hgv_gpass

hgv_gpass applies Poisson de-clumping heuristics to approximate genome-wide significance of single-nucleotide polymorphism (SNP) associations in genome-wide association studies (GWAS), accounting for linkage disequilibrium (LD) to produce adjusted p-values for identifying disease-associated variants.

Key Features:

• Efficient p-value adjustment: Implements Poisson de-clumping heuristics to approximate genome-wide significance for millions of correlated SNP comparisons in seconds and can outperform permutation tests and other multiple-comparison methods.
• Mitigation of Bonferroni conservativeness: Reduces the overconservativeness of Bonferroni correction in the presence of LD by modeling correlation among nearby SNPs.
• Scalability and sample-size independence: Accuracy and computational efficiency are reported to be nearly independent of sample size, number of SNPs, and the scale of p-values to be adjusted.
• False discovery rate estimation: Supports adoption of methods to estimate false discovery rate (FDR) and applying user-specified FDR thresholds to identify significant SNPs.
• Consistency across populations: Produces consistent p-value adjustments across different genomic regions and populations, including European and African groups.
• Correlation with genomic features: Adjusted significance values are significantly correlated with linkage disequilibrium, recombination rates, and SNP densities.
• Local significance thresholds: Incorporates SNP-specific local thresholds to detect genome-wide significant associations that account for variability in sequence features.

Scientific Applications:

• Genome-wide association studies (GWAS): Identification of SNPs associated with diseases and traits using adjusted p-values that account for LD.
• False discovery control: Application of FDR thresholds to control discovery rates in large-scale association analyses.
• Cross-population genetic analysis: Comparative p-value adjustment and association detection across populations such as European and African cohorts.
• Genomic context interpretation: Relating adjusted association significance to LD structure, recombination rates, and SNP density for interpretation of association signals.

Methodology:

Implements Poisson de-clumping heuristics to approximate genome-wide significance while accounting for linkage disequilibrium (LD).