PyHLA: tests for the association between HLA alleles and diseases

BMC Bioinformatics

Software

You-Qiang Song¹  Yanhui Fan² 

[1] School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong;Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong;School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong;Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong;Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, China;

关键词: HLA;    Association;    Interaction;    Multi-allelic;   

DOI  :  10.1186/s12859-017-1496-0

 received in 2016-07-26, accepted in 2017-01-27,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017