Hello, Its my first time to deal with Longitudinal analysis in Limma and I would like to make sure that my design reflects my research question. Research question: which gene-expressions are significantly differentially expressed over time with the change of FEV1 (continuous variable) overtime. I have 2 gene expression, FEV1, and other covariants readings for 2 timepoints.

RID<-factor(rownames(combined_pheno)) #reading ID is different for each reading 
ID<-factor(combined_pheno$SubjectID) # each subject has 1 ID and 2 RID

design<- model.matrix(~FEV1+Time+FEV1:Time+ Age+Gender+Race)
fit<-voomLmFit(exp , design, block=ID, plot=TRUE)
fit<- eBayes (fit)
result<- topTable(fit,coef="FEV1:Time",n=Inf,adjust="BH",p.value = 0.05 , sort.by = "logFC")
sessionInfo( )

My question would be is it right to assume that "ID" is counted as random effect by adding (block=ID) Thank you

Yes. The blocking variable in limma means a random effect.

I notice however that you are trying to adjust for patient differences by including patient characteristics (Age, Gender and Race) in the design matrix. It is a bit unusual to include patient as a random effect and adjust for patient differences at the same time. I guess it still might work ok if you have a lot of samples. It depends on your experimental design.