An Integrated Tool to Study MHC Region: Accurate SNV Detection and HLA Genes Typing in Human MHC Region Using Targeted High-Throughput Sequencing

The major histocompatibility complex (MHC) is one of the most variable and gene-dense regions of the human genome. Most studies of the MHC, and associated regions, focus on minor variants and HLA typing, many of which have been demonstrated to be associated with human disease susceptibility and metabolic pathways. However, the detection of variants in the MHC region, and diagnostic HLA typing, still lacks a coherent, standardized, cost effective and high coverage protocol of clinical quality and reliability. In this paper, we presented such a method for the accurate detection of minor variants and HLA types in the human MHC region, using high-throughput, high-coverage sequencing of target regions. A probe set was designed to template upon the 8 annotated human MHC haplotypes, and to encompass the 5 megabases (Mb) of the extended MHC region. We deployed our probes upon three, genetically diverse human samples for probe set evaluation, and sequencing data show that ∼97% of the MHC region, and over 99% of the genes in MHC region, are covered with sufficient depth and good evenness. 98% of genotypes called by this capture sequencing prove consistent with established HapMap genotypes. We have concurrently developed a one-step pipeline for calling any HLA type referenced in the IMGT/HLA database from this target capture sequencing data, which shows over 96% typing accuracy when deployed at 4 digital resolution. This cost-effective and highly accurate approach for variant detection and HLA typing in the MHC region may lend further insight into immune-mediated diseases studies, and may find clinical utility in transplantation medicine research. This one-step pipeline is released for general evaluation and use by the scientific community.     

HLA Typing for the Next Generation

Allele-level resolution data at primary HLA typing is the ideal for most histocompatibility testing laboratories. Many high-throughput molecular HLA typing approaches are unable to determine the phase of observed DNA sequence polymorphisms, leading to ambiguous results. The use of higher resolution methods is often restricted due to cost and time limitations. Here we report on the feasibility of using Pacific Biosciences’ Single Molecule Real-Time (SMRT) DNA sequencing technology for high-resolution and high-throughput HLA typing. Seven DNA samples were typed for HLA-A, -B and -C. The results showed that SMRT DNA sequencing technology was able to generate sequences that spanned entire HLA Class I genes that allowed for accurate allele calling. Eight novel genomic HLA class I sequences were identified, four were novel alleles, three were confirmed as genomic sequence extensions and one corrected an existing genomic reference sequence. This method has the potential to revolutionize the field of HLA typing. The clinical impact of achieving this level of resolution HLA typing data is likely to considerable, particularly in applications such as organ and blood stem cell transplantation where matching donors and recipients for their HLA is of utmost importance.     

Construction of microarrays for genotyping of DQA using unmodified 45-mer oligonucleotide

The human leukocyte antigen (HLA) class II system is strongly connected to immunological response and its compatibility between tissues is critical in transplantation. The simple robust typing analyses of HLA genes are extremely important. In this paper, we developed an approach based on microarray technology for genotyping of DQA gene. The microarrays were constructed with a total 31 unmodified 45-mer oligonucleotide. The second exon of DQA gene was amplified, and allowed to hybridize with the array. DQA genotypes were assigned by quantitative analysis of the hybridization results. The arrays were evaluated by DQA genotyping of nine reference samples and 120 clinical samples. The results demonstrate that the genotyping accuracy/concordance achieved 97.5% compared with the direct DNA sequencing. Although our methods did not perform high-resolution genotyping, it could be an alternative for serological typing in routine medical practice.     

Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals

Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs.     

High-throughput multiplex HLA genotyping by next-generation sequencing using multi-locus individual tagging

Background

Unambiguous human leukocyte antigen (HLA) typing is important in transplant matching and disease association studies. High-resolution HLA typing that is not restricted to the peptide-binding region can decrease HLA allele ambiguities. Cost and technology constraints have hampered high-throughput and efficient high resolution unambiguous HLA typing. We have developed a method for HLA genotyping that preserves the very high-resolution that can be obtained by next-generation sequencing (NGS) but also achieves substantially increased efficiency. Unambiguous HLA-A, B, C and DRB1 genotypes can be determined for 96 individuals in a single run of the Illumina MiSeq.

Results

Long-range amplification of full-length HLA genes from four loci was performed in separate polymerase chain reactions (PCR) using primers and PCR conditions that were optimized to reduce co-amplification of other HLA loci. Amplicons from the four HLA loci of each individual were then pooled and subjected to enzymatic library generation. All four loci of an individual were then tagged with one unique index combination. This multi-locus individual tagging (MIT) method combined with NGS enabled the four loci of 96 individuals to be analyzed in a single 500 cycle sequencing paired-end run of the Illumina-MiSeq. The MIT-NGS method generated sequence reads from the four loci were then discriminated using commercially available NGS HLA typing software. Comparison of the MIT-NGS with Sanger sequence-based HLA typing methods showed that all the ambiguities and discordances between the two methods were due to the accuracy of the MIT-NGS method.

Conclusions

The MIT-NGS method enabled accurate, robust and cost effective simultaneous analyses of four HLA loci per sample and produced 6 or 8-digit high-resolution unambiguous phased HLA typing data from 96 individuals in a single NGS run.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-864) contains supplementary material, which is available to authorized users.     

Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing

ABSTRACT: BACKGROUND: High-resolution HLA genotyping is a critical diagnostic and research assay. Current methods rarely achieve unambiguous high-resolution typing without making population-specific frequency inferences due to a lack of locus coverage and difficulty in exon-phase matching. Achieving high-resolution typing is also becoming more challenging with traditional methods as the database of known HLA alleles increases. RESULTS: We designed a cDNA amplicon-based pyrosequencing method to capture 94% of the HLA class I open-reading-frame with only two amplicons per sample, and an analogous method for class II HLA genes, with a primary focus on sequencing the DRB loci. We present a novel Galaxy server-based analysis workflow for determining genotype. During assay validation, we performed two GS Junior sequencing runs to determine the accuracy of the HLA class I amplicons and DRB amplicon at different levels of multiplexing. When 116 amplicons were multiplexed, we unambiguously resolved 99%of class I alleles to four- or six-digit resolution, as well as 100% unambiguous DRB calls. The second experiment, with 271 multiplexed amplicons, missed some alleles, but generated high-resolution, concordant typing for 93% of class I alleles, and 96% for DRB1 alleles. In a third, preliminary experiment we attempted to sequence novel amplicons for other class II loci with mixed success. CONCLUSIONS: The presented assay is higher-throughput and higher-resolution than existing HLA genotyping methods, and suitable for allele discovery or large cohort sampling. The validated class I and DRB primers successfully generated unambiguously high-resolution genotypes, while further work is needed to validate additional class II genotyping amplicons.     

Performance of Genotype Imputation for Low Frequency and Rare Variants from the 1000 Genomes

Genotype imputation is now routinely applied in genome-wide association studies (GWAS) and meta-analyses. However, most of the imputations have been run using HapMap samples as reference, imputation of low frequency and rare variants (minor allele frequency (MAF) < 5%) are not systemically assessed. With the emergence of next-generation sequencing, large reference panels (such as the 1000 Genomes panel) are available to facilitate imputation of these variants. Therefore, in order to estimate the performance of low frequency and rare variants imputation, we imputed 153 individuals, each of whom had 3 different genotype array data including 317k, 610k and 1 million SNPs, to three different reference panels: the 1000 Genomes pilot March 2010 release (1KGpilot), the 1000 Genomes interim August 2010 release (1KGinterim), and the 1000 Genomes phase1 November 2010 and May 2011 release (1KGphase1) by using IMPUTE version 2. The differences between these three releases of the 1000 Genomes data are the sample size, ancestry diversity, number of variants and their frequency spectrum. We found that both reference panel and GWAS chip density affect the imputation of low frequency and rare variants. 1KGphase1 outperformed the other 2 panels, at higher concordance rate, higher proportion of well-imputed variants (info>0.4) and higher mean info score in each MAF bin. Similarly, 1M chip array outperformed 610K and 317K. However for very rare variants (MAF≤0.3%), only 0–1% of the variants were well imputed. We conclude that the imputation of low frequency and rare variants improves with larger reference panels and higher density of genome-wide genotyping arrays. Yet, despite a large reference panel size and dense genotyping density, very rare variants remain difficult to impute.     

Evaluation of DRB1 high resolution typing by a new SSO-based Luminex method

HLA testing is an essential part of the process to identify a donor who may be a good match for the patients who need haematopoietic stem cells from bone marrow, peripheral blood or cord blood and the DNA typing in high resolution is now recommended as the Scientific Societies also describe in their standards. Recently the new PCR-Luminex HLA typing method, based on the reverse sequence specific oligonucleotide probes coupled with a microsphere beads in an array platform, has been well established. We report the data from 146 samples previously typed to a four digits level and used to evaluate the accuracy, sensitivity and performance of the new high definition DRB1 by PCR-Luminex kit. One hundred and forty-six samples from unrelated healthy donors, haematological patients or external proficiency tests were used in this study. The Luminex high definition DRB1 typing represents a versatile method and may be easily introduced in the routine, particularly when the technical team has already acquired experience on the technique. Only few HLA allelic combinations need an additional typing by PCR–SSP or SBT to solve the ambiguous results thus reducing the time necessary to produce a final report.     

‘Saviour Siblings’? The Distinction between PGD with HLA Tissue Typing and Preimplantation HLA Tissue Typing

One of the more controversial uses of preimplantation genetic diagnosis (PGD) involves selecting embryos with a specific tissue type so that the child to be born can act as a donor to an existing sibling who requires a haematopoietic stem cell transplant. PGD with HLA tissue typing is used to select embryos that are free of a familial genetic disease and that are also a tissue match for an existing sibling who requires a transplant. Preimplantation HLA tissue typing occurs when parents select embryos that are not at risk of a familial genetic disease to be a match for an existing sibling who requires a transplant. In Victoria, Australia, applications to use PGD with HLA tissue typing are reviewed by the Infertility Treatment Authority on a case by case basis. Preimplantation HLA tissue typing is prohibited prima facie because the embryo to be tested would not be at risk for a genetic abnormality or disease. Arguments for or against the use of PGD/HLA tissue typing are based on several key issues including the commodification and welfare of the donor child. This essay aims to show that that the same arguments apply to both PGD with HLA tissue typing and Preimplantation HLA tissue typing, and that the policy distinction between the two procedures is therefore ethically inconsistent.     

Distinctive KIR and HLA diversity in a panel of north Indian Hindus

HLA and KIR are diverse and rapidly evolving gene complexes that work together in human immunity mediated by cytolytic lymphocytes. Understanding their complex immunogenetic interaction requires study of both HLA and KIR diversity in the same human population. Here a panel of 72 unrelated north Indian Hindus was analyzed. HLA- A, B, C, DRB1, DQA1, and DQB1 alleles and their frequencies were determined by sequencing or high-resolution typing of genomic DNA; KIR genotypes were determined by gene-specific typing and by allele-level DNA typing for KIR2DL1, 2DL3, 2DL5, 3DL1, and 3DL2. From HLA analysis, the north Indian population is seen to have several characteristics shared either with Caucasian or East Asian populations, consistent with the demographic history of north India, as well as specific features, including several alleles at high frequency that are rare or absent in other populations. A majority of the north Indian KIR gene profiles have not been seen in Caucasian and Asian populations. Most striking is a higher frequency of the B group of KIR haplotypes, resulting in equal frequencies for A and B group haplotypes in north Indians. All 72 members of the north Indian panel have different HLA genotype and different KIR genotype.     

Haplotype Estimation Using Sequencing Reads

High-throughput sequencing technologies produce short sequence reads that can contain phase information if they span two or more heterozygote genotypes. This information is not routinely used by current methods that infer haplotypes from genotype data. We have extended the SHAPEIT2 method to use phase-informative sequencing reads to improve phasing accuracy. Our model incorporates the read information in a probabilistic model through base quality scores within each read. The method is primarily designed for high-coverage sequence data or data sets that already have genotypes called. One important application is phasing of single samples sequenced at high coverage for use in medical sequencing and studies of rare diseases. Our method can also use existing panels of reference haplotypes. We tested the method by using a mother-father-child trio sequenced at high-coverage by Illumina together with the low-coverage sequence data from the 1000 Genomes Project (1000GP). We found that use of phase-informative reads increases the mean distance between switch errors by 22% from 274.4 kb to 328.6 kb. We also used male chromosome X haplotypes from the 1000GP samples to simulate sequencing reads with varying insert size, read length, and base error rate. When using short 100 bp paired-end reads, we found that using mixtures of insert sizes produced the best results. When using longer reads with high error rates (5–20 kb read with 4%–15% error per base), phasing performance was substantially improved.     

A fault-tolerant method for HLA typing with PacBio data

Background

Human leukocyte antigen (HLA) genes are critical genes involved in important biomedical aspects, including organ transplantation, autoimmune diseases and infectious diseases. The gene family contains the most polymorphic genes in humans and the difference between two alleles is only a single base pair substitution in many cases. The next generation sequencing (NGS) technologies could be used for high throughput HLA typing but in silico methods are still needed to correctly assign the alleles of a sample. Computer scientists have developed such methods for various NGS platforms, such as Illumina, Roche 454 and Ion Torrent, based on the characteristics of the reads they generate. However, the method for PacBio reads was less addressed, probably owing to its high error rates. The PacBio system has the longest read length among available NGS platforms, and therefore is the only platform capable of having exon 2 and exon 3 of HLA genes on the same read to unequivocally solve the ambiguity problem caused by the “phasing” issue.

Results

We proposed a new method BayesTyping1 to assign HLA alleles for PacBio circular consensus sequencing reads using Bayes’ theorem. The method was applied to simulated data of the three loci HLA-A, HLA-B and HLA-DRB1. The experimental results showed its capability to tolerate the disturbance of sequencing errors and external noise reads.

Conclusions

The BayesTyping1 method could overcome the problems of HLA typing using PacBio reads, which mostly arise from sequencing errors of PacBio reads and the divergence of HLA genes, to some extent.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-296) contains supplementary material, which is available to authorized users.     

Molecular prediction for atherogenic risks across different cell types of leukocytes

Background

We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.

Methods

The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n?=?1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.

Results

The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.

Conclusions

The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.     

Cytotoxic antiglobulin test--a sensitive method for HLA typing

By using of 1:1000 of diluted antiglobulin sera the cytotoxic antiglobulin test also covers HLA antigens in 20% of those cases which cannot be identified by normal microlymphocytotoxic tests. The percentage of not specifically faulty positive results is lower than 5%. The method can be used for typing HLA antigens in all those cases where a decreased expressiveness of HLA antigens can be expected as well as for determining weak HLA cytotoxins. The use of undiluted antiglobulin sera, however, leads to unspecific positive results in 1/4 of the cases. The reasons for this phenomenon are being investigated and discussed.     

Deep Whole-Genome Sequencing of 100 Southeast Asian Malays

Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies.     

High-throughput DNA typing of HLA-A, -B, -C, and -DRB1 loci by a PCR–SSOP–Luminex method in the Japanese population

We have developed a new high-throughput, high-resolution genotyping method for the detection of alleles at the human leukocyte antigen (HLA)-A, -B, -C, and -DRB1 loci by combining polymerase chain reaction (PCR) and sequence-specific oligonucleotide probes (SSOPs) protocols with the Luminex 100 xMAP flow cytometry dual-laser system to quantitate fluorescently labeled oligonucleotides attached to color-coded microbeads. In order to detect the HLA alleles with a frequency of more than 0.1% in the Japanese population, we created 48 oligonucleotide probes for the HLA-A locus, 61 for HLA-B, 34 for HLA-C, and 51 for HLA-DRB1. The accuracy of the PCR–SSOP–Luminex method was determined by comparing it to the nucleotide sequencing method after subcloning into the plasmid vector using 150 multinational control samples obtained from the International HLA DNA Exchange University of California Los Angeles. In addition, we performed the PCR–SSOP–Luminex method for HLA allele typing on DNA samples collected from 1,018 Japanese volunteers. Overall, the genotyping method exhibited an accuracy of 85.91% for HLA-A, 85.03% for HLA-B, 97.32% for HLA-C, and 90.67% for HLA-DRB1 using 150 control samples, and 100% for HLA-A and -C, 99.90% for HLA-B, and 99.95% for HLA-DRB1 in 1,018 Japanese samples. The PCR–SSOP–Luminex method provides a simple, accurate, and rapid approach toward multiplex genotyping of HLA alleles to the four-digit or higher level of resolution in the Japanese population. It takes only approximately 5 h from DNA extraction to the definition of HLA four-digit alleles at the HLA-A, HLA-B, HLA-C, and HLA-DRB1 loci for 96 samples when handled by a single typist.     

HapTree: A Novel Bayesian Framework for Single Individual Polyplotyping Using NGS Data

As the more recent next-generation sequencing (NGS) technologies provide longer read sequences, the use of sequencing datasets for complete haplotype phasing is fast becoming a reality, allowing haplotype reconstruction of a single sequenced genome. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. In this paper, we describe a novel maximum-likelihood estimation framework, HapTree, for polyploid haplotype assembly of an individual genome using NGS read datasets. We evaluate the performance of HapTree on simulated polyploid sequencing read data modeled after Illumina sequencing technologies. For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes

Background

Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls.

Results

This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%.

Conclusions

In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users.