On Mon, Mar 23, 2009 at 12:32 PM, moleps islon <[email protected]> wrote:
> Thanks for the input Neil. For my use I believe I can disregard the
> genetic status of it all (ie recessive, dominant etc.,etc) and just
> treat it as a risk factor, either you have it or you dont. I'm not
> modeling the inheritance and we dont have anyone related in our
> sample.
That is implicitly a dominant model.
However, you might observe a dosage effect in relation to penetrance.
Someone who is heterozygous and carries one copy of the risk allele
may have a penetrance that differs from someone who is homozygous for
the risk allele (i.e. carries two copies). You often also find that
there is a penetrance associated with the wild-type (i.e. zero copies
of the risk allele) too.
> We know our 217 patients have 11 cancers. What we dont know
> is how many have the risk factor, but we know the general incidence of
> cancer in people without the risk factor to be 6/100000.
This is where molecular genetics steps into the arena, and you would
be best served a) genotyping your samples yourself; b) searching
public databases for estimates of the allele frequency in similar
populations.
> By simulating
> a random number of random people in the sample having the riskfactor I
> can compare the random allocation of the risk factor in the sample.
> Next, by finding the random samples resulting in incidence rates of
> 6/100000 in the risk factor negative group I can use those samples to
> infer the incidence of the risk factor because the set has produced a
> result within the constraints of the problem.
That just seems like a circular reasoning to me, and it won't
necessarily reflect the true allele frequency of the risk allele in
the population as a whole.
Regards
Neil
--
"The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data." ~ John Tukey (1986), "Sunset salvo". The American
Statistician 40(1).
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