ArticleGenetics1999
Mapping quantitative trait loci by genotyping haploid tissues.
Article in Genetics, 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Comparison of Methods to Estimate Additive-by-Additive-by-Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies.International journal of molecular sciences · 2023Article
- Genetic linkage map construction and QTL identification of juvenile growth traits in Torreya grandis.BMC genetics · 2014Article
- Computing power of quantitative trait locus association mapping for haploid loci.BMC bioinformatics · 2009Article
- Understanding quantitative genetics in the systems biology era.International journal of biological sciences · 2009Review
- A unified framework for mapping quantitative trait loci in bivalent tetraploids using single-dose restriction fragments: a case study from alfalfa.Genome research · 2002Article
- A hierarchical statistical model for estimating population properties of quantitative genes.BMC genetics · 2002Article
- Genetic basis of climatic adaptation in scots pine by bayesian quantitative trait locus analysis.Genetics · 2000Article
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1 author.
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Abstract
Mapping strategies based on a half- or full-sib family design have been developed to map quantitative trait loci (QTL) for outcrossing species. However, these strategies are dependent on controlled crosses where marker-allelic frequency and linkage disequilibrium between the marker and QTL may limit their application. In this article, a maximum-likelihood method is developed to map QTL segregating in an open-pollinated progeny population using dominant markers derived from haploid tissues from single meiotic events. Results from the haploid-based mapping strategy are not influenced by the allelic frequencies of markers and their linkage disequilibria with QTL, because the probabilities of QTL genotypes conditional on marker genotypes of haploid tissues are independent of these population parameters. Parameter estimation and hypothesis testing are implemented via expectation/conditional maximization algorithm. Parameters estimated include the additive effect, the dominant effect, the population mean, the chromosomal location of the QTL in the interval, and the residual variance within the QTL genotypes, plus two population parameters, outcrossing rate and QTL-allelic frequency. Simulation experiments show that the accuracy and power of parameter estimates are affected by the magnitude of QTL effects, heritability levels of a trait, and sample sizes used. The application and limitation of the method are discussed.
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