The calculated IC50 (Inhibitory Concentration of drugs required to reduce the fluorescence intensity by half, compared to live –controls) values were determined by fitting the variable slope Hill equation (an equation determining the non-linear drug dose-response relationship) using GraphPad Prism software (La Jolla, CA).
In an additional model, we also adjusted for protein and carbohydrates by fitting the following variables: non-nutritional covariates, alcohol, protein, carbohydrate, PUFA, MUFA and total fat.
A mixed model was fitted in ASReml 3.0 (VSN International 2009) by fitting the response variable (TYM or SGNC or SGNO) as a fixed effect and by fitting the line effects and effects due to repeated measurements as random effects.
Tests for dose response trends over the tertiles of intake were estimated by fitting the ordinal exposure variables as continuous variables and evaluated using the Wald test.
Tests for dose response trends over the different quartiles and categories of fat intake were estimated by fitting the ordinal exposure variables as continuous variables and evaluated using the Wald test.
We tested the contribution to the explained variance of each group of variables by fitting the full model again, excluding the variables from that group, and recorded the decrease in R2*.
To investigate relative contributions of the 25 environmental variables, a jackknife procedure was used, which was performed by fitting the model with n-1 variables and successively omitting one variable.
Broad-sense heritability (H) was obtained for each trait by fitting the trait (the independent variable) to the CC line label in a linear regression model that incorporates sex and age.
Shown are the number of sheep with observations (n), the average (mean), standard deviation (SD), lowest (min) and highest (max) values of the variables calculated by fitting the Wood model using the nonlinear mixed model procedure in 'R'R
Prostaglandin IC50 values were calculated and 95% confidence intervals were estimated using Prism 5.0 software (GraphPad) by fitting the log transformation of the response variable by nonlinear regression to the variable slope equation (1) and constraining the bottom to zero but allowing the Hill Slope to vary.
