Gwas Result Result #29 — Analysis Report
Contents
- gwas result (#29) completed
1. Gwas Result
2026-09-21 02:18:47 · gwas_result
GWAS tested 8 SNPs after QC (of 20 input); 0 passed significance.
| n_snps_input | 20 |
|---|---|
| n_snps_after_qc | 8 |
| maf_filtered | 0 |
| missingness_filtered | 0 |
| hwe_filtered | 12 |
| hwe_not_testable | 0 |
| ld_pruned | 0 |
ld heatmap
missingness plot
genotype pca
Method & Provenance
- Method
- GWAS — association analysis
- Engine
- Python · scikit-learn/statsmodels 1.8.0
- Input dataset
- gallery — cat_g_gwas_real_data.csv
- Started
- 2026-09-21 02:18:47.650556
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/bb45ab95d37f40eb83d4d8e3b1980205
| Parameter | Value |
|---|---|
| target_column | Protein_Y19 |
| encoding_model | additive |
| significance_threshold | 0.00625 |
| n_snps_input | 20 |
| n_snps_after_qc | 8 |
| n_samples | 283 |
| n_significant | 0 |
| lambda_gc | 33.578415 |
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')