NAs in fixed effects

Dear listmembers

I have a rather large dataset which needs one random effect to be
analysed properly (group). I also have many explanatory variables,
each with a few NAs at different places. I can easily enough fit a
model with 2 fixed effects, but as the number of fixed effects
increase so does tha NAs as they are not in the same rows. I am no
statistician, and this may be a naive question, but is there a way to
fit each fixed efefct with its full data? My first ide was to use
lme, where na.pass works, but I found this comment by Bates;

"I don't think you want to use na.pass here.  The underlying C code
for fitting lme or lmer models doesn't take kindly to finding NA's in
the data."

I couldnt make na.pass work in any other package dealing with mixed
models.

there are a few NAs in the data scattered throughout, eliminating the
data severly (although each single variable only have 0-10%NA)

This may illustrate what my problem is;

x<-c(1:10) y<-c(1:10) pa<-c(NA,2:10) pb<-c(1,NA,3:10) 
pc<-c(1:2,NA,4:10) pd<-c(1:3,NA,5:10) pe<-c(1:4,NA,6:10) 
pf<-c(1:5,NA,7:10) group<-factor(rep(c("A","B"), each=5))

Ignore that the data is not enough to analyse and only has two levels
of the random effect, that is not the important bit. I want my model
(x~y+pa+pb+pc+pd+pe+pf+(1|group)) to use 10 values on y and 9 values
on the p variables. not 4 values on all.

Is that even possible?