I am running models using deseq2. I have a question about the raw p-values and covariates. I run two models without and with covariates. There is a substantial shift upwards in the p-values for the model with covariates. The mean p-value after adjustment is above .6. Any comments?

deseqobjgenus3 <- phyloseqtodeseq2(genus4, formula(~ Western )) model3 <- DESeq(deseqobjgenus3, fitType = 'local') out3 <-as.data.frame(results(model3)) out3 names(out3) = c("mean3", "log2fc3", "se3", "stat3", "p3", "adjp3")

deseqobjgenus1 <- phyloseqtodeseq2(genus4, formula(~ v4age1 + white + hwbmi + site +dtcalors + Western )) model1 <- DESeq(deseqobjgenus1, fitType = 'local') out1 <-as.data.frame(results(model1)) out1 names(out1) = c("mean1", "log2fc1", "se1", "stat1", "p1", "adjp1")

P-values can either go up or down after controlling for covariates in a linear model. I would make the model choice on external factors other than the p-value distribution.