Re: Binary Data with missing values

Dear Andrew,

You can delete the records with missing SNP values with a transformation

In ASReml 3, you could use

  SNP1 !DV*

Otherwise use
  SNP1 !D 10

The latter also drops records with a value of 10 but I assume there are
none of those.

Also,
I expect SNP has just two states (0,1 or -1,1 probably). It would be
better
to drop the !I from its declaration and treat it as a covariable
so you are sure which way the effect is fitted.

Assuming you have in fact may SNPs to test,
there are ways using !CYCLE to test a whole lot in a single run.

On Wed, 2009-07-08 at 11:16 +0100, Clempson, Andrew Martin wrote:
> Dear All
>
>
>
> I wonder if someone could provide advice on dealing with missing
> values in binary data models.
>
>
>
> I am in the process of performing association studies between SNPs and
> pregnancy status in cows. I have genotypic information on
> approximately 90% of animals and pregnancy status data on
> approximately 80% of animals (coded as 0 = pregnant, 1 = not
> pregnant).
>
>
>
> At the moment, my model is very simple –
>
>
>
> Fertility Analysis
>
> ANIMALID !P
>
> Sire !P
>
> Dam !P
>
> Herd !I
>
> Age
>
> PregnancyStatus
>
> SNP1!I
>
> Pedigree.ped !MAKE !SORT !REPEAT
>
> Pregnancy.csv !CSV !MAXIT 100 !EXTRA 20 !MVINCLUDE !SKIP 1 !DISPLAY 15
>
> PregnancyStatus !BIN ~ mu Herd Age SNP1 !r ANIMALID
>
>
>
>
>
> The model only runs when the !MVINCLUDE qualifier is specified,
> however, I am trying to find a way of excluding missing values, as
> including them interferes with the result. Using MVEXCLUDE / MVREMOVE
> causes the model to fail.
>
>
>
> I would be very grateful for any replies,
>
> Many thanks
>
>
>
> Andrew
>
>
>
>
>
>
>
>
>
>
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-- 
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