Effects of QTL parameters and marker density on efficiency of Haley–Knott regression interval mapping of QTL with complex traits and use of artificial neural network for prediction of the efficiency of HK method in livestock

Abstract

Dominance effect refers to the allele interaction in a locus. In this study, different portions of dominance standard deviations underlying quantitative trait loci (QTL) effect were considered. The F2 design is frequently employed in QTL mapping experiments using Haley and Knott regression method for QTL mapping analysis. This simulation study is carried out to consider the effect of the total standard deviation of QTL (SDQ) with different portions of additive/dominance effects in the context of different levels of population size, marker spacing and relative position of QTL from marker bracket on power of detecting QTL, precision of estimated QTL position and additive and dominance effects. The other aims of the study were to design an optimal artificial neural network (ANN) model to predict Haley–Knott (HK) results for more combinations of simulated parameters. SDQ of QTL strongly affected the power of QTL detection, therefore, in every combination of other parameters when SDQ is either 0.5 or 0.8, power was 100%. In all scenarios, the power increased when the ratio of additive and dominant SD of QTL effects was low or high (0.25 or 0.75). Increase of additive effect compared with the dominance effect decreased the precision of QTL location. Precision of estimated additive effect and dominance effect was good but precision of dominance effect was more affected by the considered parameter combinations than the additive effect. This study developed an ANN model with minimum dimensions and minimum errors for prediction of efficiency parameters of HK method given the simulated parameters. Moreover, for the first time, this study shows the use of trained ANN model for prediction of large-scale combinations of simulated parameters.