Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis

Prediction of HLA class II alleles using SNPs in an African population

BACKGROUND: Despite the importance of the human leukocyte antigen (HLA) gene locus in research and clinical practice, direct HLA typing is laborious and expensive. Furthermore, the analysis requires specialized software and expertise which are unavailable in most developing country settings. Recently, in silico methods have been developed for predicting HLA alleles using single nucleotide polymorphisms (SNPs). However, the utility of these methods in African populations has not been systematically evaluated. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we investigate prediction of HLA class II (HLA-DRB1 and HLA-DQB1) alleles using SNPs in the Wolaita population, southern Ethiopia. The subjects comprised 297 Ethiopians with genome-wide SNP data, of whom 188 had also been HLA typed and were used for training and testing the model. The 109 subjects with SNP data alone were used for empirical prediction using the multi-allelic gene prediction method. We evaluated accuracy of the prediction, agreement between predicted and HLA typed alleles, and discriminative ability of the prediction probability supplied by the model. We found that the model predicted intermediate (two-digit) resolution for HLA-DRB1 and HLA-DQB1 alleles at accuracy levels of 96% and 87%, respectively. All measures of performance showed high accuracy and reliability for prediction. The distribution of the majority of HLA alleles in the study was similar to that previously reported for the Oromo and Amhara ethnic groups from Ethiopia. CONCLUSIONS/SIGNIFICANCE: We demonstrate that HLA class II alleles can be predicted from SNP genotype data with a high level of accuracy at intermediate (two-digit) resolution in an African population. This finding offers new opportunities for HLA studies of disease epidemiology and population genetics in developing countrie

Mendelian randomization shows a causal effect of low vitamin D on multiple sclerosis risk.

ObjectiveWe sought to estimate the causal effect of low serum 25(OH)D on multiple sclerosis (MS) susceptibility that is not confounded by environmental or lifestyle factors or subject to reverse causality.MethodsWe conducted mendelian randomization (MR) analyses using an instrumental variable (IV) comprising 3 single nucleotide polymorphisms found to be associated with serum 25(OH)D levels at genome-wide significance. We analyzed the effect of the IV on MS risk and both age at onset and disease severity in 2 separate populations using logistic regression models that controlled for sex, year of birth, smoking, education, genetic ancestry, body mass index at age 18-20 years or in 20s, a weighted genetic risk score for 110 known MS-associated variants, and the presence of one or more HLA-DRB1*15:01 alleles.ResultsFindings from MR analyses using the IV showed increasing levels of 25(OH)D are associated with a decreased risk of MS in both populations. In white, non-Hispanic members of Kaiser Permanente Northern California (1,056 MS cases and 9,015 controls), the odds ratio (OR) was 0.79 (p = 0.04, 95% confidence interval (CI): 0.64-0.99). In members of a Swedish population from the Epidemiological Investigation of Multiple Sclerosis and Genes and Environment in Multiple Sclerosis MS case-control studies (6,335 cases and 5,762 controls), the OR was 0.86 (p = 0.03, 95% CI: 0.76-0.98). A meta-analysis of the 2 populations gave a combined OR of 0.85 (p = 0.003, 95% CI: 0.76-0.94). No association was observed for age at onset or disease severity.ConclusionsThese results provide strong evidence that low serum 25(OH)D concentration is a cause of MS, independent of established risk factors

Human leukocyte antigens and genetic susceptibility to lymphoma

Familial aggregation, coupled with ethnic variation in incidence, suggests that inherited susceptibility plays a role in the development of lymphoma, and the search for genetic risk factors has highlighted the contribution of the human leukocyte antigen (HLA) complex. In a landmark study published almost 50 years ago, Hodgkin lymphoma (HL) was the first disease to be associated with HLA variation. It is now clear that Epstein-Barr virus (EBV)-positive and -negative HL are strongly associated with specific HLA polymorphisms but these differ by EBV status of the tumours. HLA class I alleles are consistently associated with EBV-positive HL while a polymorphism in HLA class II is the strongest predictor of risk of EBV-negative HL. Recent investigations, particularly genome-wide association studies (GWAS), have also revealed associations between HLA and common types of non-Hodgkin lymphoma (NHL). Follicular lymphoma is strongly associated with two distinct haplotypes in HLA class II whereas diffuse large B-cell lymphoma is most strongly associated with HLA-B*08. Although chronic lymphocytic leukaemia is associated with variation in HLA class II, the strongest signals in GWAS are from non-HLA polymorphisms, suggesting that inherited susceptibility is explained by co-inheritance of multiple low risk variants. Associations between B-cell derived lymphoma and HLA variation suggest that antigen presentation, or lack of, plays an important role in disease pathogenesis but the precise mechanisms have yet to be elucidated

Fine mapping of causal HLA variants using penalised regression

The identification of risk loci in the Human Leukocyte Antigen (HLA) region using single-SNP association tests has been hampered by the extent of linkage disequilibrium (LD). Penalised regression via Least Absolute Shrinkage and Selection Operator (LASSO) can be used as a method for selection of variables in multi-SNP analysis, and to deal with the problem of multi-collinearity among predictors. This method applies a penalty that shrinks the estimates of the regression coefficients towards zero. This is equivalent to applying a double exponential (DE) prior distribution to the coefficients with a mode at zero, corresponding to the prior belief that most of the effects are negligible in a Bayesian approach. Parameter inference is based on the posterior mode, with non-zero values indicating marker-disease associations. Single-SNP, stepwise regression and the LASSO approach were applied to case-control studies of rheumatoid arthritis, a disease which has been associated with markers from the HLA region. A generalisation of the LASSO called the HyperLasso (HLASSO), which uses the normal-exponential-gamma prior in place of the DE, was also investigated. These approaches were applied to data from the Genetics of Rheumatoid Arthritis (GoRA) study. Genotype imputation was used as a means to jointly analyse the GoRA and the Wellcome Trust Case Control Consortium (WTCCC) HLA SNPs. The North American Rheumatoid Arthritis Consortium (NARAC) study was used to validate the findings. After controlling for type-I error, the penalised approaches greatly reduced the number of positive signals compared to single-SNP analysis, suggesting that correlation among SNP loci was better handled. The HLASSO results were sparser but similar to the LASSO results. One SNP in HLA-DPB1 was replicated in the NARAC study. In both models, the robustness of the retained variables was verified by bootstrapping. The results suggest that SNP-selection using LASSO or HLASSO shows a substantial benefit in identifying risk loci in regions of high LD

The prediction of HLA genotypes from next generation sequencing and genome scan data

Genome-wide association studies have very successfully found highly significant disease associations with single nucleotide polymorphisms (SNP) in the Major Histocompatibility Complex for adverse drug reactions, autoimmune diseases and infectious diseases. However, the extensive linkage disequilibrium in the region has made it difficult to unravel the HLA alleles underlying these diseases. Here I present two methods to comprehensively predict 4-digit HLA types from the two types of experimental genome data widely available. The Virtual SNP Imputation approach was developed for genome scan data and demonstrated a high precision and recall (96% and 97% respectively) for the prediction of HLA genotypes. A reanalysis of 6 genome-wide association studies using the HLA imputation method identified 18 significant HLA allele associations for 6 autoimmune diseases: 2 in ankylosing spondylitis, 2 in autoimmune thyroid disease, 2 in Crohn's disease, 3 in multiple sclerosis, 2 in psoriasis and 7 in rheumatoid arthritis. The EPIGEN consortium also used the Virtual SNP Imputation approach to detect a novel association of HLA-A*31:01 with adverse reactions to carbamazepine. For the prediction of HLA genotypes from next generation sequencing data, I developed a novel approach using a naïve Bayes algorithm called HLA-Genotyper. The validation results covered whole genome, whole exome and RNA-Seq experimental designs in the European and Yoruba population samples available from the 1000 Genomes Project. The RNA-Seq data gave the best results with an overall precision and recall near 0.99 for Europeans and 0.98 for the Yoruba population. I then successfully used the method on targeted sequencing data to detect significant associations of idiopathic membranous nephropathy with HLA-DRB1*03:01 and HLA-DQA1*05:01 using the 1000 Genomes European subjects as controls. Using the results reported here, researchers may now readily unravel the association of HLA alleles with many diseases from genome scans and next generation sequencing experiments without the expensive and laborious HLA typing of thousands of subjects. Both algorithms enable the analysis of diverse populations to help researchers pinpoint HLA loci with biological roles in infection, inflammation, autoimmunity, aging, mental illness and adverse drug reactions

Bioinformatische Analyse des Humanen Leukozyten Antigens in Chronisch Entzündlichen Darmerkrankungen

The Human Leucocyte Antigen (HLA) has been identified as a genetic risk factor for Inflammatory Bowel Diseases (IBD). The causative role of the HLA in IBD remains to be revealed. The aim of this thesis is to gain as much understanding as possible about the HLA alleles genetically associated with Ulcerative Colitis (UC), a subtype of IBD. This cumulative thesis includes two papers published in peer-reviewed journals and an additional manuscript which is in progress. In the first publication, presented in this thesis (Section 6.3), we built an imputation panel that enabled the analysis of IBD and the associated HLA alleles and their corresponding haplotypes across different ancestries. In the second publication (Section 7.3), I analyzed the interaction of peptides with a defined set of HLA alleles associated with UC. This study was the first to use ultra-high density microarray data for predicting the binding status of HLA alleles and peptides. In the final manuscript (Section 8.3), I studied the genetics of UC in Caucasian individuals. I analyzed, what the genetics, combined with prior knowledge about different genes and their protein function, can tell us about a hypothesized peptide that might be a key player in the pathogenesis of UC. Next to some improvements in the imputation of HLA genomes and the binding prediction, this thesis points out first concrete candidate peptides and suggests a path to continue to discover more about the contribution of the HLA in UC

Imputing Amino Acid Polymorphisms in Human Leukocyte Antigens

DNA sequence variation within human leukocyte antigen (HLA) genes mediate susceptibility to a wide range of human diseases. The complex genetic structure of the major histocompatibility complex (MHC) makes it difficult, however, to collect genotyping data in large cohorts. Long-range linkage disequilibrium between HLA loci and SNP markers across the major histocompatibility complex (MHC) region offers an alternative approach through imputation to interrogate HLA variation in existing GWAS data sets. Here we describe a computational strategy, SNP2HLA, to impute classical alleles and amino acid polymorphisms at class I (HLA-A, -B, -C) and class II (-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) loci. To characterize performance of SNP2HLA, we constructed two European ancestry reference panels, one based on data collected in HapMap-CEPH pedigrees (90 individuals) and another based on data collected by the Type 1 Diabetes Genetics Consortium (T1DGC, 5,225 individuals). We imputed HLA alleles in an independent data set from the British 1958 Birth Cohort (N = 918) with gold standard four-digit HLA types and SNPs genotyped using the Affymetrix GeneChip 500 K and Illumina Immunochip microarrays. We demonstrate that the sample size of the reference panel, rather than SNP density of the genotyping platform, is critical to achieve high imputation accuracy. Using the larger T1DGC reference panel, the average accuracy at four-digit resolution is 94.7% using the low-density Affymetrix GeneChip 500 K, and 96.7% using the high-density Illumina Immunochip. For amino acid polymorphisms within HLA genes, we achieve 98.6% and 99.3% accuracy using the Affymetrix GeneChip 500 K and Illumina Immunochip, respectively. Finally, we demonstrate how imputation and association testing at amino acid resolution can facilitate fine-mapping of primary MHC association signals, giving a specific example from type 1 diabetes