Can anyone explain me what is the difference between additive(or Co-dominant) and log-additive(or multiplicative) models in genetic association analysis.
The two most popular regression models in the applied statistical genetics are the linear and the logit model. The difference between these two models lies on the left-hand side of the corresponding equations. The left hand side of a linear model is represented by a continuous variable Y; the left hand side of a logit model is the represented by a transformation (logit) of a dichotomous variable Y. Therefore, in a linear regression model the relationship between the dependent and the independent variable is linear on the logit scale. Consequently, in the association analysis between a given SNP (coded as 0, 1, 2) and a trait under study, if this trait is quantitative, the genetic effect is additive; if this trait is dichotomous, the genetic effect is log-additive.
The two most popular regression models in the applied statistical genetics are the linear and the logit model. The difference between these two models lies on the left-hand side of the corresponding equations. The left hand side of a linear model is represented by a continuous variable Y; the left hand side of a logit model is the represented by a transformation (logit) of a dichotomous variable Y. Therefore, in a linear regression model the relationship between the dependent and the independent variable is linear on the logit scale. Consequently, in the association analysis between a given SNP (coded as 0, 1, 2) and a trait under study, if this trait is quantitative, the genetic effect is additive; if this trait is dichotomous, the genetic effect is log-additive.