diff --git a/str/associatr/get_cis_numpy_files.py b/str/associatr/get_cis_numpy_files.py
index 1018da37..dc00c09a 100644
--- a/str/associatr/get_cis_numpy_files.py
+++ b/str/associatr/get_cis_numpy_files.py
@@ -10,7 +10,7 @@
  - output gene-level phenotype and covariate numpy objects for input into associatr
 
  analysis-runner  --config get_cis_numpy_files.toml --dataset "bioheart" --access-level "test" \
---description "get cis and numpy" --output-dir "str/associatr/tob_n1055/input_files"  \
+--description "get cis and numpy" --output-dir "str/associatr/tob_n1055/tester" \
 --image australia-southeast1-docker.pkg.dev/cpg-common/images/scanpy:1.9.3 \
 python3 get_cis_numpy_files.py
 
@@ -21,6 +21,7 @@
 import numpy as np
 import pandas as pd
 import scanpy as sc
+from cyvcf2 import VCF
 from scipy.stats import norm
 
 import hail as hl
@@ -31,6 +32,33 @@
 from cpg_utils.hail_batch import get_batch, image_path, init_batch, output_path
 
 
+def extract_genotypes(vcf_file, loci):
+    """
+    Helper function to extract genotypes (SNPs) from a VCF file; target loci specified as a list (can be single or multiple)
+
+    """
+    # Read the VCF file
+    vcf_reader = VCF(vcf_file)
+
+    results = pd.DataFrame()
+    results['sample_id'] = vcf_reader.samples
+
+    # Iterate through the records in the VCF file for each locus
+    for locus in loci:
+        chrom, pos = locus.split(':')
+        pos = int(pos)
+
+        for record in vcf_reader(f'{chrom}:{pos}-{pos}'):
+            if record.CHROM == chrom and record.POS == pos:
+                gt = record.gt_types
+                gt[gt == 3] = 2 #HOM ALT is coded as 3; change it to 2
+                results[locus] = gt
+                break
+
+    # Convert results to a DataFrame
+    return results
+
+
 def cis_window_numpy_extractor(
     input_h5ad_dir,
     input_pseudobulk_dir,
@@ -42,6 +70,8 @@ def cis_window_numpy_extractor(
     chrom_len,
     min_pct,
     remove_samples_file,
+    snp_input,
+    snp_loci,
 ):
     """
     Creates gene-specific cis window files and phenotype-covariate numpy objects
@@ -106,6 +136,11 @@ def cis_window_numpy_extractor(
 
         gene_pheno_cov = gene_pheno.merge(covariates, on='sample_id', how='inner')
 
+        # add SNP genotypes we would like to condition on
+        if snp_input is not None:
+            snp_genotype_df = extract_genotypes(snp_input['vcf'], snp_loci)
+            gene_pheno_cov = gene_pheno_cov.merge(snp_genotype_df, on='sample_id', how='inner')
+
         # filter for samples that were assigned a CPG ID; unassigned samples after demultiplexing will not have a CPG ID
         gene_pheno_cov = gene_pheno_cov[gene_pheno_cov['sample_id'].str.startswith('CPG')]
 
@@ -151,6 +186,13 @@ def manage_concurrency_for_job(job: hb.batch.job.Job):
             j.cpu(get_config()['get_cis_numpy']['job_cpu'])
             j.memory(get_config()['get_cis_numpy']['job_memory'])
             j.storage(get_config()['get_cis_numpy']['job_storage'])
+
+            if get_config()['get_cis_numpy']['snp_vcf_dir'] is not None:
+                snp_vcf_dir = get_config()['get_cis_numpy']['snp_vcf_dir']
+                snp_vcf_path = f'{snp_vcf_dir}/{chrom}_common_variants.vcf.bgz'
+                snp_input = get_batch().read_input_group(**{'vcf': snp_vcf_path, 'csi': snp_vcf_path + '.csi'})
+            else:  # no SNP VCF provided
+                snp_input = None
             j.call(
                 cis_window_numpy_extractor,
                 get_config()['get_cis_numpy']['input_h5ad_dir'],
@@ -163,6 +205,8 @@ def manage_concurrency_for_job(job: hb.batch.job.Job):
                 chrom_len,
                 get_config()['get_cis_numpy']['min_pct'],
                 get_config()['get_cis_numpy']['remove_samples_file'],
+                snp_input,
+                get_config()['get_cis_numpy']['snp_loci'],
             )
 
             manage_concurrency_for_job(j)