Post by jcole on Sept 15, 2015 19:07:47 GMT
Hello,
My dataset is of 3500 individuals for 48 variables in which I am predicting the total genetic effect using:
gcta64 --remove excludeIDs.list --grm-bin test_Kibs --reml --pheno phen.phen --out LM1 --mpheno 1 --thread-num 10 --reml-pred-rand
... x 48 phenotypes
The output is an HSQ for each variable which provides the genetic variance V(G), and the INDI.BLP which provides in column 4 the total genetic effect, which in theory is calculated from the genetic variance. However, when I combine all of the total genetic effects (column 4) into a single matrix 3500 x 48 and obtain the genetic covariance matrix using cov() in R, the diagonal (genetic variance) is not equal to the V(G) in the HSQ. The genetic variances from the HSQ files are in fact consistently higher than the genetic variance determined from the covariance matrix of the total genetic effect.
Can you please explain this?
Thank you!
My dataset is of 3500 individuals for 48 variables in which I am predicting the total genetic effect using:
gcta64 --remove excludeIDs.list --grm-bin test_Kibs --reml --pheno phen.phen --out LM1 --mpheno 1 --thread-num 10 --reml-pred-rand
... x 48 phenotypes
The output is an HSQ for each variable which provides the genetic variance V(G), and the INDI.BLP which provides in column 4 the total genetic effect, which in theory is calculated from the genetic variance. However, when I combine all of the total genetic effects (column 4) into a single matrix 3500 x 48 and obtain the genetic covariance matrix using cov() in R, the diagonal (genetic variance) is not equal to the V(G) in the HSQ. The genetic variances from the HSQ files are in fact consistently higher than the genetic variance determined from the covariance matrix of the total genetic effect.
Can you please explain this?
Thank you!
