Benchmarking freely available human leukocyte antigen typing algorithms across varying genes, coverages and typing resolutions

Abstract

The human leukocyte antigen (HLA) system is a group of genes coding for proteins that are central to the adaptive immune system and identifying the specific HLA allele combination of a patient is relevant in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation-sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq. This evaluation was done for the five HLA loci, HLA-A, -B, -C, -DRB1 and -DQB1 using whole-exome sequencing (WES) samples from 829 individuals. The robustness of the tools to lower coverage was evaluated by subsampling and HLA typing 230 WES samples at coverages ranging from 1X to 100X. The typing accuracy was measured across four typing resolutions. Among these, we present two clinically-relevant typing resolutions, which specifically focus on the peptide binding region. On average, across the five HLA genes, HLA*LA was found to have the highest typing accuracy. For the individual genes, HLA-A, -B and -C, Optitype's typing accuracy was highest and HLA*LA had the highest typing accuracy for HLA-DRB1 and -DQB1. The tools' robustness to lower coverage data varied widely and further depended on the specific HLA locus. For all class I loci, Optitype had a typing accuracy above 95% (according to the modification of the amino acids in the functionally relevant portion of the protein) at 50X, but increasing the depth of coverage beyond even 100X could still improve the typing accuracy of HISAT-genotype, Kourami, and STC-seq across all five HLA genes as well as HLA*LA's typing accuracy for HLA-DQB1. HLA typing is also used in studies of ancient DNA (aDNA), which often is based on lower quality sequencing data. Interestingly, we found that Optitype's typing accuracy is not notably impaired by short read length or by DNA damage, which is typical of aDNA, as long as the depth of coverage is sufficiently high.

Elevated genetic risk for multiple sclerosis originated in Steppe Pastoralist populations

“…For our ancestry path analysis, a substantial fraction of the fine-mapped MS variants were not imputed in our ancient dataset, due to quality control filtering and the difficulty of accurately inferring HLA alleles in ancient samples 22 . To address this, we LD-pruned genome-wide significant summary statistics from the same study 4 , for which we could reliably assign ancestry path labels (n = 62, see Methods).…”

“…For example, the CHG path originates in Caucasus hunter-gatherers, before merging with EHG to form the Steppe population, and then merges with other ancestries in later European populations (Figure 1). Because not all fine-mapped SNPs had ancestral path labels (missing OR=10.4%) and due to the difficulty in accurately inferring HLA alleles in ancient samples 18 , we LD-pruned genome-wide significant summary statistics from the same study 3 for which we did have ancestry path labels (n=62, see methods). This allowed us to test for polygenic selection across disease-associated variants using CLUES 19 and PALM 20 .…”

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions

“…computerome.dk/) for providing the computational resources for this project. This study is based on the NT's Master's thesis (56), which can be found at https://findit.dtu.dk/en/catalog/ 60339f5ad9001d01650f4d5d. This paper previously appeared as a preprint at (57).…”

“…This study is based on the NT’s Master’s thesis ( 56 ), which can be found at . This paper previously appeared as a preprint at ( 57 ).…”

Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity

“…Several bioinformatics tools have been developed to call HLA alleles from NGS data, offering varying degrees of accuracy and resolution (Major et al, 2013;Kiyotani et al, 2017;Larjo et al, 2017;Matey-Hernandez et al, 2018;Chen et al, 2021;Liu et al, 2021;Thuesen et al, 2022;Xin et al, 2022;Claeys et al, 2023). These tools can be categorized into alignment-based and assembly-based methods, some of which are specifically designed for specific NGS data.…”