DOCK2 is involved in the host genetics and biology of severe COVID-19.

Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge1-5. Here we conducted a genome-wide association study (GWAS) involving 2,393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3,289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target.

Multiancestry analysis of the HLA locus in Alzheimer's and Parkinson's diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes.

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson's disease (PD) and Alzheimer's disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aß42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

Genetic Risk of Primary Aldosteronism and Its Contribution to Hypertension: A Cross-Ancestry Meta-Analysis of Genome-Wide Association Studies.

BACKGROUND: Hypertension imposes substantial health and economic burden worldwide. Primary aldosteronism (PA) is one of the most common causes of secondary hypertension, causing cardiovascular events at higher risk compared with essential hypertension. However, the germline genetic contribution to the susceptibility of PA has not been well elucidated. METHOD: We conducted a genome-wide association analysis of PA in the Japanese population and a cross-ancestry meta-analysis combined with UK Biobank and FinnGen cohorts (816 PA cases and 425 239 controls) to identify genetic variants that contribute to PA susceptibility. We also performed a comparative analysis for the risk of 42 previously established blood pressure-associated variants between PA and hypertension with the adjustment of blood pressure. RESULTS: In the Japanese genome-wide association study, we identified 10 loci that presented suggestive evidence for the association with the PA risk (P<1.0×10-6). In the meta-analysis, we identified 5 genome-wide significant loci (1p13, 7p15, 11p15, 12q24, and 13q12; P<5.0×10-8), including 3 of the suggested loci in the Japanese genome-wide association study. The strongest association was observed at rs3790604 (1p13), an intronic variant of WNT2B (odds ratio, 1.50 [95% CI, 1.33-1.69]; P=5.2×10-11). We further identified 1 nearly genome-wide significant locus (8q24, CYP11B2), which presented a significant association in the gene-based test (P=7.2×10-7). Of interest, all of these loci were known to be associated with blood pressure in previous studies, presumably because of the prevalence of PA among individuals with hypertension. This assumption was supported by the observation that they had a significantly higher risk effect on PA than on hypertension. We also revealed that 66.7% of the previously established blood pressure-associated variants had a higher risk effect for PA than for hypertension. CONCLUSIONS: This study demonstrates the genome-wide evidence for a genetic predisposition to PA susceptibility in the cross-ancestry cohorts and its significant contribution to the genetic background of hypertension. The strongest association with the WNT2B variants reinforces the implication of the Wnt/ß-catenin pathway in the PA pathogenesis.

Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.

The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.

Comparative whole transcriptome analysis of Parkinson's disease focusing on the efficacy of zonisamide.

OBJECTIVE: Interindividual variations in responsiveness to zonisamide in patients with Parkinson's disease (PD) have been observed in clinical settings. To decipher the molecular mechanisms determining the efficacy of zonisamide, we conducted whole transcriptome sequencing analysis of patients with PD. METHODS: We selected 23 super-responders (SRs) and 25 non-responders (NRs) to zonisamide from patients with PD who had participated in a previous clinical trial for the approval of zonisamide for the treatment of 'wearing-off'. Whole transcriptome analysis of peripheral blood was conducted on samples taken before and 12 weeks after zonisamide treatment. We performed differential gene expression analysis to compare between the SRs and NRs at each time point. RESULTS: Differentially expressed genes in the pre-treatment samples were significantly enriched for glutamatergic synapses and insulin-like growth factor binding (Padj=7.8 × 10-3 and 0.029, respectively). The gene sets associated with these functions changed more dynamically by treatment in SRs than NRs (p=7.2 × 10-3 and 8.2 × 10-3, respectively). CONCLUSIONS: Our results suggest that the efficacy of zonisamide in PD patients is associated with glutamate-related synaptic modulation and p53-mediated dopaminergic neural loss. Their transcriptomic differences could be captured before treatment, which would lead to the realisation of future personalised treatment.

Trans-Ethnic Fine-Mapping of the Major Histocompatibility Complex Region Linked to Parkinson's Disease.

BACKGROUND: Despite evidence for the role of human leukocyte antigen (HLA) in the genetic predisposition to Parkinson's disease (PD), the complex haplotype structure and highly polymorphic feature of the major histocompatibility complex (MHC) region has hampered a unified insight on the genetic risk of PD. In addition, a majority of the reports focused on Europeans, lacking evidence on other populations. OBJECTIVES: The aim of this study is to elucidate the genetic features of the MHC region associated with PD risk in trans-ethnic cohorts. METHODS: We conducted trans-ethnic fine-mapping of the MHC region for European populations (14,650 cases and 1,288,625 controls) and East Asian populations (7712 cases and 27,372 controls). We adopted a hybrid fine-mapping approach including both HLA genotype imputation of genome-wide association study (GWAS) data and direct imputation of HLA variant risk from the GWAS summary statistics. RESULTS: Through trans-ethnic MHC fine-mapping, we identified the strongest associations at amino acid position 13 of HLA-DRß1 (P = 6.0 × 10-15 ), which explains the majority of the risk in HLA-DRB1. In silico prediction revealed that HLA-DRB1 alleles with histidine at amino acid position 13 (His13) had significantly weaker binding affinity to an α-synuclein epitope than other alleles (P = 9.6 × 10-4 ). Stepwise conditional analysis suggested additional independent associations at Ala69 in HLA-B (P = 1.0 × 10-7 ). A subanalysis in Europeans suggested additional independent associations at non-HLA genes in the class III MHC region (EHMT2; P = 2.5 × 10-7 ). CONCLUSIONS: Our study highlights the shared and distinct genetic features of the MHC region in patients with PD across ethnicities. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Predicting the impact of single nucleotide variants on splicing via sequence-based deep neural networks and genomic features.

Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing-related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence-based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally-defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN-based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.

Assessing predictions of the impact of variants on splicing in CAGI5.

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

Exclusion of Isolated Cortical Swelling Can Increase Efficacy of Baseline Alberta Stroke Program Early CT Score in the Prediction of Prognosis in Acute Ischemic Stroke Patients Treated with Thrombolysis.

BACKGROUND: The accuracy of the Alberta Stroke Program Early CT Score (ASPECTS) as a prognostic indicator in the treatment of cerebral infarction with thrombolysis remains controversial. We hypothesized that ASPECTS can more accurately predict treatment outcomes by excluding isolated cortical swelling (ICS) from ASPECTS and retrospectively tested patients treated with thrombolysis. METHODS: This retrospective cohort study included 106 patients treated with intravenous thrombolysis for cerebral infarction in our hospital. We included only patients with middle cerebral artery infarction. For the modification of ASPECTS, we removed each ICS from the ASPECTS system. We compared the correlation coefficients between the ASPECTS and modified ASPECTS with regard to treatment outcome, and performed a multivariate logistic regression analysis to evaluate the association between modified ASPECTS and outcomes. The primary outcome was a modified Rankin Scale score equal to or less than 2 on discharge and the secondary outcomes included an improvement of National Institutes of Health Stroke Scale (NIHSS) score of 4 or greater within 24 hours. RESULTS: Seventy-two patients were included in this study. The correlation coefficient of modified ASPECTS was significantly higher than that of ASPECTS in the primary outcome (r = .249 versus r = .363, P < .001) and in the improvement of NIHSS score (r = .303 versus r = .443, P < .001). Multivariate analysis revealed that a modified ASPECTS greater than 7 was significantly associated with the primary outcome (odds ratio [OR] = 1.334, 95% confidence interval [CI] = 1.071-1.661, P = .012) and the improvement of the NIHSS score (OR = 1.555, 95% CI = 1.208-2.003, P = .001). CONCLUSIONS: The present study reveals that ASPECTS may be more strongly associated with outcome by excluding ICS.

Deep Learning-Based HLA Allele Imputation Applicable to GWAS.

Human leukocyte antigen (HLA) imputation is an essential step following genome-wide association study, particularly when putative associations in HLA genes are identified, to fully understand the genetic basis of human traits. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of imputation accuracy and computational costs. Here, I describe Deep*HLA, a recently published method that employs deep learning algorithms to accurately impute HLA alleles from regional single nucleotide variants. Deep*HLA was trained and benchmarked on two reference panels of different ancestries. Deep*HLA achieved high imputation accuracy with relatively mild reduced imputation accuracy for rare alleles. I provide a detailed protocol for running Deep*HLA, including instructions for data preprocessing, model training, and imputation. Deep*HLA is implemented in Python 3 and is freely available.

Common and rare genetic variants predisposing females to unexplained recurrent pregnancy loss.

Recurrent pregnancy loss (RPL) is a major reproductive health issue with multifactorial causes, affecting 2.6% of all pregnancies worldwide. Nearly half of the RPL cases lack clinically identifiable causes (e.g., antiphospholipid syndrome, uterine anomalies, and parental chromosomal abnormalities), referred to as unexplained RPL (uRPL). Here, we perform a genome-wide association study focusing on uRPL in 1,728 cases and 24,315 female controls of Japanese ancestry. We detect significant associations in the major histocompatibility complex (MHC) region at 6p21 (lead variant=rs9263738; P = 1.4 × 10-10; odds ratio [OR] = 1.51 [95% CI: 1.33-1.72]; risk allele frequency = 0.871). The MHC associations are fine-mapped to the classical HLA alleles, HLA-C*12:02, HLA-B*52:01, and HLA-DRB1*15:02 (P = 1.1 × 10-10, 1.5 × 10-10, and 1.2 × 10-9, respectively), which constitute a population-specific common long-range haplotype with a protective effect (P = 2.8 × 10-10; OR = 0.65 [95% CI: 0.57-0.75]; haplotype frequency=0.108). Genome-wide copy-number variation (CNV) calling demonstrates rare predicted loss-of-function (pLoF) variants of the cadherin-11 gene (CDH11) conferring the risk of uRPL (P = 1.3 × 10-4; OR = 3.29 [95% CI: 1.78-5.76]). Our study highlights the importance of reproductive immunology and rare variants in the uRPL etiology.

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Genome-wide association study identifies risk loci within the major histocompatibility complex region for Hunner-type interstitial cystitis.

Hunner-type interstitial cystitis (HIC) is a rare, chronic inflammatory disease of the urinary bladder with unknown etiology and genetic background. Here, we conduct a genome-wide association study of 144 patients with HIC and 41,516 controls of Japanese ancestry. The genetic variant, rs1794275, in the major histocompatibility complex (MHC) region (chromosome 6p21.3) is associated with HIC risk (odds ratio [OR] = 2.32; p = 3.4 × 10-9). The association is confirmed in a replication set of 26 cases and 1,026 controls (p = 0.014). Fine mapping demonstrates the contribution to the disease risk of a completely linked haplotype of three human leukocyte antigen HLA-DQß1 amino acid positions, 71, 74, and 75 (OR = 1.94; p = 5 × 10-8) and of HLA-DPß1 amino acid position 178, which tags HLA-DPB1∗04:02 (OR = 2.35; p = 7.5 × 10-8). The three HLA-DQß1 amino acid positions are located together at the peptide binding groove, suggesting their functional importance in antigen presentation. Our study reveals genetic contributions to HIC risk that may be associated with class II MHC molecule antigen presentation.

A genome-wide association study for allergen component sensitizations identifies allergen component-specific and allergen protein group-specific associations.

Background: Allergic diseases are some of the most common diseases worldwide. Genome-wide association studies (GWASs) have been conducted to elucidate the genetic factors of allergic diseases. However, no GWASs for allergen component sensitization have been performed. Objective: We sought to detect genetic variants associated with differences in immune responsiveness against allergen components. Methods: The participants of the present study were recruited from the Tokyo Children's Health, Illness, and Development study, and allergen component-specific IgE level at age 9 years was measured by means of allergen microarray immunoassays. We performed GWASs for allergen component sensitization against each allergen (single allergen component sensitization, number of allergen components analyzed, n = 31), as well as against allergen protein families (allergen protein group sensitization, number of protein groups analyzed, n = 16). Results: We performed GWAS on 564 participants of the Tokyo Children's Health, Illness, and Development study and found associations between Amb a 1 sensitization and the immunoglobulin heavy-chain variable gene on chromosome 14 and between Phl p 1 sensitization and the HLA class II region on chromosome 6 (P < 5.0 × 10-8). A GWAS-significant association was also observed between the HLA class II region and profilin sensitization (P < 5.0 × 10-8). Conclusions: Our data provide the first demonstration of genetic risk for allergen component sensitization and show that this genetic risk is related to immune response genes including immunoglobulin heavy-chain variable gene and HLA.

Multifaceted analysis of cross-tissue transcriptomes reveals phenotype-endotype associations in atopic dermatitis.

Atopic dermatitis (AD) is a skin disease that is heterogeneous both in terms of clinical manifestations and molecular profiles. It is increasingly recognized that AD is a systemic rather than a local disease and should be assessed in the context of whole-body pathophysiology. Here we show, via integrated RNA-sequencing of skin tissue and peripheral blood mononuclear cell (PBMC) samples along with clinical data from 115 AD patients and 14 matched healthy controls, that specific clinical presentations associate with matching differential molecular signatures. We establish a regression model based on transcriptome modules identified in weighted gene co-expression network analysis to extract molecular features associated with detailed clinical phenotypes of AD. The two main, qualitatively differential skin manifestations of AD, erythema and papulation are distinguished by differential immunological signatures. We further apply the regression model to a longitudinal dataset of 30 AD patients for personalized monitoring, highlighting patient heterogeneity in disease trajectories. The longitudinal features of blood tests and PBMC transcriptome modules identify three patient clusters which are aligned with clinical severity and reflect treatment history. Our approach thus serves as a framework for effective clinical investigation to gain a holistic view on the pathophysiology of complex human diseases.

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.

Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method.

The killer cell immunoglobulin-like receptor (KIR) recognizes human leukocyte antigen (HLA) class I molecules and modulates the function of natural killer cells. Despite its role in immunity, the complex genomic structure has limited a deep understanding of the KIR genomic landscape. Here we conduct deep sequencing of 16 KIR genes in 1,173 individuals. We devise a bioinformatics pipeline incorporating copy number estimation and insertion or deletion (indel) calling for high-resolution KIR genotyping. We define 118 alleles in 13 genes and demonstrate a linkage disequilibrium structure within and across KIR centromeric and telomeric regions. We construct a KIR imputation reference panel (nreference = 689, imputation accuracy = 99.7%), apply it to biobank genotype (ntotal = 169,907), and perform phenome-wide association studies of 85 traits. We observe a dearth of genome-wide significant associations, even in immune traits implicated previously to be associated with KIR (the smallest p = 1.5 × 10-4). Our pipeline presents a broadly applicable framework to evaluate innate immunity in large-scale datasets.

A common deletion at BAK1 reduces enhancer activity and confers risk of intracranial germ cell tumors.

Intracranial germ cell tumors (IGCTs) are rare brain neoplasms that mainly occur in children and adolescents with a particularly high incidence in East Asian populations. Here, we conduct a genome-wide association study (GWAS) of 133 patients with IGCTs and 762 controls of Japanese ancestry. A common 4-bp deletion polymorphism in an enhancer adjacent to BAK1 is significantly associated with the disease risk (rs3831846; P = 2.4 × 10-9, odds ratio = 2.46 [95% CI: 1.83-3.31], minor allele frequency = 0.43). Rs3831846 is in strong linkage disequilibrium with a testicular GCTs susceptibility variant rs210138. In-vitro reporter assays reveal rs3831846 to be a functional variant attenuating the enhancer activity, suggesting its contribution to IGCTs predisposition through altering BAK1 expression. Risk alleles of testicular GCTs derived from the European GWAS show significant positive correlations in the effect sizes with the Japanese IGCTs GWAS (P = 1.3 × 10-4, Spearman's ρ = 0.48). These results suggest the shared genetic susceptibility of GCTs beyond ethnicity and primary sites.

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force.

Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.