ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2013
Genomewide markers as cofactors for precision mapping of quantitative trait loci.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 43 citations in OpenAlex.
- A Large-Scale Candidate-Gene Association Mapping for Drought Tolerance and Agronomic Traits in Sugarcane.International journal of molecular sciences · 2023Article
- Quantitative Trait Locus Mapping of Marsh Spot Disease Resistance in Cranberry Common Bean (International journal of molecular sciences · 2022Article
- Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton.Computational and structural biotechnology journal · 2022Article
- Identification of Candidate Gene-Based Markers for Girth Growth in Rubber Trees.Plants (Basel, Switzerland) · 2021Article
- QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize.Scientific reports · 2020Article
- QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations.Computational and structural biotechnology journal · 2020Article
- An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2.Briefings in bioinformatics · 2019Article
- Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection.PloS one · 2019Article
- Identification of Nitrogen Consumption Genetic Variants in Yeast Through QTL Mapping and Bulk Segregant RNA-Seq Analyses.G3 (Bethesda, Md.) · 2017Article
- Fine mapping by composite genome-wide association analysis.Genetics research · 2017Article
- Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs.PloS one · 2017Article
- Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology.Scientific reports · 2016Article
- Genome-wide association mapping of quantitative traits in a breeding population of sugarcane.BMC plant biology · 2016Article
- Fine-mapping and cross-validation of QTLs linked to fatty acid composition in multiple independent interspecific crosses of oil palm.BMC genomics · 2016Article
- Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data.PloS one · 2016Article
- Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet.Heredity · 2015Article
- Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Molecular breeding : new strategies in plant improvementArticle
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1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
In composite interval mapping of quantitative trait loci (QTL), subsets of background markers are used to account for the effects of QTL outside the marker interval being tested. Here, I propose a QTL mapping approach (called G model) that utilizes genomewide markers as cofactors. The G model involves backward elimination on a given chromosome after correcting for genomewide marker effects, calculated under a random effects model, at all the other chromosomes. I simulated a trait controlled by 15 or 30 QTL, mapping populations of N = 96, 192, and 384 recombinant inbreds, and N M = 192 and 384 evenly spaced markers. In the C model, which utilized subsets of background markers, the number of QTL detected and the number of false positives depended on the number of cofactors used, with five cofactors being too few with N = 384 and 20-40 cofactors being too many with N = 96. A window size of 0 cM for excluding cofactors maintained the number of true QTL detected while decreasing the number of false positives. The number of true QTL detected was generally higher with the G model than with the C model, and the G model led to good control of the type I error rate in simulations where the null hypothesis of no marker-QTL linkage was true. Overall, the results indicated that the G model is useful in QTL mapping because it is less subjective and has equal, if not better, performance when compared with the traditional approach of using subsets of markers to account for background QTL.
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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.