GWAS Result Help
Target: Trait_value | Encoding: additive | SNPs after QC: 10 / 10 | Samples: 20 | Significant: 0 | λGC: 1.620
Moderate test-statistic inflation (λGC = 1.62).
Values above ~1.05 suggest residual confounding (population structure or
relatedness). With few testable SNPs λGC is also a noisy
estimate. Interpret borderline associations carefully.
Learn more: Manhattan plot · Q-Q plot · Effect size · MAF · LD · Genomic control · Multiple testing
| Metric | Value |
|---|---|
| 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 |
| SNP ▲▼ | Chromosome ▲▼ | Position ▲▼ | Beta ▲▼ | SE ▲▼ | R² ▲▼ | P-value ▲▼ | Corrected P ▲▼ |
|---|---|---|---|---|---|---|---|
| SNP_001 | -0.5504 | 0.7774 | 0.0271 | 5.86e-01 | 1.00e+00 | ||
| SNP_002 | -0.0118 | 0.7881 | 0.0000 | 9.91e-01 | 1.00e+00 | ||
| SNP_003 | -1.1528 | 0.7998 | 0.1035 | 2.77e-01 | 1.00e+00 | ||
| SNP_004 | -1.2455 | 0.5951 | 0.1957 | 1.25e-01 | 1.00e+00 | ||
| SNP_005 | -0.3341 | 0.7056 | 0.0123 | 7.15e-01 | 1.00e+00 | ||
| SNP_006 | 1.1842 | 0.6542 | 0.1540 | 1.79e-01 | 1.00e+00 | ||
| SNP_007 | -0.7805 | 0.6858 | 0.0671 | 3.86e-01 | 1.00e+00 | ||
| SNP_008 | 0.2619 | 0.7303 | 0.0071 | 7.82e-01 | 1.00e+00 | ||
| SNP_009 | 0.0945 | 0.7675 | 0.0008 | 9.24e-01 | 1.00e+00 | ||
| SNP_010 | -0.7852 | 0.7661 | 0.0551 | 4.34e-01 | 1.00e+00 |
Static figures
Linkage disequilibrium (r²)
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Genotype missingness
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Genotype PCA
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Method & Provenance
- Method
- GWAS — association analysis
- Method key
gwas(result type:gwas_result, result #27)- Engine
- Python · scikit-learn/statsmodels v1.8.0
- Dataset(s)
- #724 gallery — example_gwas.csv
- Status
- completed
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/ce9e69bd9b5841ff84c3245d1bf700a6 Bookmark or share this URL — it does not depend on your session.
- Created
- 2026-09-21 02:18:38.023727
Parameters used
| 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 |
Execution trace
Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.
# app.services.gwas_qc_service — SNP QC + allele encodingfrom sklearn.linear_model import LogisticRegressionfor snp in snp_matrix.columns: # per-SNP association test model = LogisticRegression(max_iter=1000).fit(X_snp, y) p = association_pvalue(model)fdr = statsmodels.stats.multitest.multipletests(p_values, method='fdr_bh')