Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

Statistical methods for cis-Mendelian randomization with two-sample summary-level data.

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

Type I interferon blockade with anifrolumab in patients with systemic lupus erythematosus modulates key immunopathological pathways in a gene expression and proteomic analysis of two phase 3 trials.

INTRODUCTION: Anifrolumab is a type I interferon (IFN) receptor 1 (IFNAR1) blocking antibody approved for treating patients with systemic lupus erythematosus (SLE). Here, we investigated the immunomodulatory mechanisms of anifrolumab using longitudinal transcriptomic and proteomic analyses of the 52-week, randomised, phase 3 TULIP-1 and TULIP-2 trials. METHODS: Patients with moderate to severe SLE were enrolled in TULIP-1 and TULIP-2 and received intravenous anifrolumab or placebo alongside standard therapy. Whole-blood expression of 18 017 genes using genome-wide RNA sequencing (RNA-seq) (pooled TULIP; anifrolumab, n=244; placebo, n=258) and 184 plasma proteins using Olink and Simoa panels (TULIP-1; anifrolumab, n=124; placebo, n=132) were analysed. We compared treatment groups via gene set enrichment analysis using MetaBase pathway analysis, blood transcriptome modules, in silico deconvolution of RNA-seq and longitudinal linear mixed effect models for gene counts and protein levels. RESULTS: Compared with placebo, anifrolumab modulated >2000 genes by week 24, with overlapping results at week 52, and 41 proteins by week 52. IFNAR1 blockade with anifrolumab downregulated multiple type I and II IFN-induced gene modules/pathways and type III IFN-λ protein levels, and impacted apoptosis-associated and neutrophil extracellular traps-(NET)osis-associated transcriptional pathways, innate cell activating chemokines and receptors, proinflammatory cytokines and B-cell activating cytokines. In silico deconvolution of RNA-seq data indicated an increase from baseline of mucosal-associated invariant and γδT cells and a decrease of monocytes following anifrolumab treatment. DISCUSSION: Type I IFN blockade with anifrolumab modulated multiple inflammatory pathways downstream of type I IFN signalling, including apoptotic, innate and adaptive mechanisms that play key roles in SLE immunopathogenesis.

Tailored Bayes: a risk modeling framework under unequal misclassification costs.

Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications, this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this article, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimize predictive performance with respect to unbalanced misclassification costs. We use simulation studies to showcase when TB is expected to outperform standard Bayesian methods in the context of logistic regression. We then apply TB to three real-world applications, a cardiac surgery, a breast cancer prognostication task, and a breast cancer tumor classification task and demonstrate the improvement in predictive performance over standard methods.

Conditional inference in cis-Mendelian randomization using weak genetic factors.

Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t-tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly-used identification-robust tests for the setting where estimated factors are used as instruments. Second, we propose a test which appropriately adjusts for first-stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol-lowering drug targets aimed at preventing coronary heart disease.

Two-stage penalized regression screening to detect biomarker-treatment interactions in randomized clinical trials.

High-dimensional biomarkers such as genomics are increasingly being measured in randomized clinical trials. Consequently, there is a growing interest in developing methods that improve the power to detect biomarker-treatment interactions. We adapt recently proposed two-stage interaction detecting procedures in the setting of randomized clinical trials. We also propose a new stage 1 multivariate screening strategy using ridge regression to account for correlations among biomarkers. For this multivariate screening, we prove the asymptotic between-stage independence, required for familywise error rate control, under biomarker-treatment independence. Simulation results show that in various scenarios, the ridge regression screening procedure can provide substantially greater power than the traditional one-biomarker-at-a-time screening procedure in highly correlated data. We also exemplify our approach in two real clinical trial data applications.

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.

Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a 2-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. We propose to extend our previous approach for the joint analysis of marginal summary statistics to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate estimates as prior information yields an analysis similar to Mendelian randomization (MR) and TWAS approaches. hJAM is applicable to multiple correlated SNPs and intermediates to yield conditional estimates for the intermediates on the outcome, thus providing advantages over alternative approaches. We investigated the performance of hJAM in comparison with existing MR and TWAS approaches and demonstrated that hJAM yields an unbiased estimate, maintains correct type-I error, and has increased power across extensive simulations. We applied hJAM to 2 examples: estimating the causal effects of body mass index (GIANT Consortium) and type 2 diabetes (DIAGRAM data set, GERA Cohort, and UK Biobank) on myocardial infarction (UK Biobank) and estimating the causal effects of the expressions of the genes for nuclear casein kinase and cyclin dependent kinase substrate 1 and peptidase M20 domain containing 1 on the risk of prostate cancer (PRACTICAL and GTEx).

Bayesian variable selection with a pleiotropic loss function in Mendelian randomization.

Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.

A flexible and parallelizable approach to genome-wide polygenic risk scores.

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.

In Severe Alcoholic Hepatitis, Serum Keratin-18 Fragments Are Diagnostic, Prognostic, and Theragnostic Biomarkers.

INTRODUCTION: Up to 40% of patients with severe alcoholic hepatitis (AH) die within 6 months of presentation, making prompt diagnosis and appropriate treatment essential. We determined the associations between serum keratin-18 (K18) and histological features, prognosis, and differential response to prednisolone in patients with severe AH. METHODS: Total (K18-M65) and caspase-cleaved K18 (K18-M30) were quantified in pretreatment sera from 824 patients enrolled in the Steroids or Pentoxifylline for Alcoholic Hepatitis trial (87 with suitable histological samples) and disease controls. RESULTS: K18 fragments were markedly elevated in severe AH and strongly predicted steatohepatitis (alcoholic steatohepatitis) on biopsy (area under receiver operating characteristics: 0.787 and 0.807). Application of published thresholds to predict alcoholic steatohepatitis would have rendered biopsy unnecessary in 84% of all AH cases. K18-M30 and M65 were associated with 90-day mortality, independent of age and Model for End-stage Liver Disease score in untreated patients. The association for K18-M65 was independent of both age and Model for End-stage Liver Disease in prednisolone-treated patients. Modelling of the effect of prednisolone on 90-day mortality as a function of pretreatment serum K18 levels indicated benefit in those with high serum levels of K18-M30. At low pretreatment serum K18 levels, prednisolone was potentially harmful. A threshold of K18-M30 5 kIU/L predicted therapeutic benefit from prednisolone above this level (odds ratio: 0.433, 95% confidence interval: 0.19-0.95, P = 0.0398), but not below (odds ratio: 1.271, 95% confidence interval: 0.88-1.84, P = 0.199). Restricting prednisolone usage to the former group would have reduced exposure by 87%. DISCUSSION: In a large cohort of patients with severe AH, serum K18 strongly correlated with histological severity, independently associated with 90-day mortality, and predicted response to prednisolone therapy. Quantification of serum K18 levels could assist in clinical decision-making.

Fine mapping chromatin contacts in capture Hi-C data.

BACKGROUND: Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest. RESULTS: We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets. CONCLUSIONS: Our results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

Insight into Genotype-Phenotype Associations through eQTL Mapping in Multiple Cell Types in Health and Immune-Mediated Disease.

Genome-wide association studies (GWAS) have transformed our understanding of the genetics of complex traits such as autoimmune diseases, but how risk variants contribute to pathogenesis remains largely unknown. Identifying genetic variants that affect gene expression (expression quantitative trait loci, or eQTLs) is crucial to addressing this. eQTLs vary between tissues and following in vitro cellular activation, but have not been examined in the context of human inflammatory diseases. We performed eQTL mapping in five primary immune cell types from patients with active inflammatory bowel disease (n = 91), anti-neutrophil cytoplasmic antibody-associated vasculitis (n = 46) and healthy controls (n = 43), revealing eQTLs present only in the context of active inflammatory disease. Moreover, we show that following treatment a proportion of these eQTLs disappear. Through joint analysis of expression data from multiple cell types, we reveal that previous estimates of eQTL immune cell-type specificity are likely to have been exaggerated. Finally, by analysing gene expression data from multiple cell types, we find eQTLs not previously identified by database mining at 34 inflammatory bowel disease-associated loci. In summary, this parallel eQTL analysis in multiple leucocyte subsets from patients with active disease provides new insights into the genetic basis of immune-mediated diseases.

JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.

Recently, large scale genome-wide association study (GWAS) meta-analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one-at-a-time. This complicates the ability of fine-mapping to identify a small set of SNPs for further functional follow-up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re-analysis of published marginal summary statistics under joint multi-SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible Jump MCMC implementations of JAM and present some comparisons of performance. In a series of realistic simulation studies, JAM demonstrated identical performance to various alternatives designed for single region settings. In multi-region settings, where the only multivariate alternative involves stepwise selection, JAM offered greater power and specificity. We also present an application to real published results from MAGIC (meta-analysis of glucose and insulin related traits consortium) - a GWAS meta-analysis of more than 15,000 people. We re-analysed several genomic regions that produced multiple significant signals with glucose levels 2 hr after oral stimulation. Through joint multivariate modelling, JAM was able to formally rule out many SNPs, and for one gene, ADCY5, suggests that an additional SNP, which transpired to be more biologically plausible, should be followed up with equal priority to the reported index.

GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm.

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n  =  3,175), when compared with the largest published meta-GWAS (n > 100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.

Accuracy of Gene Scores when Pruning Markers by Linkage Disequilibrium.

OBJECTIVE: Gene scores are often used to model the combined effects of genetic variants. When variants are in linkage disequilibrium, it is common to prune all variants except the most strongly associated. This avoids duplicating information but discards information when variants have independent effects. However, joint modelling of correlated variants increases the sampling error in the gene score. In recent applications, joint modelling has offered only small improvements in accuracy over pruning. We aimed to quantify the relationship between pruning and joint modelling in relation to sample size. METHODS: We derived the coefficient of determination R2 for a gene score constructed from pruned markers, and for one constructed from correlated markers with jointly estimated effects. RESULTS: Pruned scores tend to have slightly lower R2 than jointly modelled scores, but the differences are small at sample sizes up to 100,000. If the proportion of correlated variants is high, joint modelling can obtain modest improvements asymptotically. CONCLUSIONS: The small gains observed to date from joint modelling can be explained by sample size. As studies become larger, joint modelling will be useful for traits affected by many correlated variants, but the improvements may remain small. Pruning remains a useful heuristic for current studies.

Genetic factors associated with response to intravitreal ranibizumab in Korean patients with neovascular age-related macular degeneration.

PURPOSE: To investigate the association between genetic risk variants for age-related macular degeneration (AMD) and response to intravitreal ranibizumab in Korean patients with neovascular AMD. METHODS: This prospective study included 273 treatment-naive patients (273 eyes) who underwent 5 monthly injections (Months 0, 1, 2, 3, and 4) of intravitreal ranibizumab for neovascular AMD. Patients were genotyped for 23 single-nucleotide polymorphisms within 12 AMD-relevant genes. For each polymorphism, genotypic association with good response at Month 5, predetermined as visual improvement of ≥ 8 Early Treatment Diabetic Retinopathy Study letters from baseline, was investigated with logistic regression analysis adjusted for age, gender, smoking, baseline Early Treatment Diabetic Retinopathy Study letter, central retinal thickness, lesion area, and type of choroidal neovascularization. RESULTS: At Month 5, visual acuity improved by 9.1 ± 17.6 letters from baseline, and 136 patients (49.8%) were classified as good responders. In logistic regression, no tested polymorphism showed statistically significant association with favorable visual outcome at Month 5. When unadjusted for multiple tests, AA genotype for VEGF rs699947 had an increased chance of good response compared with other genotypes (odds ratio, 3.61; 95% confidence interval, 1.42-9.18; P = 0.0071). CONCLUSION: In this Korean neovascular AMD cohort, there was no statistically significant effect of genotype on early visual outcome after ranibizumab treatment.

A Randomized Phase I Study of the Anti-Interleukin-33 Antibody Tozorakimab in Healthy Adults and Patients With Chronic Obstructive Pulmonary Disease.

Tozorakimab is a human monoclonal antibody that neutralizes interleukin (IL)-33. IL-33 is a broad-acting epithelial "alarmin" cytokine upregulated in lung tissue of patients with chronic obstructive pulmonary disease (COPD). This first-in-human, phase I, randomized, double-blind, placebo-controlled study (NCT03096795) evaluated the safety, tolerability, pharmacokinetics (PKs), immunogenicity, target engagement, and pharmacodynamics (PDs) of tozorakimab. This was a 3-part study. In part 1, 56 healthy participants with a history of mild atopy received single escalating doses of either intravenous or subcutaneous tozorakimab or placebo. In part 2, 24 patients with mild COPD received multiple ascending doses of subcutaneous tozorakimab or placebo. In part 3, 8 healthy Japanese participants received a single intravenous dose of tozorakimab or placebo. The safety data collected included treatment-emergent adverse events (TEAEs), vital signs, and clinical laboratory parameters. Biological samples for PKs, immunogenicity, target engagement, and PD biomarker analyses were collected. No meaningful differences in the frequencies of TEAEs were observed between the tozorakimab and placebo arms. Three tozorakimab-treated participants with COPD experienced treatment-emergent serious adverse events. Subcutaneous or intravenous tozorakimab demonstrated linear, time-independent PKs with a mean half-life of 11.7-17.3 days. Treatment-emergent anti-drug antibody frequency was low. Engagement of tozorakimab with endogenous IL-33 in serum and nasal airways was demonstrated. Tozorakimab significantly reduced serum IL-5 and IL-13 levels in patients with COPD compared with placebo. Overall, tozorakimab was well tolerated, with a linear, time-independent serum PK profile. Additionally, biomarker studies demonstrated proof of mechanism. Overall, these data support the further clinical development of tozorakimab in COPD and other inflammatory diseases.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Bayesian semiparametric meta-analysis for genetic association studies.

We present a Bayesian semiparametric model for the meta-analysis of candidate gene studies with a binary outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping genetic markers in the same genetic region. Meta-analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequilibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta-analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian semiparametric model which models the observed genotype group frequencies conditional to the case/control status and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach allows borrowing of strength across studies and across markers. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model. Full posterior inference is performed through Markov chain Monte Carlo algorithms. The approach is demonstrated on simulated and real data.