Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations

Understanding the genetic background of complex diseases requires expansion studies beyond univariate associations. Therefore, it is important to use interaction assessments risk factors in order discover whether, and how variants act together on disease development. The principle analysis explore magnitude combined effect causation. In this study, we simulations investigate different scenarios causation show two interact. We mainly focus most commonly used models, additive multiplicative scales, since there often confusion regarding their interpretation. Our results that when are involved same chain events, an called synergism. Synergism described as a deviation from additivity, which broader term. also confirm relevant estimate relationships, because they correspond independent at low prevalence. Importantly, evaluate threshold more than required for causation, multifactorial model. found simple mathematical relationship (square root) between additive-to-multiplicative linear scale (AMLES), where 0 corresponds 1 multiplicative. propose AMLES metric could be test effects relationships time, given can simultaneously reveal additive, intermediate relationships. Finally, utility our simulation study was demonstrated using real data by analyzing interpreting gene-gene odds ratios rheumatoid arthritis case-control cohort.