Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters.

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases.

shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores.

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context.

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.

Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches.

Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

Bayesian clustering with uncertain data.

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc.

Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency.

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies.

We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.

BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.

MOTIVATION: While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE). RESULTS: Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples. AVAILABILITY AND IMPLEMENTATION: We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

A more accurate method for colocalisation analysis allowing for multiple causal variants.

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.

Comparison of sparse biclustering algorithms for gene expression datasets.

MOTIVATION: Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. RESULTS: We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. AVAILABILITY: Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.

Probiotics for management of functional abdominal pain disorders in children.

BACKGROUND: Functional abdominal pain is pain occurring in the abdomen that cannot be fully explained by another medical condition and is common in children. It has been hypothesised that the use of micro-organisms, such as probiotics and synbiotics (a mixture of probiotics and prebiotics), might change the composition of bacterial colonies in the bowel and reduce inflammation, as well as promote normal gut physiology and reduce functional symptoms. OBJECTIVES: To assess the efficacy and safety of probiotics in the treatment of functional abdominal pain disorders in children. SEARCH METHODS: We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL) and two clinical trials registers from inception to October 2021. SELECTION CRITERIA: Randomised controlled trials (RCTs) that compare probiotic preparations (including synbiotics) to placebo, no treatment or any other interventional preparation in patients aged between 4 and 18 years of age with a diagnosis of functional abdominal pain disorder according to the Rome II, Rome III or Rome IV criteria. DATA COLLECTION AND ANALYSIS: The primary outcomes were treatment success as defined by the primary studies, complete resolution of pain, improvement in the severity of pain and improvement in the frequency of pain. Secondary outcomes included serious adverse events, withdrawal due to adverse events, adverse events, school performance or change in school performance or attendance, social and psychological functioning or change in social and psychological functioning, and quality of life or change in quality life measured using any validated scoring tool. For dichotomous outcomes, we calculated the risk ratio (RR) and corresponding 95% confidence interval (95% CI). For continuous outcomes, we calculated the mean difference (MD) and corresponding 95% CI. MAIN RESULTS: We included 18 RCTs assessing the effectiveness of probiotics and synbiotics in reducing the severity and frequency of pain, involving a total of 1309 patients. Probiotics may achieve more treatment success when compared with placebo at the end of the treatment, with 50% success in the probiotic group versus 33% success in the placebo group (RR 1.57, 95% CI 1.05 to 2.36; 554 participants; 6 studies; I2 = 70%; low-certainty evidence).  It is not clear whether probiotics are more effective than placebo for complete resolution of pain, with 42% success in the probiotic group versus 27% success in the placebo group (RR 1.55, 95% CI 0.94 to 2.56; 460 participants; 6 studies; I2 = 70%; very low-certainty evidence). We judged the evidence to be of very low certainty due to high inconsistency and risk of bias. We were unable to draw meaningful conclusions from our meta-analyses of the pain severity and pain frequency outcomes due to very high unexplained heterogeneity leading to very low-certainty evidence. None of the included studies reported serious adverse events. Meta-analysis showed no difference in withdrawals due to adverse events between probiotics (1/275) and placebo (1/269) (RR 1.00, 95% CI 0.07 to 15.12). The results were identical for the total patients with any reported adverse event outcome. However, these results are of very low certainty due to imprecision from the very low numbers of events and risk of bias. Synbiotics may result in more treatment success at study end when compared with placebo, with 47% success in the probiotic group versus 35% success in the placebo group (RR 1.34, 95% CI 1.03 to 1.74; 310 participants; 4 studies; I2 = 0%; low certainty). One study used Bifidobacterium coagulans/fructo-oligosaccharide, one used Bifidobacterium lactis/inulin, one used Lactobacillus rhamnosus GG/inulin and in one study this was not stated).  Synbiotics may result in little difference in complete resolution of pain at study end when compared with placebo, with 52% success in the probiotic group versus 32% success in the placebo group (RR 1.65, 95% CI 0.97 to 2.81; 131 participants; 2 studies; I2 = 18%; low-certainty evidence). We were unable to draw meaningful conclusions from our meta-analyses of pain severity or frequency of pain due to very high unexplained heterogeneity leading to very low-certainty evidence.  None of the included studies reported serious adverse events. Meta-analysis showed little to no difference in withdrawals due to adverse events between synbiotics (8/155) and placebo (1/147) (RR 4.58, 95% CI 0.80 to 26.19), or in any reported adverse events (3/96 versus 1/93, RR 2.88, 95% CI 0.32 to 25.92). These results are of very low certainty due to imprecision from the very low numbers of events and risk of bias. There were insufficient data to analyse by subgroups of specific functional abdominal pain syndrome (irritable bowel syndrome, functional dyspepsia, abdominal migraine, functional abdominal pain - not otherwise specified) or by specific strain of probiotic. There was insufficient evidence on school performance or change in school performance/attendance, social and psychological functioning, or quality of life to draw conclusions about the effects of probiotics or synbiotics on these outcomes. AUTHORS' CONCLUSIONS: The results from this review demonstrate that probiotics and synbiotics may be more efficacious than placebo in achieving treatment success, but the evidence is of low certainty. The evidence demonstrates little to no difference between probiotics or synbiotics and placebo in complete resolution of pain. We were unable to draw meaningful conclusions about the impact of probiotics or synbiotics on the frequency and severity of pain as the evidence was all of very low certainty due to significant unexplained heterogeneity or imprecision. There were no reported cases of serious adverse events when using probiotics or synbiotics amongst the included studies, although a review of RCTs may not be the best context to assess long-term safety. The available evidence on adverse effects was of very low certainty and no conclusions could be made in this review. Safety will always be a priority in paediatric populations when considering any treatment. Reporting of all adverse events, adverse events needing withdrawal, serious adverse events and, particularly, long-term safety outcomes are vital to meaningfully move forward the evidence base in this field. Further targeted and appropriately designed RCTs are needed to address the gaps in the evidence base. In particular, appropriate powering of studies to confirm the safety of specific strains not yet investigated and studies to investigate long-term follow-up of patients are both warranted.

Vitamin D for the treatment of inflammatory bowel disease.

BACKGROUND: Vitamin D possesses immunomodulatory properties and has been implicated in the pathogenesis and severity of inflammatory bowel disease (IBD). Animal studies and emerging epidemiological evidence have demonstrated an association between vitamin D deficiency and worse disease activity. However, the role of vitamin D for the treatment of IBD is unclear. OBJECTIVES: To evaluate the benefits and harms of vitamin D supplementation as a treatment for IBD. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search date was Jun 2023. SELECTION CRITERIA: We included randomised controlled trials (RCTs) in people of all ages with active or inactive IBD comparing any dose of vitamin D with another dose of vitamin D, another intervention, placebo, or no intervention. We defined doses as: vitamin D (all doses), any-treatment-dose vitamin D (greater than 400 IU/day), high-treatment-dose vitamin D (greater than 1000 IU/day), low-treatment-dose vitamin D (400 IU/day to 1000 IU/day), and supplemental-dose vitamin D (less than 400 IU/day). DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Our primary outcomes were 1. clinical response for people with active disease, 2. clinical relapse for people in remission, 3. quality of life, and 4. withdrawals due to adverse events. Our secondary outcomes were 5. disease activity at end of study, 6. normalisation of vitamin D levels at end of study, and 7. total serious adverse events. We used GRADE to assess certainty of evidence for each outcome. MAIN RESULTS: We included 22 RCTs with 1874 participants. Study duration ranged from four to 52 weeks. Ten studies enroled people with Crohn's disease (CD), five enroled people with ulcerative colitis (UC), and seven enroled people with CD and people with UC. Seventeen studies included adults, three included children, and two included both. Four studies enroled people with active disease, six enroled people in remission, and 12 enroled both. We assessed each study for risk of bias across seven individual domains. Five studies were at low risk of bias across all seven domains. Ten studies were at unclear risk of bias in at least one domain but with no areas of high risk of bias. Seven studies were at high risk of bias for blinding of participants and assessors. Vitamin D (all doses) versus placebo or no treatment Thirteen studies compared vitamin D against placebo or no treatment. We could not draw any conclusions on clinical response for UC as the certainty of the evidence was very low (risk ratio (RR) 4.00, 95% confidence interval (CI) 1.51 to 10.57; 1 study, 60 participants). There were no data on CD. There may be fewer clinical relapses for IBD when using vitamin D compared to placebo or no treatment (RR 0.57, 95% CI 0.34 to 0.96; 3 studies, 310 participants). The certainty of the evidence was low. We could not draw any conclusions on quality of life for IBD (standardised mean difference (SMD) -0.13, 95% CI -3.10 to 2.83 (the SMD value indicates a negligent decrease in quality of life, and the corresponding CIs indicate that the effect can range from a large decrease to a large increase in quality of life); 2 studies, 243 participants) or withdrawals due to adverse events for IBD (RR 1.97, 95% CI 0.18 to 21.27; 12 studies, 1251 participants; note 11 studies reported withdrawals but recorded 0 events in both groups. Thus, the RR and CIs were calculated from 1 study rather than 12). The certainty of the evidence was very low. High-treatment-dose vitamin D versus low-treatment-dose vitamin D Five studies compared high treatment vitamin D doses against low treatment vitamin D doses. There were no data on clinical response. There may be no difference in clinical relapse for CD (RR 0.48, 95% CI 0.23 to 1.01; 1 study, 34 participants). The certainty of the evidence was low. We could not draw any conclusions on withdrawals due to adverse events for IBD as the certainty of the evidence was very low (RR 0.89, 95% CI 0.06 to 13.08; 3 studies, 104 participants; note 2 studies reported withdrawals but recorded 0 events in both groups. Thus, the RR and CIs were calculated from 1 study rather than 3). The data on quality of life and disease activity could not be meta-analysed, were of very low certainty, and no conclusions could be drawn. Any-treatment-dose vitamin D versus supplemental-dose vitamin D Four studies compared treatment doses of vitamin D against supplemental doses. There were no data on clinical response and relapse. There were no data on quality of life that could be meta-analysed. We could not draw any conclusions on withdrawals due to adverse events for IBD as the certainty of the evidence was very low (RR 3.09, 95% CI 0.13 to 73.17; 4 studies, 233 participants; note 3 studies reported withdrawals but recorded 0 events in both groups. Thus, the RR and CIs were calculated from 1 study rather than 4). AUTHORS' CONCLUSIONS: There may be fewer clinical relapses when comparing vitamin D with placebo, but we cannot draw any conclusions on differences in clinical response, quality of life, or withdrawals, due to very low-certainty evidence. When comparing high and low doses of vitamin D, there were no data for clinical response, but there may be no difference in relapse for CD. We cannot draw conclusions on the other outcomes due to very low certainty evidence. Finally, comparing vitamin D (all doses) to supplemental-dose vitamin D, there were no data on clinical relapse or response, and we could not draw conclusions on other outcomes due to very low certainty evidence or missing data. It is difficult to make any clear recommendations for future research on the basis of the findings of this review. Future studies must be clear on the baseline populations, the purpose of vitamin D treatment, and, therefore, study an appropriate dosing strategy. Stakeholders in the field may wish to reach consensus on such issues prior to new studies.

Autoimmunity Is a Significant Feature of Idiopathic Pulmonary Arterial Hypertension.

Rationale: Autoimmunity is believed to play a role in idiopathic pulmonary arterial hypertension (IPAH). It is not clear whether this is causative or a bystander of disease and if it carries any prognostic or treatment significance. Objectives: To study autoimmunity in IPAH using a large cross-sectional cohort. Methods: Assessment of the circulating immune cell phenotype was undertaken using flow cytometry, and the profile of serum immunoglobulins was generated using a standardized multiplex array of 19 clinically validated autoantibodies in 473 cases and 946 control subjects. Additional glutathione S-transferase fusion array and ELISA data were used to identify a serum autoantibody to BMPR2 (bone morphogenetic protein receptor type 2). Clustering analyses and clinical correlations were used to determine associations between immunogenicity and clinical outcomes. Measurements and Main Results: Flow cytometric immune profiling demonstrates that IPAH is associated with an altered humoral immune response in addition to raised IgG3. Multiplexed autoantibodies were significantly raised in IPAH, and clustering demonstrated three distinct clusters: "high autoantibody," "low autoantibody," and a small "intermediate" cluster exhibiting high concentrations of ribonucleic protein complex. The high-autoantibody cluster had worse hemodynamics but improved survival. A small subset of patients demonstrated immunoglobulin reactivity to BMPR2. Conclusions: This study establishes aberrant immune regulation and presence of autoantibodies as key features in the profile of a significant proportion of patients with IPAH and is associated with clinical outcomes.

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.

Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis.

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS.

BACKGROUND: Genome-wide association studies (GWAS) are limited in power to detect associations that exceed the stringent genome-wide significance threshold. This limitation can be alleviated by leveraging relevant auxiliary data, such as functional genomic data. Frameworks utilising the conditional false discovery rate have been developed for this purpose, and have been shown to increase power for GWAS discovery whilst controlling the false discovery rate. However, the methods are currently only applicable for continuous auxiliary data and cannot be used to leverage auxiliary data with a binary representation, such as whether SNPs are synonymous or non-synonymous, or whether they reside in regions of the genome with specific activity states. RESULTS: We describe an extension to the cFDR framework for binary auxiliary data, called "Binary cFDR". We demonstrate FDR control of our method using detailed simulations, and show that Binary cFDR performs better than a comparator method in terms of sensitivity and FDR control. We introduce an all-encompassing user-oriented CRAN R package ( https://annahutch.github.io/fcfdr/ ; https://cran.r-project.org/web/packages/fcfdr/index.html ) and demonstrate its utility in an application to type 1 diabetes, where we identify additional genetic associations. CONCLUSIONS: Our all-encompassing R package, fcfdr, serves as a comprehensive toolkit to unite GWAS and functional genomic data in order to increase statistical power to detect genetic associations.

Consensus clustering for Bayesian mixture models.

BACKGROUND: Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. RESULTS: In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three 'omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. CONCLUSTIONS: Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

Multi-tissue transcriptome-wide association studies.

A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.

Fine-mapping genetic associations.

Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.

Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy.

Cytokines are essential regulatory components of the immune system, and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlies them, remain unknown. Here, we aimed to identify and characterize genetic variants with pleiotropic effects on cytokines. Using three population-based cohorts (n = 9,263), we performed multivariate genome-wide association studies (GWAS) for a correlation network of 11 circulating cytokines, then combined our results in meta-analysis. We identified a total of eight loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. In addition, conditional analyses revealed a further four secondary signals at three known cytokine loci. Integration, through the use of Bayesian colocalization analysis, of publicly available GWAS summary statistics with the cytokine network associations revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins, on metabolic traits such as lipoprotein and lipid levels, on blood-cell-related traits such as platelet count, and on disease traits such as coronary artery disease and type 2 diabetes.