diff --git a/src/1.JWAS/src/JWAS.jl b/src/1.JWAS/src/JWAS.jl
index 58ee13f6..5e794bc6 100644
--- a/src/1.JWAS/src/JWAS.jl
+++ b/src/1.JWAS/src/JWAS.jl
@@ -134,8 +134,8 @@ export dataset
         * If `causal_structure` is provided, e.g., causal_structure = [0.0 0.0 0.0;1.0 0.0 0.0;1.0 0.0 0.0] for
           trait 2 -> trait 1 and trait 3 -> trait 1 (row index affacts column index), phenotypic causal networks will be incorporated using structure equation models.
 * Genomic Prediction
-    * Predicted values for individuals of interest can be obtained based on an user-defined prediction equation `prediction_equation`, e.g., "y1:animal + y1:age".
-    For now, genomic data is always included. Genetic values including effects defined with genotype and pedigre information are returned if `prediction_equation`= false, defaulting to `false`.
+    * Predicted values for individuals of interest can be obtained based on a user-defined prediction equation `prediction_equation`, e.g., "y1:animal + y1:age".
+    For now, genomic data is always included. Genetic values including effects defined with genotype and pedigree information are returned if `prediction_equation`= false, defaulting to `false`.
     * Individual estimted genetic values and prediction error variances (PEVs) are returned if `outputEBV`=true, defaulting to `true`. Heritability and genetic
     variances are returned if `output_heritability`=`true`, defaulting to `true`. Note that estimation of heritability is computaionally intensive.
 * Miscellaneous Options
diff --git a/src/1.JWAS/src/build_MME.jl b/src/1.JWAS/src/build_MME.jl
index ad34e6f6..438bdce0 100644
--- a/src/1.JWAS/src/build_MME.jl
+++ b/src/1.JWAS/src/build_MME.jl
@@ -112,6 +112,7 @@ function build_model(model_equations::AbstractString, R = false; df = 4.0,
 
   #create mme with genotypes
   filter!(x->x.random_type != "genotypes",modelTerms)
+  filter!(x->x[2].random_type != "genotypes",dict)
   mme = MME(nModels,modelVec,modelTerms,dict,lhsVec,R == false ? R : Float32.(R),Float32(df))
   if length(genotypes) != 0
     mme.M = genotypes
diff --git a/src/1.JWAS/src/output.jl b/src/1.JWAS/src/output.jl
index 73e37f5d..61bf0afa 100644
--- a/src/1.JWAS/src/output.jl
+++ b/src/1.JWAS/src/output.jl
@@ -44,6 +44,7 @@ function prediction_setup(model)
         println("Default or user-defined prediction equation are not available.")
         model.MCMCinfo.outputEBV = false
     end
+    filter!(e->(e in keys(model.modelTermDict)),prediction_equation) #remove "genotypes" for now
     model.MCMCinfo.prediction_equation = prediction_equation
 end