I do GWAS with Bovine genotype data(Bulls).

My phenotypes are breeding values for the Bulls.

Each phenotype has an uncertainty depending on how many daughters is used when calculating the Breeding Value.

I would really, really like to be abel to take this into account by making the number of daughtes qualifier to the response variable in the mixed model..

In eg. ASreml you can specify the Linearixed Model like this:

"dyd !WT ndau ~ mu !r snp1 and(snp2,1) animal"

!WT is defined in the ASreml manual:

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!WT

Weighted analyses are achieved by using !WT weight as a qualifier to the response variable. An example of this is

y !WT wt ~ mu A X

where y is the name of the response variable and wt is the name of a variate in the data containing weights. If these are relative weights (to be scaled by the units variance) then this is all that is required. If they are absolute weights, that is, the reciprocal of known variances, use the !S2==1 qualifier to fix the unit variance. When a structure is present in the residuals the weights are applied as a matrix product. If S is the structure and W is the diagonal matrix constructed from the square root of the values of the variate weight, then R inverse = W ( S inverse) W. Negative weights are treated as zeros.

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In the mixable manual, I noticed the option:

GWFGLS(formula, data, subset, weights,) for the Genome-wide FGLS.

But I guess this is not the same thing..?

Any ideas?

kind Regards,

Tim Knutsen.