Simulated dataset for three traits. Markers, QTL and phenotypes are simulated for three traits. Here we extend the simulation scheme described by Cheng et al. (2018) for the case of three traits. So we simulated 100 evenly spaced loci on each of 4 chromosomes of length 10 cM. We selected 10 loci on each chromosome as QTL. We sampled states from Bernoulli distribution with p=0.5. After that we simulated 500 generations to obtain linkage disequilibrium using 500 males and 500 females that were mated at random. Random mating was continued for 5 more generations and population size was increased to 4000 males and 4000 females. The QTL on chromosome 1 has effect on trait 1, wherehas chomosomes 1 and 2 had effects on traits 2 and 3 respectively. The QTL on chromosome 4 had effects on the 3 traits. The effects of QTL on chromosome 4 were simulated from a multivariate normal distribution with null mean and variance covariance matrix:
1.00 0.75 0.50
0.75 1.00 0.75
0.50 0.75 1.00
The genetic values were scaled to have variance 1.0. The phenotypes for these traits were obtained by adding residuals to genetic values, residuals were simulated from a multivariate normal distribution with null mean and covariance matrix:
6.0 6.0 1.0
6.0 8.0 2.0
1.0 2.0 1.0
In total, 8000 individuals were simulated and the genetic covariance matrix is:
1.00 0.34 0.07
0.34 1.00 0.21
0.07 0.21 1.00
data(simulated3t)Matrix simulated3t.X contains the marker information. Matrix simulated3t.pheno contains the phenotypical information.
Cheng, H., K. Kadir, J., Zeng, D. Garrick and R. Fernando. 2018. Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors. Genetics, 209(1): 89-103.