Multi-population classical HLA type imputation

Human leukocyte antigen (HLA) Class I and II genetic relationships with brain imaging measures: a systematic review and meta-analysis

This systematic review and meta-analysis synthesizes current evidence on associations between genetic polymorphisms of human leukocyte antigen (HLA) class I and II molecules, which play key roles in antigen presentation and immune response regulation, and brain imaging phenotypes across various neurological and psychiatric conditions. The strongest associations were observed in multiple sclerosis (MS), where HLA-DRB1*15:01 was linked to increased lesion volume and disease progression. Meta-analyses revealed significant pooled effect sizes for both T1 and T2 lesion volumes. Emerging evidence also suggests that variants in HLA class I and II genes contribute to brain structural changes associated with age-related cognitive decline, schizophrenia and Gulf War illness. Our findings revealed greater patterns of association between HLA class II polymorphisms with brain imaging implications as compared to class I in neurodegenerative conditions. Considering HLA class II roles in exogenous protein/peptide presentation, this highlights the potential importance of neuroimmune-environmental interactions as contributing factors to disease pathogenesis and progression. Population-based studies from the UK Biobank highlight the potential influence of HLA class I and II genetic polymorphisms on brain structure beyond disease-specific contexts, suggesting broader implications for neurodevelopment and neurodegeneration. Despite these emerging insights, the limited availability of studies using imaging modalities beyond structural MRI, along with inconsistent findings within specific diseases and across conditions, underscores the need for further investigation into the mechanistic contributions of specific genetic variants on impacting brain structural and functional outcomes. Future research should include larger, more diverse study cohorts and employ advanced genotyping technologies to provide a more comprehensive investigations of HLA's role on brain health across the lifespan.

Polymorphisms in HLA genes among Brazilian patients hospitalized with COVID-19: Insights from a multicentric study

Immunogenetic factors such as human leukocyte antigen (HLA) alleles, have yielded contrasting associations with protection or increased chances of hospitalization due to COVID-19 worldwide. This case-control study included 834 patients with confirmed COVID-19 diagnosis from five Brazilian states: Ceará (n = 110), Mato Grosso (n = 192), Pará (n = 209), Rio de Janeiro (n = 211) and Rio Grande do Sul (n = 112). Genotyping was performed using the Axiom™ Human Genotyping SARS-CoV-2 array, targeting single nucleotide polymorphisms in HLA class I and II genes; HLA alleles were imputed for eight loci. Among the 15 preselected candidate alleles, only DQA1∗05:01 (p = 0.015) in the state of Ceará remained significantly associated with hospitalization. The meta-analysis of the most frequent alleles in all states revealed that HLA-DPA1∗01:03 (p = 0.0229, OR = 0.76, 95 % CI = 0.60-0.96) and HLA-DPB1∗04:01 (p = 0.0474, OR = 0.78, 95 % CI = 0.611.00) were associated with protection against hospitalization, whereas HLA-DPA1∗02:01 (p = 0.0259, OR = 1.37, 95 % CI = 1.04-1.80), HLA-DQA1∗05:01 (p = 0.0133, OR = 1.40, 95 % CI = 1.07-1.82), and HLA-DRB1∗03:01 (p = 0.0276, OR: 1.59, 95 % CI: 1.05-2.40) associated with increased risk of hospitalization. HLA evolutionary divergence (HED) scores were significantly higher among the non-hospitalized group for the HLA-A locus, which has been shown to be a protective factor for the most severe forms and consequently hospitalization due to COVID-19.

Advancements in Umbilical Cord Biobanking: A Comprehensive Review of Current Trends and Future Prospects

Biobanking has emerged as a transformative concept in advancing the medical field, particularly with the exponential growth of umbilical cord (UC) biobanking in recent decades. UC blood and tissue provide a rich source of primitive hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) for clinical transplantation, offering distinct advantages over alternative adult stem cell sources. However, to fully realize the therapeutic potential of UC-derived stem cells and establish a comprehensive global UC-biobanking network, it is imperative to optimize and standardize UC processing, cryopreservation methods, quality control protocols, and regulatory frameworks, alongside developing effective consent provisions. This review aims to comprehensively explore recent advancements in UC biobanking, focusing on the establishment of rigorous safety and quality control procedures, the standardization of biobanking operations, and the optimization and automation of UC processing and cryopreservation techniques. Additionally, the review examines the expanded clinical applications of UC stem cells, addresses the challenges associated with umbilical cord biobanking and UC-derived stem cell therapies, and discusses the promising role of artificial intelligence (AI) in enhancing various operational aspects of biobanking, streamlining data processing, and improving data analysis accuracy while ensuring compliance with safety and quality standards. By addressing these critical areas, this review seeks to provide insights into the future direction of UC biobanking and its potential to significantly impact regenerative medicine.

Adolescent sleep patterns, genetic predisposition, and risk of multiple sclerosis

STUDY OBJECTIVES

Shift work, insufficient sleep, and poor sleep quality at young age have been associated with increased risk of multiple sclerosis (MS). This study aimed to investigate the potential interaction between aspects of inadequate sleep (short sleep, phase shift, and poor sleep quality) during adolescence and HLA-DRB1*15:01 in relation to MS risk.

METHODS

We used a Swedish population-based case-control study (1253 cases and 1766 controls). Participants with different sleep patterns during adolescence and HLA-DRB1*15:01 status were compared regarding MS risk by calculating odds ratios with 95% confidence intervals (CI) using logistic regression models. Additive interaction between aspects of inadequate sleep and HLA-DRB1*15:01 status was assessed by calculating the attributable proportion due to interaction (AP) with 95% CI.

RESULTS

Short sleep duration (<7 hours/night) during adolescence acted synergistically with HLA-DRB1*15:01, increasing the risk of MS (AP 0.38, 95% CI: 0.01 to 0.75, p = .04). Similarly, subjective low sleep quality during adolescence interacted with HLA-DRB1*15:01 regarding risk of MS (AP 0.30, 95% CI: 0.06 to 0.56, p = .03), whereas phase shift did not significantly influence the risk of the disease, irrespective of HLA-DRB1*15:01 status.

CONCLUSIONS

Our findings underscore the importance of addressing inadequate sleep during adolescence, particularly in the context of the HLA-DRB1*15:01 allele, as it appears to amplify the risk of subsequently developing MS.

Efficient HLA imputation from sequential SNPs data by transformer

Human leukocyte antigen (HLA) genes are associated with a variety of diseases, yet the direct typing of HLA alleles is both time-consuming and costly. Consequently, various imputation methods leveraging sequential single nucleotide polymorphisms (SNPs) data have been proposed, employing either statistical or deep learning models, such as the convolutional neural network (CNN)-based model, DEEP*HLA. However, these methods exhibit limited imputation efficiency for infrequent alleles and necessitate a large size of reference dataset. In this context, we have developed a Transformer-based model to HLA allele imputation, named "HLA Reliable IMpuatioN by Transformer (HLARIMNT)" designed to exploit the sequential nature of SNPs data. We evaluated HLARIMNT's performance using two distinct reference panels; Pan-Asian reference panel (n = 530) and Type 1 Diabetes genetics Consortium (T1DGC) reference panel (n = 5225), alongside a combined panel (n = 1060). HLARIMNT demonstrated superior accuracy to DEEP*HLA across several indices, particularly for infrequent alleles. Furthermore, we explored the impact of varying training data sizes on imputation accuracy, finding that HLARIMNT consistently outperformed across all data size. These findings suggest that Transformer-based models can efficiently impute not only HLA types but potentially other gene types from sequential SNPs data.

Quantifying uncertainty of molecular mismatch introduced by mislabeled ancestry using haplotype-based HLA genotype imputation

Introduction

Modern histocompatibility algorithms depend on the comparison and analysis of high-resolution HLA protein sequences and structures, especially when considering epitope-based algorithms, which aim to model the interactions involved in antibody or T cell binding. HLA genotype imputation can be performed in the cases where only low/intermediate-resolution HLA genotype is available or if specific loci are missing, and by providing an individuals' race/ethnicity/ancestry information, imputation results can be more accurate. This study assesses the effect of imputing high-resolution genotypes on molecular mismatch scores under a variety of ancestry assumptions.

Methods

We compared molecular matching scores from "ground-truth" high-resolution genotypes against scores from genotypes which are imputed from low-resolution genotypes. Analysis was focused on a simulated patient-donor dataset and confirmed using two real-world datasets, and deviations were aggregated based on various ancestry assumptions.

Results

We observed that using multiple imputation generally results in lower error in molecular matching scores compared to single imputation, and that using the correct ancestry assumptions can reduce error introduced during imputation.

Discussion

We conclude that for epitope analysis, imputation is a valuable and low-risk strategy, as long as care is taken regarding epitope analysis context, ancestry assumptions, and (multiple) imputation strategy.

Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. We also fitted the models for Infinium Global Screening Array (GSA, Illumina, Inc.) and Axiom Precision Medicine Research Array (PMRA, Thermo Fisher Scientific Inc.) SNP content, with mean accuracies of 99.1% (97.2, 100) and 98.9% (97.4, 100), respectively.

Association of Human Leukocyte Antigen Alleles with COVID-19 Severity and Mortality in a Spanish Population

Background and Objectives: The aim of the following cross-sectional study is to determine the association between human leukocyte antigen (HLA) alleles and outcomes in patients presenting to the emergency department (ED) with SARS-CoV-2 infection. Methods and Materials: Genotyping was made using the Axiom Human Genotyping SARS-CoV-2 Research Array. Statistical analysis was made with Fisher's exact test and multivariable logistic regression, adjusted for sex, age and clinical variables. Results: Of 190 patients, 11.1% were discharged from the ED; 57.9% were admitted to the COVID-19 ward, without intensive care unit (ICU) admission; 15.3% survived an ICU admission; and 15.8% died. After multivariable analysis, two HLA alleles protected against hospital admission (HLA-C*05:01, adjusted odds ratio [aOR] 0.2, 95% confidence interval [CI] 0.055-0.731; and HLA-DQB1*02:02, aOR 0.046, CI 0.002-0.871) and one was associated with higher risk for ICU admission or death (HLA-DQA1*05:01, aOR 2.517, CI 1.086-5.833). Conclusions: In this population, HLA-C*05:01 and HLA-DQB1*02:02 are associated with a protective effect against hospital admission and HLA-DQA1*05:01 is associated with higher risk of ICU admission or death in the multivariable analysis. This may help stratify risk in COVID-19 patients.

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

HLA Associations of Intrathecal IgG Production against Specific Viruses in Multiple Sclerosis

OBJECTIVE

Specific human leucocyte antigen (HLA) alleles are not only associated with higher risk to develop multiple sclerosis (MS) and other autoimmune diseases, but also with the severity of various viral and bacterial infections. Here, we analyzed the most specific biomarker for MS, that is, the polyspecific intrathecal IgG antibody production against measles, rubella, and varicella zoster virus (MRZ reaction), for possible HLA associations in MS.

METHODS

We assessed MRZ reaction from 184 Swiss patients with MS and clinically isolated syndrome (CIS) and 89 Swiss non-MS/non-CIS control patients, and performed HLA sequence-based typing, to check for associations of positive MRZ reaction with the most prevalent HLA alleles. We used a cohort of 176 Swedish MS/CIS patients to replicate significant findings.

RESULTS

Whereas positive MRZ reaction showed a prevalence of 38.0% in MS/CIS patients, it was highly specific (97.7%) for MS/CIS. We identified HLA-DRB1*15:01 and other tightly linked alleles of the HLA-DR15 haplotype as the strongest HLA-encoded risk factors for a positive MRZ reaction in Swiss MS/CIS (odds ratio [OR], 3.90, 95% confidence interval [CI] 2.05-7.46, padjusted = 0.0004) and replicated these findings in Swedish MS/CIS patients (OR 2.18, 95%-CI 1.16-4.02, padjusted = 0.028). In addition, female MS/CIS patients had a significantly higher probability for a positive MRZ reaction than male patients in both cohorts combined (padjusted <0.005).

INTERPRETATION

HLA-DRB1*15:01, the strongest genetic risk factor for MS, and female sex, 1 of the most prominent demographic risk factors for developing MS, predispose in MS/CIS patients for a positive MRZ reaction, the most specific CSF biomarker for MS. ANN NEUROL 2024;95:1112-1126.

Head trauma results in manyfold increased risk of multiple sclerosis in genetically susceptible individuals

BACKGROUND

Large register-based studies have reported an association between head trauma and increased risk of multiple sclerosis (MS). We aimed to investigate possible interactions between head trauma and MS-associated HLA genes in relation to MS risk.

METHODS

We used a Swedish population-based case-control study (2807 incident cases, 5950 matched controls with HLA genotypes available for 2057 cases, 2887 controls). Subjects with and without a history of self-reported head trauma were compared regarding MS risk, by calculating ORs with 95% CIs using logistic regression models. Additive interaction between head trauma, HLA-DRB1*1501 and absence of HLA-A*0201, was assessed by calculating the attributable proportion (AP) due to interaction.

RESULTS

A history of head trauma was associated with a 30% increased risk of subsequently developing MS (OR 1.34, 95% CI 1.17 to 1.53), with a trend showing increased risk of MS with increasing number of head impacts (p=0.03). We observed synergistic effects between recent head trauma and HLA-DRB1*15:01 as well as absence of HLA*02:01 in relation to MS risk (each AP 0.40, 95% CI 0.1 to 0.7). Recent head trauma in individuals with both genetic risk factors rendered an 18-fold increased risk of MS, compared with those with neither the genetic risk factors nor a history of head trauma (OR 17.7, 95% CI 7.13 to 44.1).

CONCLUSIONS

Our findings align with previous observations of a dose-dependent association between head trauma and increased risk of MS and add a novel aspect of this association by revealing synergistic effects between recent head trauma and MS-associated HLA genes.

Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes

The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.

High-resolution HLA genotyping improves PIRCHE-II assessment of molecular mismatching in kidney transplantation

HLA matching in solid organ transplant is performed with the aim of assessing immunologic compatibility in order to avoid hyperacute rejection and assess the risk of future rejection events. Molecular mismatch algorithms are intended to improve granularity in histocompatibility assessment and risk stratification. PIRCHE-II uses HLA genotyping to predict indirectly presented mismatched donor HLA peptides, though most clinical validation studies rely on imputing high resolution (HR) genotypes from low resolution (LR) typing data. We hypothesized that use of bona fide HR typing could overcome limitations in imputation, improving accuracy and predictive ability for donor-specific antibody development and acute rejection. We performed a retrospective analysis of adult and pediatric kidney transplant donor/recipient pairs (N = 419) with HR typing and compared the use of imputed LR genotyping verses HR genotyping for PIRCHE-II analysis and outcomes. Imputation success was highly dependent on the reference population used, as using historic Caucasian reference populations resulted in 10 % of pairs with unsuccessful imputation while multiethnic reference populations improved successful imputation with only 1 % unable to be imputed. Comparing PIRCHE-II analysis with HR and LR genotyping produced notably different results, with 20 % of patients discrepantly classified to immunologic risk groups. These data emphasize the importance of using multiethnic reference panels when performing imputation and indicate HR HLA genotyping has clinically meaningful benefit for PIRCHE-II analysis compared to imputed LR typing.

High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Optic neuritis (ON) is associated with numerous immune-mediated inflammatory diseases, but 50% patients are ultimately diagnosed with multiple sclerosis (MS). Differentiating MS-ON from non-MS-ON acutely is challenging but important; non-MS ON often requires urgent immunosuppression to preserve vision. Using data from the United Kingdom Biobank we showed that combining an MS-genetic risk score (GRS) with demographic risk factors (age, sex) significantly improved MS prediction in undifferentiated ON; one standard deviation of MS-GRS increased the Hazard of MS 1.3-fold (95% confidence interval 1.07-1.55, P < 0.01). Participants stratified into quartiles of predicted risk developed incident MS at rates varying from 4% (95%CI 0.5-7%, lowest risk quartile) to 41% (95%CI 33-49%, highest risk quartile). The model replicated across two cohorts (Geisinger, USA, and FinnGen, Finland). This study indicates that a combined model might enhance individual MS risk stratification, paving the way for precision-based ON treatment and earlier MS disease-modifying therapy.

Genotypes predisposing for celiac disease and autoimmune diabetes and risk of infections in early childhood

OBJECTIVES

Infections in early childhood have been associated with risk of celiac disease (CD) and type 1 diabetes (T1D). We investigated whether this is driven by susceptibility genes for autoimmune disease by comparing infection frequency by genetic susceptibility variants for CD or T1D.

METHODS

We genotyped 373 controls and 384 children who developed CD or T1D in the population-based Norwegian Mother, Father and Child Cohort study (MoBa) study for human leukocyte antigen (HLA)-DQ, FUT2, SH2B3, and PTPN22, and calculated a weighted non-HLA genetic risk score (GRS) for CD and T1D based on over 40 SNPs. Parents reported infections in questionnaires when children were 6 and 18 months old. We used negative binomial regression to estimate incidence rate ratio (IRR) for infections by genotype.

RESULTS

HLA genotypes for CD and T1D or non-HLA GRS for T1D were not associated with infections. The non-HLA GRS for CD was associated with a nonsignificantly lower frequency of infections (aIRR: 0.95, 95% CI: 0.87-1.03 per weighted allele score), and significantly so when restricting to healthy controls (aIRR: 0.89, 0.81-0.99). Participants homozygous for rs601338(A;A) at FUT2, often referred to as nonsecretors, had a nonsignificantly lower risk of infections (aIRR: 0.91, 95% CI: 0.83-1.01). SH2B3 and PTPN22 genotypes were not associated with infections. The association between infections and risk of CD (OR: 1.15 per five infections) was strengthened after adjustment for HLA genotype and non-HLA GRS (OR: 1.24).

CONCLUSIONS

HLA variants and non-HLA GRS conferring susceptibility for CD were not associated with increased risk of infections in early childhood and is unlikely to drive the observed association between infections and risk of CD or T1D in many studies.

Genetic determinants of IgG antibody response to COVID-19 vaccination

Human humoral immune responses to SARS-CoV-2 vaccines exhibit substantial inter-individual variability and have been linked to vaccine efficacy. To elucidate the underlying mechanism behind this variability, we conducted a genome-wide association study (GWAS) on the anti-spike IgG serostatus of UK Biobank participants who were previously uninfected by SARS-CoV-2 and had received either the first dose (n = 54,066) or the second dose (n = 46,232) of COVID-19 vaccines. Our analysis revealed significant genome-wide associations between the IgG antibody serostatus following the initial vaccine and human leukocyte antigen (HLA) class II alleles. Specifically, the HLA-DRB1∗13:02 allele (MAF = 4.0%, OR = 0.75, p = 2.34e-16) demonstrated the most statistically significant protective effect against IgG seronegativity. This protective effect was driven by an alteration from arginine (Arg) to glutamic acid (Glu) at position 71 on HLA-DRβ1 (p = 1.88e-25), leading to a change in the electrostatic potential of pocket 4 of the peptide binding groove. Notably, the impact of HLA alleles on IgG responses was cell type specific, and we observed a shared genetic predisposition between IgG status and susceptibility/severity of COVID-19. These results were replicated within independent cohorts where IgG serostatus was assayed by two different antibody serology tests. Our findings provide insights into the biological mechanism underlying individual variation in responses to COVID-19 vaccines and highlight the need to consider the influence of constitutive genetics when designing vaccination strategies for optimizing protection and control of infectious disease across diverse populations.

Associations of classical HLA alleles with depression and anxiety

Immune dysregulation has been widely observed in patients with psychiatric disorders. This study aims to examine the association between HLA alleles and depression and anxiety. Using data from the UK Biobank, we performed regression analyses to assess the association of 359 HLA alleles with depression and anxiety, as determined by Patient Health Questionnaire (PHQ) score (n = 120,033), self-reported depression (n = 121,685), general anxiety disorder (GAD-7) score (n = 120,590), and self-reported anxiety (n = 108,310). Subsequently, we conducted gene environmental interaction study (GEIS) to evaluate the potential effects of interactions between HLA alleles and environmental factors on the risk of depression and anxiety. Sex stratification was implemented in all analysis. Our study identified two significant HLA alleles associated with self-reported depression, including HLA-C*07:01 (β = -0.015, p = 5.54 × 10-5 ) and HLA-B*08:01 (β = -0.015, p = 7.78 × 10-5 ). Additionally, we identified four significant HLA alleles associated with anxiety score, such as HLA-DRB1*07:01 (β = 0.084, p = 9.28 × 10-5 ) and HLA-B*57:01 (β = 0.139, p = 1.22 × 10-4 ). GEIS revealed that certain HLA alleles interacted with environmental factors to influence mental health outcomes. For instance, HLA-A*02:07 × cigarette smoking was associated with depression score (β = 0.976, p = 1.88 × 10-6 ). Moreover, sex stratification analysis revealed significant sex-based differences in the interaction effects of certain HLA alleles with environmental factors. Our findings indicate the considerable impact of HLA alleles on the risks of depression and anxiety, providing valuable insights into the functional relevance of immune dysfunction in these conditions.

HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases

The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease.

GWAS for the composite traits of hematuria and albuminuria

Our GWAS of hematuria in the UK Biobank identified 6 loci, some of which overlap with loci for albuminuria suggesting pleiotropy. Since clinical syndromes are often defined by combinations of traits, generating a combined phenotype can improve power to detect loci influencing multiple characteristics. Thus the composite trait of hematuria and albuminuria was chosen to enrich for glomerular pathologies. Cases had both hematuria defined by ICD codes and albuminuria defined as uACR > 3 mg/mmol. Controls had neither an ICD code for hematuria nor an uACR > 3 mg/mmol. 2429 cases and 343,509 controls from the UK Biobank were included. eGFR was lower in cases compared to controls, with the exception of the comparison in females using CKD-EPI after age adjustment. Variants at 4 loci met genome-wide significance with the following nearest genes: COL4A4, TRIM27, ETV1 and CUBN. TRIM27 is part of the extended MHC locus. All loci with the exception of ETV1 were replicated in the Geisinger MyCode cohort. The previous GWAS of hematuria reported COL4A3-COL4A4 variants and HLA-B*0801 within MHC, which is in linkage disequilibrium with the TRIM27 variant (D' = 0.59). TRIM27 is highly expressed in the tubules. Additional loci included a coding sequence variant in CUBN (p.Ala2914Val, MAF = 0.014 (A), p = 3.29E-8, OR = 2.09, 95% CI = 1.61-2.72). Overall, GWAS for the composite trait of hematuria and albuminuria identified 4 loci, 2 of which were not previously identified in a GWAS of hematuria.

A GWAS in the pandemic epicenter highlights the severe COVID-19 risk locus introgressed by Neanderthals

Large GWAS indicated that genetic factors influence the response to SARS-CoV-2. However, sex, age, concomitant diseases, differences in ancestry, and uneven exposure to the virus impacted the interpretation of data. We aimed to perform a GWAS of COVID-19 outcome in a homogeneous population who experienced a high exposure to the virus and with a known infection status. We recruited inhabitants of Bergamo province-that in spring 2020 was the epicenter of the SARS-Cov-2 pandemic in Europe-via an online questionnaire followed by personal interviews. Cases and controls were matched by age, sex and risk factors. We genotyped 1195 individuals and replicated the association at the 3p21.31 locus with severity, but with a stronger effect size that further increased in gravely ill patients. Transcriptome-wide association study highlighted eQTLs for LZTFL1 and CCR9. We also identified 17 loci not previously reported, suggestive for an association with either COVID-19 severity or susceptibility.

Assessing HLA imputation accuracy in a West African population

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We, therefore, sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from 315 Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes (1kg-All), 1000 Genomes African (1kg-Afr), 1000 Genomes Gambian (1kg-Gwd), H3Africa, and the HLA Multi-ethnic datasets. HLA-A, HLA-B, and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA, and Minimac4, and concordance rate was used as an assessment metric. The best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel, with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in a West African population.

Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores

BACKGROUND

Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways.

METHODS

In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients.

FINDINGS

We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region.

INTERPRETATION

This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA.

FUNDING

This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Observed associations between indicators of socioeconomic status and risk of multiple sclerosis in Sweden are explained by a few lifestyle-related factors

BACKGROUND AND PURPOSE

The association between socioeconomic status (SES) and the risk of multiple sclerosis (MS) is unclear. The aim was to study whether a potential association between indicators of SES and MS risk in Sweden is explained by lifestyle/environmental factors.

METHODS

Using the Swedish MS registry and the Swedish patient registries, a register study was performed comprising all cases diagnosed with MS in Sweden between 1990 and 2018 (N = 24,729) and five randomly selected controls per case, matched by year and age at disease onset, sex and residential area at disease onset. Data from two matched case-control studies combined comprising data on environment/lifestyle factors (7193 cases, 9609 controls, inclusion period 2005-2018) were also utilized. For all participants, information regarding ancestry, formal education (available 1990-2018) and family income (available 1998-2018) was retrieved from the National Board of Health and Welfare.

RESULTS

The registry study revealed no association between education and MS risk, whereas an income exceeding the upper quartile was associated with lower MS risk compared to having an income in the lowest quartile (odds ratio 0.86, 95% confidence interval 0.82-0.90). These findings were replicated in the crude analyses of the case-control study. However, after adjustment for confounding, no association was observed between income and risk of MS.

CONCLUSIONS

Education and income were not associated with occurrence of MS after adjustment for a few lifestyle-related factors (smoking, alcohol consumption, body mass index and sun exposure habits), indicating that SES has no influence on MS risk besides its association with these lifestyle factors in the Swedish context.

Association between exposure to combustion-related air pollution and multiple sclerosis risk

BACKGROUND

Smoking and occupational pulmonary irritants contribute to multiple sclerosis (MS) development. We aimed to study the association between ambient air pollution and MS risk and potential interaction with the human leukocyte antigen (HLA)-DRB1*15:01 allele.

METHODS

Exposure to combustion-related air pollution was estimated as outdoor levels of nitrogen oxides (NOx) at the participants' residence locations, by spatially resolved dispersion modelling for the years 1990-18. Using two population-based case-control studies (6635 cases, 8880 controls), NOx levels were associated with MS risk by calculating odds ratios (OR) with 95% confidence intervals (CI) using logistic regression models. Interaction between high NOx levels and the HLA-DRB1*15:01 allele regarding MS risk was calculated by the attributable proportion due to interaction (AP). In addition, a register study was performed comprising all MS cases in Sweden who had received their diagnosis between 1993 and 2018 (n = 22 173), with 10 controls per case randomly selected from the National Population register.

RESULTS

Residential air pollution was associated with MS risk. NOx levels (3-year average) exceeding the 90th percentile (24.6 µg/m3) were associated with an OR of 1.37 (95% CI 1.10-1.76) compared with levels below the 25th percentile (5.9 µg/m3), with a trend of increasing risk of MS with increasing levels of NOx (P <0.0001). A synergistic effect was observed between high NOx levels (exceeding the lower quartile among controls) and the HLA-DRB1*15:01 allele regarding MS risk (AP 0.26, 95% CI 0.13-0.29).

CONCLUSIONS

Our findings indicate that moderate levels of combustion-related ambient air pollution may play a role in MS development.

Saliva microbiome, dietary, and genetic markers are associated with suicidal ideation in university students

Here, salivary microbiota and major histocompatibility complex (MHC) human leukocyte antigen (HLA) alleles were compared between 47 (12.6%) young adults with recent suicidal ideation (SI) and 325 (87.4%) controls without recent SI. Several bacterial taxa were correlated with SI after controlling for sleep issues, diet, and genetics. Four MHC class II alleles were protective for SI including DRB1*04, which was absent in every subject with SI while present in 21.7% of controls. Increased incidence of SI was observed with four other MHC class II alleles and two MHC class I alleles. Associations between these HLA alleles and salivary bacteria were also identified. Furthermore, rs10437629, previously associated with attempted suicide, was correlated here with SI and the absence of Alloprevotella rava, a producer of an organic acid known to promote brain energy homeostasis. Hence, microbial-genetic associations may be important players in the diathesis-stress model for suicidal behaviors.

Differences in Clinical Features and Comorbid Burden between HLA-C∗06:02 Carrier Groups in >9,000 People with Psoriasis

The identification of robust endotypes-disease subgroups of clinical relevance-is fundamental to stratified medicine. We hypothesized that HLA-C∗06:02 status, the major genetic determinant of psoriasis, defines a psoriasis endotype of clinical relevance. Using two United Kingdom-based cross-sectional datasets-an observational severe-psoriasis study (Biomarkers of Systemic Treatment Outcomes in Psoriasis; n = 3,767) and a large population-based bioresource (UK Biobank, including n = 5,519 individuals with psoriasis)-we compared demographic, environmental, and clinical variables of interest in HLA-C∗06:02-positive (one or two copies of the HLA-C∗06:02 allele) with those in HLA-C∗06:02‒negative (no copies) individuals of European ancestry. We used multivariable regression analyses to account for mediation effects established a priori. We confirm previous observations that HLA-C∗06:02-positive status is associated with earlier age of psoriasis onset and extend findings to reveal an association with disease expressivity in females (Biomarkers of Systemic Treatment Outcomes in Psoriasis: P = 2.7 × 10-14, UK Biobank: P = 1.0 × 10-8). We also show HLA-C∗06:02-negative status to be associated with characteristic clinical features (large plaque disease, OR for HLA-C∗06:02 = 0.73, P = 7.4 × 10-4; nail involvement, OR = 0.70, P = 2.4 × 10-6); higher central adiposity (Biomarkers of Systemic Treatment Outcomes in Psoriasis: waist circumference difference of 2.0 cm, P = 8.4 × 10-4; UK Biobank: waist circumference difference of 1.4 cm, P = 1.5 × 10-4), especially in women; and a higher prevalence of other cardiometabolic comorbidities. These findings extend the clinical phenotype delineated by HLA-C∗06:02 and highlight its potential as an important biomarker to consider in future multimarker stratified medicine approaches.

GWAS of Hematuria

BACKGROUND AND OBJECTIVES

Glomerular hematuria has varied causes but can have a genetic basis, including Alport syndrome and IgA nephropathy.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used summary statistics to identify genetic variants associated with hematuria in White British UK Biobank participants. Individuals with glomerular hematuria were enriched by excluding participants with genitourinary conditions. A strongly associated locus on chromosome 2 (COL4A4-COL4A3) was identified. The region was reimputed using the Trans-Omics for Precision Medicine Program followed by sequential rounds of regional conditional analysis, conditioning on previous genetic signals. Similarly, we applied conditional analysis to identify independent variants in the MHC region on chromosome 6 using imputed HLA haplotypes.

RESULTS

In total, 16,866 hematuria cases and 391,420 controls were included. Cases had higher urinary albumin-creatinine compared with controls (women: 13.01 mg/g [8.05-21.33] versus 12.12 mg/g [7.61-19.29]; P<0.001; men: 8.85 mg/g [5.66-16.19] versus 7.52 mg/g [5.04-12.39]; P<0.001) and lower eGFR (women: 88±14 versus 90±13 ml/min per 1.72 m²; P<0.001; men: 87±15 versus 90±13 ml/min per 1.72 m²; P<0.001), supporting enrichment of glomerular hematuria. Variants at six loci (PDPN, COL4A4-COL4A3, HLA-B, SORL1, PLLP, and TGFB1) met genome-wide significance (P<5E-8). At chromosome 2, COL4A4 p.Ser969X (rs35138315; minor allele frequency=0.00035; P<7.95E-35; odds ratio, 87.3; 95% confidence interval, 47.9 to 159.0) had the most significant association, and two variants in the locus remained associated with hematuria after conditioning for this variant: COL4A3 p.Gly695Arg (rs200287952; minor allele frequency=0.00021; P<2.16E-7; odds ratio, 45.5; 95% confidence interval, 11.8 to 168.0) and a common COL4A4 intron 25 variant (not previously reported; rs58261427; minor allele frequency=0.214; P<2.00E-9; odds ratio, 1.09; 95% confidence interval, 1.06 to 1.12). Of the HLA haplotypes, HLA-B (*0801; minor allele frequency=0.14; P<4.41E-24; odds ratio, 0.84; 95% confidence interval, 0.82 to 0.88) displayed the most statistically significant association. For remaining loci, we identified three novel associations, which were replicated in the deCODE dataset for dipstick hematuria (nearest genes: PDPN, SORL1, and PLLP).

CONCLUSIONS

Our study identifies six loci associated with hematuria, including independent variants in COL4A4-COL4A3 and HLA-B. Additionally, three novel loci are reported, including an association with an intronic variant in PDPN expressed in the podocyte.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_04_26_CJN13711021.mp3.

Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA)

PURPOSE

The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA.

PARTICIPANTS

A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit).

FINDINGS TO DATE

Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease.

FUTURE PLANS

The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

The utility of imputation for molecular mismatch analysis in solid organ transplantation

HLA genotyping has undergone a rapid progression in resolution since the development of DNA-based typing methods. Despite the advent of high-resolution next-generation sequencing, the bulk of solid organ genotyping is performed at intermediate resolution, which provides multiple possible two-field results for each classical HLA loci. As a result, several methodologies have been developed to impute the most likely allele-level (two-field) HLA genotype for the purposes of donor-recipient compatibility analysis. The advent of molecular mismatch analysis, however, has placed a new emphasis on the accuracy of imputation. While seminal molecular mismatch studies have relied on the imputation of intermediate resolution genotyping, several recent studies have performed analysis showing that imputation generates inaccuracies in epitope identification. While the clinical impact of these errors is not clear, it is important that these concerns do not preclude future progress in understanding the utility of molecular mismatch analysis in transplantation. In the future, advances in genotyping methods will result in routine two-field resolution that will abrogate these concerns. In the meantime, however, studies are needed in order to address the role of molecular mismatch in diverse patient populations and to carefully address the potential of molecular mismatch analysis in the context of imputation.

Allele imputation for the killer cell immunoglobulin-like receptor KIR3DL1/S1

Highly polymorphic interaction of KIR3DL1 and KIR3DS1 with HLA class I ligands modulates the effector functions of natural killer (NK) cells and some T cells. This genetically determined diversity affects severity of infections, immune-mediated diseases, and some cancers, and impacts the course of immunotherapies, including transplantation. KIR3DL1 is an inhibitory receptor, and KIR3DS1 is an activating receptor encoded by the KIR3DL1/S1 gene that has more than 200 diverse and divergent alleles. Determination of KIR3DL1/S1 genotypes for medical application is hampered by complex sequence and structural variation, requiring targeted approaches to generate and analyze high-resolution allele data. To overcome these obstacles, we developed and optimized a model for imputing KIR3DL1/S1 alleles at high-resolution from whole-genome SNP data. We designed the model to represent a substantial component of human genetic diversity. Our Global imputation model is effective at genotyping KIR3DL1/S1 alleles with an accuracy ranging from 88% in Africans to 97% in East Asians, with mean specificity of 99% and sensitivity of 95% for alleles >1% frequency. We used the established algorithm of the HIBAG program, in a modification named Pulling Out Natural killer cell Genomics (PONG). Because HIBAG was designed to impute HLA alleles also from whole-genome SNP data, PONG allows combinatorial diversity of KIR3DL1/S1 with HLA-A and -B to be analyzed using complementary techniques on a single data source. The use of PONG thus negates the need for targeted sequencing data in very large-scale association studies where such methods might not be tractable.

STXBP6 and B3GNT6 Genes are Associated With Selective IgA Deficiency

Immunoglobulin A Deficiency (IgAD) is a polygenic primary immune deficiency, with a strong genetic association to the human leukocyte antigen (HLA) region. Previous genome-wide association studies (GWAS) have identified five non-HLA risk loci (IFIH1, PVT1, ATG13-AMBRA1, AHI1 and CLEC16A). In this study, we investigated the genetic interactions between different HLA susceptibility haplotypes and non-MHC genes in IgAD. To do this, we stratified IgAD subjects and healthy controls based on HLA haplotypes (N = 10,993), and then performed GWAS to identify novel genetic regions contributing to IgAD susceptibility. After replicating previously published HLA risk haplotypes, we compared individuals carrying at least one HLA risk allele (HLA-B*08:01-DRB1*03:01-DQB1*02:01 or HLA-DRB1*07:01-DQB1*02:02 or HLA-DRB1*01-DQB1*05:01) with individuals lacking an HLA risk allele. Subsequently, we stratified subjects based on the susceptibility alleles/haplotypes and performed gene-based association analysis using 572,856 SNPs and 24,125 genes. A significant genome-wide association in STXBP6 (rs4097492; p = 7.63 × 10^-9) was observed in the cohort carrying at least one MHC risk allele. We also identified a significant gene-based association for B3GNT6 (P _Gene = 2.1 × 10^-6) in patients not carrying known HLA susceptibility alleles. Our findings indicate that the etiology of IgAD differs depending on the genetic background of HLA susceptibility haplotypes.

OUP accepted manuscript

Genetic variants in the human leukocyte antigen and killer cell immunoglobulin-like receptor regions have been associated with many brain-related diseases, but how they shape brain structure and function remains unclear. To identify the genetic variants in HLA and KIR genes associated with human brain phenotypes, we performed a genetic association study of ∼30 000 European unrelated individuals using brain MRI phenotypes generated by the UK Biobank (UKB). We identified 15 HLA alleles in HLA class I and class II genes significantly associated with at least one brain MRI-based phenotypes (P < 5 × 10⁻⁸). These associations converged on several main haplotypes within the HLA. In particular, the human leukocyte antigen alleles within an ancestral haplotype 8.1 were associated with multiple MRI measures, including grey matter volume, cortical thickness (TH) and diffusion MRI (dMRI) metrics. These alleles have been strongly associated with schizophrenia. Additionally, associations were identified between HLA-DRB1*04∼DQA1*03:01∼DQB1*03:02 and isotropic volume fraction of diffusion MRI in multiple white matter tracts. This haplotype has been reported to be associated with Parkinson’s disease. These findings suggest shared genetic associations between brain MRI biomarkers and brain-related diseases. Additionally, we identified 169 associations between the complement component 4 (C4) gene and imaging phenotypes. We found that C4 gene copy number was associated with cortical TH and dMRI metrics. No KIR gene copy numbers were associated with image-derived phenotypes at genome-wide threshold. To address the multiple testing burden in the phenome-wide association study, we performed a multi-trait association analysis using trait-based association test that uses extended Simes procedure and identified MRI image-specific associations. This study contributes to insight into how critical immune genes affect brain-related traits as well as the development of neurological and neuropsychiatric disorders.

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.

Immune system regulation and role of the human leukocyte antigen in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a debilitating condition that adversely affect mental and physical health. Recent studies have increasingly explored the role of the immune system in risk for PTSD and its related symptoms. Dysregulation of the immune system may lead to central nervous system tissue damage and impair learning and memory processes. Individuals with PTSD often have comorbid inflammatory or auto-immune disorders. Evidence shows associations between PTSD and multiple genes that are involved in immune-related or inflammatory pathways. In this review, we will summarize the evidence of immune dysregulation in PTSD, outlining the contributions of distinct cell types, genes, and biological pathways. We use the Human Leukocyte Antigen (HLA) locus to illustrate the contribution of genetic variation to function in different tissues that contribute to PTSD etiology, severity, and comorbidities.

Sex-Specific Causal Relations between Steroid Hormones and Obesity-A Mendelian Randomization Study

Steroid hormones act as important regulators of physiological processes including gene expression. They provide possible mechanistic explanations of observed sex-dimorphisms in obesity and coronary artery disease (CAD). Here, we aim to unravel causal relationships between steroid hormones, obesity, and CAD in a sex-specific manner. In genome-wide meta-analyses of four steroid hormone levels and one hormone ratio, we identified 17 genome-wide significant loci of which 11 were novel. Among loci, seven were female-specific, four male-specific, and one was sex-related (stronger effects in females). As one of the loci was the human leukocyte antigen (HLA) region, we analyzed HLA allele counts and found four HLA subtypes linked to 17-OH-progesterone (17-OHP), including HLA-B*14*02. Using Mendelian randomization approaches with four additional hormones as exposure, we detected causal effects of dehydroepiandrosterone sulfate (DHEA-S) and 17-OHP on body mass index (BMI) and waist-to-hip ratio (WHR). The DHEA-S effect was stronger in males. Additionally, we observed the causal effects of testosterone, estradiol, and their ratio on WHR. By mediation analysis, we found a direct sex-unspecific effect of 17-OHP on CAD while the other four hormone effects on CAD were mediated by BMI or WHR. In conclusion, we identified the sex-specific causal networks of steroid hormones, obesity-related traits, and CAD.

The increased risk of multiple sclerosis associated with HLA-DRB1*15:01 and smoking is modified by alcohol consumption

Previous studies have observed an inverse association between alcohol consumption and multiple sclerosis (MS) risk. We aimed to investigate possible interactions between alcohol consumption, MS-associated human leukocyte antigen (HLA) genes and smoking regarding MS risk. We used a Swedish population-based case-control study (2059 incident cases, 2887 controls) matched by age, sex, and residential area. Subjects with different genotypes and alcohol consumption habits were compared regarding MS risk, by calculating odds ratios with 95% confidence intervals using logistic regression models. Interaction on the additive scale between non-drinking and both genotype and smoking were assessed by calculating the attributable proportion due to interaction (AP). There was a dose-dependent inverse association between alcohol consumption and MS risk (p for trend < 0.0001). A potentiating effect was observed between non-drinking and presence of DRB1*15:01 (AP 0.3, 95% CI 0.2-0.5) which was of similar magnitude irrespective of smoking habits. Non-drinking also interacted with smoking to increase MS risk (AP 0.2, 95% CI 0.06-0.4). Non-drinking interacts with DRB1*15:01 and smoking to increase the risk of MS. Better understanding of the mechanisms behind our findings may help to define ways to achieve protection against MS by other means than alcohol consumption.

Factors affecting the risk of relapsing-onset and progressive-onset multiple sclerosis

OBJECTIVE

It has been debated whether the different clinical disease courses in multiple sclerosis (MS) are the consequence of different pathogenic mechanisms, with distinct risk factors, or if all MS clinical phenotypes are variations of similar underlying disease mechanisms. We aimed to study environmental risk factors and their interactions with human leucocyte antigen DRB1*15:01 with regards to relapsing-onset and progressive-onset MS.

METHODS

We used two Swedish population-based case-control studies, including 7520 relapsing-onset cases, 540 progressive-onset cases and 11 386 controls matched by age, sex and residential area. Logistic regression was used to estimate ORs with 95% CIs for associations between the different MS phenotypes and a number of environmental and lifestyle factors. Interaction between the DRB1*15:01 allele and environmental risk factors was evaluated on the additive scale.

RESULTS

All environmental and lifestyle factors associated with risk of developing MS apply to both relapsing-onset and progressive-onset disease. Smoking, obesity and Epstein-Barr virus nuclear antigen-1 (EBNA-1) antibody levels were associated with increased risk of both MS phenotypes, whereas snuff use, alcohol consumption and sun exposure were associated with reduced risk. Additive interactions between DRB1*15:01 and smoking, obesity, EBNA-1 antibody levels and sun exposure, respectively, occurred to increase MS risk regardless of the clinical phenotype.

INTERPRETATION

Our finding that the same environmental and lifestyle factors affect both relapsing-onset and progressive-onset MS supports the notion that the different clinical phenotypes share common underlying disease mechanisms.

Prediction of Type 1 Diabetes at Birth: Cord Blood Metabolites vs Genetic Risk Score in the Norwegian Mother, Father, and Child Cohort

BACKGROUND AND AIM

Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes.

METHODS

Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low-molecular-weight metabolites (including amino acids, small organic acids, and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father, and Child cohort. We analyzed the data using logistic regression (estimating odds ratios per SD [adjusted odds ratio (aOR)]), area under the receiver operating characteristic curve (AUC), and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA single-nucleotide polymorphisms, and a 4-category HLA risk group.

RESULTS

The strongest associations for metabolites were aminoadipic acid (aOR = 1.23; 95% CI, 0.97-1.55), indoxyl sulfate (aOR = 1.15; 95% CI, 0.87-1.51), and tryptophan (aOR = 0.84; 95% CI, 0.65-1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified 6 clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), whereas the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78.

CONCLUSIONS

In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

Low sun exposure acts synergistically with high Epstein-Barr nuclear antigen 1 (EBNA-1) antibody levels in multiple sclerosis etiology

BACKGROUND

Among multiple sclerosis (MS) patients, an association has been observed between low levels of vitamin D and high Epstein-Barr nuclear antigen 1 (EBNA-1) antibody levels. However, whether sun exposure/vitamin D moderates the role of Epstein-Barr virus (EBV) infection in MS etiology is unclear. We aimed to investigate potential synergistic effects between low sun exposure and elevated EBNA-1 antibody levels regarding MS risk.

METHODS

We used a population-based case-control study involving 2017 incident cases of MS and 2443 matched controls. We used logistic regression models to calculate the odds ratios of MS with 95% confidence intervals (CIs) in subjects with different sun exposure habits and EBNA-1 status. Potential interaction on the additive scale was evaluated by calculating the attributable proportion due to interaction (AP).

RESULTS

Low sun exposure acted synergistically with high EBNA-1 antibody levels (AP 0.2, 95% CI 0.03-0.3) in its association to increased MS risk. The interaction was present regardless of HLA-DRB1*15:01 status.

CONCLUSIONS

Low sun exposure may either directly, or indirectly by affecting vitamin D levels, synergistically reinforce pathogenic mechanisms, such as aspects of the adaptive immune response, related to MS risk conveyed by EBV infection.

The BS variant of C4 protects against age-related loss of white matter microstructural integrity

Age-related loss of white matter microstructural integrity is a major determinant of cognitive decline, dementia, and gait disorders. However, the mechanisms and molecular pathways that contribute to this loss of integrity remain elusive. We performed a GWAS of white matter microstructural integrity as quantified by diffusion MRI metrics (mean diffusivity, MD; and fractional anisotropy, FA) in up to 31,128 individuals from UK Biobank (age 45-81 years) based on a 2 degrees of freedom (2df) test of single nucleotide polymorphism (SNP) and SNP x age effects. We identified 18 loci that were associated at genome-wide significance with either MD (N = 16) or FA (N = 6). Among the top loci was a region on chromosome 6 encoding the human major histocompatibility complex (MHC). Variants in the MHC region were strongly associated with both MD (best SNP: 6:28866209_TTTTG_T, beta(SE)=-0.069(0.009); 2df p = 6.5x10-15) and FA (best SNP: rs3129787, beta(SE)=-0.056(0.008); 2df p = 3.5x10-12). Of the imputed HLA alleles and complement component 4 (C4) structural haplotype variants in the human MHC, the strongest association was with the C4-BS variant (for MD: beta(SE)=-0.070(0.010); p = 2.7x10-11; for FA: beta(SE)=-0.054(0.011); p = 1.6x10-7). After conditioning on C4-BS no associations with HLA alleles remained significant. The protective influence of C4-BS was stronger in older subjects (age ≥ 65; interaction p = 0.0019 (MD), p = 0.015 (FA)) and in subjects without a history of smoking (interaction p = 0.00093 (MD), p = 0.021 (FA)). Taken together, our findings demonstrate a role of the complement system and of gene-environment interactions in age-related loss of white matter microstructural integrity.

DRB1-environment interactions in multiple sclerosis etiology: results from two Swedish case-control studies

OBJECTIVE

We aimed to investigate the influence of environmental risk factors for multiple sclerosis (MS) in different genetic contexts, and study if interactions between environmental factors and human leucocyte antigen (HLA) genes differ in magnitude according to heterozygocity and homozygocity for HLA-DRB1*15:01.

METHODS

Using population-based case-control studies (6985 cases, 6569 controls), subjects with different genotypes and smoking, EBNA-1 status and adolescent Body Mass status, were compared regarding MS risk, by calculating OR with 95% CI employing logistic regression. The interaction between different genotypes and each environmental factor was evaluated on the additive scale.

RESULTS

The effect of each DRB1*15:01 allele on MS risk was additive on the log-odds scale for each additional allele. Interaction between DRB1*15:01 and each assessed environmental factor was of similar magnitude regardless of the number of DRB1*15:01 alleles, although ORs were affected. When any of the environmental factors were present in DRB1*15:01 carriers without the protective A*02:01 allele, a three-way interaction occurred and rendered high ORs, especially among DRB1*15:01 homozygotes (OR 20.0, 95% CI 13.1 to 30.5 among smokers, OR 21.9, 95% CI 15.0 to 31.8 among those with elevated EBNA-1 antibody levels, and OR 44.3, 95% CI 13.5 to 145 among those who reported adolescent overweight/obesity).

CONCLUSIONS

The strikingly increased MS risk among DRB*15:01 homozygotes exposed to any of the environmental factors is a further argument in favour of these factors acting on immune-related mechanisms. The data further reinforce the importance of preventive measures, in particular for those with a genetic susceptibility to MS.

High antibody levels against human herpesvirus-6A interact with lifestyle factors in multiple sclerosis development

BACKGROUND

Infection with human herpesvirus 6A (HHV-6A) has been suggested to increase multiple sclerosis (MS) risk. However, potential interactions between HHV-6A and environmental/lifestyle risk factors for MS have not previously been studied.

METHODS

We used two Swedish population-based case-control studies comprising 5993 cases and 5995 controls. Using logistic regression models, subjects with different HHV-6A antibody levels, environmental exposures, and lifestyle habits were compared regarding MS risk, by calculating odds ratios (ORs) with 95% confidence intervals (CIs). Potential interactions between high HHV-6A antibody levels and common environmental exposures and lifestyle factors were evaluated on the additive scale.

RESULTS

High HHV-6A antibody levels were associated with increased risk of developing MS (OR = 1.5, 95% CI = 1.4-1.6). Regarding MS risk, significant interactions were observed between high HHV-6A antibody levels and both smoking (attributable proportion (AP) = 0.2, 95% CI = 0.1-0.3), low ultraviolet radiation (UVR) exposure (AP = 0.3, 95% CI = 0.1-0.4), and low vitamin D levels (AP = 0.3, 95% CI = 0.0-0.6).

CONCLUSION

High HHV-6A antibody levels are associated with increased MS risk and act synergistically with common environmental/lifestyle risk factors for MS. Further research is needed to investigate potential mechanisms underlying the interactions presented in this study.

Large-Scale Imputation of KIR Copy Number and HLA Alleles in North American and European Psoriasis Case-Control Cohorts Reveals Association of Inhibitory KIR2DL2 With Psoriasis

Killer cell immunoglobulin-like receptors (KIR) regulate immune responses in NK and CD8+ T cells via interaction with HLA ligands. KIR genes, including KIR2DS1, KIR3DL1, and KIR3DS1 have previously been implicated in psoriasis susceptibility. However, these previous studies were constrained to small sample sizes, in part due to the time and expense required for direct genotyping of KIR genes. Here, we implemented KIR*IMP to impute KIR copy number from single-nucleotide polymorphisms (SNPs) on chromosome 19 in the discovery cohort (n=11,912) from the PAGE consortium, University of California San Francisco, and the University of Dundee, and in a replication cohort (n=66,357) from Kaiser Permanente Northern California. Stratified multivariate logistic regression that accounted for patient ancestry and high-risk HLA alleles revealed that KIR2DL2 copy number was significantly associated with psoriasis in the discovery cohort (p ≤ 0.05). The KIR2DL2 copy number association was replicated in the Kaiser Permanente replication cohort. This is the first reported association of KIR2DL2 copy number with psoriasis and highlights the importance of KIR genetics in the pathogenesis of psoriasis.

Development and validation of a universal blood donor genotyping platform: a multinational prospective study

Each year, blood transfusions save millions of lives. However, under current blood-matching practices, sensitization to non-self-antigens is an unavoidable adverse side effect of transfusion. We describe a universal donor typing platform that could be adopted by blood services worldwide to facilitate a universal extended blood-matching policy and reduce sensitization rates. This DNA-based test is capable of simultaneously typing most clinically relevant red blood cell (RBC), human platelet (HPA), and human leukocyte (HLA) antigens. Validation was performed, using samples from 7927 European, 27 South Asian, 21 East Asian, and 9 African blood donors enrolled in 2 national biobanks. We illustrated the usefulness of the platform by analyzing antibody data from patients sensitized with multiple RBC alloantibodies. Genotyping results demonstrated concordance of 99.91%, 99.97%, and 99.03% with RBC, HPA, and HLA clinically validated typing results in 89 371, 3016, and 9289 comparisons, respectively. Genotyping increased the total number of antigen typing results available from 110 980 to >1 200 000. Dense donor typing allowed identification of 2 to 6 times more compatible donors to serve 3146 patients with multiple RBC alloantibodies, providing at least 1 match for 176 individuals for whom previously no blood could be found among the same donors. This genotyping technology is already being used to type thousands of donors taking part in national genotyping studies. Extraction of dense antigen-typing data from these cohorts provides blood supply organizations with the opportunity to implement a policy of genomics-based precision matching of blood.

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10^-120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

Accurate imputation of human leukocyte antigens with CookHLA

The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched.

Associations between human leukocyte antigens and renal function

Human leukocyte antigens (HLA) have been associated with renal function, but previous studies report contradictory findings with little consensus on the exact nature or impact of this observation. This study included 401,307 white British subjects aged 39–73 when they were recruited by UK Biobank. Subjects’ HLA types were imputed using HLA*IMP:02 software. Regression analysis was used to compare 362 imputed HLA types with estimated glomerular filtration rate (eGFR) as a primary outcome and clinical indications as secondary outcome measures. 22 imputed HLA types were associated with increased eGFR (and therefore increased renal function). Decreased eGFR (decreased renal function) was associated with 11 imputed HLA types, seven of which were also associated with increased risk of end-stage renal disease and/or chronic kidney disease. Many of these HLA types are commonly inherited together in established haplotypes, for example: HLA-A*01:01, B*08:01, C*07:01, DRB1*03:01, DQB1*02:01. This haplotype has a population frequency of 9.5% in England and each allele was associated with decreased renal function. 33 imputed HLA types were associated with kidney function in white British subjects. Linkage disequilibrium in HLA heritance suggests that this is not random and particularly affects carriers of established haplotypes. This could have important applications for the diagnosis and treatment of renal disease and global population health.