Immunoseq: the identification of functionally relevant variants through targeted capture and sequencing of active regulatory regions in human immune cells

IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

The temporal behavior of the murine T cell receptor repertoire following Immunotherapy

Immunotherapy is now an essential tool for cancer treatment, and the unique features of an individual's T cell receptor repertoire are known to play a key role in its effectiveness. The repertoire, famously vast due to a cascade of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire over several time points following treatment with anti-CTLA-4, in a syngeniec mouse model for colorectal cancer, generating a longitudinal dataset of T cell clones and their abundance. The dynamics of the repertoire can be observed in response to treatment over a period of four weeks, as clonal expansion of specific clones ascends and descends. The data made available here can be used to determine treatment and predict its effect, while also providing a unique look at the behavior of the immune system over time.

TCR_Explore: A novel webtool for T cell receptor repertoire analysis

T cells expressing either alpha-beta or gamma-delta T cell receptors (TCR) are critical sentinels of the adaptive immune system, with receptor diversity being essential for protective immunity against a broad array of pathogens and agents. Programs available to profile TCR clonotypic signatures can be limiting for users with no coding expertise. Current analytical pipelines can be inefficient due to manual processing steps, open to data entry errors and have multiple analytical tools with unique inputs that require coding expertise. Here we present a bespoke webtool designed for users irrespective of coding expertise, coined 'TCR_Explore', enabling analysis either derived via Sanger sequencing or next generation sequencing (NGS) platforms. Further, TCR_Explore incorporates automated quality control steps for Sanger sequencing. The creation of flexible and publication ready figures are enabled for different sequencing platforms following universal conversion to the TCR_Explore file format. TCR_Explore will enhance a user's capacity to undertake in-depth TCR repertoire analysis of both new and pre-existing datasets for identification of T cell clonotypes associated with health and disease. The web application is located at https://tcr-explore.erc.monash.edu for users to interactively explore TCR repertoire datasets.

GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data

BACKGROUND

In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential.

RESULTS

This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire.

CONCLUSION

The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py .

Tumor infiltrating lymphocytes as an endpoint in cancer vaccine trials

Checkpoint inhibitors have invigorated cancer immunotherapy research, including cancer vaccination. Classic early phase trial design and endpoints used in developing chemotherapy are not suited for evaluating all forms of cancer treatment. Peripheral T cell response dynamics have demonstrated inconsistency in assessing the efficacy of cancer vaccination. Tumor infiltrating lymphocytes (TILs), reflect the local tumor microenvironment and may prove a superior endpoint in cancer vaccination trials. Cancer vaccines may also promote success in combination immunotherapy treatment of weakly immunogenic tumors. This review explores the impact of TILs as an endpoint for cancer vaccination in multiple malignancies, summarizes the current literature regarding TILs analysis, and discusses the challenges of providing validity and a standardized implementation of this approach.

T-cell repertoire diversity: friend or foe for protective antitumor response?

Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.

Safety and efficacy of atezolizumab with obinutuzumab and bendamustine in previously untreated follicular lymphoma

Obinutuzumab (G) chemoimmunotherapy demonstrated improved progression-free survival (PFS) vs rituximab-based chemoimmunotherapy in patients with previously untreated follicular lymphoma (FL) in the GALLIUM trial. Atezolizumab (atezo) is a programmed death-ligand 1 inhibitor with a complementary mechanism of action to G by restoring cytotoxic T-cell function. We evaluated the safety and efficacy of atezo-G-bendamustine in patients with previously untreated FL in a phase Ib/II trial (#NCT02596971). A safety run-in phase was followed by an expansion phase with atezo-G-bendamustine induction and atezo-G maintenance for ≤24 months. Forty patients with previously untreated FL were enrolled and treated with atezo-G-bendamustine. The primary endpoint, complete response (CR) rate, assessed by an independent review committee (IRC; modified Lugano 2014 criteria) was 75.0% (95% confidence interval [CI], 61.3% to 85.8%). Three-year investigator-assessed PFS and overall survival rates were 80.9% (95% CI, 63.9% to 90.5%) and 89.3% (95% CI, 73.9% to 95.9%), respectively. At baseline, 21/40 patients had circulating lymphoma-specific clonotypes and underwent repeat testing at end of induction; all were minimal residual disease negative (10-5 sensitivity), with 16 (76.2%) CRs, 3 (14.3%) partial responses, and 2 (9.5%) with stable disease (IRC assessed). Grade 5 (fatal) adverse events (AEs) were reported in 5 patients. The efficacy of atezo-G-bendamustine in previously untreated FL did not appear superior to G-bendamustine efficacy as seen in the GALLIUM trial, and the addition of atezo to G-bendamustine was associated with an increased risk of AEs. Particularly due to the unfavorable safety profile, this regimen cannot be recommended in patients with previously untreated FL. This trial was registered at www.clinicaltrials.gov as #NCT02596971.

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.

Systemic Inflammation and Tumour-Infiltrating T-Cell Receptor Repertoire Diversity Are Predictive of Clinical Outcome in High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements

Simple Summary

The current version of the World-Health-Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues acknowledges the provisional entity of high-grade B-cell lymphoma, with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/TH) which is associated with dire prognosis compared to triple-negative diffuse-large-B-cell-lymphoma (tnDLBCL). There is growing evidence for the essential prognostic role of the tumor-microenvironment (TME) and especially the extent of tumor-infiltration by the adaptive immune-system through tumor-infiltrating-lymphocytes (TIL) across a variety of cancers. More precisely, the clonal-architecture of the tumor-infiltrating T-cell-receptor (TCR)-repertoire has recently emerged as a key determinant of risk-stratification in patients with hematological malignancies. Moreover, inflammation-based prognostic-scores, such as the Glasgow-prognostic-score (GPS) were shown to reflect the TME. We therefore performed a large scale next-generation-sequencing (NGS) and clinicopathological study of the TCR-β-chain-repertoire in HGBL-DH/TH revealing several entity-exclusive clonotypes distinct from tnDLBCL, suggestive of tumor-neoantigen-selection and correlate our findings with the GPS in context of clinical outcome in HGBL-DH/TH.

Abstract

High-grade B-cell lymphoma, with MYC and BCL2 and/or BCL6 rearrangements (double/triple-hit high grade B-cell lymphoma, HGBL-DH/TH) constitutes a provisional entity among B-cell malignancies with an aggressive behavior and dire prognosis. While evidence for the essential prognostic role of the composition of the tumor-microenvironment (TME) in hematologic malignancies is growing, its prognostic impact in HGBL-DH/TH remains unknown. In this study, we outline the adaptive immune response in a cohort of 47 HGBL-DH/TH and 27 triple-negative diffuse large B-cell lymphoma (tnDLBCL) patients in a large-scale, next-generation sequencing (NGS) investigation of the T-cell receptor (TCR) β-chain repertoire and supplement our findings with data on the Glasgow-Prognostic Score (GPS) at diagnosis, as a score-derived measure of systemic inflammation. We supplement these studies with an immunophenotypic investigation of the TME. Our findings demonstrate that the clonal architecture of the TCR repertoire of HGBL-DH/TH differs significantly from tnDLBCL. Moreover, several entity-exclusive clonotypes, suggestive of tumor-neoantigen selection are identified. Additionally, both productive clonality and percentage of maximum frequency clone as measures of TCR repertoire diversity and tumor-directed activity of the adaptive immune system had significant impact on overall survival (OS; productive clonality: p = 0.0273; HR: 2.839; CI: 1.124–7.169; maximum productive frequency: p = 0.0307; HR: 2.167; CI: 1.074–4.370) but not PFS (productive clonality: p = 0.4459; maximum productive frequency: p = 0.5567) in HGBL-DH/TH patients, while GPS was a significant predictor of both OS and PFS (OS: p < 0.0001; PFS: p = 0.0002). Subsequent multivariate analysis revealed GPS and the revised international prognostic index (R-IPI) to be the only prognosticators holding significant impact for OS (GPS: p = 0.038; R-IPI: p = 0.006) and PFS (GPS: p = 0.029; R-IPI: p = 0.006) in HGBL-DH/TH. Through the identification of expanded, recurrent and entity-exclusive TCR-clonotypes we provide indications for a distinct subset of tumor-neoantigenic elements exclusively shared among HGBL-DH/TH. Further, we demonstrate an adverse prognostic role for both systemic inflammation and uniform adaptive immune response.

Exhaustion of tumour-infiltrating T-cell receptor repertoire diversity is an age-dependent indicator of immunological fitness independently predictive of clinical outcome in Burkitt lymphoma

Burkitt lymphoma (BL) is an aggressive B-cell-malignancy derived from germinal-centre B-cells. Curative therapy traditionally requires intensive immunochemotherapy. Recently, immuno-oncological approaches, modulating the T-cell tumour response, were approved for the treatment of a variety of malignancies. The architecture of the tumour-infiltrating T-cell receptor (TCR) repertoire in BL remains insufficiently characterized. We therefore performed a large-scale, next-generation sequencing study of the complimentary-determining region (CDR)-3 region of the TCRβ chain repertoire in a large cohort of all epidemiological subtypes of BL (n = 82) and diffuse large B-cell lymphoma (DLBCL; n = 34). Molecular data were subsequently assessed for correlation with clinical outcome. Our investigations revealed an age-dependent immunoprofile in BL as in DLBCL. Moreover, we found several public clonotypes in numerous patients suggestive of shared tumour neoantigen selection exclusive to BL and distinct from DLBCL regardless of Epstein-Barr virus and/or human immunodeficiency virus status. Compared with baseline, longitudinal analysis unveiled significant repertoire restrictions upon relapse (P = 0·0437) while productive TCR repertoire clonality proved to be a useful indicator of both overall and progression-free-survival [OS: P = 0·0001; hazard ratio (HR): 6·220; confidence interval (CI): 2·263-11·78; PFS: P = 0·0025; HR: 3·086; CI: 1·555-7·030]. Multivariate analysis confirmed its independence from established prognosticators, including age at diagnosis and comorbidities. Our findings establish the clinical relevance of the architecture and clonality of the TCR repertoire and its age-determined dynamics in BL.

Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group

BACKGROUND

The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies.

METHODS

In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence.

RESULTS

A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies.

CONCLUSIONS

Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort

The HLA genes, the most polymorphic genes in the human genome, constitute the strongest single genetic susceptibility factor for autoimmune diseases, transplantation alloimmunity and infections. HLA imputation via statistical inference of alleles based on single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with alleles is a powerful first-step screening tool. Due to different LD structures between populations, the accuracy of HLA imputation may benefit from matching the imputation reference with the study population. To evaluate the potential advantage of using population-specific reference in HLA imputation, we constructed an HLA reference panel consisting of 1150 Finns with 5365 major histocompatibility complex region SNPs consistent between genome builds. We evaluated the accuracy of the panel against a European panel in an independent test set of 213 Finnish subjects. We show that the Finnish panel yields a lower imputation error rate (1.24% versus 1.79%). More than 30% of imputation errors occurred in haplotypes enriched in Finland. The frequencies of imputed HLA alleles were highly correlated with clinical-grade HLA allele frequencies and allowed accurate replication of established HLA–disease associations in ∼102 000 biobank participants. The results show that a population-specific reference increases imputation accuracy in a relatively isolated population within Europe and can be successfully applied to biobank-scale genome data collections.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites

DNase I-hypersensitive sites (DHSs) serve key roles in the regulation of gene transcription as markers of cis-regulatory elements (CREs). Recent advances in next-generation sequencing have enabled the genome-wide location and annotation of DHSs in a variety of cells. Numerous studies have confirmed that DHSs are involved in several processes in cell fate decision and development. DHSs have also been indicated in cancer and inherited diseases as driver distal regulatory elements. Here, the definition of DHSs is reviewed, in addition to high-throughput methods of DHS identification. Furthermore, the function of DHSs in gene expression is probed. The roles of DHSs in disease occurrence are also reviewed and discussed. Concomitant advances in the identification of essential roles of DHSs will assist in disclosing the underlying molecular mechanisms, supplementing gene transcription and enlarging the molecular basis of DHS-related bioprocesses, phenotypes, distinct traits and diseases.

Rheumatoid arthritis-relevant DNA methylation changes identified in ACPA-positive asymptomatic individuals using methylome capture sequencing

Objective

To compare DNA methylation in subjects positive vs negative for anti-citrullinated protein antibodies (ACPA), a key serological marker of rheumatoid arthritis (RA) risk.

Methods

With banked serum from a random subset (N = 3600) of a large general population cohort, we identified ACPA-positive samples and compared them to age- and sex-matched ACPA-negative controls. We used a custom-designed methylome panel to conduct targeted bisulfite sequencing of 5 million CpGs located in regulatory or hypomethylated regions of DNA from whole blood (red blood cell lysed). Using binomial regression models, we investigated the differentially methylated regions (DMRs) between ACPA-positive vs ACPA-negative subjects. An independent set of T cells from RA patients was used to “validate” the differentially methylated sites.

Results

We measured DNA methylation in 137 subjects, of whom 63 were ACPA-positive, 66 were ACPA-negative, and 8 had self-reported RA. We identified 1303 DMRs of relevance, of which one third (402) had underlying genetic effects. These DMRs were enriched in intergenic CpG islands (CGI) and CGI shore regions. Furthermore, the genes associated with these DMRs were enriched in pathways related to Epstein-Barr virus infection and immune response. In addition, 80 (38%) of 208 RA-specific DMRs were replicated in T cells from RA samples.

Conclusions

Sequencing-based high-resolution methylome mapping revealed biologically relevant DNA methylation changes in asymptomatic individuals positive for ACPA that overlap with those seen in RA. Pathway analyses suggested roles for viral infections, which may represent the effect of environmental triggers upstream of disease onset.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0699-9) contains supplementary material, which is available to authorized users.

Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease After Allogeneic HSCT

Genetic mismatches in protein coding genes between allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipient and donor can elicit an alloimmunity response via peptides presented by the recipient HLA receptors as minor histocompatibility antigens (mHAs). While the impact of individual mHAs on allo-HSCT outcome such as graft-vs.-host and graft-vs.-leukemia effects has been demonstrated, it is likely that established mHAs constitute only a small fraction of all immunogenic non-synonymous variants. In the present study, we have analyzed the genetic mismatching in 157 exome-sequenced sibling allo-HSCT pairs to evaluate the significance of polymorphic HLA class I associated peptides on clinical outcome. We applied computational mismatch estimation approaches based on experimentally verified HLA ligands available in public repositories, published mHAs, and predicted HLA-peptide affinites, and analyzed their associations with chronic graft-vs.-host disease (cGvHD) grades. We found that higher estimated recipient mismatching consistently increased the risk of severe cGvHD, suggesting that HLA-presented mismatching influences the likelihood of long-term complications in the patient. Furthermore, computational approaches focusing on estimation of HLA-presentation instead of all non-synonymous mismatches indiscriminately may be beneficial for analysis sensitivity and could help identify novel mHAs.

Long-read sequence capture of the haemoglobin gene clusters across codfish species

Combining high-throughput sequencing with targeted sequence capture has become an attractive tool to study specific genomic regions of interest. Most studies have so far focused on the exome using short-read technology. These approaches are not designed to capture intergenic regions needed to reconstruct genomic organization, including regulatory regions and gene synteny. Here, we demonstrate the power of combining targeted sequence capture with long-read sequencing technology for comparative genomic analyses of the haemoglobin (Hb) gene clusters across eight species separated by up to 70 million years. Guided by the reference genome assembly of the Atlantic cod (Gadus morhua) together with genome information from draft assemblies of selected codfishes, we designed probes covering the two Hb gene clusters. Use of custom-made barcodes combined with PacBio RSII sequencing led to highly continuous assemblies of the LA (~100 kb) and MN (~200 kb) clusters, which include syntenic regions of coding and intergenic sequences. Our results revealed an overall conserved genomic organization of the Hb genes within this lineage, yet with several, lineage-specific gene duplications. Moreover, for some of the species examined, we identified amino acid substitutions at two sites in the Hbb1 gene as well as length polymorphisms in its regulatory region, which has previously been linked to temperature adaptation in Atlantic cod populations. This study highlights the use of targeted long-read capture as a versatile approach for comparative genomic studies by generation of a cross-species genomic resource elucidating the evolutionary history of the Hb gene family across the highly divergent group of codfishes.

Exploring rare and low-frequency variants in the Saguenay-Lac-Saint-Jean population identified genes associated with asthma and allergy traits

The Saguenay–Lac-Saint-Jean (SLSJ) region is located in northeastern Quebec and is known for its unique demographic history and founder effect. As founder populations are enriched with population-specific variants, we characterized the variants distribution in SLSJ and compared it with four European populations (Finnish, Sweden, United Kingdom and France), of which the Finnish population is another founder population. Targeted sequencing of the coding and non-coding immune regulatory regions of the SLSJ asthma familial cohort and the four European populations were performed. Rare and low-frequency coding and non-coding regulatory variants identified in the SLSJ population were then investigated for variant- and gene-level associations with asthma and allergy-related traits (eosinophil percentage, immunoglobulin (Ig) E levels and lung function). Our data showed that (1) rare or deleterious variants were not enriched in the two founder populations as compared with the three non-founder European populations; (2) a larger proportion of founder population-specific variants occurred with higher frequencies; and (3) low-frequency variants appeared to be more deleterious. Furthermore, a rare variant, rs1386931, located in the 3ʹ-UTR of CXCR6 and intron of FYCO1 was found to be associated with eosinophil percentage. Gene-based analyses identified NRP2, MRPL44 and SERPINE2 to be associated with various asthma and allergy-related traits. Our study demonstrated the usefulness of using a founder population to identify new genes associated with asthma and allergy-related traits; thus better understand the genes and pathways implicated in pathophysiology.

Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation

Allogeneic haematopoietic stem cell transplantation currently represents the primary potentially curative treatment for cancers of the blood and bone marrow. While relapse occurs in approximately 30% of patients, few risk-modifying genetic variants have been identified. The present study evaluates the predictive potential of patient genetics on relapse risk in a genome-wide manner. We studied 151 graft recipients with HLA-matched sibling donors by sequencing the whole-exome, active immunoregulatory regions, and the full MHC region. To assess the predictive capability and contributions of SNPs and INDELs, we employed machine learning and a feature selection approach in a cross-validation framework to discover the most informative variants while controlling against overfitting. Our results show that germline genetic polymorphisms in patients entail a significant contribution to relapse risk, as judged by the predictive performance of the model (AUC = 0.72 [95% CI: 0.63-0.81]). Furthermore, the top contributing variants were predictive in two independent replication cohorts (n = 258 and n = 125) from the same population. The results can help elucidate relapse mechanisms and suggest novel therapeutic targets. A computational genomic model could provide a step toward individualized prognostic risk assessment, particularly when accompanied by other data modalities.

Hidden genomic MHC disparity between HLA-matched sibling pairs in hematopoietic stem cell transplantation

Matching classical HLA alleles between donor and recipient is an important factor in avoiding adverse immunological effects in HSCT. Siblings with no differences in HLA alleles, either due to identical-by-state or identical-by-descent status, are considered to be optimal donors. We carried out a retrospective genomic sequence and SNP analysis of 336 fully HLA-A, -B, -DRB1 matched and 14 partially HLA-matched sibling HSCT pairs to determine the level of undetected mismatching within the MHC segment as well as to map their recombination sites. The genomic sequence of 34 genes locating in the MHC region revealed allelic mismatching at 1 to 8 additional genes in partially HLA-matched pairs. Also, fully matched pairs were found to have mismatching either at HLA-DPB1 or at non-HLA region within the MHC segment. Altogether, 3.9% of fully HLA-matched HSCT pairs had large genomic mismatching in the MHC segment. Recombination sites mapped to certain restricted locations. The number of mismatched nucleotides correlated with the risk of GvHD supporting the central role of full HLA matching in HSCT. High-density genome analysis revealed that fully HLA-matched siblings may not have identical MHC segments and even single allelic mismatching at any classical HLA gene often implies larger genomic differences along MHC.

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise.

Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome

Background

The functional impact of genetic variation has been extensively surveyed, revealing that genetic changes correlated to phenotypes lie mostly in non-coding genomic regions. Studies have linked allele-specific genetic changes to gene expression, DNA methylation, and histone marks but these investigations have only been carried out in a limited set of samples.

Results

We describe a large-scale coordinated study of allelic and non-allelic effects on DNA methylation, histone mark deposition, and gene expression, detecting the interrelations between epigenetic and functional features at unprecedented resolution. We use information from whole genome and targeted bisulfite sequencing from 910 samples to perform genotype-dependent analyses of allele-specific methylation (ASM) and non-allelic methylation (mQTL). In addition, we introduce a novel genotype-independent test to detect methylation imbalance between chromosomes. Of the ~2.2 million CpGs tested for ASM, mQTL, and genotype-independent effects, we identify ~32% as being genetically regulated (ASM or mQTL) and ~14% as being putatively epigenetically regulated. We also show that epigenetically driven effects are strongly enriched in repressed regions and near transcription start sites, whereas the genetically regulated CpGs are enriched in enhancers. Known imprinted regions are enriched among epigenetically regulated loci, but we also observe several novel genomic regions (e.g., HOX genes) as being epigenetically regulated. Finally, we use our ASM datasets for functional interpretation of disease-associated loci and show the advantage of utilizing naïve T cells for understanding autoimmune diseases.

Conclusions

Our rich catalogue of haploid methylomes across multiple tissues will allow validation of epigenome association studies and exploration of new biological models for allelic exclusion in the human genome.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1173-7) contains supplementary material, which is available to authorized users.