Abstract
BackgroundRecently, several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Therefore, tools for downstream association analysis are highly desirable. Although several tools have been designed for multi-allelic marker association analysis, they were designed only for microsatellite markers and do not scale well with increasing data volumes, or they were designed for large-scale data but provided a limited number of tests.ResultsWe have developed a Python package called PyHLA, which implements several methods for HLA association analysis, to fill the gap. PyHLA is a tailor-made, easy to use, and flexible tool designed specifically for the association analysis of the HLA types imputed from genome-wide genotyping and NGS data. PyHLA provides functions for association analysis, zygosity tests, and interaction tests between HLA alleles and diseases. Monte Carlo permutation and several methods for multiple testing corrections have also been implemented.ConclusionsPyHLA provides a convenient and powerful tool for HLA analysis. Existing methods have been integrated and desired methods have been added in PyHLA. Furthermore, PyHLA is applicable to small and large sample sizes and can finish the analysis in a timely manner on a personal computer with different platforms. PyHLA is implemented in Python. PyHLA is a free, open source software distributed under the GPLv2 license. The source code, tutorial, and examples are available at https://github.com/felixfan/PyHLA.
Highlights
Several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data
Given the continuously increasing amounts of HLA types being generated, integrating the workflow for their downstream association analysis is highly desirable. Several existing tools, such as CLUMP [8], PyPop [9] and SKDM [10], can be used to analyze HLA types. These tools are not ideal for association analysis of HLA types inferred from genome-wide association studies (GWAS) and NGS data as they were designed for analyzing microsatellite markers or provided limited functions
We present PyHLA, a Python-based standalone tool, for the association analysis between diseases and HLA types inferred from GWAS and NGS data
Summary
Several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Given the continuously increasing amounts of HLA types being generated, integrating the workflow for their downstream association analysis is highly desirable. Several existing tools, such as CLUMP [8], PyPop [9] and SKDM [10], can be used to analyze HLA types. We present PyHLA, a Python-based standalone tool, for the association analysis between diseases and HLA types inferred from GWAS and NGS data
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