SweHLA: the high confidence HLA typing bio-resource drawn from 1000 Swedish genomes

Abstract

There is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of Sanger sequenced lab typing. Here we aimed to combine results from available software programs, minimizing the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1000 Swedish genomes, and a framework for future HLA interrogation. HLA 2nd-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, HLA-B, HLA-C; class II: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). A high confidence population set (SweHLA) was determined using an n−1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per gene, 875 to 988 of the 1000 samples were genotyped in SweHLA; 920 samples had at least seven loci called. While a small fraction of reference alleles were common to all software (class I = 1.9% and class II = 4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency >2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software solutions. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.

Treatment- and population-specific genetic risk factors for anti-drug antibodies against interferon-beta: a GWAS

“…Three MHC class I HLA alleles reported to be associated with ADA by Núñez et al [ 16 ] did not replicate in the present study (Additional file 23 ). All three alleles are more frequent in Spain than in Germany and Sweden, with, e.g., HLA-B*14:02 showing a frequency of 1% in Sweden [ 47 ], 2% in Germany, and 4% in Spain [ 48 ]. To reliably assess whether the associations of these alleles are specific to Spanish populations or whether the lack of correction for multiple testing led to type I errors in the original study, independent replication studies on Spanish patients are required.…”

“…Interestingly, these alleles also show substantial population-specific differences [ 47 , 48 , 61 ]: The IFNβ-1a s.c. risk allele HLA-DRB1*15:01 is less frequent in Southern Europe and for Ashkenazi, Southern Hispanic, and African ancestries (e.g., Italy 5.6–6.4%, Southwestern Spain 5.2–8.6%). At the same time, it is especially frequent in individuals with ancestry from other parts of Europe (e.g., Northern Spain 16.7–32.1%, Germany 12.9–17.2%, Sweden 16.1%).…”

Association of Protective HLA-A With HLA-B∗27 Positive Ankylosing Spondylitis

“…An n-1 concordance method (Nordin et al, 2020) was used to ensure high quality genotyping across the MHC. The inputs for this were raw HLA genotype calls generated from four separate software programs, with consensus variant calls reported with 2-field resolution.…”

“…In brief, this meant that for a variant to be called in the final set, the result had to be identical across three out of four programs (Supplementary Figure 3). As noted previously (Nordin et al, 2020), this procedure can account for software biases, such as reference version and algorithm choice. Called chromosome 6 SNPs were the base data for imputation (SNP2HLA, Jia et al, 2013), whereas reads mapped to chromosome 6, plus unmapped reads, were used as inputs for inference tools [HLA-VBSeq (Nariai et al, 2015), HLAscan (Ka et al, 2017), and HLA-HD (Kawaguchi et al, 2017)].…”

“…Called chromosome 6 SNPs were the base data for imputation (SNP2HLA, Jia et al, 2013), whereas reads mapped to chromosome 6, plus unmapped reads, were used as inputs for inference tools [HLA-VBSeq (Nariai et al, 2015), HLAscan (Ka et al, 2017), and HLA-HD (Kawaguchi et al, 2017)]. SNP2HLA, HLA-VBSeq and HLAscan were previously used to genotype SweGen at eight HLA genes (Nordin et al, 2020), however this was expanded to 17 genes (HLA-A, -B, -C, -DOA, -DOB, -DPA1, -DPB1, -DQA1, -DQB1, -DRA, -DRB1, -E, -F, -G, MICA, MICB, and TAP2) with the inclusion of HLA-HD. The impact of biases on genotyping this set of genes was assessed with average read depth across each for the three sample populations [10 bp bins in BEDtools (Quinlan and Hall, 2010) v2.26.0], shared variant availability across software references, and concordance rate between the high confidence set and each software.…”