Imputation of KIR Types from SNP Variation Data

Geny: A Genotyping Tool for Allelic Decomposition of Killer Cell Immunoglobulin-Like Receptor Genes

Accurate genotyping of Killer cell Immunoglobulin-like Receptor (KIR) genes plays a pivotal role in enhancing our understanding of innate immune responses, disease correlations, and the advancement of personalized medicine. However, due to the high variability of the KIR region and high level of sequence similarity among different KIR genes, the currently available genotyping methods are unable to accurately infer copy numbers, genotypes and haplotypes of individual KIR genes from next-generation sequencing data. Here we introduce Geny, a new computational tool for precise genotyping of KIR genes. Geny utilizes available KIR haplotype databases and proposes a novel combination of expectation-maximization filtering schemes and integer linear programming-based combinatorial optimization models to resolve ambiguous reads, provide accurate copy number estimation and estimate the haplotype of each copy for the genes within the KIR region. We evaluated Geny on a large set of simulated short-read datasets covering the known validated KIR region assemblies and a set of Illumina short-read samples sequenced from 25 validated samples from the Human Pangenome Reference Consortium collection and showed that it outperforms the existing genotyping tools in terms of accuracy, precision and recall. We envision Geny becoming a valuable resource for understanding immune system response and consequently advancing the field of patient-centric medicine.

A machine-learning method for biobank-scale genetic prediction of blood group antigens

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank-scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.

The absence of seroconversion after exposition to hepatitis C virus is not related to KIR-HLA genotype combinations (GEHEP-012 study)

BACKGROUND & AIMS

It has been reported that specific killer-cell immunoglobulin-like receptors (KIRs) and HLA genotype combinations, such as KIR2DS4/HLA-C1 with presence of KIRDL2 or KIRDL3, homozygous KIRDL3/HLA-C1 and KIR3DL1/≥2HLA-Bw4, are strongly associated with the lack of active infection and seroconversion after exposition to hepatitis C virus (HCV).

OBJECTIVE

To determine whether these KIR-HLA combinations are relevant factors involved in that phenotype.

PATIENTS AND METHODS

In this retrospective case-control study, genotype data from a genome-wide association study previously performed on low susceptibility to HCV-infection carried out on 27 high-risk HCV-seronegative (HRSN) individuals and 743 chronically infected (CI) subjects were used. HLA alleles were imputed using R package HIBAG v1.2223 and KIR genotypes were imputed using the online resource KIR*IMP v1.2.0.

RESULTS

It was possible to successfully impute at least one KIR-HLA genotype combination previously associated with the lack of infection and seroconversion after exposition to HCV in a total of 23 (85.2%) HRSN individuals and in 650 (87.5%) CI subjects. No KIR-HLA genotype combination analyzed was related to the HRSN condition.

CONCLUSIONS

Our results suggest that those KIR-HLA genotype combinations are not relevant factors involved in the lack of infection and seroconversion after exposition to HCV. More studies will be needed to completely understand this phenotype.

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer-cell immunoglobulin-like receptor (KIR) genes. These genes are of special interest in the context of allogeneic hematopoietic stem cell transplantation. For such complex gene regions, current haplotype reconstruction methods are not feasible as they cannot cope with the complexity of the data. We present an expectation-maximization (EM)-algorithm to estimate haplotype frequencies (HTFs) which deals with the missing data components, and takes into account linkage disequilibrium (LD) between genes. To cope with the exponential increase in the number of haplotypes as genes are added, we add three components to a standard EM-algorithm implementation. First, reconstruction is performed iteratively, adding one gene at a time. Second, after each step, haplotypes with frequencies below a threshold are collapsed in a rare haplotype group. Third, the HTF of the rare haplotype group is profiled in subsequent iterations to improve estimates. A simulation study evaluates the effect of combining information of multiple genes on the estimates of these frequencies. We show that estimated HTFs are approximately unbiased. Our simulation study shows that the EM-algorithm is able to combine information from multiple genes when LD is high, whereas increased ambiguity levels increase bias. Linear regression models based on this EM, show that a large number of haplotypes can be problematic for unbiased effect size estimation and that models need to be sparse. In a real data analysis of KIR genotypes, we compare HTFs to those obtained in an independent study. Our new EM-algorithm-based method is the first to account for the full genetic architecture of complex gene regions, such as the KIR gene region. This algorithm can handle the numerous observed ambiguities, and allows for the collapsing of haplotypes to perform implicit dimension reduction. Combining information from multiple genes improves haplotype reconstruction.

Innate receptors modulating adaptive T cell responses: KIR-HLA interactions and T cell-mediated control of chronic viral infections

Killer-cell immunoglobulin-like receptors (KIRs) are mainly expressed on natural killer (NK) cells and are key regulators of innate immune responses. NK cells are the first responders in the face of infection and help promote placentation during pregnancy; the importance of KIRs in these NK-mediated processes is well-established. However, mounting evidence suggests that KIRs also have a prominent and long-lasting effect on the adaptive immune system. Here, we review the evidence for the impact of KIRs on T cell responses with a focus on the clinical significance of this interaction.

Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data

Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes. Therefore, although many tools have been developed to genotype HLA genes using common sequencing data, none of them work for KIR genes. Even specialized KIR genotypers could not resolve all the KIR genes. Here we describe T1K, a novel computational method for the efficient and accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, or whole-exome sequencing data. T1K jointly considers alleles across all genotyped genes, so it can reliably identify present genes and distinguish homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. Moreover, T1K can call novel single-nucleotide variants and process single-cell data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 expression was enriched in tumor-specific CD8+ T cells. T1K may open the opportunity for HLA and KIR genotyping across various sequencing applications.

Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity

Natural Killer cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect Natural Killer cell gene expression is poorly understood. Here we perform expression quantitative trait locus mapping of negatively selected Natural Killer cells from a population of healthy Europeans (n = 245). We find a significant subset of genes demonstrate expression quantitative trait loci specific to Natural Killer cells and these are highly informative of human disease, in particular autoimmunity. A Natural Killer cell transcriptome-wide association study across five common autoimmune diseases identifies further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of Natural Killer cells, demonstrating markedly different expression quantitative trait loci from other immune cells, with implications for disease mechanisms.

Natural Killer cells are key mediators of anti-tumour immunosurveillance and anti-viral immunity. Here, the authors map regulatory genetic variation in primary Natural Killer cells, providing new insights into their role in human health and disease.

Immunogenomics of Killer Cell Immunoglobulin-Like Receptor (KIR) and HLA Class I: Coevolution and Consequences for Human Health

Interactions of killer cell immunoglobin-like receptors (KIR) with human leukocyte antigens (HLA) class I regulate effector functions of key cytotoxic cells of innate and adaptive immunity. The extreme diversity of this interaction is genetically determined, having evolved in the ever-changing environment of pathogen exposure. Diversity of KIR and HLA genes is further facilitated by their independent segregation on separate chromosomes. That fetal implantation relies on many of the same types of immune cells as infection control places certain constraints on the evolution of KIR interactions with HLA. Consequently, specific inherited combinations of receptors and ligands may predispose to specific immune-mediated diseases, including autoimmunity. Combinatorial diversity of KIR and HLA class I can also differentiate success rates of immunotherapy directed to these diseases. Progress toward both etiopathology and predicting response to therapy is being achieved through detailed characterization of the extent and consequences of the combinatorial diversity of KIR and HLA. Achieving these goals is more tractable with the development of integrated analyses of molecular evolution, function, and pathology that will establish guidelines for understanding and managing risks. Here, we present what is known about the coevolution of KIR with HLA class I and the impact of their complexity on immune function and homeostasis.

Interaction between maternal killer immunoglobulin-like receptors and offspring HLAs and susceptibility of childhood ALL

Acute lymphoblastic leukemia (ALL) in children is associated with a distinct neonatal cytokine profile. The basis of this neonatal immune phenotype is unknown but potentially related to maternal-fetal immune receptor interactions. We conducted a case-control study of 226 case child-mother pairs and 404 control child-mother pairs to evaluate the role of interaction between HLA genotypes in the offspring and maternal killer immunoglobulin-like receptor (KIR) genotypes in the etiology of childhood ALL, while considering potential mediation by neonatal cytokines and the immune-modulating enzyme arginase-II (ARG-II). We observed different associations between offspring HLA-maternal KIR activating profiles and the risk of ALL in different predicted genetic ancestry groups. For instance, in Latino subjects who experience the highest risk of childhood leukemia, activating profiles were significantly associated with a lower risk of childhood ALL (odds ratio [OR] = 0.59; 95% confidence interval [CI], 0.49-0.71) and a higher level of ARG-II at birth (coefficient = 0.13; 95% CI, 0.04-0.22). HLA-KIR activating profiles were also associated with a lower risk of ALL in non-Latino Asians (OR = 0.63; 95% CI, 0.38-1.01), although they had a lower tumor necrosis factor-α level (coefficient = -0.27; 95% CI, -0.49 to -0.06). Among non-Latino White subjects, no significant association was observed between offspring HLA-maternal KIR interaction and ALL risk or cytokine levels. The current study reports the association between offspring HLA-maternal KIR interaction and the development of childhood ALL with variation by predicted genetic ancestry. We also observed some associations between activating profiles and immune factors related to cytokine control; however, cytokines did not demonstrate causal mediation of the activating profiles on ALL risk.

Contribution of HLA and KIR Alleles to Systemic Sclerosis Susceptibility and Immunological and Clinical Disease Subtypes

Systemic sclerosis (SSc) is an autoinflammatory, fibrotic condition of unknown aetiology. The presence of detectable autoantibodies against diverse nuclear antigens, as well as strong HLA associations with disease, suggest autoimmune involvement, however the links between endogenous and exogenous risk factors and SSc pathology remain undetermined. We have conducted a genetic analysis of HLA inheritance in two independent and meta-analysed cohorts of 1,465 SSc cases and 13,273 controls, including stratified association analyses in clinical and autoantibody positive subgroups of disease. Additionally, we have used patient genotypes to impute gene dosages across the KIR locus, encoding paired activating and inhibitory lymphocyte receptors for Class I HLA ligands, to conduct the largest analysis of KIR-HLA epistatic interactions in SSc to date. We confirm previous Class II HLA associations with SSc risk and report a new Class I association with haplotype HLA-B*44:03-HLA-C*16:01 at genome-wide significance (GWS). We further report statistically significant HLA associations with clinical and serological subtypes of disease through direct case-case comparison, and report a new association of HLA-DRB1*15:01, previously shown to bind topoisomerase-1 derived peptides, with anti-topoisomerase (ATA) positive disease. Finally, we identify genetic epistasis between KIRs and HLA class I ligands, suggesting genetic modulation of lymphocyte activation may further contribute to an individual’s underlying disease risk. Taken together, these findings support future functional investigation into endogenous immunological and environmental stimuli for disrupted immune tolerance in SSc.

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 (n_reference = 689, imputation accuracy = 99.7%), apply it to biobank genotype (n_total = 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.

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.

Phenome-wide association study of the major histocompatibility complex region in the Korean population identifies novel association signals

Human leukocyte antigen (HLA) gene variants in the major histocompatibility complex (MHC) region are associated with numerous complex human diseases and quantitative traits. Previous phenome-wide association studies (PheWAS) for this region demonstrated that HLA association patterns to the phenome have both population-specific and population-shared components. We performed MHC PheWAS in the Korean population by analyzing associations between phenotypes and genetic variants in the MHC region using the Korea Biobank Array project data samples from the Korean Genome and Epidemiology Study (KoGES) cohorts. Using this single-population dataset, we curated and analyzed 82 phenotypes for 125 673 Korean individuals after imputing HLA using CookHLA, a recently developed imputation framework. More than one-third of these phenotypes showed significant associations, confirming 56 known associations and discovering 13 novel association signals that were not reported previously. In addition, we analyzed heritability explained by the variants in the MHC region and genetic correlations among phenotypes based on the MHC variants.

KIR gene content imputation from single-nucleotide polymorphisms in the Finnish population

The killer cell immunoglobulin-like receptor (KIR) gene cluster on chromosome 19 encodes cell surface glycoproteins that bind class I human leukocyte antigen (HLA) molecules as well as some other ligands. Through regulation of natural killer (NK) cell activity KIRs participate in tumour surveillance and clearing viral infections. KIR gene gene copy number variation associates with the outcome of transplantations and susceptibility to immune-mediated diseases. Inferring KIR gene content from genetic variant data is therefore desirable for immunogenetic analysis, particularly in the context of growing biobank genome data collections that rely on genotyping by microarray. Here we describe a stand-alone and freely available gene content imputation for 12 KIR genes. The models were trained using 807 Finnish biobank samples genotyped for 5900 KIR-region SNPs and analysed for KIR gene content with targeted sequencing. Cross-validation results demonstrate a high mean overall accuracy of 98.5% (95% CI [97.0-99.2]%) which compares favourably with previous methods including short-read sequencing based approaches.

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.

Killer-cell immunoglobulin-like receptor/human leukocyte antigen-C combination and 'great obstetrical syndromes' (Review)

Recurrent pregnancy loss (RPL), pre-eclampsia (PE), fetal growth restriction (FGR), and preterm delivery are examples of ‘great obstetrical syndromes’ (GOS). Placental dysfunction is the most common pathogenesis of GOS. In human pregnancies, the effects of uterine natural killer cells involve angiogenesis, promoting the remodeling of uterine spiral artery, and improving the invasion of trophoblast cells. The uNK cells supply killer immunoglobulin-like receptors (KIRs), which come into contact with human leukocyte antigen-C (HLA-C) ligands expressed by extravillous trophoblast cells (EVTs). Numerous studies have investigated the association between GOS and KIR/HLA-C combination. However, the outcomes have not been conclusive. The present review aimed to reveal the association between GOS and KIR/HLA-C combination to screen out high-risk pregnancies, strengthen the treatment of pregnancy complications, and reduce the frequency of adverse maternal and fetal outcomes. It has been reported that a female with a KIR AA genotype and a neonate with a paternal HLA-C2 molecule is more prone to develop GOS and have a small fetus since less cytokines were secreted by uNK cells. Conversely, the combination of KIR BB haplotype (including the activating KIR2DS1) and HLA-C2 can induce the production of cytokines and increase trophoblast invasion, leading to the birth of a large fetus. KIR/HLA-C combinations may be applicable in selecting third-party gametes or surrogates. Detection of maternal KIR genes and HLA-C molecules from the couple could serve as useful markers for predicting and diagnosing GOS.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

Using de novo assembly to identify structural variation of eight complex immune system gene regions

Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14⁺ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data. Continued reductions in the cost of these technologies will enable application of these methods to larger samples and provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

The human immune system is incredibly versatile underlying its capacity to defend the body against thousands of pathogens. At a molecular level, it recognizes pathogens using large libraries of antibodies and related protein receptors. These molecules are encoded by gene families that are particularly difficult to analyze due to their unusually complex patterns of similarities and differences between genes and individuals. To overcome this, we applied several sequencing methods to DNA from a single individual and developed methods to reconstruct the underlying sequence at eight of the immune-associated regions. Importantly, we used DNA extracted from monocytes to avoid capturing the further rearrangements that occur in active immune cells. We generated accurate assemblies by integrating multiple complementary data types, although we noted a small subset of locations that remain challenging. Moreover, we found that this individual contains multiple structural differences between the two inherited chromosomes and compared to previously analyzed genomes, affecting the copy number of immune system genes. Application of these methods in larger numbers of individuals will clearly uncover much more variation than is currently known, and might lead to new understanding of the effect of genetic variation on the broad range of human diseases determined by the immune response.

High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING

The killer-cell immunoglobulin-like receptor (KIR) complex on chromosome 19 encodes receptors that modulate the activity of natural killer cells, and variation in these genes has been linked to infectious and autoimmune disease, as well as having bearing on pregnancy and transplant outcomes. The medical relevance and high variability of KIR genes makes short-read sequencing an attractive technology for interrogating the region, providing a high-throughput, high-fidelity sequencing method that is cost-effective. However, because this gene complex is characterized by extensive nucleotide polymorphism, structural variation including gene fusions and deletions, and a high level of homology between genes, its interrogation at high resolution has been thwarted by bioinformatic challenges, with most studies limited to examining presence or absence of specific genes. Here, we present the PING (Pushing Immunogenetics to the Next Generation) pipeline, which incorporates empirical data, novel alignment strategies and a custom alignment processing workflow to enable high-throughput KIR sequence analysis from short-read data. PING provides KIR gene copy number classification functionality for all KIR genes through use of a comprehensive alignment reference. The gene copy number determined per individual enables an innovative genotype determination workflow using genotype-matched references. Together, these methods address the challenges imposed by the structural complexity and overall homology of the KIR complex. To determine copy number and genotype determination accuracy, we applied PING to European and African validation cohorts and a synthetic dataset. PING demonstrated exceptional copy number determination performance across all datasets and robust genotype determination performance. Finally, an investigation into discordant genotypes for the synthetic dataset provides insight into misaligned reads, advancing our understanding in interpretation of short-read sequencing data in complex genomic regions. PING promises to support a new era of studies of KIR polymorphism, delivering high-resolution KIR genotypes that are highly accurate, enabling high-quality, high-throughput KIR genotyping for disease and population studies.

MiDAS-Meaningful Immunogenetic Data at Scale

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.

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.

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

HIV-1 and human genetic variation

Over the past four decades, research on the natural history of HIV infection has described how HIV wreaks havoc on human immunity and causes AIDS. HIV host genomic research, which aims to understand how human genetic variation affects our response to HIV infection, has progressed from early candidate gene studies to recent multi-omic efforts, benefiting from spectacular advances in sequencing technology and data science. In addition to invading cells and co-opting the host machinery for replication, HIV also stably integrates into our own genome. The study of the complex interactions between the human and retroviral genomes has improved our understanding of pathogenic mechanisms and suggested novel preventive and therapeutic approaches against HIV infection.

Accurate and Efficient KIR Gene and Haplotype Inference From Genome Sequencing Reads With Novel K-mer Signatures

The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body's reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individual's KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%-100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI's accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.

IMAGINE Network's Mind And Gut Interactions Cohort (MAGIC) Study: a protocol for a prospective observational multicentre cohort study in inflammatory bowel disease and irritable bowel syndrome

INTRODUCTION

Gut microbiome and diet may be important in irritable bowel syndrome (IBS), inflammatory bowel disease (IBD) and comorbid psychiatric conditions, but the mechanisms are unclear. We will create a large cohort of patients with IBS, IBD and healthy controls, and follow them over time, collecting dietary and mental health information and biological samples, to assess their gastrointestinal (GI) and psychological symptoms in association with their diet, gut microbiome and metabolome.

METHODS AND ANALYSIS

This 5-year observational prospective cohort study is recruiting 8000 participants from 15 Canadian centres. Persons with IBS who are 13 years of age and older or IBD ≥5 years will be recruited. Healthy controls will be recruited from the general public and from friends or relatives of those with IBD or IBS who do not have GI symptoms. Participants answer surveys and provide blood, urine and stool samples annually. Surveys assess disease activity, quality of life, physical pain, lifestyle factors, psychological status and diet. The main outcomes evaluated will be the association between the diet, inflammatory, genetic, microbiome and metabolomic profiles in those with IBD and IBS compared with healthy controls using multivariate logistic regression. We will also compare these profiles in those with active versus quiescent disease and those with and without psychological comorbidity.

ETHICS AND DISSEMINATION

Approval has been obtained from the institutional review boards of all centres taking part in the study. We will develop evidence-based knowledge translation initiatives for patients, clinicians and policymakers to disseminate results to relevant stakeholders.Trial registration number: NCT03131414.

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

Epistatic interactions between killer immunoglobulin-like receptors and human leukocyte antigen ligands are associated with ankylosing spondylitis

The killer immunoglobulin-like receptors (KIRs), found predominantly on the surface of natural killer (NK) cells and some T-cells, are a collection of highly polymorphic activating and inhibitory receptors with variable specificity for class I human leukocyte antigen (HLA) ligands. Fifteen KIR genes are inherited in haplotypes of diverse gene content across the human population, and the repertoire of independently inherited KIR and HLA alleles is known to alter risk for immune-mediated and infectious disease by shifting the threshold of lymphocyte activation. We have conducted the largest disease-association study of KIR-HLA epistasis to date, enabled by the imputation of KIR gene and HLA allele dosages from genotype data for 12,214 healthy controls and 8,107 individuals with the HLA-B*27-associated immune-mediated arthritis, ankylosing spondylitis (AS). We identified epistatic interactions between KIR genes and their ligands (at both HLA subtype and allele resolution) that increase risk of disease, replicating analyses in a semi-independent cohort of 3,497 cases and 14,844 controls. We further confirmed that the strong AS-association with a pathogenic variant in the endoplasmic reticulum aminopeptidase gene ERAP1, known to alter the HLA-B*27 presented peptidome, is not modified by carriage of the canonical HLA-B receptor KIR3DL1/S1. Overall, our data suggests that AS risk is modified by the complement of KIRs and HLA ligands inherited, beyond the influence of HLA-B*27 alone, which collectively alter the proinflammatory capacity of KIR-expressing lymphocytes to contribute to disease immunopathogenesis.

Cells of the immune system utilise various cell-surface receptors to differentiate between healthy and infected or malignant cells, enabling targeted inflammatory responses while minimising damage to self-tissue. In instances where the immune system fails to correctly differentiate healthy from diseased tissue, or inflammatory activity is poorly regulated, autoimmune or autoinflammatory conditions can develop. Here we have investigated a possible role for a class of immune-cell activating and inhibitory receptors in the pathogenesis of ankylosing spondylitis (AS), a common but poorly understood inflammatory arthritis in which the immune system causes severe damage to the joints of the pelvis and spine. Using genetic information from 12,214 healthy controls and 8,107 individuals with AS we were able to identify combinations of independently inherited immune cell receptors and their ligands that increase or decrease an individual’s risk of disease. This research provides new insight into the nature of co-inherited genetic factors that may collectively alter the proinflammatory capacity of immune cells, contributing to the immunopathogenesis of immune-mediated diseases.

Identifying the immune interactions underlying HLA class I disease associations

Variation in the risk and severity of many autoimmune diseases, malignancies and infections is strongly associated with polymorphisms at the HLA class I loci. These genetic associations provide a powerful opportunity for understanding the etiology of human disease. HLA class I associations are often interpreted in the light of 'protective' or 'detrimental' CD8⁺ T cell responses which are restricted by the host HLA class I allotype. However, given the diverse receptors which are bound by HLA class I molecules, alternative interpretations are possible. As well as binding T cell receptors on CD8⁺ T cells, HLA class I molecules are important ligands for inhibitory and activating killer immunoglobulin-like receptors (KIRs) which are found on natural killer cells and some T cells; for the CD94:NKG2 family of receptors also expressed mainly by NK cells and for leukocyte immunoglobulin-like receptors (LILRs) on myeloid cells. The aim of this study is to develop an immunogenetic approach for identifying and quantifying the relative contribution of different receptor-ligand interactions to a given HLA class I disease association and then to use this approach to investigate the immune interactions underlying HLA class I disease associations in three viral infections: Human T cell Leukemia Virus type 1, Human Immunodeficiency Virus type 1 and Hepatitis C Virus as well as in the inflammatory condition Crohn's disease.

Blood natural killer cell deficiency reveals an immunotherapy strategy for atopic dermatitis

Atopic dermatitis (AD) is a widespread, chronic skin disease associated with aberrant allergic inflammation. Current treatments involve either broad or targeted immunosuppression strategies. However, enhancing the immune system to control disease remains untested. We demonstrate that patients with AD harbor a blood natural killer (NK) cell deficiency that both has diagnostic value and improves with therapy. Multidimensional protein and RNA profiling revealed subset-level changes associated with enhanced NK cell death. Murine NK cell deficiency was associated with enhanced type 2 inflammation in the skin, suggesting that NK cells play a critical immunoregulatory role in this context. On the basis of these findings, we used an NK cell-boosting interleukin-15 (IL-15) superagonist and observed marked improvement in AD-like disease in mice. These findings reveal a previously unrecognized application of IL-15 superagonism, currently in development for cancer immunotherapy, as an immunotherapeutic strategy for AD.

Analysis of Killer Immunoglobulin-Like Receptor Genes in Colorectal Cancer

Natural killer cells (NK cells) play a major role in the immune response to cancer. An important element of NK target recognition is the binding of human leucocyte antigen (HLA) class I molecules by killer immunoglobulin-like receptors (KIRs). Colorectal carcinoma (CRC) is one of the most common types of inflammation-based cancer. The purpose of the present study was to investigate the presence of KIR genes and HLA class I and II alleles in 1074 CRC patients and 1272 controls. We imputed data from single-nucleotide polymorphism (SNP) Illumina OncoArray to identify associations at HLA (HLA–A, B, C, DPB1, DQA1, DQB1, and DRB1) and KIRs (HIBAG and KIR*IMP, respectively). For association analysis, we used PLINK (v1.9), the PyHLA software, and R version 3.4.0. Only three SNP markers showed suggestive associations (p < 10⁻³; rs16896742, rs28367832, and rs9277952). The frequency of KIR2DS3 was significantly increased in the CRC patients compared to healthy controls (p < 0.005). Our results suggest that the implication of NK cells in CRC may not act through allele combinations in KIR and HLA genes. Much larger studies in ethnically homogeneous populations are needed to rule out the possible role of allelic combinations in KIR and HLA genes in CRC risk.

A Targeted Genetic Association Study of the Rare Type of Osteomyelitis

Chronic nonbacterial osteomyelitis is a rare bone disorder that can be found in the jaw. It is often associated with systemic conditions, including autoimmune deficiencies. However, little is known about how the genetic and immunologic background of patients influences the disease. Here, we focus on human leukocyte antigen (HLA), killer cell immunoglobulin-like receptors (KIRs), and their specific combinations that have been difficult to analyze owing to their high diversity. We employed a recently developed technology of simultaneous typing of HLA alleles and KIR haplotype and investigated alleles of the 35 HLA loci and KIR haplotypes composed of centromeric and telomeric motifs in 18 cases and 18 controls for discovery and 472 independent controls for validation. We identified an amino acid substitution of threonine at position 94 of HLA-C in combination with the telomeric KIR genotype of haplotype tA01/tB01 that had significantly higher frequency (>20%) in the case population than in both control populations. Multiple logistic regression analysis based on a dominant model with adjustments for age and sex revealed and validated its statistical significance and high predictive accuracy (C-statistic ≥0.85). Structure-based analysis revealed that the combination of the amino acid change in HLA-C and the telomeric genotype tA01/tB01 could be associated with lower stability of HLA-C. This is the first case-control study of a rare disease that employed the latest sequencing technology enabling simultaneous typing and investigated amino acid polymorphisms at HLA loci in combination with KIR haplotype.

HLA and KIR Associations of Cervical Neoplasia

Background

Cervical cancer is the fourth most common cancer in women, and we recently reported human leukocyte antigen (HLA) alleles showing strong associations with cervical neoplasia risk and protection. HLA ligands are recognized by killer immunoglobulin-like receptors (KIRs) expressed on a range of immune cell subsets, governing their proinflammatory activity. We hypothesized that the inheritance of particular HLA-KIR combinations would increase cervical neoplasia risk.

Methods

Here, we used HLA and KIR dosages imputed from single-nucleotide polymorphism genotype data from 2143 cervical neoplasia cases and 13858 healthy controls of European decent.

Results

The following 4 novel HLA alleles were identified in association with cervical neoplasia, owing to their linkage disequilibrium with known cervical neoplasia-associated HLA-DRB1 alleles: HLA-DRB3*9901 (odds ratio [OR], 1.24; P = 2.49 × 10-9), HLA-DRB5*0101 (OR, 1.29; P = 2.26 × 10-8), HLA-DRB5*9901 (OR, 0.77; P = 1.90 × 10-9), and HLA-DRB3*0301 (OR, 0.63; P = 4.06 × 10-5). We also found that homozygosity of HLA-C1 group alleles is a protective factor for human papillomavirus type 16 (HPV16)-related cervical neoplasia (C1/C1; OR, 0.79; P = .005). This protective association was restricted to carriers of either KIR2DL2 (OR, 0.67; P = .00045) or KIR2DS2 (OR, 0.69; P = .0006).

Conclusions

Our findings suggest that HLA-C1 group alleles play a role in protecting against HPV16-related cervical neoplasia, mainly through a KIR-mediated mechanism.

A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals

BACKGROUND

The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20-69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition.

RESULTS

Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics.

CONCLUSION

In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT01699893.

Autoantibodies to Killer Cell Immunoglobulin-Like Receptors in Patients With Systemic Lupus Erythematosus Induce Natural Killer Cell Hyporesponsiveness

Natural killer (NK) cell cytotoxicity toward self-cells is restrained by the inhibitory HLA class I-binding receptors CD94/NKG2A and the killer cell immunoglobulin-like receptors (KIRs). CD94/NKG2A and KIRs are also essential for NK cell education, which is a dynamic functional maturation process where a constitutive binding of inhibitory receptors to cognate HLA class I molecules is required for NK cells to maintain their full cytotoxic capacity. Previously, we described autoantibodies to CD94/NKG2A in patients with systemic lupus erythematosus (SLE). In this study we analyzed sera from 191 patients with SLE, 119 patients with primary Sjögren's syndrome (pSS), 48 patients with systemic sclerosis (SSc), and 100 healthy donors (HD) for autoantibodies to eight different KIRs. Anti-KIR autoantibodies were identified in sera from 23.0% of patients with SLE, 10.9% of patients with pSS, 12.5% of patients with SSc, and 3.0% of HD. IgG from anti-KIR-positive SLE patients reduced the degranulation and cytotoxicity of NK cells toward K562 tumor cells. The presence of anti-KIR-autoantibodies reacting with >3 KIRs was associated with an increased disease activity (p < 0.0001), elevated serum levels of IFN-α (p < 0.0001), nephritis (p = 0.001), and the presence of anti-Sm (p = 0.007), and anti-RNP (p = 0.003) autoantibodies in serum. Together these findings suggest that anti-KIR autoantibodies may contribute to the reduced function of NK cells in SLE patients, and that a defective NK cell function may be a risk factor for the development of lupus nephritis.

Fluctuating and Geographically Specific Selection Characterize Rapid Evolution of the Human KIR Region

The killer-cell immunoglobulin-like receptor (KIR) region comprises a fast-evolving family of genes that encode receptors for natural killer (NK) cells and have crucial role in host defense. Evolution of KIR was examined in the context of the human genome. Gene-content diversity and single nucleotide polymorphisms (SNP) in the KIR genes and flanking regions were compared to >660,000 genome-wide SNPs in over 800 individuals from 52 populations of the human genome diversity panel (HGDP). KIR allelic diversity was further examined using next generation sequencing in a subset of 56 individuals. We identified the SNP rs587560 located in KIR3DL3 as a marker of KIR2DL2 and KIR2DL3 and, consequently, Cen A and Cen B haplotypes. We also show that combinations of two KIR2DL4 SNPs (rs35656676 and rs592645) distinguish KIR3DL1 from KIR3DS1 and also define the major KIR3DL1 high- and low-expressing alleles lineages. Comparing the diversity of the SNPs within the KIR region to remainder of the genome, we observed a high diversity for the centromeric KIR region consistent with balancing selection (p < 0.01); in contrast, centromeric KIR diversity is significantly reduced in East Asian populations (p < 0.01), indicating purifying selection. By analyzing SNP haplotypes in a region spanning ~500 kb that includes the KIR cluster, we observed evidence of strong positive selection in Africa for high-expressing KIR3DL1 alleles, favored over the low-expressing alleles (p < 0.01). In sharp contrast, the strong positive selection (p < 0.01) that we also observed in the telomeric KIR region in Oceanic populations tracked with a high frequency of KIR3DS1. In addition, we demonstrated that worldwide frequency of high-expression KIR3DL1 alleles was correlated with virus with virus (r = 0.64, p < 10⁻⁶) and protozoa (r = 0.69, p < 10⁻⁶) loads, which points to selection globally on KIR3DL1 high-expressing alleles attributable to pathogen exposure.

Pregnancy Immunogenetics and Genomics: Implications for Pregnancy-Related Complications and Autoimmune Disease

Pregnancy presents a singular physiological scenario during which the maternal immune system must accommodate the semiallogeneic fetus. Fluctuations between pro- and anti-inflammatory states are required throughout gestation to facilitate uterine tissue remodeling, fetal growth and development, and finally birth. Tolerance for the fetus must be established and maintained without fundamentally compromising the maternal immune system function, so that both the mother and fetus are protected from foreign insults. Here, we review our current understanding of how genetic variation at both maternal and fetal loci affects implantation and placenta formation, thereby determining the likelihood of a successful pregnancy outcome or the development of pregnancy-related complications. We also consider the impact of pregnancy on both the maternal and fetal systemic immune systems and the related implications for modulating ongoing autoimmune diseases and triggering their development.

Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival

Natural killer (NK) cells have increasingly become a target of interest for immunotherapies. NK cells express killer immunoglobulin-like receptors (KIRs), which play a vital role in immune response to tumors by detecting cellular abnormalities. The genomic region encoding the 16 KIR genes displays high polymorphic variability in human populations, making it difficult to resolve individual genotypes based on next generation sequencing data. As a result, the impact of polymorphic KIR variation on cancer phenotypes has been understudied. Currently, labor-intensive, experimental techniques are used to determine an individual's KIR gene copy number profile. Here, we develop an algorithm to determine the germline copy number of KIR genes from whole exome sequencing data and apply it to a cohort of nearly 5000 cancer patients. We use a k-mer based approach to capture sequences unique to specific genes, count their occurrences in the set of reads derived from an individual and compare the individual's k-mer distribution to that of the population. Copy number results demonstrate high concordance with population copy number expectations. Our method reveals that the burden of inhibitory KIR genes is associated with survival in two tumor types, highlighting the potential importance of KIR variation in understanding tumor development and response to immunotherapy.

Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease characterised by progressive destruction of intrahepatic bile ducts. The strongest genetic association is with HLA-DQA1*04:01, but at least three additional independent HLA haplotypes contribute to susceptibility. We used dense single nucleotide polymorphism (SNP) data in 2861 PBC cases and 8514 controls to impute classical HLA alleles and amino acid polymorphisms using state-of-the-art methodologies. We then demonstrated through stepwise regression that association in the HLA region can be largely explained by variation at five separate amino acid positions. Three-dimensional modelling of protein structures and calculation of electrostatic potentials for the implicated HLA alleles/amino acid substitutions demonstrated a correlation between the electrostatic potential of pocket P6 in HLA-DP molecules and the HLA-DPB1 alleles/amino acid substitutions conferring PBC susceptibility/protection, highlighting potential new avenues for future functional investigation.

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease that exhibits strong genetic associations, especially with variants in the human leukocyte antigen (HLA) gene region. Here we use dense single nucleotide polymorphism (SNP) data from the largest PBC study to date (2861 cases, 8514 controls) to investigate the likely underlying causes of this association, via performing imputation of HLA classical alleles and amino acids. We show that the HLA association can be largely explained by variation at five separate amino acid positions, one of which shows functional relevance to electrostatic potentials of HLA-DP molecules.

Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines

BACKGROUND

Humoral immune responses to infectious agents or vaccination vary substantially among individuals, and many of the factors responsible for this variability remain to be defined. Current evidence suggests that human genetic variation influences (i) serum immunoglobulin levels, (ii) seroconversion rates, and (iii) intensity of antigen-specific immune responses. Here, we evaluated the impact of intrinsic (age and sex), environmental, and genetic factors on the variability of humoral response to common pathogens and vaccines.

METHODS

We characterized the serological response to 15 antigens from common human pathogens or vaccines, in an age- and sex-stratified cohort of 1000 healthy individuals (Milieu Intérieur cohort). Using clinical-grade serological assays, we measured total IgA, IgE, IgG, and IgM levels, as well as qualitative (serostatus) and quantitative IgG responses to cytomegalovirus, Epstein-Barr virus, herpes simplex virus 1 and 2, varicella zoster virus, Helicobacter pylori, Toxoplasma gondii, influenza A virus, measles, mumps, rubella, and hepatitis B virus. Following genome-wide genotyping of single nucleotide polymorphisms and imputation, we examined associations between ~ 5 million genetic variants and antibody responses using single marker and gene burden tests.

RESULTS

We identified age and sex as important determinants of humoral immunity, with older individuals and women having higher rates of seropositivity for most antigens. Genome-wide association studies revealed significant associations between variants in the human leukocyte antigen (HLA) class II region on chromosome 6 and anti-EBV and anti-rubella IgG levels. We used HLA imputation to fine map these associations to amino acid variants in the peptide-binding groove of HLA-DRβ1 and HLA-DPβ1, respectively. We also observed significant associations for total IgA levels with two loci on chromosome 2 and with specific KIR-HLA combinations.

CONCLUSIONS

Using extensive serological testing and genome-wide association analyses in a well-characterized cohort of healthy individuals, we demonstrated that age, sex, and specific human genetic variants contribute to inter-individual variability in humoral immunity. By highlighting genes and pathways implicated in the normal antibody response to frequently encountered antigens, these findings provide a basis to better understand disease pathogenesis.

TRIALS REGISTRATION

ClinicalTrials.gov , NCT01699893.

Applying genomics in heart transplantation

While advances in patient care and immunosuppressive pharmacotherapies have increased the lifespan of heart allograft recipients, there are still significant comorbidities post-transplantation and 5-year survival rates are still significant, at approximately 70%. The last decade has seen massive strides in genomics and other omics fields, including transcriptomics, with many of these advances now starting to impact heart transplant clinical care. This review summarizes a number of the key advances in genomics which are relevant for heart transplant outcomes, and we highlight the translational potential that such knowledge may bring to patient care within the next decade.

Association of Natural Killer Cell Ligand Polymorphism HLA-C Asn80Lys With the Development of Anti-SSA/Ro-Associated Congenital Heart Block

OBJECTIVE

Fetal exposure to maternal anti-SSA/Ro antibodies is necessary but not sufficient for the development of autoimmune congenital heart block (CHB), suggesting that other factors, such as fetal genetic predisposition, are important. Given the previously described association between major histocompatibility complex alleles and CHB risk, we undertook the present study to test the hypothesis that a variant form of HLA-C Asn80Lys, which binds with high affinity to an inhibitory killer cell immunoglobulin-like receptor (KIR) and thus renders natural killer (NK) cells incapable of restricting inflammation, contributes to the development of CHB.

METHODS

Members of 192 pedigrees in the US and Europe (194 cases of CHB, 91 unaffected siblings, 152 fathers, 167 mothers) and 1,073 out-of-study controls were genotyped on the Immunochip single-nucleotide polymorphism microarray. Imputation was used to identify associations at HLA-C Asn80Lys (Asn, C1; Lys, C2) and KIR. Tests for association were performed using logistic regression. McNemar's test and the pedigree disequilibrium test (PDT) were used for matched analyses between affected and unaffected children.

RESULTS

Compared with out-of-study controls of the same sex, the C2 allele was less frequent in the mothers (odds ratio [OR] 0.63, P = 0.0014) and more frequent in the fathers (OR 1.40, P = 0.0123), yielding a significant sex-by-C2 interaction (P = 0.0002). The C2 allele was more frequent in affected siblings than in unaffected siblings (OR 3.67, P = 0.0025), which was consistent with the PDT results (P = 0.016); these results were observed in both sexes and across the US and European cohorts. There was no difference in the frequency of the inhibitory KIR genotype (KIR AA) between affected and unaffected children (P = 0.55).

CONCLUSION

These data establish C2 as a novel genetic risk factor associated with CHB. This observation supports a model in which fetuses with C2 ligand expression and maternal anti-SSA/Ro positivity may have impaired NK cell surveillance, resulting in unchecked cardiac inflammation and scarring.

Genetic susceptibility to infectious diseases: Current status and future perspectives from genome-wide approaches

Genome-wide association studies (GWASs) have been widely applied to identify genetic factors that affect complex diseases or traits. Presently, the GWAS Catalog includes > 2800 human studies. Of these, only a minority have investigated the susceptibility to infectious diseases or the response to therapies for the treatment or prevention of infections. Despite their limited application in the field, GWASs have provided valuable insights by pinpointing associations to both innate and adaptive immune response loci, as well as novel unexpected risk factors for infection susceptibility. Herein, we discuss some issues and caveats of GWASs for infectious diseases, we review the most recent findings ensuing from these studies, and we provide a brief summary of selected GWASs for infections in non-human mammals. We conclude that, although the general trend in the field of complex traits is to shift from GWAS to next-generation sequencing, important knowledge on infectious disease-related traits can be still gained by GWASs, especially for those conditions that have never been investigated using this approach. We suggest that future studies will benefit from the leveraging of information from the host's and pathogen's genomes, as well as from the exploration of models that incorporate heterogeneity across populations and phenotypes. Interactions within HLA genes or among HLA variants and polymorphisms located outside the major histocompatibility complex may also play an important role in shaping the susceptibility and response to invading pathogens.

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Relatively few GWASs for infectious diseases were performed.

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Phenotype heterogeneity and case/control misclassification can affect GWAS power.

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Adaptive and innate immunity loci were identified in several infectious disease GWASs.

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Unexpected loci (e.g., lncRNAs) were also associated with infection susceptibility.

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GWASs should integrate host and pathogen diversity and use complex association models.

Influence of Human Leukocyte Antigen (HLA) Alleles and Killer Cell Immunoglobulin-Like Receptors (KIR) Types on Heparin-Induced Thrombocytopenia (HIT)

Heparin-induced thrombocytopenia (HIT) is an unpredictable, life-threatening, immune-mediated reaction to heparin. Variation in human leukocyte antigen (HLA) genes is now used to prevent immune-mediated adverse drug reactions. Combinations of HLA alleles and killer cell immunoglobulin-like receptors (KIR) are associated with multiple autoimmune diseases and infections. The objective of this study is to evaluate the association of HLA alleles and KIR types, alone or in the presence of different HLA ligands, with HIT. HIT cases and heparin-exposed controls were identified in BioVU, an electronic health record coupled to a DNA biobank. HLA sequencing and KIR type imputation using Illumina OMNI-Quad data were performed. Odds ratios for HLA alleles and KIR types and HLA*KIR interactions using conditional logistic regressions were determined in the overall population and by race/ethnicity. Analysis was restricted to KIR types and HLA alleles with a frequency greater than 0.01. The p values for HLA and KIR association were corrected by using a false discovery rate q<0.05 and HLA*KIR interactions were considered significant at p<0.05. Sixty-five HIT cases and 350 matched controls were identified. No statistical differences in baseline characteristics were observed between cases and controls. The HLA-DRB3*01:01 allele was significantly associated with HIT in the overall population (odds ratio 2.81 [1.57-5.02], p=2.1×10-4 , q=0.02) and in individuals with European ancestry, independent of other alleles. No KIR types were associated with HIT, although a significant interaction was observed between KIR2DS5 and the HLA-C1 KIR binding group (p=0.03). The HLA-DRB3*01:01 allele was identified as a potential risk factor for HIT. This class II HLA gene and allele represent biologically plausible candidates for influencing HIT pathogenesis. We found limited evidence of the role of KIR types in HIT pathogenesis. Replication and further study of the HLA-DRB3*01:01 association is necessary.

A large population-based association study between HLA and KIR genotypes and measles vaccine antibody responses

Human antibody response to measles vaccine is highly variable in the population. Host genes contribute to inter-individual antibody response variation. The killer cell immunoglobulin-like receptors (KIR) are recognized to interact with HLA molecules and possibly influence humoral immune response to viral antigens. To expand on and improve our previous work with HLA genes, and to explore the genetic contribution of KIR genes to the inter-individual variability in measles vaccine-induced antibody responses, we performed a large population-based study in 2,506 healthy immunized subjects (ages 11 to 41 years) to identify HLA and KIR associations with measles vaccine-induced neutralizing antibodies. After correcting for the large number of statistical tests of allele effects on measles-specific neutralizing antibody titers, no statistically significant associations were found for either HLA or KIR loci. However, suggestive associations worthy of follow-up in other cohorts include B*57:01, DQB1*06:02, and DRB1*15:05 alleles. Specifically, the B*57:01 allele (1,040 mIU/mL; p = 0.0002) was suggestive of an association with lower measles antibody titer. In contrast, the DQB1*06:02 (1,349 mIU/mL; p = 0.0004) and DRB1*15:05 (2,547 mIU/mL; p = 0.0004) alleles were suggestive of an association with higher measles antibodies. Notably, the associations with KIR genotypes were strongly nonsignificant, suggesting that KIR loci in terms of copy number and haplotypes are not likely to play a major role in antibody response to measles vaccination. These findings refine our knowledge of the role of HLA and KIR alleles in measles vaccine-induced immunity.

Deciphering the killer-cell immunoglobulin-like receptor system at super-resolution for natural killer and T-cell biology

Killer-cell immunoglobulin-like receptors (KIRs) are components of two fundamental biological systems essential for human health and survival. First, they contribute to host immune responses, both innate and adaptive, through their expression by natural killer cells and T cells. Second, KIR play a key role in regulating placentation, and hence reproductive success. Analogous to the diversity of their human leucocyte antigen class I ligands, KIR are extremely polymorphic. In this review, we describe recent developments, fuelled by methodological advances, that are helping to decipher the KIR system in terms of haplotypes, polymorphisms, expression patterns and their ligand interactions. These developments are delivering deeper insight into the relevance of KIR in immune system function, evolution and disease.

qKAT: a high-throughput qPCR method for KIR gene copy number and haplotype determination

Killer cell immunoglobulin-like receptors (KIRs), expressed on natural killer cells and T cells, have considerable biomedical relevance playing significant roles in immunity, pregnancy and transplantation. The KIR locus is one of the most complex and polymorphic regions of the human genome. Extensive sequence homology and copy number variation makes KIRs technically laborious and expensive to type. To aid the investigation of KIRs in human disease we developed a high-throughput, multiplex real-time polymerase chain reaction method to determine gene copy number for each KIR locus. We used reference DNA samples to validate the accuracy and a cohort of 1698 individuals to evaluate capability for precise copy number discrimination. The method provides improved information and identifies KIR haplotype alterations that were not previously visible using other approaches.

Defining KIR and HLA Class I Genotypes at Highest Resolution via High-Throughput Sequencing

The physiological functions of natural killer (NK) cells in human immunity and reproduction depend upon diverse interactions between killer cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands: HLA-A, HLA-B, and HLA-C. The genomic regions containing the KIR and HLA class I genes are unlinked, structurally complex, and highly polymorphic. They are also strongly associated with a wide spectrum of diseases, including infections, autoimmune disorders, cancers, and pregnancy disorders, as well as the efficacy of transplantation and other immunotherapies. To facilitate study of these extraordinary genes, we developed a method that captures, sequences, and analyzes the 13 KIR genes and HLA-A, HLA-B, and HLA-C from genomic DNA. We also devised a bioinformatics pipeline that attributes sequencing reads to specific KIR genes, determines copy number by read depth, and calls high-resolution genotypes for each KIR gene. We validated this method by using DNA from well-characterized cell lines, comparing it to established methods of HLA and KIR genotyping, and determining KIR genotypes from 1000 Genomes sequence data. This identified 116 previously uncharacterized KIR alleles, which were all demonstrated to be authentic by sequencing from source DNA via standard methods. Analysis of just two KIR genes showed that 22% of the 1000 Genomes individuals have a previously uncharacterized allele or a structural variant. The method we describe is suited to the large-scale analyses that are needed for characterizing human populations and defining the precise HLA and KIR factors associated with disease. The methods are applicable to other highly polymorphic genes.

Differential frequency of NKG2C/KLRC2 deletion in distinct African populations and susceptibility to Trachoma: a new method for imputation of KLRC2 genotypes from SNP genotyping data

NKG2C is an activating receptor that is preferentially expressed on natural killer (NK) cells. The gene encoding NKG2C (killer cell lectin-like receptor C2, KLRC2) is present at different copy numbers in the genomes of different individuals. Deletion at the NKG2C locus was investigated in a case-control study of 1522 individuals indigenous to East- and West-Africa and the association with the ocular Chlamydia trachomatis infection and its sequelae was explored. The frequency of homozygous KLRC2 deletion was 13.7 % in Gambians and 4.7 % in Tanzanians. A significantly higher frequency of the deletion allele was found in West-Africans from the Gambia and Guinea-Bissau (36.2 % p = 2.105 × 10(-8), 26.8 % p = 0.050; respectively) in comparison to East-African Tanzanians where the frequency of the deletion is comparable to other human populations (20.9 %). We found no evidence for an association between the numbers of KLRC2 gene copies and the clinical manifestations of trachoma (follicular trachoma or conjunctival scarring). A new method for imputation of KLRC2 genotypes from single nucleotide polymorphism (SNP) data in 2621 individuals from the Gambia further confirmed these results. Our data suggest that NKG2C does not play a major role in trachomatous disease. We found that the deletion allele is present at different frequencies in different populations but the reason behind these differences is currently not understood. The new method offers the potential to use SNP arrays from genome wide association studies to study the frequency of KLRC2 deletion in other populations and its association with other diseases.