ArticleBMC genetics2009
Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium.
Article in BMC genetics, 2009. 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.
- Identification of functional features underlying heat stress response in Sprague-Dawley rats using mixed linear models.Scientific reports · 2022Article
- Analysis of Variance Components for Genetic Markers with Unphased Genotypes.Frontiers in genetics · 2016Article
- A revised Fisher model on analysis of quantitative trait loci with multiple alleles.Frontiers in genetics · 2014Article
- Current applications of models of genetic effects with interactions across the genome.Current genomics · 2012Article
- On coding genotypes for genetic markers with multiple alleles in genetic association study of quantitative traits.BMC genetics · 2011Article
- Multiallelic models of genetic effects and variance decomposition in non-equilibrium populations.Genetica · 2011Article
- Statistical analysis of genetic interactions.Genetics research · 2010Review
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2 authors.
Funding
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Abstract
backgroundCockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F(infinity) genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F(infinity) model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).
resultsIn this paper, we further explore the differences and links between the F(infinity) and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the Finfinity models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F(infinity) model. A new way of linking the Cockerham and F(infinity) model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F(infinity) models are applied.
conclusionThe Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F(infinity) regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F(infinity) model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F(infinity) model can then be calculated by translating formulas derived for the Cockerham models.
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