gls function, very old results

Hello R-users,

I am currently trying to learn how to use the function gls of the nlme
library. I fitted the following model:
Generalized least squares fit by REML
Model: response ~ array + dye + genes + variety + variety * genes +
array * genes + dye * genes
Data: data

I have 11 arrays, 2 dyes, 2 varieties, 3200 genes, and 2 replications
for each.
Therefore I should have the corresponding degrees of freedom and number
of coefficients, but instead I have the following:
Coefficients:
  (Intercept)         array           dye         genes       variety
 5.955503e+00  2.695750e-02  4.120987e-01 -2.499571e-04  2.686421e-01
  array:genes     dye:genes genes:variety
 1.319176e-06 -7.112527e-05  2.660801e-05

Degrees of freedom: 110386 total; 110378 residual
Residual standard error: 1.030704

anova(fit)

Denom. DF: 110378
              numDF F-value p-value
(Intercept)       1 7590769  <.0001
array             1   21263  <.0001
dye               1    3069  <.0001
genes             1    4277  <.0001
variety           1    2493  <.0001
array:genes       1      38  <.0001
dye:genes         1      99  <.0001
genes:variety     1      15   1e-04

So I would like to know what I am doing wrong?
I use the following command:

and my dataset looks like this:
   array variety dye genes response flag
1     79       1   1     1 8.395252    0
2     79       1   1     1 8.583917    0
3     79       1   1     2 8.544225    0
4     79       1   1     2 8.423542    0
5     79       1   1     3 7.502186    0
6     79       1   1     3 7.524021    0
7     79       1   1     4 8.188411    0
8     79       1   1     4 8.072779    0
9     79       1   1     5 7.629976    0
10    79       1   1     5 7.524021    0
11    79       1   1     6 7.684784    0
12    79       1   1     6 7.610358    0
13    79       1   1     7 8.366138    0
14    79       1   1     7 8.369621    0
15    79       1   1     8 7.166266    0
16    79       1   1     8 7.038784    0
17    79       1   1     9 7.474205    0
18    79       1   1     9 7.805067    0
19    79       1   1    10 8.339501    0
20    79       1   1    10 8.407155    0

Any suggestion would be greatly appreciated.
Thank you,
raphael

data$dye<- factor(data$dye,labels=c("red","blue"))