Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium
1 Division of Biostatistics, Department of Population Health, Medical College of Wisconsin, Milwaukee, WI 53226, USA
2 Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
BMC Genetics 2009, 10:52 doi:10.1186/1471-2156-10-52Published: 4 September 2009
Cockerham 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∞ 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∞ 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).
In this paper, we further explore the differences and links between the F∞ 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 F∞ 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∞ model. A new way of linking the Cockerham and F∞ model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F∞ models are applied.
The 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∞ 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∞ 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∞ model can then be calculated by translating formulas derived for the Cockerham models.