Gwas Result Result #27 — Analysis Report
Contents
- gwas result (#27) completed
1. Gwas Result
2026-09-21 02:18:38 · gwas_result
GWAS tested 10 SNPs after QC (of 10 input); 0 passed significance.
| n_snps_input | 10 |
|---|---|
| n_snps_after_qc | 10 |
| maf_filtered | 0 |
| missingness_filtered | 0 |
| hwe_filtered | 0 |
| 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 — example_gwas.csv
- Started
- 2026-09-21 02:18:38.023727
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/ce9e69bd9b5841ff84c3245d1bf700a6
| Parameter | Value |
|---|---|
| target_column | Trait_value |
| encoding_model | additive |
| significance_threshold | 0.005 |
| n_snps_input | 10 |
| n_snps_after_qc | 10 |
| n_samples | 20 |
| n_significant | 0 |
| lambda_gc | 1.620086 |
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')