ArticleBMC genetics2007
Separation of base allele and sampling term effects gives new insights in variance component QTL analysis.
Article in BMC genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions.The plant genome · 2020Article
- Identification of quantitative genetic components of fitness variation in farmed, hybrid and native salmon in the wild.Heredity · 2015Article
- Article
- Fine mapping and replication of QTL in outbred chicken advanced intercross lines.Genetics, selection, evolution : GSE · 2011Article
- Modelling dominance in a flexible intercross analysis.BMC genetics · 2009Article
- Precision of genetic parameters and breeding values estimated in marker assisted BLUP genetic evaluation.Genetics, selection, evolution : GSE · 2009Article
- An improved method for quantitative trait loci detection and identification of within-line segregation in F2 intercross designs.Genetics · 2008Article
- Pedigree-free animal models: the relatedness matrix reloaded.Proceedings. Biological sciences · 2008Article
- Increasing the efficiency of variance component quantitative trait loci analysis by using reduced-rank identity-by-descent matrices.Genetics · 2007Article
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2 authors.
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Abstract
backgroundVariance component (VC) models are commonly used for Quantitative Trait Loci (QTL) mapping in outbred populations. Here, the QTL effect is given as a random effect and a critical part of the model is the relationship between the phenotypic values and the random effect. In the traditional VC model, each individual has a unique QTL effect and the relationship between these random effects is given as a covariance structure (known as the identity-by-descent (IBD) matrix).
resultsWe present an alternative notation of the variance component model, where the elements of the random effect are independent base generation allele effects and sampling term effects. The relationship between the phenotypic vales and the random effect is given by an incidence matrix, which results in a novel, but statistically equivalent, version of the traditional VC model. A general algorithm to estimate this incidence matrix is presented. Since the model is given in terms of base generation allele effects and sampling term effects, these effects can be estimated separately using best linear unbiased prediction (BLUP). From simulated data, we showed that biallelic QTL effects could be accurately clustered using the BLUP obtained from our model notation when markers are fully informative, and that the accuracy increased with the size of the QTL effect. We also developed a measure indicating whether a base generation marker homozygote is a QTL heterozygote or not, by comparing the variances of the sampling term BLUP and the base generation allele BLUP. A ratio greater than one gives strong support for a QTL heterozygote.
conclusionWe developed a simple presentation of the VC QTL model for identification of base generation allele effects in QTL linkage analysis. The base generation allele effects and sampling term effects were separated in our model notation. This clarifies the assumptions of the model and should also enhance the development of genome scan methods.
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