diff --git a/docs/output files.rst b/docs/output files.rst
index 07d541c5..4be43dea 100644
--- a/docs/output files.rst	
+++ b/docs/output files.rst	
@@ -240,7 +240,7 @@ The output includes the estimated intercept and covariate coefficients for the r
 effect estimates. 
 
 This file has three columns: the first gives the name of the regression coefficient 
-(i.e. direct_effect, population_effect, etc.), the second gives the corresponding regression coefficient,
+(i.e. proband, for proband PGS; parental, for parental PGS; etc.), the second gives the corresponding regression coefficient,
 and the third gives the standard error. For example, in the two-generation analysis, 
 if your PGS file has columns proband, paternal, maternal,
 then the effects file contains the estimate of the direct effect 
diff --git a/docs/simulation.rst b/docs/simulation.rst
index 0342011c..c5ac3715 100644
--- a/docs/simulation.rst
+++ b/docs/simulation.rst
@@ -68,8 +68,12 @@ To estimate direct effect and average NTC of the PGS, use the following command:
     ``pgs.py direct --pgs direct.pgs.txt --phenofile phenotype.txt``
 
 This will output a population effect estimate (1 generation model) to direct.1.effects.txt, and 
-direct effect and average NTC estimates to (2 generation model) to direct.2.effects.txt. The 
-sampling variance-covariance matrix of the estimates is output to direct.1.vcov.txt (for the 1 generation model) and
+direct effect and average NTC estimates to (2 generation model) to direct.2.effects.txt. The
+population and direct effect estimates are the coefficients on the proband PGS in the 1 and 2
+generation models, so are indicated by the 'proband' row. The average NTC estimate is the
+coefficient on the parental PGS in the two-generation model. The first column gives the name
+of the covariate/PGS column, the second column gives the estimated regression coefficient,
+and the third column gives the standard error of the estimate. The sampling variance-covariance matrix of the estimates is output to direct.1.vcov.txt (for the 1 generation model) and
 direct.2.vcov.txt (for the 2 generation model).
 
 As we are using the true direct effects as weights, the PGS captures all of the heritability,
diff --git a/snipar/pgs.py b/snipar/pgs.py
index ffab25ef..6d18d972 100644
--- a/snipar/pgs.py
+++ b/snipar/pgs.py
@@ -585,15 +585,15 @@ def fit_pgs_model(y, pg, ngen, ibdrel_path=None, covariates=None, fit_sib=False,
     ## Fit model
     if ngen==1:
         print('Fitting 1 generation model (proband only)')
-        alpha, alpha_cols = make_and_fit_model(y, pg, ['population_effect'], ibdrel_path=ibdrel_path, covariates=covariates, sparse_thresh=sparse_thresh)
+        alpha, alpha_cols = make_and_fit_model(y, pg, ['proband'], ibdrel_path=ibdrel_path, covariates=covariates, sparse_thresh=sparse_thresh)
     elif ngen==2 or ngen==3:
         if fit_sib:
             if 'sib' in pg.sid:
-                pg_cols = ['direct_effect','sib_indirect']
+                pg_cols = ['proband','sibling']
             else:
                 raise(ValueError('Sibling PGS not found (use --fit_sib when calculating PGS)'))
         else:
-            pg_cols = ['direct_effect']
+            pg_cols = ['proband']
         if parsum:
             if 'maternal' in pg.sid and 'paternal' in pg.sid:
                 parcols = np.sort(np.array([np.where(pg.sid=='maternal')[0][0],np.where(pg.sid=='paternal')[0][0]]))
@@ -607,9 +607,9 @@ def fit_pgs_model(y, pg, ngen, ibdrel_path=None, covariates=None, fit_sib=False,
                 pass
             else:
                 raise(ValueError('Maternal and paternal PGS not found so cannot sum (--parsum option given)'))
-            pg_cols.append('avg_parental_NTC')
+            pg_cols.append('parental')
         else:
-            pg_cols += ['paternal_NTC','maternal_NTC']
+            pg_cols += ['paternal','maternal']
         if ngen==2:
             print('Fitting 2 generation model (proband and observed/imputed parents)')
             alpha, alpha_cols = make_and_fit_model(y, pg, pg_cols, ibdrel_path=ibdrel_path, covariates=covariates, sparse_thresh=sparse_thresh)
diff --git a/snipar/scripts/simulate.py b/snipar/scripts/simulate.py
index 7a2ebfc1..49d6f462 100644
--- a/snipar/scripts/simulate.py
+++ b/snipar/scripts/simulate.py
@@ -1,4 +1,14 @@
 #!/usr/bin/env python
+"""Simulates genotype-phenotype data using forward simulation. Phenotypes can be affected 
+by direct genetic effects, indirect genetic effects (vertical transmission), and assortative mating.
+
+Args:
+@parser@
+
+Results:
+    genotype data in .bed format; full pedigree including phenotype and genetic components for all generations
+        
+"""
 import numpy as np
 import h5py, argparse
 from snipar.ibd import write_segs_from_matrix
@@ -30,7 +40,7 @@
 parser.add_argument('--save_par_gts',action='store_true',help='Save the genotypes of the parents of the final generation',default=False)
 parser.add_argument('--impute',action='store_true',help='Impute parental genotypes from phased sibling genotypes & IBD',default=False)
 parser.add_argument('--unphased_impute',action='store_true',help='Impute parental genotypes from unphased sibling genotypes & IBD',default=False)
-
+__doc__ = __doc__.replace("@parser@", get_parser_doc(parser))
 def main(args):
     """"Calling this function with args is equivalent to running this script from commandline with the same arguments.
     Args: