Genes · 2024Article
- Identification of Novel Loci Precisely Modulating Pre-Harvest Sprouting Resistance and Red Color Components of the Seed Coat inPlants (Basel, Switzerland) · 2024Article
- Smart breeding approaches in post-genomics era for developing climate-resilient food crops.Frontiers in plant science · 2022Review
- Machine learning in plant science and plant breeding.iScience · 2021Review
- Reproductive tissues-specific meta-QTLs and candidate genes for development of heat-tolerant rice cultivars.Plant molecular biology · 2020Article
- The role of epistasis and its interaction with environment in fine-tuning heading time in barley.Journal of experimental botany · 2020Article
- QTL Mapping for Seed Germination Response to Drought Stress inFrontiers in plant science · 2020Article
- QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.G3 (Bethesda, Md.) · 2019Article
- Article
- Prioritising candidate genes causing QTL using hierarchical orthologous groups.Bioinformatics (Oxford, England) · 2018Article
- Mapping and confirmation of loci for salt tolerance in a novel soybean germplasm, Fiskeby III.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Fine-mapping of QTLs for individual and total isoflavone content in soybean (Glycine max L.) using a high-density genetic map.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.Scientific reports · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.
resultsThe prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.
conclusionsOur approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.
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