# Gwas Result Result #28 — Analysis Report

Generated 2026-09-21 02:39:34 · 1 run(s) · CAT-G Data Analyzer

## 1. gwas result (#28)

2026-09-21 02:18:44 · gwas_result · status: completed

*GWAS tested 1 SNPs after QC (of 7 input); 0 passed significance.*

| Metric | Value |
|---|---|
| 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](data:image/png;base64,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)

*missingness plot*

### Method & Provenance

- **Method:** GWAS — association analysis
- **Engine:** Python · scikit-learn/statsmodels 1.8.0
- **Input datasets:** 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')
```
