Further insights of the variance component method for detecting QTL in livestock and aquacultural species: relaxing the assumption of additive effects

Abstract

-Complex traits may show some degree of dominance at the gene level that may influence the statistical power of simple models, i.e. assuming only additive effects to detect quantitative trait loci (QTL) using the variance component method. Little has been published on this topic even in species where relatively large family sizes can be obtained, such as poultry, pigs, and aquacultural species. This is important, when the idea is to select regions likely to be harbouring dominant QTL or in marker assisted selection. In this work, we investigated the empirical power and accuracy to both detect and localise dominant QTL with or without incorporating dominance effects explicitly in the model of analysis. For this purpose, populations with variable family sizes and constant population size and different values for dominance variance were simulated. The results show that when using only additive effects there was little loss in power to detect QTL and estimates of position, using or not using dominance, were empirically unbiased. Further, there was little gain in accuracy of positioning the QTL with most scenarios except when simulating an overdominant QTL.QTL / additive effect / dominance / power / REML

Modelling dominance in a flexible intercross analysis

“…We expect, however, that major genes having moderate dominance effects will be detected with the simpler additive version of FIA. These results are similar to the ones obtained by Martinez [ 9 ] where he showed that the power of VC-based models does not increase substantially by including dominance effects as long as the QTL effects are not overdominant. The difference in power for HK-regression with or without dominance included in the model seem to be small as long as the QTL effects are not overdominant.…”

“…Hence, for a single QTL model there is no covariance between additive and dominance effects. The estimates of and may be strongly correlated, however, since the IBD-values in Π and Δ are correlated [ 9 ].…”

“…FIA is a variance component based method which models dependencies between founder QTL allele effects. This difference between FIA and the model studied by Martinez [ 9 ] implies that Martinez' results cannot be directly applied on FIA.…”

“…We may expect major genes to have considerable dominance effects [ 8 ] but this does not necessarily imply that the power of a QTL analysis will increase by including dominance effects in the statistical model. In a recent paper by Martinez [ 9 ], the power to detect a QTL having a dominance effect using a variance component (VC) model was studied. He found that the gain in power using a model with both additive and dominance effects was not substantial compared to a model with only additive effects as long as the QTL effect was not overdominant.…”