Hi!

I'm using DESeq2 on an experimental setup that assesses the effect of a treatment (control & heatwave) on 3 different genotypes (with replicates at the genotype level).

I have used Wald tests to assess differences in the treatment effect between pairs of genotypes (i.e., genotypeA vs. genotypeB, genotypeA vs. genotypeC, and genotypeB vs. genotypeC). I would also like to use an LRT to assess differences in the treatment effect between all 3 genotypes simultaneously, but i'm not 100% confident that i am using the right full/reduced models to do this.

One option is to use the full model with the interaction term, and reduced model with no interaction term:

ddsoption1 <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design= ~genotype + treatment + genotype:treatment)

LRT_test1 <- DESeq(ddsoption1, test="LRT", reduced= ~genotype + treatment)

Or, perhaps I should be specifying a full model with no interaction term and reduced model with just treatment:

ddsoption2 <- DESeqDataSetFromMatrix(countData= counts, colData= coldata, design= ~genotype + treatment)

LRT_test2 <- DESeq(ddsoption2, test="LRT", reduced= ~treatment)

Is anyone able to help clarify which model setup i should be using to address my question?

Thanks in advance for any help, its much appreciated :)

Think about what you want your null model to be. You already know what the full model is (the one in the first chunk). Should the null be that the genotype effect is the same across all treatments (#1) or that there is no genotype effect (#2). Then based on your choice of null model you should use the first or second chunk but you should use the full model as specified in the first chunk.