For trait 6 I do not see anything wrong.

None of the hub genes (genes with the highest module membership) in the turquoise module show a strong correlation with the trait 6. Also, the majority of the genes in the turquoise module show a correlation (pearson) < 0.1 with that trait:

geneTraitSignificance = as.data.frame(cor(datExpr, trait6, use = "p"));

This explain why the Module eigengene (1st Principal component) of the turquoise module poorly correlate with the trait 6 (Module-trait relationship table).

Perhaps, I would plot the heatmap of each module to see if the gene expression in your modules is in agreement with your experiment design. I suppose that the gene in the turquoise module are co-expressed only in few samples

To plot the heatmaps, try this:

library(gplots)
col = colorpanel(300, 'purple','black','yellow')
colorsA1 = names(table(moduleColors))
pdf("Heatmap.pdf",height=9,width=9)
for (c in 1:length(colorsA1)){
      moduleColors == colorsA1[c]
      heatmap.2(t(datExpr[moduleColors==colorsA1[c]]), scale = "row", col=col, density.info ="none", trace="none", cexCol=0.5, cexRow=0.8, margin=c(19,11), main = colorsA1[c], Colv = FALSE)
      }; dev.off()