HOGVAX: Exploiting epitope overlaps to maximize population coverage in vaccine design with application to SARS-CoV-2

The efficacy of epitope vaccines depends on the included epitopes as well as the probability that the selected epitopes are presented by the major histocompatibility complex (MHC) proteins of a vaccinated individual. Designing vaccines that effectively immunize a high proportion of the population is challenging because of high MHC polymorphism, diverging MHC-peptide binding affinities, and physical constraints on epitope vaccine constructs. Here, we present HOGVAX, a combinatorial optimization approach for epitope vaccine design. To optimize population coverage within the constraint of limited vaccine construct space, HOGVAX employs a hierarchical overlap graph (HOG) to identify and exploit overlaps between selected peptides and explicitly models the structure of linkage disequilibrium in the MHC. In a SARS-CoV-2 case study, we demonstrate that HOGVAX-designed vaccines contain substantially more epitopes than vaccines built from concatenated peptides and predict vaccine efficacy in over 98% of the population with high numbers of presented peptides in vaccinated individuals.

Complete sequences of six major histocompatibility complex haplotypes, including all the major MHC class II structures

Accurate and comprehensive immunogenetic reference panels are key to the successful implementation of population-scale immunogenomics. The 5Mbp Major Histocompatibility Complex (MHC) is the most polymorphic region of the human genome and associated with multiple immune-mediated diseases, transplant matching and therapy responses. Analysis of MHC genetic variation is severely complicated by complex patterns of sequence variation, linkage disequilibrium and a lack of fully resolved MHC reference haplotypes, increasing the risk of spurious findings on analyzing this medically important region. Integrating Illumina, ultra-long Nanopore, and PacBio HiFi sequencing as well as bespoke bioinformatics, we completed five of the alternative MHC reference haplotypes of the current (GRCh38/hg38) build of the human reference genome and added one other. The six assembled MHC haplotypes encompass the DR1 and DR4 haplotype structures in addition to the previously completed DR2 and DR3, as well as six distinct classes of the structurally variable C4 region. Analysis of the assembled haplotypes showed that MHC class II sequence structures, including repeat element positions, are generally conserved within the DR haplotype supergroups, and that sequence diversity peaks in three regions around HLA-A, HLA-B+C, and the HLA class II genes. Demonstrating the potential for improved short-read analysis, the number of proper read pairs recruited to the MHC was found to be increased by 0.06%-0.49% in a 1000 Genomes Project read remapping experiment with seven diverse samples. Furthermore, the assembled haplotypes can serve as references for the community and provide the basis of a structurally accurate genotyping graph of the complete MHC region.

Integrated genomic surveillance enables tracing of person-to-person SARS-CoV-2 transmission chains during community transmission and reveals extensive onward transmission of travel-imported infections, Germany, June to July 2021

Background

Tracking person-to-person SARS-CoV-2 transmission in the population is important to understand the epidemiology of community transmission and may contribute to the containment of SARS-CoV-2. Neither contact tracing nor genomic surveillance alone, however, are typically sufficient to achieve this objective.

Aim

We demonstrate the successful application of the integrated genomic surveillance (IGS) system of the German city of Düsseldorf for tracing SARS-CoV-2 transmission chains in the population as well as detecting and investigating travel-associated SARS-CoV-2 infection clusters.

Methods

Genomic surveillance, phylogenetic analysis, and structured case interviews were integrated to elucidate two genetically defined clusters of SARS-CoV-2 isolates detected by IGS in Düsseldorf in July 2021.

Results

Cluster 1 (n = 67 Düsseldorf cases) and Cluster 2 (n = 36) were detected in a surveillance dataset of 518 high-quality SARS-CoV-2 genomes from Düsseldorf (53% of total cases, sampled mid-June to July 2021). Cluster 1 could be traced back to a complex pattern of transmission in nightlife venues following a putative importation by a SARS-CoV-2-infected return traveller (IP) in late June; 28 SARS-CoV-2 cases could be epidemiologically directly linked to IP. Supported by viral genome data from Spain, Cluster 2 was shown to represent multiple independent introduction events of a viral strain circulating in Catalonia and other European countries, followed by diffuse community transmission in Düsseldorf.

Conclusion

IGS enabled high-resolution tracing of SARS-CoV-2 transmission in an internationally connected city during community transmission and provided infection chain-level evidence of the downstream propagation of travel-imported SARS-CoV-2 cases.

Rapid Selection of Sotrovimab Escape Variants in Severe Acute Respiratory Syndrome Coronavirus 2 Omicron-Infected Immunocompromised Patients

BACKGROUND

Monoclonal antibodies (mAbs) that target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are predominantly less effective against Omicron variants. Immunocompromised patients often experience prolonged viral shedding, resulting in an increased risk of viral escape.

METHODS

In an observational, prospective cohort, 57 patients infected with Omicron variants who received sotrovimab alone or in combination with remdesivir were followed. The study end points were a decrease in SARS-CoV-2 RNA <106 copies/mL in nasopharyngeal swabs at day 21 and the emergence of escape mutations at days 7, 14, and 21 after sotrovimab administration. All SARS-CoV-2 samples were analyzed using whole-genome sequencing. Individual variants within the quasispecies were subsequently quantified and further characterized using a pseudovirus neutralization assay.

RESULTS

The majority of patients (43 of 57, 75.4%) were immunodeficient, predominantly due to immunosuppression after organ transplantation or hematologic malignancies. Infections by Omicron/BA.1 comprised 82.5%, while 17.5% were infected by Omicron/BA.2. Twenty-one days after sotrovimab administration, 12 of 43 (27.9%) immunodeficient patients had prolonged viral shedding compared with 1 of 14 (7.1%) immunocompetent patients (P = .011). Viral spike protein mutations, some specific for Omicron (e.g., P337S and/or E340D/V), emerged in 14 of 43 (32.6%) immunodeficient patients, substantially reducing sensitivity to sotrovimab in a pseudovirus neutralization assay. Combination therapy with remdesivir significantly reduced emergence of escape variants.

CONCLUSIONS

Immunocompromised patients face a considerable risk of prolonged viral shedding and emergence of escape mutations after early therapy with sotrovimab. These findings underscore the importance of careful monitoring and the need for dedicated clinical trials in this patient population.

Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes

Typical genotyping workflows map reads to a reference genome before identifying genetic variants. Generating such alignments introduces reference biases and comes with substantial computational burden. Furthermore, short-read lengths limit the ability to characterize repetitive genomic regions, which are particularly challenging for fast k-mer-based genotypers. In the present study, we propose a new algorithm, PanGenie, that leverages a haplotype-resolved pangenome reference together with k-mer counts from short-read sequencing data to genotype a wide spectrum of genetic variation-a process we refer to as genome inference. Compared with mapping-based approaches, PanGenie is more than 4 times faster at 30-fold coverage and achieves better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (≥50 bp) and variants in repetitive regions, enabling the inclusion of these classes of variants in genome-wide association studies. PanGenie efficiently leverages the increasing amount of haplotype-resolved assemblies to unravel the functional impact of previously inaccessible variants while being faster compared with alignment-based workflows.

Characterization of SARS-CoV-2 infection clusters based on integrated genomic surveillance, outbreak analysis and contact tracing in an urban setting

Background

Tracing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing.

Methods

In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform.

Results

Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of 4 outbreaks at a maximum care hospital, and genetically based identification of 5 putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population.

Conclusions

Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.

Modifying splice site usage with ModCon: Maintaining the genetic code while changing the underlying mRNP code

Codon degeneracy of amino acid sequences permits an additional “mRNP code” layer underlying the genetic code that is related to RNA processing. In pre-mRNA splicing, splice site usage is determined by both intrinsic strength and sequence context providing RNA binding sites for splicing regulatory proteins. In this study, we systematically examined modification of splicing regulatory properties in the neighborhood of a GT site, i.e. potential splice site, without altering the encoded amino acids.

We quantified the splicing regulatory properties of the neighborhood around a potential splice site by its Splice Site HEXplorer Weight (SSHW) based on the HEXplorer score algorithm. To systematically modify GT site neighborhoods, either minimizing or maximizing their SSHW, we designed the novel stochastic optimization algorithm ModCon that applies a genetic algorithm with stochastic crossover, insertion and random mutation elements supplemented by a heuristic sliding window approach.

To assess the achievable range in SSHW in human splice donors without altering the encoded amino acids, we applied ModCon to a set of 1000 randomly selected Ensembl annotated human splice donor sites, achieving substantial and accurate changes in SSHW. Using ModCon optimization, we successfully switched splice donor usage in a splice site competition reporter containing coding sequences from FANCA, FANCB or BRCA2, while retaining their amino acid coding information.

The ModCon algorithm and its R package implementation can assist in reporter design by either introducing novel splice sites, silencing accidental, undesired splice sites, and by generally modifying the entire mRNP code while maintaining the genetic code.

State-of-the-art genome inference in the human MHC

The Major Histocompatibility Complex (MHC) on the short arm of chromosome 6 is associated with more diseases than any other region of the genome; it encodes the antigen-presenting Human Leukocyte Antigen (HLA) proteins and is one of the key immunogenetic regions of the genome. Accurate genome inference and interpretation of MHC association signals have traditionally been hampered by the region's uniquely complex features, such as high levels of polymorphism; inter-gene sequence homologies; structural variation; and long-range haplotype structures. Recent algorithmic and technological advances have, however, significantly increased the accessibility of genetic variation in the MHC; these developments include (i) accurate SNP-based HLA type imputation; (ii) genome graph approaches for variation-aware genome inference from next-generation sequencing data; (iii) long-read-based diploid de novo assembly of the MHC; (iv) cost-effective targeted MHC sequencing methods. Applied to hundreds of thousands of samples over the last years, these technologies have already enabled significant biological discoveries, for example in the field of autoimmune disease genetics. Remaining challenges concern the development of integrated methods that leverage haplotype-resolved de novo assembly of the MHC for the development of improved MHC genotyping methods for short reads and the construction of improved reference panels for SNP-based imputation. Improved genome inference in the MHC can crucially contribute to an improved genetic and functional understanding of many immune-related phenotypes and diseases.

Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

BACKGROUND

Mobile genetic elements are found in genomes throughout the microbial world, mediating genome plasticity and important prokaryotic phenotypes. Even the cell wall-less mycoplasmas, which are known to harbour a minimal set of genes, seem to accumulate mobile genetic elements. In Mycoplasma hominis, a facultative pathogen of the human urogenital tract and an inherently very heterogeneous species, four different MGE-classes had been detected until now: insertion sequence ISMhom-1, prophage MHoV-1, a tetracycline resistance mediating transposon, and ICEHo, a species-specific variant of a mycoplasma integrative and conjugative element encoding a T4SS secretion system (termed MICE).

RESULTS

To characterize the prevalence of these MGEs, genomes of 23 M. hominis isolates were assembled using whole genome sequencing and bioinformatically analysed for the presence of mobile genetic elements. In addition to the previously described MGEs, a new ICEHo variant was found, which we designate ICEHo-II. Of 15 ICEHo-II genes, five are common MICE genes; eight are unique to ICEHo-II; and two represent a duplication of a gene also present in ICEHo-I. In 150 M. hominis isolates and based on a screening PCR, prevalence of ICEHo-I was 40.7%; of ICEHo-II, 28.7%; and of both elements, 15.3%. Activity of ICEHo-I and -II was demonstrated by detection of circularized extrachromosomal forms of the elements through PCR and subsequent Sanger sequencing.

CONCLUSIONS

Nanopore sequencing enabled the identification of mobile genetic elements and of ICEHo-II, a novel MICE element of M. hominis, whose phenotypic impact and potential impact on pathogenicity can now be elucidated.

A diploid assembly-based benchmark for variants in the major histocompatibility complex

Most human genomes are characterized by aligning individual reads to the reference genome, but accurate long reads and linked reads now enable us to construct accurate, phased de novo assemblies. We focus on a medically important, highly variable, 5 million base-pair (bp) region where diploid assembly is particularly useful - the Major Histocompatibility Complex (MHC). Here, we develop a human genome benchmark derived from a diploid assembly for the openly-consented Genome in a Bottle sample HG002. We assemble a single contig for each haplotype, align them to the reference, call phased small and structural variants, and define a small variant benchmark for the MHC, covering 94% of the MHC and 22368 variants smaller than 50 bp, 49% more variants than a mapping-based benchmark. This benchmark reliably identifies errors in mapping-based callsets, and enables performance assessment in regions with much denser, complex variation than regions covered by previous benchmarks.

Distinct genetic architectures and environmental factors associate with host response to the γ2-herpesvirus infections

Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr Virus (EBV) establish life-long infections and are associated with malignancies. Striking geographic variation in incidence and the fact that virus alone is insufficient to cause disease, suggests other co-factors are involved. Here we present epidemiological analysis and genome-wide association study (GWAS) in 4365 individuals from an African population cohort, to assess the influence of host genetic and non-genetic factors on virus antibody responses. EBV/KSHV co-infection (OR = 5.71(1.58-7.12)), HIV positivity (OR = 2.22(1.32-3.73)) and living in a more rural area (OR = 1.38(1.01-1.89)) are strongly associated with immunogenicity. GWAS reveals associations with KSHV antibody response in the HLA-B/C region (p = 6.64 × 10^-09). For EBV, associations are identified for VCA (rs71542439, p = 1.15 × 10^-12). Human leucocyte antigen (HLA) and trans-ancestry fine-mapping substantiate that distinct variants in HLA-DQA1 (p = 5.24 × 10^-44) are driving associations for EBNA-1 in Africa. This study highlights complex interactions between KSHV and EBV, in addition to distinct genetic architectures resulting in important differences in pathogenesis and transmission.

HLA*LA-HLA typing from linearly projected graph alignments

Summary

HLA*LA implements a new graph alignment model for human leukocyte antigen (HLA) type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data) and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.

Availability and implementation

HLA*LA is implemented in C++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3).

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic structure of SARS-CoV-2 reflects clonal superspreading and multiple independent introduction events, North-Rhine Westphalia, Germany, February and March 2020

We whole-genome sequenced 55 SARS-CoV-2 isolates from Germany to investigate SARS-CoV-2 outbreaks in 2020 in the Heinsberg district and Düsseldorf. While the genetic structure of the Heinsberg outbreak indicates a clonal origin, reflecting superspreading dynamics from mid-February during the carnival season, distinct viral strains were circulating in Düsseldorf in March, reflecting the city’s international links. Limited detection of Heinsberg strains in the Düsseldorf area despite geographical proximity may reflect efficient containment and contact-tracing efforts.

Ultraplexing: increasing the efficiency of long-read sequencing for hybrid assembly with k-mer-based multiplexing

Hybrid genome assembly has emerged as an important technique in bacterial genomics, but cost and labor requirements limit large-scale application. We present Ultraplexing, a method to improve per-sample sequencing cost and hands-on time of Nanopore sequencing for hybrid assembly by at least 50% compared to molecular barcoding while maintaining high assembly quality. Ultraplexing requires the availability of Illumina data and uses inter-sample genetic variability to assign reads to isolates, which obviates the need for molecular barcoding. Thus, Ultraplexing can enable significant sequencing and labor cost reductions in large-scale bacterial genome projects.

Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps

Metagenomic sequence classification should be fast, accurate and information-rich. Emerging long-read sequencing technologies promise to improve the balance between these factors but most existing methods were designed for short reads. MetaMaps is a new method, specifically developed for long reads, capable of mapping a long-read metagenome to a comprehensive RefSeq database with >12,000 genomes in <16 GB or RAM on a laptop computer. Integrating approximate mapping with probabilistic scoring and EM-based estimation of sample composition, MetaMaps achieves >94% accuracy for species-level read assignment and r² > 0.97 for the estimation of sample composition on both simulated and real data when the sample genomes or close relatives are present in the classification database. To address novel species and genera, which are comparatively harder to predict, MetaMaps outputs mapping locations and qualities for all classified reads, enabling functional studies (e.g. gene presence/absence) and detection of incongruities between sample and reference genomes.

Sequencing platforms, such as Oxford Nanopore or Pacific Biosciences generate long-read data that preserve long-range genomic information but have high error rates. Here, the authors develop MetaMaps, a computational tool for strain-level metagenomic assignment and compositional estimation using long reads.

Genetic fine mapping of systemic lupus erythematosus MHC associations in Europeans and African Americans

Genetic variation within the major histocompatibility complex (MHC) contributes substantial risk for systemic lupus erythematosus, but high gene density, extreme polymorphism and extensive linkage disequilibrium (LD) have made fine mapping challenging. To address the problem, we compared two association techniques in two ancestrally diverse populations, African Americans (AAs) and Europeans (EURs). We observed a greater number of Human Leucocyte Antigen (HLA) alleles in AA consistent with the elevated level of recombination in this population. In EUR we observed 50 different A-C-B-DRB1-DQA-DQB multilocus haplotype sequences per hundred individuals; in the AA sample, these multilocus haplotypes were twice as common compared to Europeans. We also observed a strong narrow class II signal in AA as opposed to the long-range LD observed in EUR that includes class I alleles. We performed a Bayesian model choice of the classical HLA alleles and a frequentist analysis that combined both single nucleotide polymorphisms (SNPs) and classical HLA alleles. Both analyses converged on a similar subset of risk HLA alleles: in EUR HLA- B*08:01 + B*18:01 + (DRB1*15:01 frequentist only) + DQA*01:02 + DQB*02:01 + DRB3*02 and in AA HLA-C*17:01 + B*08:01 + DRB1*15:03 + (DQA*01:02 frequentist only) + DQA*02:01 + DQA*05:01+ DQA*05:05 + DQB*03:19 + DQB*02:02. We observed two additional independent SNP associations in both populations: EUR rs146903072 and rs501480; AA rs389883 and rs114118665. The DR2 serotype was best explained by DRB1*15:03 + DQA*01:02 in AA and by DRB1*15:01 + DQA*01:02 in EUR. The DR3 serotype was best explained by DQA*05:01 in AA and by DQB*02:01 in EUR. Despite some differences in underlying HLA allele risk models in EUR and AA, SNP signals across the extended MHC showed remarkable similarity and significant concordance in direction of effect for risk-associated variants.

De novo assembly of haplotype-resolved genomes with trio binning

Complex allelic variation hampers the assembly of haplotype-resolved sequences from diploid genomes. We developed trio binning, an approach that simplifies haplotype assembly by resolving allelic variation before assembly. In contrast with prior approaches, the effectiveness of our method improved with increasing heterozygosity. Trio binning uses short reads from two parental genomes to first partition long reads from an offspring into haplotype-specific sets. Each haplotype is then assembled independently, resulting in a complete diploid reconstruction. We used trio binning to recover both haplotypes of a diploid human genome and identified complex structural variants missed by alternative approaches. We sequenced an F1 cross between the cattle subspecies Bos taurus taurus and Bos taurus indicus and completely assembled both parental haplotypes with NG50 haplotig sizes of >20 Mb and 99.998% accuracy, surpassing the quality of current cattle reference genomes. We suggest that trio binning improves diploid genome assembly and will facilitate new studies of haplotype variation and inheritance.

NovoGraph: Genome graph construction from multiple long-read de novo assemblies

Genome graphs are emerging as an important novel approach to the analysis of high-throughput sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and uses a simple criterion of homologous-identical recombination to convert the multiple sequence alignment into a graph. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

NovoGraph: Human genome graph construction from multiple long-read de novo assemblies

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Variation in human chromosome 21 ribosomal RNA genes characterized by TAR cloning and long-read sequencing

Despite the key role of the human ribosome in protein biosynthesis, little is known about the extent of sequence variation in ribosomal DNA (rDNA) or its pre-rRNA and rRNA products. We recovered ribosomal DNA segments from a single human chromosome 21 using transformation-associated recombination (TAR) cloning in yeast. Accurate long-read sequencing of 13 isolates covering ∼0.82 Mb of the chromosome 21 rDNA complement revealed substantial variation among tandem repeat rDNA copies, several palindromic structures and potential errors in the previous reference sequence. These clones revealed 101 variant positions in the 45S transcription unit and 235 in the intergenic spacer sequence. Approximately 60% of the 45S variants were confirmed in independent whole-genome or RNA-seq data, with 47 of these further observed in mature 18S/28S rRNA sequences. TAR cloning and long-read sequencing enabled the accurate reconstruction of multiple rDNA units and a new, high-quality 44 838 bp rDNA reference sequence, which we have annotated with variants detected from chromosome 21 of a single individual. The large number of variants observed reveal heterogeneity in human rDNA, opening up the possibility of corresponding variations in ribosome dynamics.

Nanopore sequencing and assembly of a human genome with ultra-long reads

We report the sequencing and assembly of a reference genome for the human GM12878 Utah/Ceph cell line using the MinION (Oxford Nanopore Technologies) nanopore sequencer. 91.2 Gb of sequence data, representing ∼30× theoretical coverage, were produced. Reference-based alignment enabled detection of large structural variants and epigenetic modifications. De novo assembly of nanopore reads alone yielded a contiguous assembly (NG50 ∼3 Mb). We developed a protocol to generate ultra-long reads (N50 > 100 kb, read lengths up to 882 kb). Incorporating an additional 5× coverage of these ultra-long reads more than doubled the assembly contiguity (NG50 ∼6.4 Mb). The final assembled genome was 2,867 million bases in size, covering 85.8% of the reference. Assembly accuracy, after incorporating complementary short-read sequencing data, exceeded 99.8%. Ultra-long reads enabled assembly and phasing of the 4-Mb major histocompatibility complex (MHC) locus in its entirety, measurement of telomere repeat length, and closure of gaps in the reference human genome assembly GRCh38.

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

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.

Determining an individual’s HLA type (the sequence of the exons of the HLA genes) is important in many areas of biomedical research. For example, HLA types shape immune epitope repertoires, which are relevant in cancer immunotherapy, and influence autoimmune and infectious disease risk. Whole-genome sequencing data, currently being generated for hundreds of thousands of individuals, contains the information necessary for HLA typing–but inferring accurate HLA types from these is a challenging problem. First, the HLA genes are the most polymorphic genes in the human genome; second, these genes and their variant alleles exhibit high degrees of sequence similarity (due to a shared evolutionary origin). This makes it difficult to establish which specific HLA gene a given observed sequencing read derives from. We show that this problem can be addressed using a Population Reference Graph (PRG): for each gene, the PRG contains not only the reference sequence but also variant alleles, thus enabling, using a novel sequence-to-graph mapping algorithm, the accurate mapping of reads to HLA genes. We also show that HLA*PRG, the algorithm implementing our approach, achieves–based on standard whole-genome sequencing data–accuracies comparable to those of specialized gold-standard methods. HLA*PRG is open source and freely available.

Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

HLA*IMP--an integrated framework for imputing classical HLA alleles from SNP genotypes

MOTIVATION

Genetic variation at classical HLA alleles influences many phenotypes, including susceptibility to autoimmune disease, resistance to pathogens and the risk of adverse drug reactions. However, classical HLA typing methods are often prohibitively expensive for large-scale studies. We previously described a method for imputing classical alleles from linked SNP genotype data. Here, we present a modification of the original algorithm implemented in a freely available software suite that combines local data preparation and QC with probabilistic imputation through a remote server.

RESULTS

We introduce two modifications to the original algorithm. First, we present a novel SNP selection function that leads to pronounced increases (up by 40% in some scenarios) in call rate. Second, we develop a parallelized model building algorithm that allows us to process a reference set of over 2500 individuals. In a validation experiment, we show that our framework produces highly accurate HLA type imputations at class I and class II loci for independent datasets: at call rates of 95-99%, imputation accuracy is between 92% and 98% at the four-digit level and over 97% at the two-digit level. We demonstrate utility of the method through analysis of a genome-wide association study for psoriasis where there is a known classical HLA risk allele (HLA-C*06:02). We show that the imputed allele shows stronger association with disease than any single SNP within the region. The imputation framework, HLA*IMP, provides a powerful tool for dissecting the architecture of genetic risk within the HLA.

AVAILABILITY

HLA*IMP, implemented in C++ and Perl, is available from http://oxfordhla.well.ox.ac.uk and is free for academic use.