Post by hnorpois on Apr 16, 2014 12:15:38 GMT
Hello,
I have a question concerning the random effect g in the MLM.
In the model
y=Xb+g+e (with
y: nx1 vector of phenotypes
b: fixed effects
g: random effect
and e as error term) I dont understand how to measure g, that seems to rely on the GRM (a n x n matrix for n phenotypes). g is a n x 1 matrix. Is g the first component of a pca (on the n x n of GRM) or the first component of a multidimensional scaling? I dont think so. So how do you get a "polygenetic effect" for each id (corresponding to a phenotype)?
I am sorry to ask this question here but I did not find an answer in the papers.
Thanks
Hermann
I have a question concerning the random effect g in the MLM.
In the model
y=Xb+g+e (with
y: nx1 vector of phenotypes
b: fixed effects
g: random effect
and e as error term) I dont understand how to measure g, that seems to rely on the GRM (a n x n matrix for n phenotypes). g is a n x 1 matrix. Is g the first component of a pca (on the n x n of GRM) or the first component of a multidimensional scaling? I dont think so. So how do you get a "polygenetic effect" for each id (corresponding to a phenotype)?
I am sorry to ask this question here but I did not find an answer in the papers.
Thanks
Hermann
