Frequently Asked Questions (FAQ)

General Questions

Q1: When should I use EDGE instead of standard additive GWAS?

A: Use EDGE when:

You suspect nonadditive inheritance patterns
Previous additive GWAS found few associations
You have large sample size (N > 2,000 under the case to control ratio at 70/30)
You’re following up candidate genes with known non-additive effects
You want to explore genetic architecture

Standard additive GWAS is sufficient for initial exploratory analyses or when sample size is limited.

Q2: What sample size do I need for EDGE analysis?

A: Minimum recommendations:

Binary outcomes: 1,400 cases + 600 controls
Continuous outcomes: 2,000 total samples

Optimal recommendations:

Binary outcomes: 5,000+ cases and controls
Continuous outcomes: 10,000+ samples

Larger samples provide more stable α estimates and better power.

Q3: How do I choose between optimization methods?

A: Decision guide:

Start with default (BFGS) - works for >95% of cases
Use L-BFGS for very large datasets (N > 100,000)
Try Newton if BFGS has convergence issues
Use Nelder-Mead for maximum robustness (slower)

Q4: Should I always use outcome transformations?

A: No, only when needed:

Check outcome distribution first (histogram, Q-Q plot)
Use transformations for non-normal distributions
RINT is safest choice for unknown distributions
No transformation needed if outcome is already normal

Technical Questions

Q5: Why do some SNPs fail to converge?

A: Common reasons:

Low MAF: Rare variants have insufficient heterozygotes
Perfect separation: In logistic regression, genotype perfectly predicts outcome
Multicollinearity: Genotype correlated with covariates
Numerical instability: Extreme coefficient values

Solutions:

# 1. Filter rare variants
genotype_filtered = genotype.loc[:, maf > 0.05]

# 2. Try different optimization method
edge = EDGEAnalysis(
    outcome_type='continuous',
    ols_method='nm',  # More robust
    max_iter=5000
)

# 3. Check skipped SNPs
skipped = edge.get_skipped_snps()
# Review for patterns (e.g., all in same region, all rare)

Q6: How do I interpret alpha values?

A: Alpha interpretation guide:

Alpha	Pattern	Biological Example
α ≈ 0	Recessive	Cystic fibrosis (CFTR mutations)
α ≈ 0.5	Additive	Height, most complex traits
α ≈ 1	Dominant	Huntington’s disease (HTT expansion)
α > 1	Over-dominant	Sickle cell trait (heterozygote advantage)
α < 0	Under-recessive	Heterozygote disadvantage (rare)

Q7: What if lambda (genomic inflation) is high?

A: Troubleshooting high λ (>1.05):

# 1. Add more principal components
covariates_extended = covariates + [f'pc{i}' for i in range(11, 21)]

# 2. Check for stratification
# Run PCA and examine PC plots by case/control status

# 3. Verify QC filters
# - Check for related individuals
# - Verify genotype quality
# - Check for batch effects

# 4. Consider genomic control correction (post-hoc)
gwas_results['pval_gc'] = adjust_genomic_control(
    gwas_results['pval'],
    lambda_gc
)

Q8: How do I handle related individuals?

A: Current options:

Remove related individuals (recommended for v0.1.2):

# Identify unrelated set using KING or PLINK
# kinship threshold: 0.0884 (3rd degree relatives)
unrelated_ids = identify_unrelated(kinship_matrix, threshold=0.0884)
genotype_unrelated = genotype.loc[unrelated_ids]

Wait for mixed model support (planned for v0.2.0):
- Will account for relatedness using GRM
- Allows use of full dataset including families
- More powerful for related samples

Q9: Can I use EDGE with imputed genotypes?

A: Yes, with considerations:

# 1. Filter by imputation quality
high_quality = imputation_info > 0.8  # INFO score
genotype_imputed = genotype.loc[:, high_quality]

# 2. Use dosages (0-2 continuous) instead of hard calls
# EDGE works with dosages

# 3. Be aware of uncertainty
# Imputed variants may have less stable alpha estimates
# Prefer directly genotyped SNPs for alpha calculation

Q10: How do I replicate EDGE findings in an independent cohort?

A: Replication workflow:

# Step 1: Extract alpha values from discovery cohort
discovery_alpha = pd.read_csv('discovery_alpha_values.csv')
alpha_dict = dict(zip(
    discovery_alpha['variant_id'],
    discovery_alpha['alpha_value']
))

# Step 2: Apply same alpha values to replication cohort
edge_replication = EDGEAnalysis(outcome_type='continuous')

replication_results = edge_replication.apply_alpha(
    genotype_data=replication_geno,
    phenotype_df=replication_pheno,
    outcome='trait',
    covariates=['age', 'sex', 'pc1', 'pc2'],
    alpha_values=discovery_alpha  # Use discovery alphas
)

# Step 3: Check for consistency
sig_variants = discovery_gwas[discovery_gwas['pval'] < 5e-8]['variant_id']

replication_sig = replication_results[
    replication_results['variant_id'].isin(sig_variants)
]

# Count replicated loci (p < 0.05, same direction)
replicated = (
    (replication_sig['pval'] < 0.05) &
    (np.sign(replication_sig['coef']) == np.sign(discovery_gwas['coef']))
).sum()

print(f"Replication rate: {replicated / len(sig_variants):.1%}")

Interpretation Questions

Q11: Can EDGE detect epistasis (SNP-SNP interactions)?

A: No, EDGE models single-SNP effects:

EDGE captures nonadditive effects at single loci (dominance)
Does not model interactions between loci (epistasis)
For epistasis, use dedicated interaction testing methods
EDGE and epistasis testing are complementary approaches

Q12: How do I know if EDGE found a truly novel locus?

A: Validation checklist:

# 1. Check it's not in LD with known loci
def check_ld_independence(novel_snp, known_snps, ld_matrix):
    """Check if novel SNP is independent of known loci."""
    max_ld_r2 = 0
    for known_snp in known_snps:
        r2 = ld_matrix[novel_snp, known_snp]
        if r2 > max_ld_r2:
            max_ld_r2 = r2
    return max_ld_r2 < 0.1  # Independent if r² < 0.1

# 2. Verify it's not a QC artifact
# - Check MAF, HWE, call rate
# - Look for clustered missingness
# - Verify in multiple genotyping platforms

# 3. Examine alpha biological plausibility
# - Does inheritance pattern make sense?
# - Are nearby genes relevant to phenotype?

# 4. Replicate in independent cohort
# - Must replicate with similar alpha value
# - Check effect direction consistency

Q13: What does it mean if alpha is negative?

A: Negative alpha indicates under-recessive pattern:

Heterozygotes have opposite effect direction from homozygotes
Example: If homozygotes increase trait, heterozygotes decrease it
Rare but possible biological scenarios:
- Antagonistic pleiotropy
- Complex regulatory mechanisms
- Possible false positive (check carefully!)

Recommendation: Scrutinize negative alpha SNPs carefully for QC issues.

Q14: Should I filter SNPs based on alpha values?

A: Generally no, but consider:

Don’t filter alpha values for:

Primary GWAS analysis (test all variants)
Exploratory analysis
Novel discovery

Consider filtering for:

Extremely unstable alphas (|α| > 5): likely convergence issues
Follow-up functional studies: focus on biologically plausible patterns
Replication: use only well-estimated alphas (SE < 0.5)

# Flag potentially unstable alphas
alpha_df['stable'] = (
    (alpha_df['alpha_value'].abs() < 5) &
    (alpha_df['std_err_het'] < 0.5) &
    (alpha_df['std_err_hom'] < 0.5)
)

print(f"Stable alpha estimates: {alpha_df['stable'].sum()} / {len(alpha_df)}")

Q15: How do I compare EDGE results across different traits?

A: Cross-trait comparison:

# Load results for multiple traits
traits = ['bmi', 'height', 'triglycerides']
alpha_by_trait = {}

for trait in traits:
    alpha_df = pd.read_csv(f'results/{trait}_alpha_values.csv')
    alpha_by_trait[trait] = alpha_df

# Find variants tested in all traits
common_variants = set(alpha_by_trait[traits[0]]['variant_id'])
for trait in traits[1:]:
    common_variants &= set(alpha_by_trait[trait]['variant_id'])

# Compare alpha values
comparison = pd.DataFrame({
    trait: alpha_by_trait[trait].set_index('variant_id')['alpha_value']
    for trait in traits
}).loc[list(common_variants)]

# Calculate correlation
print("Alpha correlation across traits:")
print(comparison.corr())

# Identify trait-specific non-additive effects
# (different alpha values across traits)
comparison['alpha_range'] = comparison.max(axis=1) - comparison.min(axis=1)
trait_specific = comparison.nlargest(20, 'alpha_range')
print("\nVariants with trait-specific inheritance patterns:")
print(trait_specific)

Practical Implementation Questions

Q16: Can I run EDGE on multiple chromosomes in parallel?

A: Yes, recommended for large datasets:

from joblib import Parallel, delayed

def analyze_chromosome(chrom, train_g, train_p, test_g, test_p):
    """Run EDGE on a single chromosome."""
    # Filter to chromosome
    chrom_snps = [col for col in train_g.columns if col.startswith(f'chr{chrom}:')]

    edge = EDGEAnalysis(outcome_type='continuous')
    alpha_df, gwas_df = edge.run_full_analysis(
        train_genotype=train_g[chrom_snps],
        train_phenotype=train_p,
        test_genotype=test_g[chrom_snps],
        test_phenotype=test_p,
        outcome='trait',
        covariates=['age', 'sex'],
        output_prefix=f'results/chr{chrom}'
    )

    return alpha_df, gwas_df

# Run in parallel
results = Parallel(n_jobs=22)(
    delayed(analyze_chromosome)(chrom, train_g, train_p, test_g, test_p)
    for chrom in range(1, 23)
)

# Combine results
all_alpha = pd.concat([r[0] for r in results], ignore_index=True)
all_gwas = pd.concat([r[1] for r in results], ignore_index=True)

Q17: How much disk space do I need?

A: Storage requirements:

# Estimates for typical GWAS

# Input data
# - Genotypes: ~4 bytes per genotype
#   Example: 500K SNPs × 50K samples = 100 GB

# - Phenotypes: negligible (~1 MB)

# Output data
# - Alpha values: ~500 bytes per SNP
#   Example: 500K SNPs = 250 MB

# - GWAS results: ~1 KB per SNP
#   Example: 500K SNPs = 500 MB

# Total: ~100 GB input + 1 GB output

# Recommendations:
# - 200 GB free space for safety
# - SSD for faster I/O
# - Compress intermediate files

Q18: Can I use EDGE with X chromosome variants?

A: Limited support in v0.1.2:

# Current workaround for X chromosome

# 1. Analyze males and females separately
males = phenotype[phenotype['sex'] == 'male'].index
females = phenotype[phenotype['sex'] == 'female'].index

# 2. For males: recode genotypes (0/1 → 0/2)
#    Males are hemizygous, so 0=ref, 1=alt (recode as 0 and 2)
genotype_X_males = genotype_X.loc[males].replace({1: 2})

# 3. Run EDGE separately
edge_females = EDGEAnalysis(outcome_type='continuous')
alpha_f, gwas_f = edge_females.run_full_analysis(
    train_genotype_X.loc[females_train],
    phenotype.loc[females_train],
    test_genotype_X.loc[females_test],
    phenotype.loc[females_test],
    outcome, covariates
)

# Note: Proper X chromosome support coming in v0.2.0

Q19: How do I save and load EDGE results for later use?

A: Best practices for saving results:

# Save results
alpha_df.to_csv('alpha_values.csv', index=False)
gwas_results.to_csv('gwas_results.csv', index=False)

# For large datasets, use compressed format
alpha_df.to_csv('alpha_values.csv.gz', index=False, compression='gzip')

# Or use Parquet for faster loading
alpha_df.to_parquet('alpha_values.parquet')
gwas_results.to_parquet('gwas_results.parquet')

# Load results later
alpha_df = pd.read_csv('alpha_values.csv')
# or
alpha_df = pd.read_parquet('alpha_values.parquet')

# Apply saved alphas to new data
edge = EDGEAnalysis(outcome_type='continuous')
new_gwas = edge.apply_alpha(
    new_genotype,
    new_phenotype,
    outcome,
    covariates,
    alpha_values=alpha_df  # Use saved alphas
)

Q20: What are the memory requirements?

A: Memory usage guide:

# Rough estimates

# Genotype data: 8 bytes per value (pandas float64)
# Example: 100K SNPs × 10K samples = 8 GB

# For large datasets:
# 1. Process by chromosome
# 2. Use chunking

def analyze_in_chunks(genotype, phenotype, chunk_size=10000):
    """Process SNPs in chunks to reduce memory."""
    all_results = []

    n_snps = len(genotype.columns)
    n_chunks = (n_snps + chunk_size - 1) // chunk_size

    for i in range(n_chunks):
        start_idx = i * chunk_size
        end_idx = min((i + 1) * chunk_size, n_snps)

        chunk_snps = genotype.columns[start_idx:end_idx]
        geno_chunk = genotype[chunk_snps]

        edge = EDGEAnalysis(outcome_type='continuous')
        alpha_chunk, gwas_chunk = edge.run_full_analysis(
            geno_chunk.loc[train_ids],
            phenotype.loc[train_ids],
            geno_chunk.loc[test_ids],
            phenotype.loc[test_ids],
            outcome='trait',
            covariates=['age', 'sex']
        )

        all_results.append((alpha_chunk, gwas_chunk))

        # Clear memory
        del geno_chunk
        import gc
        gc.collect()

    # Combine results
    alpha_combined = pd.concat([r[0] for r in all_results], ignore_index=True)
    gwas_combined = pd.concat([r[1] for r in all_results], ignore_index=True)

    return alpha_combined, gwas_combined

Frequently Asked Questions (FAQ)

General Questions

Technical Questions

Interpretation Questions

Practical Implementation Questions

See Also