Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases

Modeling gene interactions in polygenic prediction via geometric deep learning

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.

Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets

T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.

Identifying Atopic Dermatitis Risk Loci in 1,094,060 Individuals with Subanalysis of Disease Severity and Onset

Atopic dermatitis (AD) is a common inflammatory skin disease highly attributable to genetic factors. In this study, we report results from a genome-wide meta-analysis of AD in 37,541 cases and 1,056,519 controls with data from the FinnGen project, the Estonian Biobank, the UK Biobank, the EAGLE Consortium, and the BioBank Japan. We detected 77 independent AD-associated loci, of which 10 were, to our knowledge, previously unreported. The associated loci showed enrichment in various immune regulatory processes. We further performed subgroup analyses of mild and severe AD and of early- and late-onset AD, with data from the FinnGen project. Fifty-five of the 79 tested variants in the associated loci showed larger effect estimates for severe than for mild AD as determined through administered treatment. The age of onset, as determined by the first hospital visit with AD diagnosis, was lower in patients with particular AD-risk alleles. Our findings add to the knowledge of the genetic background of AD and may underlie the development of new therapeutic strategies.

Computational estimation of clonal diversity in autoimmunity

Diversity is the cornerstone of the adaptive immune system, crucial for its effectiveness against constantly evolving pathogens that pose threats to higher vertebrates. Accurately measuring and interpreting this diversity presents challenges for immunologists, as changes in diversity and clonotype composition can tip the balance between protective immunity and autoimmunity. In this review, we present the current methods commonly used to measure diversity from single-cell T-cell receptor and B-cell receptor sequencing. We also discuss two case studies where single-cell sequencing and diversity estimations have led to breakthroughs in autoimmune disease discovery and therapeutic innovation, and reflect upon the necessity and importance of accurately defining and measuring lymphocyte diversity in these contexts.

The influence of HLA genetic variation on plasma protein expression

Genetic variation in the human leukocyte antigen (HLA) loci is associated with risk of immune-mediated diseases, but the molecular effects of HLA polymorphism are unclear. Here we examined the effects of HLA genetic variation on the expression of 2940 plasma proteins across 45,330 Europeans in the UK Biobank, with replication analyses across multiple ancestry groups. We detected 504 proteins affected by HLA variants (HLA-pQTL), including widespread trans effects by autoimmune disease risk alleles. More than 80% of the HLA-pQTL fine-mapped to amino acid positions in the peptide binding groove. HLA-I and II affected proteins expressed in similar cell types but in different pathways of both adaptive and innate immunity. Finally, we investigated potential HLA-pQTL effects on disease by integrating HLA-pQTL with fine-mapped HLA-disease signals in the UK Biobank. Our data reveal the diverse effects of HLA genetic variation and aid the interpretation of associations between HLA alleles and immune-mediated diseases.

Repurposing anti-osteoporosis drugs for autoimmune diseases: A two-sample Mendelian randomization study

Background

Despite the increasing availability of therapeutic drugs for autoimmune diseases, many patients still struggle to achieve their treatment goals. Our aim was to identify whether drugs originally used to treat bone density could be applied to the treatment of autoimmune diseases through Mendelian randomization (MR).

Methods

Using summary statistics from genome-wide association studies, we used a two-sample MR design to estimate the correlation between autoimmune diseases and BMD-related drug targets. Data from the DrugBank and ChEMBL databases were used to identify the drug targets of anti-osteoporosis medications. The Wald ratio test or inverse-variance weighting method was used to assess the impact of genetic variation in drug target(s) on autoimmune disease therapy.

Results

Through our analysis, we discovered a negative correlation between genetic variability in a specific gene (ESR1) in raloxifene/colecalciferol and various autoimmune disorders such as ankylosing spondylitis, endometriosis, IgA nephropathy, rheumatoid arthritis, sarcoidosis, systemic lupus erythematosus, and type 1 diabetes.

Conclusion

These results indicate a possible link between genetic differences in the drug targeting ESR1 and susceptibility to autoimmune disorders. Hence, our study offers significant support for the possible use of drugs targeting ESR1 for the management of autoimmune disorders. MR and drug repurposing are utilized to investigate the relationship between autoimmune diseases and bone mineral density, with a focus on ESR1.

Exploring the role of HLA variants in neuroblastoma susceptibility through whole exome sequencing

Although a number of susceptibility loci for neuroblastoma (NB) have been identified by genome-wide association studies, it is still unclear whether variants in the HLA region contribute to NB susceptibility. In this study, we conducted a comprehensive genetic analysis of variants in the HLA region among 724 NB patients and 2863 matched controls from different cohorts. We exploited whole-exome sequencing data to accurately type HLA alleles with an ensemble approach on the results from three different typing tools, and carried out rigorous sample quality control to ensure a fine-scale ancestry matching. The frequencies of common HLA alleles were compared between cases and controls by logistic regression under additive and non-additive models. Population stratification was taken into account adjusting for ancestry-informative principal components. We detected significant HLA associations with NB. In particular, HLA-DQB1*05:02 (OR = 1.61; padj = 5.4 × 10-3) and HLA-DRB1*16:01 (OR = 1.60; padj = 2.3 × 10-2) alleles were associated to higher risk of developing NB. Conditional analysis highlighted the HLA-DQB1*05:02 allele and its residue Ser57 as key to this association. DQB1*05:02 allele was not associated to clinical features worse outcomes in the NB cohort. Nevertheless, a risk score derived from the allelic combinations of five HLA variants showed a substantial predictive value for patient survival (HR = 1.53; p = 0.032) that was independent from established NB prognostic factors. Our study leveraged powerful computational methods to explore WES data and HLA variants and to reveal complex genetic associations. Further studies are needed to validate the mechanisms of these interactions that contribute to the multifaceted pattern of factors underlying the disease initiation and progression.

SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.

Evolutionary trade-offs constraining the MHC gene expansion: beyond simple TCR depletion model

The immune system is as much shaped by the pressure of pathogens as it is by evolutionary trade-offs that constrain its structure and function. A perfect example comes from the major histocompatibility complex (MHC), molecules that initiate adaptive immune response by presentation of foreign antigens to T cells. The remarkable, population-level polymorphism of MHC genes is assumed to result mainly from a co-evolutionary arms race between hosts and pathogens, while the limited, within-individual number of functional MHC loci is thought to be the consequence of an evolutionary trade-off between enhanced pathogen recognition and excessive T cell depletion during negative selection in the thymus. Certain mathematical models and infection studies suggest that an intermediate individual MHC diversity would thus be optimal. A recent, more direct test of this hypothesis has shown that the effects of MHC diversity on T-cell receptor (TCR) repertoires may differ between MHC classes, supporting the depletion model only for MHC class I. Here, we used the bank vole (Myodes=Cletronomys glareolus), a rodent species with variable numbers of expressed MHC genes, to test how an individual MHC diversity influences the proportions and TCR repertoires of responding T cell subsets. We found a non-linear relationship between MHC diversity and T cell proportions (with intermediate MHC numbers coinciding with the largest T cell proportions), perhaps reflecting an optimality effect of balanced positive and negative thymic selection. The association was strongest for the relationship between MHC class I and splenic CD8+ T cells. The CD8+ TCR richness alone was unaffected by MHC class I diversity, suggesting that MHC class I expansion may be limited by decreasing T cell counts, rather than by direct depletion of TCR richness. In contrast, CD4+ TCR richness was positively correlated with MHC class II diversity, arguing against a universal TCR depletion. It also suggests that different evolutionary forces or trade-offs may limit the within-individual expansion of the MHC class II loci.

HLA Genes: A Hallmark of Functional Genetic Variation and Complex Evolution

The major histocompatibility complex (MHC) with its highly polymorphic HLA genes represents one of the most intensely studied genomic regions in the genome. MHC proteins play a key role in antigen-specific immunity and are associated with a wide range of complex diseases. Despite decades of research and many advances in the field, the characterization and interpretation of its genetic and genomic variability remain challenging. Here an overview is provided of the MHC, the nature of its exceptional variability, and the complex evolutionary processes assumed to drive this variability. Highlighted are also recent advances in the field that promise to improve our understanding of the variability in the MHC and in antigen-specific immunity more generally.

Genetic variants in the complement system and their potential link in the aetiology of type 1 diabetes

Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin-secreting beta cells in the pancreas. This process results in life-long dependence on exogenous insulin for survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre-clinical through to clinically apparent disease.

Role of signaling lymphocytic activation molecule family of receptors in the pathogenesis of rheumatoid arthritis: insights and application

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and joint damage. The signaling lymphocytic activation molecule (SLAMF) family of receptors are expressed on various hematopoietic and non-hematopoietic cells and can regulate both immune cell activation and cytokine production. Altered expression of certain SLAMF receptors contributes to aberrant immune responses in RA. In RA, SLAMF1 is upregulated on T cells and may promote inflammation by participating in immune cell-mediated responses. SLAMF2 and SLAMF4 are involved in regulating monocyte tumor necrosis factor production and promoting inflammation. SLAMF7 activates multiple inflammatory pathways in macrophages to drive inflammatory gene expression. SLAMF8 inhibition can reduce inflammation in RA by blocking ERK/MMPs signaling. Of note, there are differences in SLAMF receptor (SFR) expression between normal and arthritic joint tissues, suggesting a role as potential diagnostic biomarkers. This review summarizes recent advances on the roles of SLAMF receptors 1, 2, 4, 7, and 8 in RA pathogenesis. However, further research is needed to elucidate the mechanisms of SLAMF regulation of immune cells in RA. Understanding interactions between SLAMF receptors and immune cells will help identify selective strategies for targeting SLAMF signaling without compromising normal immunity. Overall, the SLAMF gene family holds promise as a target for precision medicine in RA, but additional investigation of the underlying immunological mechanisms is needed. Targeting SLAMF receptors presents opportunities for new diagnostic and therapeutic approaches to dampen damaging immune-mediated inflammation in RA.

Could NCOA5 a novel candidate gene for multiple sclerosis susceptibility?

BACKGROUND

Multiple sclerosis (MS) is an inflammatory immune-mediated demyelinating disease that causes a challenging and disabling condition. Environmental and genetic factors play a role in appearing the state of the disease. Recent studies have shown that nuclear cofactor genes may play a role in the pathogenesis of MS. NCOA5 is a nuclear receptor coactivator independent of AF2 that modulates ERa-mediated transcription. This gene is involved in the pathogenesis of diseases such as psoriasis, Behcet's disease, and cancer.

METHODS AND RESULTS

We investigated the relationship between the rs2903908 polymorphism of the NCOA5 gene and MS among 157 unrelated MS patients and 160 healthy controls by RT-PCR. The frequencies of the CC, CT, and TT genotypes were 19.87%, 37.82%, and 42.31%, respectively, for the MS group and 5.63%, 43.75%, and 50.62%, respectively, for the control group. The CC genotype and the C allele were found to be significantly higher in the patient group (the p values were 0.0002 and 0.003, respectively).

CONCLUSIONS

The fact that the CC genotype was found to be significantly higher in the patient group compared to the control group (p = 0.0002) and that it had a statistically significantly higher OR value (OR, 95% CI = 4.16, 1.91-9.05) suggests that the C allele may recessively predispose to MS for this polymorphism. These results suggest for the first time that the NCOA5 gene may have an effect on the occurrence of MS through different molecular pathways, which are discussed in the manuscript.

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

Context

Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.

Objective

We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

Methods

We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.

Results

The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.

Conclusion

In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Genetic risk variants for childhood nephrotic syndrome and corticosteroid response

Introduction

The etiology of most cases of nephrotic syndrome (NS) remains unknown, therefore patients are phenotypically categorized based on response to corticosteroid therapy as steroid sensitive NS (SSNS), or steroid resistant NS (SRNS). Genetic risk factors have been identified for SSNS from unbiased genome-wide association studies (GWAS), however it is unclear if these loci are disease risk loci in other forms of NS such as SRNS. Additionally, it remains unknown if these risk loci are associated with response to therapy. Thus, we investigated the association between SSNS risk loci and therapy response in a large, multi-race cohort of children along the entire spectrum of childhood-onset NS.

Methods

We enrolled 1,000 patients with childhood-onset NS comprised of SSNS and SRNS. Genotyping was done using TaqMan and Direct Sanger Sequencing for 9 previously reported childhood SSNS risk loci. We compared the allele frequencies (AF) and variant burden between NS vs. controls and SRNS vs. SSNS.

Results

All 9 risk loci were associated with NS compared with healthy controls (p = 3.5 × 10-3-<2.2 × 10-16). Variant burden greater than 7 was associated with risk of SRNS (OR 7.4, 95% CI 4.6-12.0, p = 8.2 × 10-16).

Conclusion

Our study showed that genetic risk loci for childhood SSNS are associated with pattern of therapy response, may help predict disease outcome, and set the stage for individualized treatment of NS.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.

Autoimmunity in immune checkpoint inhibitor-induced immune-related adverse events: A focus on autoimmune skin toxicity and pneumonitis

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy. However, their use is frequently associated with immune-related adverse events (irAEs) potentially affecting any organ. The mechanisms mediating such irAEs remain poorly understood and biomarkers to predict the development of irAEs are lacking. Growing evidence shows the importance of self-antigens in mediating irAEs during ICI therapy, in particular the well-described melanocyte differentiation antigens in melanoma patients. This review will focus on two novel classes of self-antigens involved in mediating autoimmune skin toxicity and pneumonitis in non-small cell lung cancer patients treated with immunotherapy. T cells specific for these self-antigens are thought to not only mediate irAEs but are thought to simultaneously mediate anti-tumor responses and are therefore associated with both autoimmune toxicity and response to ICI therapy. We further discuss emerging cellular and proteomic immune signatures of irAEs that may serve as biomarkers to help predict which patients are at higher risk of developing these irAEs. The determination of new tumor antigens involved in ICI therapy and the identification of related biomarkers brings us a step forward in the mechanistic understanding of ICIs and will help to monitor patients at higher risk of developing irAEs. Lastly, we discuss the current challenges in collecting research samples for the study of ICI-related mechanisms and in distinguishing between immune signatures of response and those of irAEs.

Fast kernel-based association testing of non-linear genetic effects for biobank-scale data

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

Asian-type DEL (RHD*DEL1) with an allo-anti-D: A paradoxical observation in a healthy multiparous woman

BACKGROUND

The DEL phenotype is the D variant expressing the least amounts of D antigen per red cell. Asian-type DEL (RHD:c:1227G > A) is the most prevalent DEL in East Asia without any anti-D alloimmunization reported before. We investigated the first observation of an anti-D in any DEL phenotype, reported in the Japanese language at a 1987 conference, only 3 years after the discovery of DEL.

METHODS

We contacted the proband 35 years after the initial report. Standard hemagglutination, adsorption/elution, and flow cytometry tests were performed, as was nucleotide sequencing for the RHD, RHCE, and HLA class I and class II genes.

RESULTS

The healthy multiparous Japanese woman, a regular blood donor, still had the anti-D of titer 8 representing an alloantibody by standard serologic methods. Unexpectedly, she carried an Asian-type DEL without any additional RHD gene variation. All 12 HLA alleles identified were known in the Japanese population. Interestingly, one of her HLA-DRB1 and a variant of her HLA-DQB1 alleles had previously been associated with anti-D immunization.

CONCLUSION

We described an allo-anti-D, maintained for more than three decades, in an Asian-type DEL. The combination of two implicated HLA alleles were rare and could have contributed to the anti-D immunization. Continued monitoring of anti-D immunization events in patients with DEL is warranted, and we discuss possible mechanisms for further study. As only this single observation has been recognized in the last 35 years, the current recommendation is affirmed: Individuals with Asian-type DEL should be treated as Rh D-positive for transfusion and Rh immune prophylaxis purposes.

Deep integrative models for large-scale human genomics

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

Triggers and regulation: the gut microbiome in rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis is a chronic inflammatory disease marked by systemic symptoms and joint degeneration. Interestingly, the development and progression of rheumatoid arthritis have been linked to the microbiome, notably the gut microbiome. Dysbiosis, an alteration in the gut microbiome, has been connected to the etiology and pathogenesis of rheumatoid arthritis. For instance, dysbiosis increases intestinal permeability and promotes the movement of bacteria and their products, which in turn triggers and aggravates systemic inflammation.

AREAS COVERED

The correlation between the gut microbiome and RA. Triggers of RA including dysbiosis. The therapeutic potential of the gut microbiome in RA due to its critical function in influencing the immune response. The fecal microbiota transplantation (FMT), a therapeutic strategy that involves the transfer of healthy fecal microbiota from a donor to a recipient, has produced encouraging results in the treatment of several autoimmune illnesses, including rheumatoid arthritis.

EXPERT OPINION

The role of the gut microbiome in RA is critical and serves as a basis for etiology and pathogenesis, as well as having therapeutic implications. In our opinion, FMT is an excellent example of this correlation. Still, more investigations and well-designed studies are needed in order to make firm conclusions and recommendations.

Pathogenesis of autoimmune disease

Autoimmune diseases are a diverse group of conditions characterized by aberrant B cell and T cell reactivity to normal constituents of the host. These diseases occur widely and affect individuals of all ages, especially women. Among these diseases, the most prominent immunological manifestation is the production of autoantibodies, which provide valuable biomarkers for diagnosis, classification and disease activity. Although T cells have a key role in pathogenesis, they are technically more difficult to assay. In general, autoimmune disease results from an interplay between a genetic predisposition and environmental factors. Genetic predisposition to autoimmunity is complex and can involve multiple genes that regulate the function of immune cell populations. Less frequently, autoimmunity can result from single-gene mutations that affect key regulatory pathways. Infection seems to be a common trigger for autoimmune disease, although the microbiota can also influence pathogenesis. As shown in seminal studies, patients may express autoantibodies many years before the appearance of clinical or laboratory signs of disease - a period called pre-clinical autoimmunity. Monitoring autoantibody expression in at-risk populations may therefore enable early detection and the initiation of therapy to prevent or attenuate tissue damage. Autoimmunity may not be static, however, and remission can be achieved by some patients treated with current agents.

Do female bluethroats without extra-pair offspring have more MHC-compatible social mates?

Abstract

Genes of the major histocompatibility complex (MHC) are crucial for adaptive immunity in jawed vertebrates, and theory predicts that there should be mate choice for optimizing MHC constitution in the offspring. In a previous study, we demonstrated a non-random female choice of extra-pair males in the bluethroat (Luscinia svecica), yielding offspring that was closer to an intermediate MHC class II (MHCII) allele count than their within-pair halfsiblings. The present study tests whether social pairs with only within-pair young (WPY) in their brood, in the same study population, had a combined MHC-constitution closer to a presumed intermediate optimum, than social pairs with extra-pair young (EPY), with a corresponding pattern in their offspring. As expected, we found that WPY from pure WPY-broods were more MHC-optimal than WPY from mixed broods, but only in broods of young (second year) males. Correspondingly, there was a tendency for social pairs with only WPY in their brood to be more MHC-compatible than social pairs with EPY in their brood, when the male was young. Older bluethroat males have considerably larger testes than young males, and their higher sperm competitiveness could help them secure paternity in their own brood, also when they are not MHC-compatible. In other words, in the sexual conflict over paternity, females may be more likely to realise their preference for a MHC-compatible mate when paired to a young male. As a possible fitness indicator, immune responsiveness to an injected antigen (PHA) was elevated for offspring closer to “the golden mean” in MHCII allele count.

Significance statement

This study contributes to our understanding of MHC-based mate choice in extra-pair mating systems, by showing that female bluethroats (Luscinia svecica) with an MHCII-compatible social mate tend to have no extra-pair young in their brood, but only when the social male is young. This elucidates a possible sexual conflict, in which older social males are able to override female preferences and prevent other males from gaining paternity in their brood through higher sperm production. Studying systems in which extra-pair paternity occurs offers an insight into the genetic benefits of mate choice, as extra-pair males, in contrast to social males, generally contribute only sperm. Further, the strict and thorough genotyping scheme applied in this study enabled us to demonstrate a preference for “the golden mean” in MHC-diversity in a species with one of the highest MHC class II-diversity known to date.

Association of Combined Exposure to Ambient Air Pollutants, Genetic Risk, and Incident Rheumatoid Arthritis: A Prospective Cohort Study in the UK Biobank

BACKGROUND

Evidence for a potential link between air pollution and rheumatoid arthritis (RA) is inconsistent, and the modified effect of genetic susceptibility on the relationship between air pollution and RA has not been well studied.

OBJECTIVE

Using a general population cohort from the UK Biobank, this study aimed to investigate the associations between various air pollutants and the risk of incident RA and to further estimate the impact of combined exposure to ambient air pollutants on the risk of developing RA under the modification effect of genetic predisposition.

METHODS

A total of 342,973 participants with completed genotyping data and who were free of RA at baseline were included in the study. An air pollution score was constructed by summing the concentrations of each pollutant weighted by the regression coefficients with RA from single-pollutant models to assess the combined effect of air pollutants, including particulate matter (PM) with diameters ≤ 2.5 μ m (PM 2.5 ), between 2.5 and 10 μ m (PM 2.5 - 10 ), and ≤ 10 μ m (PM 10 ), as well as nitrogen dioxide (NO 2 ) and nitrogen oxides (NO x ). In addition, the polygenic risk score (PRS) of RA was calculated to characterize individual genetic risk. The Cox proportional hazard model was used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) of associations of single air pollutant, air pollution score, or PRS with incident RA.

RESULTS

During a median follow-up time of 8.1 y, 2,034 incident events of RA were recorded. The HRs (95% CIs) of incident RA per interquartile range increment in PM 2.5 , PM 2.5 - 10 , PM 10 , NO 2 , and NO x were 1.07 (1.01, 1.13), 1.00 (0.96, 1.04), 1.01 (0.96, 1.07), 1.03 (0.98, 1.09), and 1.07 (1.02, 1.12), respectively. We also found a positive exposure-response relationship between air pollution score and RA risk (p Trend = 0.000053 ). The HR (95% CI) of incident RA was 1.14 (1.00, 1.29) in the highest quartile group compared with the lowest quartile group of the air pollution score. Furthermore, the results of the combined effect of air pollution score and PRS on the RA risk showed that the risk of RA incidence in the highest genetic risk and air pollution score group was almost twice that of the lowest genetic risk and air pollution score group [incidence rate (IR) per 100,000 person-years: 98.46 vs. 51.19, and HR = 1.73 (95% CI: 1.39, 2.17) vs. 1 (reference)], although no statistically significant interaction between the air pollution and genetic risk for incident RA was found (p Interaction > 0.05 ).

DISCUSSION

The results revealed that long-term combined exposure to ambient air pollutants might increase the risk of RA, particularly in those with high genetic risk. https://doi.org/10.1289/EHP10710.

Digging into the HLA pockets: A new association with acute leukaemias

Acute leukaemias represent a highly heterogeneous group of clonal proliferations of myeloid or lymphoid blasts. In the last decade, the contribution of immunogenetics to cancer biology has elicited a renewed interest in the structures of immune adaptive responses, due to the growing body of evidence concerning their involvement into disease pathogenesis and treatment. The report by Boukouaci and colleagues suggests new associations between patterns of distribution of specific human leukocyte antigen motifs and leukaemogenesis. Commentary on Boukouaci Wahid et al. Comparative analysis of the variability of the HLA peptide-binding pockets in patients with acute leukemias" Br J Haematol 2023;200:203-215.

Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives

Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation that affects synovial tissues of multiple joints. Granzymes (Gzms) are serine proteases that are released into the immune synapse between cytotoxic lymphocytes and target cells. They enter target cells with the help of perforin to induce programmed cell death in inflammatory and tumor cells. Gzms may have a connection with RA. First, increased levels of Gzms have been found in the serum (GzmB), plasma (GzmA, GzmB), synovial fluid (GzmB, GzmM), and synovial tissue (GzmK) of patients with RA. Moreover, Gzms may contribute to inflammation by degrading the extracellular matrix and promoting cytokine release. They are thought to be involved in RA pathogenesis and have the potential to be used as biomarkers for RA diagnosis, although their exact role is yet to be fully elucidated. The purpose of this review was to summarize the current knowledge regarding the possible role of the granzyme family in RA, with the aim of providing a reference for future research on the mechanisms of RA and the development of new therapies.

Effect of Polymorphisms in the FCN1, FCN2, and FCN3 Genes on the Susceptibility to Develop Rheumatoid Arthritis: A Systematic Review

Genetic association studies in rheumatoid arthritis conducted in various populations have yielded heterogeneous results. The present systematic review was conducted to synthesize the results of the studies in order to establish the impact of polymorphisms in the ficolin-coding genes FCN1, FCN2, and FCN3 on the susceptibility to develop rheumatoid arthritis. A systematic literature review was performed using the following keywords "gene (FCN1/FCN2/FCN3)", "Polymorphism/Genetic Variant", and "rheumatoid arthritis" in different databases until January 2022. Authors assessed articles by title/abstract and then assessed by full text for data extraction. The risk of bias was assessed using the Newcastle-Ottawa scale. Data synthesis was performed qualitatively and quantitatively. A total of 1519 articles were eligible for inclusion in this review, 3 were identified as relevant for the quantitative synthesis with 670 patients and 1019 controls. For the FCN1 gene, an association was found in the dominant and recessive genetic models of the variants rs2989727 (genotype TT = OR: 0.577, 95% CI: 0.430-0.769) and rs1071583 (genotype GG = OR: 1.537, 95% CI: 1.153-2.049, p = 0.0032) with the development of rheumatoid arthritis as a protective or susceptibility factor. FCN2 and FCN3 genes did not show association with disease development. The FCN1 gene variants rs2989727 and rs1071583 are associated with the risk of developing rheumatoid arthritis in populations from Brazil and Belgium, but not in FCN2 and FCN3 gene variants.

Control of naive and effector CD4 T cell receptor repertoires by rheumatoid-arthritis-risk HLA alleles

OBJECTIVE

Human Leukocyte Antigen (HLA) alleles regulate susceptibility to rheumatoid arthritis (RA) and immune-mediated diseases. This study aims to elucidate the impact of HLA alleles to T cell subsets.

METHODS

We performed genome-wide and HLA allele association analysis for T cell receptor (TCR) beta chain repertoire in 13 purified T cell subsets from the ImmuNexUT database, consisting of 407 donors with ten immune-mediated diseases and healthy controls.

RESULTS

HLA class II alleles were associated with TRBV gene usage and the public clones of CD4 T cells, while HLA class I alleles were associated with CD8 T cells. RA-risk and immune-mediated diseases-risk HLA alleles were associated with TRBV gene usage of naive and effector CD4 T cell subsets and public clones accumulating in Th17. Clonal diversity was independent of HLA alleles and was correlated with transcriptome changes that reflect TCR signaling.

CONCLUSION

This study revealed in vivo evidence that both HLA alleles and environmental factors shape naive and effector TCR repertoires in RA and immune-mediated diseases patients.

Genetics of Primary Biliary Cholangitis

Primary biliary cholangitis (PBC) is a rare disease of the liver characterized by an autoimmune attack on the small bile ducts. PBC is a complex trait, meaning that a large list of genetic factors interacts with environmental agents to determine its onset. Genome-wide association studies have had a huge impact in fostering research in PBC, but many steps need still to be done compared with other autoimmune diseases of similar prevalence. This review presents the state-of-the-art regarding the genetic architecture of PBC and provides some thoughtful reflections about possible future lines of research, which can be helpful to fill the missing heritability gap in PBC.

Missing Causality and Heritability of Autoimmune Hepatitis

BACKGROUND

Autoimmune hepatitis has an unknown cause and genetic associations that are not disease-specific or always present. Clarification of its missing causality and heritability could improve prevention and management strategies.

AIMS

Describe the key epigenetic and genetic mechanisms that could account for missing causality and heritability in autoimmune hepatitis; indicate the prospects of these mechanisms as pivotal factors; and encourage investigations of their pathogenic role and therapeutic potential.

METHODS

English abstracts were identified in PubMed using multiple key search phases. Several hundred abstracts and 210 full-length articles were reviewed.

RESULTS

Environmental induction of epigenetic changes is the prime candidate for explaining the missing causality of autoimmune hepatitis. Environmental factors (diet, toxic exposures) can alter chromatin structure and the production of micro-ribonucleic acids that affect gene expression. Epistatic interaction between unsuspected genes is the prime candidate for explaining the missing heritability. The non-additive, interactive effects of multiple genes could enhance their impact on the propensity and phenotype of autoimmune hepatitis. Transgenerational inheritance of acquired epigenetic marks constitutes another mechanism of transmitting parental adaptations that could affect susceptibility. Management strategies could range from lifestyle adjustments and nutritional supplements to precision editing of the epigenetic landscape.

CONCLUSIONS

Autoimmune hepatitis has a missing causality that might be explained by epigenetic changes induced by environmental factors and a missing heritability that might reflect epistatic gene interactions or transgenerational transmission of acquired epigenetic marks. These unassessed or under-evaluated areas warrant investigation.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

New insights on the role of human leukocyte antigen complex in primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a rare autoimmune cholangiopathy. Genetic studies have shown that the strongest statistical association with PBC has been mapped in the human leukocyte antigen (HLA) locus, a highly polymorphic area that mostly contribute to the genetic variance of the disease. Furthermore, PBC presents high variability throughout different population groups, which may explain the different geoepidemiology of the disease. A major role in defining HLA genetic contribution has been given by genome-wide association studies (GWAS) studies; more recently, new technologies have been developed to allow a deeper understanding. The study of the altered peptides transcribed by genetic alterations also allowed the development of novel therapeutic strategies in the context of immunotolerance. This review summarizes what is known about the immunogenetics of PBC with a focus on the HLA locus, the different distribution of HLA alleles worldwide, and how HLA modifications are associated with the pathogenesis of PBC. Novel therapeutic strategies are also outlined.

How genetic risk contributes to autoimmune liver disease

Genome-wide association studies (GWAS) for autoimmune hepatitis (AIH) and GWAS/genome-wide meta-analyses (GWMA) for primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) have been successful over the past decade, identifying about 100 susceptibility loci in the human genome, with strong associations with the HLA locus and many susceptibility variants outside the HLA locus with relatively low risk. However, identifying causative variants and genes and determining their effects on liver cells and their immunological microenvironment is far from trivial. Polygenic risk scores (PRSs) based on current genome-wide data have limited potential to predict individual disease risk. Interestingly, results of mediated expression score regression analysis provide evidence that a substantial portion of gene expression at susceptibility loci is mediated by genetic risk variants, in contrast to many other complex diseases. Genome- and transcriptome-wide comparisons between AIH, PBC, and PSC could help to better delineate the shared inherited component of autoimmune liver diseases (AILDs), and statistical fine-mapping, chromosome X-wide association testing, and genome-wide in silico drug screening approaches recently applied to GWMA data from PBC could potentially be successfully applied to AIH and PSC. Initial successes through single-cell RNA sequencing (scRNA-seq) experiments in PBC and PSC now raise high hopes for understanding the impact of genetic risk variants in the context of liver-resident immune cells and liver cell subpopulations, and for bridging the gap between genetics and disease.

Review article: Diagnosis of coeliac disease: a perspective on current and future approaches

Diagnostics will play a central role in addressing the ongoing dramatic rise in global prevalence of coeliac disease, and in deploying new non-dietary therapeutics. Clearer understanding of the immunopathogenesis of coeliac disease and the utility of serology has led to partial acceptance of non-biopsy diagnosis in selected cases. Non-biopsy diagnosis may expand further because research methods for measuring gluten-specific CD4+ T cells and the acute recall response to gluten ingestion in patients is now relatively straightforward. This perspective on diagnosis in the context of the immunopathogenesis of coeliac disease sets out to highlight current consensus, limitations of current practices, gluten food challenge for diagnosis and the potential for diagnostics that measure the underlying cause for coeliac disease, gluten-specific immunity.

How Thymocyte Deletion in the Cortex May Curtail Antigen-Specific T-Regulatory Cell Development in the Medulla

CD4⁺ T cell responses to self-antigens are pivotal for immunological self-tolerance. Activation of Foxp3^– T-conventional (T-conv) cells can precipitate autoimmune disease, whereas activation of Foxp3⁺ T-regulatory (T-reg) cells is essential to prevent autoimmune disease. This distinction indicates the importance of the thymus in controlling the differentiation of self-reactive CD4⁺ T cells. Thymocytes and thymic antigen-presenting cells (APC) depend on each other for normal maturation and differentiation. In this Hypothesis and Theory article, we propose this mutual dependence dictates which self-antigens induce T-reg cell development in the thymic medulla. We postulate self-reactive CD4⁺ CD8^– thymocytes deliver signals that stabilize and amplify the presentation of their cognate self-antigen by APC in the thymic medulla, thereby seeding a niche for the development of T-reg cells specific for the same self-antigen. By limiting the number of antigen-specific CD4⁺ thymocytes in the medulla, thymocyte deletion in the cortex may impede the formation of medullary T-reg niches containing certain self-antigens. Susceptibility to autoimmune disease may arise from cortical deletion creating a “hole” in the self-antigen repertoire recognized by T-reg cells.

The Genetics of Autoimmune Myositis

The idiopathic inflammatory myopathies (IIM) are rare, heterogeneous systemic autoimmune disorders, characterized by inflammation of skeletal muscle and multi-organ involvement. Studies to identify genetic risk factors and dysregulated gene expression in IIM aim to increase our understanding of disease pathogenesis. Genome-wide association studies have confirmed the HLA region as the most strongly associated region in IIM, with different associations between clinically-defined subgroups. Associated genes are involved in both the innate and adaptive immune response, while identification of variants reported in other autoimmune disorders suggests shared biological pathways. Targeted imputation analysis has identified key associated amino acid residues within HLA molecules that may influence antigen recognition. These amino acids increase risk for specific clinical phenotypes and autoantibody subgroups, and suggest that serology-defined subgroups may be more homogeneous. Recent data support the contribution of rare genetic variation to disease susceptibility in IIM, including mitochondrial DNA variation in sporadic inclusion body myositis and somatic mutations and loss of heterozygosity in cancer-associated myositis. Gene expression studies in skeletal muscle, blood and skin from individuals with IIM has confirmed the role of interferon signalling and other dysregulated pathways, and identified cell-type specific signatures. These dysregulated genes differentiate IIM subgroups and identify potential biomarkers. Here, we review recent genetic studies in IIM, and how these inform our understanding of disease pathogenesis and provide mechanistic insights into biological pathways.

HLA autoimmune risk alleles restrict the hypervariable region of T cell receptors

Polymorphisms in the human leukocyte antigen (HLA) genes strongly influence autoimmune disease risk. HLA risk alleles may influence thymic selection to increase the frequency of T cell receptors (TCRs) reactive to autoantigens (central hypothesis). However, research in human autoimmunity has provided little evidence supporting the central hypothesis. Here we investigated the influence of HLA alleles on TCR composition at the highly diverse complementarity determining region 3 (CDR3), which confers antigen recognition. We observed unexpectedly strong HLA-CDR3 associations. The strongest association was found at HLA-DRB1 amino acid position 13, the position that mediates genetic risk for multiple autoimmune diseases. We identified multiple CDR3 amino acid features enriched by HLA risk alleles. Moreover, the CDR3 features promoted by the HLA risk alleles are more enriched in candidate pathogenic TCRs than control TCRs (for example, citrullinated epitope-specific TCRs in patients with rheumatoid arthritis). Together, these results provide genetic evidence supporting the central hypothesis.

Rheumatoid Arthritis and Associated Lung Diseases: A Comprehensive Review

Rheumatoid arthritis (RA) is a prevalent autoimmune disorder affecting 0.5-1% of the population in North America and Europe. Pulmonary manifestations in rheumatoid arthritis patients result in significant morbidity and mortality. Management of these pulmonary manifestations in RA patients causes various challenges for the physicians. This review article has discussed the current state of knowledge of these pulmonary manifestations, including interstitial lung diseases, airway-related diseases, pulmonary vasculature, and pleural involvement in RA patients. This review article has also explored various pharmacological options, including steroids, disease-modifying antirheumatic drugs (DMARDs), immunosuppressive drugs, and biologic agents. Non-pharmacological options include conservative treatment, supplemental oxygen, pulmonary rehabilitation, smoking cessation, and lung transplantation.

Contemporary selection on MHC genes in a free-living ruminant population

Genes within the major histocompatibility complex (MHC) are the most variable identified in vertebrates. Pathogen-mediated selection is believed to be the main force maintaining MHC diversity. However, relatively few studies have demonstrated contemporary selection on MHC genes. Here, we examine associations between MHC variation and several fitness measurements including total fitness and five fitness components, in 3400 wild Soay sheep (Ovis aries) monitored between 1989 and 2012. In terms of total fitness, measured as lifetime breeding success of all individuals born, we found haplotypes named C and D were associated with decreased and increased male total fitness respectively. In terms of fitness components, juvenile survival was associated with haplotype divergence while individual haplotypes (C, D and F) were associated with adult fitness components. Consistent with the increased male total fitness, the rarest haplotype D has increased in frequency throughout the study period more than expected under neutral expectations. Our results demonstrate contemporary natural selection is acting on MHC class II genes in Soay sheep and the mode of selection on specific fitness components can be different mode from selection on total fitness.

HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases

Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.

The similarity of class II HLA genotypes defines patterns of autoreactivity in idiopathic bone marrow failure disorders

Idiopathic aplastic anemia (IAA) is a rare autoimmune bone marrow failure (BMF) disorder initiated by a human leukocyte antigen (HLA)-restricted T-cell response to unknown antigens. As in other autoimmune disorders, the predilection for certain HLA profiles seems to represent an etiologic factor; however, the structure-function patterns involved in the self-presentation in this disease remain unclear. Herein, we analyzed the molecular landscape of HLA complexes of a cohort of 300 IAA patients and almost 3000 healthy and disease controls by deeply dissecting their genotypic configurations, functional divergence, self-antigen binding capabilities, and T-cell receptor (TCR) repertoire specificities. Specifically, analysis of the evolutionary divergence of HLA genotypes (HED) showed that IAA patients carried class II HLA molecules whose antigen-binding sites were characterized by a high level of structural homology, only partially explained by specific risk allele profiles. This pattern implies reduced HLA binding capabilities, confirmed by binding analysis of hematopoietic stem cell (HSC)-derived self-peptides. IAA phenotype was associated with the enrichment in a few amino acids at specific positions within the peptide-binding groove of DRB1 molecules, affecting the interface HLA-antigen-TCR β and potentially constituting the basis of T-cell dysfunction and autoreactivity. When analyzing associations with clinical outcomes, low HED was associated with risk of malignant progression and worse survival, underlying reduced tumor surveillance in clearing potential neoantigens derived from mechanisms of clonal hematopoiesis. Our data shed light on the immunogenetic risk associated with IAA etiology and clonal evolution and on general pathophysiological mechanisms potentially involved in other autoimmune disorders.

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the 'lowest number of feature subset' with the 'maximal average AUC over the nested cross validation' and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Associations between MHC class II variation and phenotypic traits in a free-living sheep population

Pathogen-mediated selection (PMS) is thought to maintain the high level of allelic diversity observed in the major histocompatibility complex (MHC) class II genes. A comprehensive way to demonstrate contemporary selection is to examine associations between MHC variation and individual fitness. As individual fitness is hard to measure, many studies examine associations between MHC variation and phenotypic traits, including direct or indirect measures of adaptive immunity thought to contribute to fitness. Here, we tested associations between MHC class II variation and five phenotypic traits measured in free-living sheep captured in August: weight, strongyle faecal egg count, and plasma IgA, IgE and IgG immunoglobulin titres against the gastrointestinal nematode parasite Teladorsagia circumcincta. We found no association between MHC class II variation and weight or strongyle faecal egg count. We did, however, find associations between MHC class II variation and immunoglobulin levels which varied with isotype, age and sex. Our results suggest associations between MHC and phenotypic traits are more likely to be found for traits more closely associated with pathogen defence than integrative traits such as bodyweight and highlight the association between MHC variation and antibodies in wild populations.

Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion

BACKGROUND

Recent studies have suggested that individual variants do not sufficiently explain the variable expressivity of phenotypes observed in complex disorders. For example, the 16p12.1 deletion is associated with developmental delay and neuropsychiatric features in affected individuals, but is inherited in > 90% of cases from a mildly-affected parent. While children with the deletion are more likely to carry additional "second-hit" variants than their parents, the mechanisms for how these variants contribute to phenotypic variability are unknown.

METHODS

We performed detailed clinical assessments, whole-genome sequencing, and RNA sequencing of lymphoblastoid cell lines for 32 individuals in five large families with multiple members carrying the 16p12.1 deletion. We identified contributions of the 16p12.1 deletion and "second-hit" variants towards a range of expression changes in deletion carriers and their family members, including differential expression, outlier expression, alternative splicing, allele-specific expression, and expression quantitative trait loci analyses.

RESULTS

We found that the deletion dysregulates multiple autism and brain development genes such as FOXP1, ANK3, and MEF2. Carrier children also showed an average of 5323 gene expression changes compared with one or both parents, which matched with 33/39 observed developmental phenotypes. We identified significant enrichments for 13/25 classes of "second-hit" variants in genes with expression changes, where 4/25 variant classes were only enriched when inherited from the noncarrier parent, including loss-of-function SNVs and large duplications. In 11 instances, including for ZEB2 and SYNJ1, gene expression was synergistically altered by both the deletion and inherited "second-hits" in carrier children. Finally, brain-specific interaction network analysis showed strong connectivity between genes carrying "second-hits" and genes with transcriptome alterations in deletion carriers.

CONCLUSIONS

Our results suggest a potential mechanism for how "second-hit" variants modulate expressivity of complex disorders such as the 16p12.1 deletion through transcriptomic perturbation of gene networks important for early development. Our work further shows that family-based assessments of transcriptome data are highly relevant towards understanding the genetic mechanisms associated with complex disorders.

A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.

Genetic overlap between type 1 diabetes and other autoimmune diseases

Type 1 diabetes (T1D) is a chronic disease caused by the destruction of pancreatic β cells, which is driven by autoreactive T lymphocytes. It has been described that a high proportion of T1D patients develop other autoimmune diseases (AIDs), such as autoimmune thyroid disease, celiac disease, or vitiligo, which suggests the existence of common etiological factors among these disorders. In this regard, genetic studies have identified a high number of loci consistently associated with T1D that also represent established genetic risk factors for other AIDs. In addition, studies focused on identifying the shared genetic component in autoimmunity have described several common susceptibility loci with a potential role in T1D. Elucidation of this genetic overlap has been useful in identifying key molecular pathways with a pathogenic role in multiple disorders. In this review, we summarize recent advances in understanding the shared genetic component between T1D and other AIDs and discuss how the identification of common pathogenic mechanisms can help in the development of new therapeutic approaches as well as in improving the use of existing drugs.

HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders

Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10-3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.

Genome-wide association study reveals an association between the HLA-DPB1∗02:01:02 allele and wheat-dependent exercise-induced anaphylaxis

Wheat-dependent exercise-induced anaphylaxis (WDEIA) is a life-threatening food allergy triggered by wheat in combination with the second factor such as exercise. The identification of potential genetic risk factors for this allergy might help high-risk individuals before consuming wheat-containing food. We aimed to identify genetic variants associated with WDEIA. A genome-wide association study was conducted in a discovery set of 77 individuals with WDEIA and 924 control subjects via three genetic models. The associations were confirmed in a replication set of 91 affected individuals and 435 control individuals. Summary statistics from the combined set were analyzed by meta-analysis with a random-effect model. In the discovery set, a locus on chromosome 6, rs9277630, was associated with WDEIA in the dominant model (OR = 3.95 [95% CI, 2.31-6.73], p = 7.87 × 10-8). The HLA-DPB1∗02:01:02 allele displayed the most significant association with WDEIA (OR = 4.51 [95% CI, 2.66-7.63], p = 2.28 × 10-9), as determined via HLA imputation following targeted sequencing. The association of the allele with WDEIA was confirmed in replication samples (OR = 3.82 [95% CI, 2.33-6.26], p = 3.03 × 10-8). A meta-analysis performed in the combined set revealed that the HLA-DPB1∗02:01:02 allele was significantly associated with an increased risk of WDEIA (OR = 4.13 [95% CI, 2.89-5.93], p = 1.06 × 10-14). Individuals carrying the HLA-DPB1∗02:01:02 allele have a significantly increased risk of WDEIA. Further validation of these findings in independent multiethnic cohorts is needed.

The genetic architecture of primary biliary cholangitis

Primary biliary cholangitis (PBC) is a rare autoimmune disease of the liver affecting the small bile ducts. From a genetic point of view, PBC is a complex trait and several genetic and environmental factors have been called in action to explain its etiopathogenesis. Similarly to other complex traits, PBC has benefited from the introduction of genome-wide association studies (GWAS), which identified many variants predisposing or protecting toward the development of the disease. While a progressive endeavour toward the characterization of candidate loci and downstream pathways is currently ongoing, there is still a relatively large portion of heritability of PBC to be revealed. In addition, genetic variation behind progression of the disease and therapeutic response are mostly to be investigated yet. This review outlines the state-of-the-art regarding the genetic architecture of PBC and provides some hints for future investigations, focusing on the study of gene-gene interactions, the application of whole-genome sequencing techniques, and the investigation of X chromosome that can be helpful to cover the missing heritability gap in PBC.