High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

<div><p>Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application.</p></div

Runtime and memory requirements comparison of HLA*PRG, PHLAT and HLAReporter on NA12878.

<p>Upper part: NA12878 2 x 100bp reads from the Platinum cohort; lower part: NA12878 2 x 250bp reads from the 1000 Genomes cohort. We provide a detailed analysis in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005151#pcbi.1005151.s014" target="_blank">S1 Text</a>.</p

Schematic representation of HLA type inference using HLA*PRG.

<p><b>a</b> Broad-scale structure of the HLA PRG. The included genes are separated by spacer blocks consisting of N characters. <b>b</b> Fine-scale structure of the PRG input sequences. Exons, introns and UTRs are embedded in regional haplotypes (padding sequence). Exon sequences typically outnumber intron sequences. The red line indicates the region covered by IMGT genomic sequences. X-axis not to scale. <b>c</b> For each gene represented in the PRG, multiple sequence alignments representing up to 3 sources of sequence data are merged for PRG construction: exonic sequences, genomic (UTR, exons, introns) sequences, regional haplotypes (“xMHC Ref.”). Using alleles present in both the current and the next-higher-level MSA (identifiers printed in red), the merging algorithm determines consensus boundaries (blue bars) to connect the MSAs of different input sequence types. For each segment so-defined, we use the MSA corresponding to the highest-resolution input sequence type (sequence characters therefore ignored are printed in grey). <b>d</b> The PRG corresponding to the input sequences shown in c, and a seed-and-extend alignment of a sequencing read to the PRG. PRG nodes are represented by boxes and edges by labelled arrows. The four blue markers correspond to the consensus MSA boundaries shown in c. The aligned sequence of the read is displayed below the PRG, and the alignment path (the sequence of edges and nodes traversed in the PRG) is highlighted. The red component of the alignment path corresponds to the exact-match “seed” component of the alignment (spanning a graph-encoded gap), whereas the orange components correspond to the “extend” component of the alignment (where mismatches are allowed).</p

HLA type inference accuracy for HLA*PRG and two state-of-the-art algorithms.

<p>HLA type inference accuracy for HLA*PRG and two state-of-the-art algorithms.</p

Effect of maternal infections on antibody concentrations among infants at one year of age.

<p>Effect of maternal infections on antibody concentrations among infants at one year of age.</p

Subgroup analysis of the effect of randomised maternal treatment on antibody concentrations among infants at one year of age in groups according to maternal infection status.

<p>Subgroup analysis of the effect of randomised maternal treatment on antibody concentrations among infants at one year of age in groups according to maternal infection status.</p

Baseline characteristics of mothers whose children were enrolled into the study and provided samples at nine years of age.

<p>Baseline characteristics of mothers whose children were enrolled into the study and provided samples at nine years of age.</p

Fourteen SNPs identified as a cluster using noise-augmented clustering approach for our <i>cis</i>IL6R instrument.

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MR Wald ratio estimates between the canonical rs2228145 / Asp358Ala SNP.

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Odds ratios for IVW MR estimates for each outcome with <i>cis</i>CRP exposures and are on the scale of lnCRP decrease.

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