PyHLA

PyHLA performs association analysis of imputed human leukocyte antigen (HLA) types derived from genome-wide genotyping and next-generation sequencing (NGS) data to identify HLA alleles, zygosity states, and allele interactions associated with disease.

Key Features:

• Association Analysis: Implements statistical association tests for imputed HLA types from genome-wide genotyping and NGS data to detect allele-disease associations.
• Zygosity and Interaction Tests: Provides zygosity tests to assess homozygosity/heterozygosity and interaction tests between HLA alleles and diseases or other factors.
• Statistical Rigor: Employs Monte Carlo permutation methods and multiple testing correction techniques to control false discoveries.
• Scalability: Handles both small and large sample sizes and supports analyses beyond methods limited to microsatellite markers.
• Integration of Methods: Integrates existing HLA analysis methodologies alongside additional implemented methods for comprehensive analysis.

Scientific Applications:

• Immunogenetics and disease association: Investigation of HLA allele contributions to autoimmune diseases, infectious diseases, and other immune-related conditions.
• Gene-environment and allele interaction studies: Exploration of interactions between HLA alleles and diseases or environmental factors.
• Population and genetic studies using imputed HLA: Analysis of HLA types imputed from genome-wide genotyping and NGS datasets for genetic association and population studies.

Methodology:

Implemented in Python; performs association analysis of imputed HLA types; includes zygosity and interaction tests; applies Monte Carlo permutation methods and multiple testing correction techniques; integrates existing and additional HLA analysis methods and supports genome-wide genotyping and NGS data.