Gwas Result Result #28 — Analysis Report
Contents
- gwas result (#28) completed
1. Gwas Result
2026-09-21 02:18:44 · gwas_result
GWAS tested 1 SNPs after QC (of 7 input); 0 passed significance.
| n_snps_input | 7 |
|---|---|
| n_snps_after_qc | 1 |
| maf_filtered | 2 |
| missingness_filtered | 0 |
| hwe_filtered | 4 |
| hwe_not_testable | 0 |
| ld_pruned | 0 |
missingness plot
Method & Provenance
- Method
- GWAS — association analysis
- Engine
- Python · scikit-learn/statsmodels 1.8.0
- Input dataset
- gallery — example_gwas_protein.csv
- Started
- 2026-09-21 02:18:44.740736
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/abdb9fb3c3284f0dbf813675034bb8b8
| Parameter | Value |
|---|---|
| target_column | Protein_Y19 |
| encoding_model | additive |
| significance_threshold | 0.05 |
| n_snps_input | 7 |
| n_snps_after_qc | 1 |
| n_samples | 95 |
| n_significant | 0 |
| lambda_gc | 0.032914 |
Command history
# python
# app.services.gwas_qc_service — SNP QC + allele encoding
# python
from sklearn.linear_model import LogisticRegression
# python
for snp in snp_matrix.columns: # per-SNP association test
# python
model = LogisticRegression(max_iter=1000).fit(X_snp, y)
# python
p = association_pvalue(model)
# python
fdr = statsmodels.stats.multitest.multipletests(p_values, method='fdr_bh')