My aim is to do paired testing for each condition against control

I have a dataset with the following experimental design

conditions: 0,8,24,72 hours of treatment

VirusUsed: wildtype (wt) and mutant (mt)

donors: donor1, donor2, donor3

NB - only one uninfected control for each donor

So accordingly, I designed a paired comparison

Time <- factor(metadata$Time) 
Virus <- factor(metadata$VirusUsed)
Donor <- factor(metadata$SampleCode)
Group = factor(paste0(Virus, ".", Time))
Group = relevel(Group, ref = "uninf.0")
design = model.matrix(~Donor+Group)

Resulting in the following design matrix

   (Intercept) Donor2 Donor3 mt.24 mt.72 mt.8 wt.24 wt.72 wt.8
1            1      0      0     0     0    0     0     0    1
2            1      1      0     0     0    0     0     0    1
3            1      0      1     0     0    0     0     0    1
4            1      0      0     0     0    1     0     0    0
5            1      1      0     0     0    1     0     0    0
6            1      0      1     0     0    1     0     0    0
7            1      0      0     0     0    0     1     0    0
8            1      1      0     0     0    0     1     0    0
9            1      0      1     0     0    0     1     0    0
10           1      0      0     1     0    0     0     0    0
11           1      1      0     1     0    0     0     0    0
12           1      0      1     1     0    0     0     0    0
13           1      0      0     0     0    0     0     1    0
14           1      1      0     0     0    0     0     1    0
15           1      0      1     0     0    0     0     1    0
16           1      0      0     0     1    0     0     0    0
17           1      1      0     0     1    0     0     0    0
18           1      0      1     0     1    0     0     0    0
19           1      0      0     0     0    0     0     0    0
20           1      1      0     0     0    0     0     0    0
21           1      0      1     0     0    0     0     0    0

and following this I ran the following code

x <- calcNormFactors(x)
x <- estimateDisp(x, design = design)
fit = glmQLFit(x , design = design)

results = list()
for(i in 4:9){
    f = glmQLFTest(fit, coef = i)
    results[[colnames(design)[i]]] <- f 
} #this loops all conditions to compare against control

However, I have the following genes that keep coming up as significant (FDR 1E-7), and upon inspection of the raw counts (below), there is a donor specific response to the virus; this effect still stands if looking at cpm values.

      wt8_1 wt8_2 wt8_3 mt8_1 mt8_2 mt8_3 wt24_1 wt24_2 wt24_3 mt24_1 mt24_2
gene1  2090     2     0  2059     0     2   2422      0      6   3273      6
gene2   193     0     0   156     0     0    121      0      0    153      0
gene3   150     0     0   207     0     0    208      0      0    243      0
gene4  1803     2     0  1862     2     2   2586      0      2   3551      0
gene5  1574     0     0  1669     2     2   1248      2      0   1570      0
gene6 13018     6     4 12188     2     2   8693      2      2  11832      4
gene7  3260     4     8  3665     0     0   4212      4      2   5516      0
      mt24_3 wt72_1 wt72_2 wt72_3 mt72_1 mt72_2 mt72_3 uninf0_1 uninf0_2
gene1      4   2218      0      6   2371      2      2        6        2
gene2      0    113      0      0    114      0      2        0        0
gene3      0    179      0      0    156      0      0        0        0
gene4      0   1970      2      4   2118      2      0        0        4
gene5      2    976      2      0   1010      0      0        0        0
gene6      8   7615      8      8   8517      8      6        4        2
gene7      2   4565      2      0   4778      2      0        2        2
      uninf0_3
gene1        2
gene2        0
gene3        0
gene4        0
gene5        0
gene6        2
gene7        0

Question: I understand that these genes will be significant for Donor1, but given paired testing design and insignificance for the other two donors;

(a) why does edgeR still compute a singificant FDR; does edgeR average the significance between the three donors after calculating them paired? a paired t-test for these genes do not turn out significant;

(b) can I filter these genes out; or is there a way edgeR to calculate significance only if all three paired donors reach significance?

PS: I lump all samples under one big model matrix because I also compare wt vs mt; but that's not within the scope of this question.