How to simulate phenotype from two random multivariant normal distributed variable and calculate statistical power!

Question

[gaaraqrq]

Greeting Homo Sapiens,

I am a Biology student and just stepped into the area of Statistical Genomics. I have gone through several posts from the forum and I must say the quality response from the community helped me to solve my 20-30% problem. Still I am finding some gap in my research area for which I am trying to write this question. Lets hope I can explain my problem and make you guys understand easily 0_0

So basically the aim of my study is to

Perform Simulation through Mixed linear model Calculate Power of Statistical Models Find Type 1 error in my model Now my model is a bit tricky (at least for me since I am from Bioscience department). I am trying to fit a model with Genotypic Variance, two random variables and these variables should be multivariant normally distributed. Next I am going to select some fixed positions from Genotype variance variable and multiply with two random multivariant normal distributed variable to simulate Phenotype (This will help me with finding Phenotypic Variance explained by my model). Finally I want to calculate the Power and Type 1 error of my model. The steps are mentioned below:

1. Create Genotype Variance from Genotype .raw file
2. Select any three positions from the variance variable
3. Create First random effect by using mvnorm function from MASS package
4. Create Second random effect by using mvnorm function
5. Combine first three random effect columns with variance variable
6. Create Simulation effect from two random variables (First three columns)
7. Combine the simulated effect with variance variables i.e. effect_indiv_geno<-t(GEN_QTN)%%effect_1_simu %% effect_2_simu
8. Create residual from rnorm
9. Calculate Phenotype from effect_indiv_geno and residual

I need help from here onwards as I am stuck. I am not sure how to plan the Power calculation process and then follow type 1 error for the model. Looking forward to hearing from you guys. Thanks and Stay Safe.