ArticleBriefings in bioinformatics2019
An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2.
Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- Unraveling the regulatory network of barley grain metabolism through the integrative analysis of multiomics and mQTL.Nature communications · 2025Article
- Conditional QTL/QTN mapping for seed width and mining candidate genes based on soybean FW-RIL population.Molecular genetics and genomics : MGG · 2025Article
- Detection of genes associated with soybean protein content using a genome-wide association study.Plant molecular biology · 2025Article
- A novel trait to reduce the mechanical damage of peach fruits at harvest: The first genetic dissection study for peduncle length.Molecular breeding : new strategies in plant improvement · 2025Article
- An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes.Plants (Basel, Switzerland) · 2024Article
- Finding stable and closely linked QTLs against spot blotch in different planting dates during the adult stage in barley.Scientific reports · 2024Article
- Developing and validating a nomogram for cognitive impairment in the older people based on the NHANES.Frontiers in neuroscience · 2023Article
- A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.Computational and structural biotechnology journal · 2023Article
- Identification of QTLs and their candidate genes for the number of maize tassel branches in FFrontiers in plant science · 2023Article
- Quantitative Trait Locus Mapping of Marsh Spot Disease Resistance in Cranberry Common Bean (International journal of molecular sciences · 2022Article
- A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an FPlant communications · 2022Article
- postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification.BMC research notes · 2022Article
- Identification and analysis of oil candidate genes reveals the molecular basis of cottonseed oil accumulation in Gossypium hirsutum L.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Genome-Wide Association Study Statistical Models: A Review.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Identification of QTL underlying the main stem related traits in a doubled haploid barley population.Frontiers in plant science · 2022Article
- Genetic dissection of cassava brown streak disease in a genomic selection population.Frontiers in plant science · 2022Article
- Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review.Pathogens (Basel, Switzerland) · 2021Review
- Genome-wide association studies: assessing trait characteristics in model and crop plants.Cellular and molecular life sciences : CMLS · 2021Review
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Authors and funding
6 authors.
Funding
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Abstract
In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P-value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.
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