Detection of epistatic interactions in association mapping populations: an example from tetraploid potato.

Epistatic interactions among loci are expected to contribute substantially to variation of quantitative traits. The objectives of our research were to (i) compare a classical mixed-model approach with a combined mixed-model and analysis of variance approach for detecting epistatic interactions; (ii) examine using computer simulations the statistical power to detect additive-additive, additive-dominance and dominance-dominance epistatic interactions and (iii) detect epistatic interactions between candidate genes for resistance to leaf blight in a set of tetraploid potato clones. Our study was based on the genotypic and phenotypic data of 184 tetraploid potato cultivars as well as computer simulations. The number of significant (α* =1 × 10(-6)) epistatic interactions ranged for the three examined traits from 3 to 32. Our findings suggested that the combined mixed-model and analysis of variance approach leads in comparison with the classical mixed-model approach not to an increased rate of false-positives. The results of the computer simulations suggested that, if molecular markers are available that are in high LD (D'>0.9) with the trait-coding loci, the statistical power to detect epistatic interactions, which explain 5-10% of the phenotypic variance, was of a size that seems promising for their detection.