Hello how can I generate a list of my most differentially expressed genes from my RNA seq data on DESeq2 using the raw pvalue (ie. unadjusted) cutoff of <0.05 instead of using DESeq2's Benjamini & Hochberg corrected pvalue cutoff of <0.05

setwd("Your_working_directory")

# Import count table#
countdata <- read.table("family_13_01_revised_RNA-seq.counts_fixed.txt", header=TRUE, row.names=1)

# Remove .bam or .sam from filenames#
colnames(countdata) <- gsub("\\.[sb]am$", "", colnames(countdata))

# Convert to matrix#
countdata <- as.matrix(countdata)
head(countdata)

# Assign condition (affected versus unaffected)#
condition <- factor(c("affected","affected","affected","unaffected","unaffected","unaffected"),levels=c("affected","unaffected"))
condition <- relevel(condition, ref = "unaffected")



#load DESeq2#
library(DESeq2)

# Create a coldata frame and instantiate the DESeqDataSet#
coldata <- data.frame(row.names=colnames(countdata),condition)

dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design = ~ condition)
dds


#pre-filtering to keep only rows that have at least 1 reads total#
keep <- rowSums(counts(dds)) > 1
dds <- dds[keep,]

# Run the DESeq#
dds <- DESeq(dds)


# Regularized log transformation for clustering/heatmaps
rld <- rlogTransformation(dds)
head(assay(rld))
hist(assay(rld))

# Colors for plots below#
library(RColorBrewer)
(mycols <- brewer.pal(8, "Dark2")[1:length(unique(condition))])

# Sample distance heatmap
sampleDists <- as.matrix(dist(t(assay(rld))))
library(gplots)
#png("qc-heatmap_baker.png", w=1000, h=1000, pointsize=20)
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
          col=colorpanel(100, "black", "white"),
          ColSideColors=mycols[condition], RowSideColors=mycols[condition],
          margin=c(10, 10), main="Sample Distance Matrix")
#dev.off()


# Principal components analysis#
rld_pca <- function (rld, intgroup = "condition", ntop = 500, colors=NULL, legendpos="bottomleft", main="PCA Biplot", textcx=1, ...) {
  require(genefilter)
  require(calibrate)
  require(RColorBrewer)
  rv = rowVars(assay(rld))
  select = order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
  pca = prcomp(t(assay(rld)[select, ]))
  fac = factor(apply(as.data.frame(colData(rld)[, intgroup, drop = FALSE]), 1, paste, collapse = " : "))
  if (is.null(colors)) {
    if (nlevels(fac) >= 3) {
      colors = brewer.pal(nlevels(fac), "Paired")
    }   else {
      colors = c("black", "red")
    }
  }
  pc1var <- round(summary(pca)$importance[2,1]*100, digits=1)
  pc2var <- round(summary(pca)$importance[2,2]*100, digits=1)
  pc1lab <- paste0("PC1 (",as.character(pc1var),"%)")
  pc2lab <- paste0("PC2 (",as.character(pc2var),"%)")
  plot(PC2~PC1, data=as.data.frame(pca$x), bg=colors[fac], pch=21, xlab=pc1lab, ylab=pc2lab, main=main, ...)
  with(as.data.frame(pca$x), textxy(PC1, PC2, labs=rownames(as.data.frame(pca$x)), cex=textcx))
  legend(legendpos, legend=levels(fac), col=colors, pch=20)
  #     rldyplot(PC2 ~ PC1, groups = fac, data = as.data.frame(pca$rld),
  #            pch = 16, cerld = 2, aspect = "iso", col = colours, main = draw.key(key = list(rect = list(col = colours),
  #                                                                                         terldt = list(levels(fac)), rep = FALSE)))
}
#png("qc-pca.png", 1000, 1000, pointsize=20)
rld_pca(rld, colors=mycols, intgroup="condition", xlim=c(-30, 30))
#dev.off()


# Get differential expression results#
res <- results(dds)
table(res$padj<0.05)

## Order by adjusted p-value #
res <- res[order(res$padj), ]
## Merge with normalized count data#
resdata <- merge(as.data.frame(res), as.data.frame(counts(dds, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
head(resdata)
#get significant results (FDR<0.05)
## Write results#
write.csv(resdata, file="sig_diffexpr-results.csv")

Why would you want to use raw p-value? These could have a very high number of false positives, hence adjustment / correction methods.

E.g. say you pick top 100 genes by p-value out of 10,000, which all have p-value < 0.01. These could be 100% false positives (FDR = 100%).