Related Experiment Video
Updated: May 21, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
A comparison of two methods to estimate additive-by-additive interaction of QTL effects by a simulation study
1Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland. jboc@up.poznan.pl
Abstract:
Additive-by-additive epistasis plays an important role in the genetic architecture of complex traits. The parameter connected with the additive-by-additive interaction can influence decisions concerning usefulness of the breeding material for the generation of new genotypes with characteristics improved over the parental forms. This study presents comparisons of two estimation methods of additive-by-additive interactions of QTL effects by the Monte Carlo simulation study. In the first method we assume that we observed only the plant phenotype, while in the second method we have additional information from the molecular marker observations. The obtained results show that the additive-by-additive interaction effect calculated on the basis of the marker observations is always smaller than the total additive-by-additive interaction effect obtained from phenotypic observations only. The lack of influence of the distance between markers and the number of linkage groups on the estimation of effects of additive-by-additive epistasis interaction genes by the two methods shows that both these methods may be used for different genetic maps and for different plant species.
Related Concept Videos
Pharmacodynamic Models: Additive and Proportional Drug Effect Model
Quantitative Aspects of Drug-Receptor Interaction
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Pharmacodynamic Models: Emax Drug–Concentration Effect Model

