-
Notifications
You must be signed in to change notification settings - Fork 45
Single Step Analysis
Hao Cheng edited this page Feb 15, 2021
·
5 revisions
# Step 1: Load packages
using JWAS,DataFrames,CSV,Statistics,JWAS.Datasets
# Step 2: Read data
phenofile = dataset("phenotypes.csv")
pedfile = dataset("pedigree.csv")
genofile = dataset("genotypes.csv")
phenotypes = CSV.read(phenofile,DataFrame,delim = ',',header=true,missingstrings=["NA"])
pedigree = get_pedigree(pedfile,separator=",",header=true);
genotypes = get_genotypes(genofile,separator=',',method="BayesC");
first(phenotypes,5)
# Step 3: Build Model Equations
model_equation ="y1 = intercept + x1 + x2 + x2*x3 + ID + dam + genotypes";
model = build_model(model_equation);
# Step 4: Set Factors or Covariates
set_covariate(model,"x1");
# Step 5: Set Random or Fixed Effects
set_random(model,"x2");
set_random(model,"ID dam",pedigree);
# Step 6: Run Analysis
out=runMCMC(model,phenotypes,single_step_analysis=true,pedigree=pedigree);
# Step 7: Check Accuruacy
results = innerjoin(out["EBV_y1"], phenotypes, on = :ID)
accuruacy = cor(results[:EBV],results[:bv1])
# Step 1: Load packages
using JWAS,DataFrames,CSV,Statistics,JWAS.Datasets
# Step 2: Read data
phenofile = dataset("phenotypes.csv")
pedfile = dataset("pedigree.csv")
genofile = dataset("genotypes.csv")
phenotypes = CSV.read(phenofile,DataFrame,delim = ',',header=true,missingstrings=["NA"])
pedigree = get_pedigree(pedfile,separator=",",header=true);
genotypes = get_genotypes(genofile,separator=',',method="BayesC");
first(phenotypes,5)
# Step 3: Build Model Equations
model_equation ="y1 = intercept + x1 + x2 + x2*x3 + ID + dam + genotypes
y2 = intercept + x1 + x2 + ID + genotypes
y3 = intercept + x1 + ID + genotypes";
model = build_model(model_equation);
# Step 4: Set Factors or Covariates
set_covariate(model,"x1");
# Step 5: Set Random or Fixed Effects
set_random(model,"x2");
set_random(model,"ID dam",pedigree);
# Step 6: Run Analysis
out=runMCMC(model,phenotypes,single_step_analysis=true,pedigree=pedigree);
# Step 7: Check Accuruacy
results = innerjoin(out["EBV_y1"], phenotypes, on = :ID)
accuruacy = cor(results[:EBV],results[:bv1])
To run analysis without genomic data, just remove "genotypes" in the model_equation from the script above.
Joint Analysis of Continuous, Censored and Categorical Traits
Integrating Phenotypic Causal Networks in GWAS
single trait and multiple trait GBLUP by providing the relationship matrix directly
User-defined Prediction Equation
Description of Mixed Effects Model
Constraint on variance components