An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Genetic variations related to the prostate cancer risk: A field synopsis and revaluation by Bayesian approaches of genome-wide association studies

Prostate cancer (PCa) is a complex disease influenced by many factors, with the genetic contribution for this neoplasia having a great role in its risk. The literature brings an increased number of Genome-Wide Association Studies (GWAS's) that attempt to elucidate the genetic associations with PCa. However, these genome studies have a considerable rate of false-positive data whose results may be biased. Therefore, we aimed to apply Bayesian approaches on significant associations among polymorphisms and PCa from GWAS's data. A literature search was performed for data published before April 20, 2024, whereby two investigators used a specific combination of keywords and Boolean operators in the search ("prostate carcinoma or prostate cancer or PCa" and "polymorphism or genetic variation" and "Genome-Wide Association Study or GWAS"). The records were retrieved, and the data were extracted with further application of two different Bayesian approaches: The False Positive Report Probability (FPRP) and the Bayesian False-Discovery Probability (BFDP), both at the prior probabilities of 10-3 and 10-6. The data were considered as noteworthy at the level of FPRP <0.2 and BFDP <0.8. Besides, in-silico analyses by gene-gene network and gene enrichment were performed to evaluate the role of the noteworthy genes in PCa. As results, 13 GWAS's were included, with 2,520 values for FPRP and 1,368 values for BFDP being obtained. Our study showed an extensive number of gene variations as noteworthy candidate biomarkers for PCa risk, with highlighting for those occurred in the 8q24 locus and in the MSMB, ITGA6, SUN2, FGF10, INCENP, MLPH, and KLK3 genes.

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.

Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes

BACKGROUND AND HYPOTHESIS

The gut-brain axis plays important roles in both gastrointestinal diseases (GI diseases) and schizophrenia (SCZ). Moreover, both GI diseases and SCZ exhibit notable abnormalities in brain subcortical volumes. However, the genetic mechanisms underlying the comorbidity of these diseases and the shared alterations in brain subcortical volumes remain unclear.

STUDY DESIGN

Using the genome-wide association studies data of SCZ, 14 brain subcortical volumes, and 8 GI diseases, the global polygenic overlap and local genetic correlations were identified, as well as the shared genetic variants among those phenotypes. Furthermore, we conducted multi-trait colocalization analyses to bolster our findings. Functional annotations, cell-type enrichment, and protein-protein interaction (PPI) analyses were carried out to reveal the critical etiology and pathology mechanisms.

STUDY RESULTS

The global polygenic overlap and local genetic correlations informed the close relationships between SCZ and both GI diseases and brain subcortical volumes. Moreover, 84 unique lead-shared variants were identified. The associated genes were linked to vital biological processes within the immune system. Additionally, significant correlations were observed with key immune cells and the PPI analysis identified several histone-associated hub genes. These findings highlighted the pivotal roles played by the immune system for both SCZ and GI diseases, along with the shared alterations in brain subcortical volumes.

CONCLUSIONS

These findings revealed the shared genetic architecture contributing to SCZ and GI diseases, as well as their shared alterations in brain subcortical volumes. These insights have substantial implications for the concurrent development of intervention and therapy targets for these diseases.

Bayesian meta-analysis of penetrance for cancer risk

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene.

GCPBayes pipeline: a tool for exploring pleiotropy at the gene level

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Gene variations related to the hepatocellular carcinoma: Results from a field synopsis and Bayesian revaluation

Hepatocellular carcinoma (HCC) is considered as the second cause of cancer-related deaths worldwide. Genetic variations are associated with HCC risk, an issue that has been the subject of several meta-analyses. However, meta-analyses have an important limitation on the likelihood of false positive data. Henceforth, this study aimed to assess the level of noteworthiness in the meta-analyses by means of a Bayesian approach. A systematic search was performed for meta-analyses with associations between gene polymorphisms and HCC. The calculations for the False-Positive Rate Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) were performed to assess the noteworthiness with a statistical power of 1.2 and 1.5 of Odds Ratio at a prior probability of 10^-3 and 10^-5. The quality of studies was evaluated by the Venice criteria. As additional analyses, the gene-gene and protein-protein networks were designed for these genes and products. As results, we found 33 meta-analytic studies on 45 polymorphisms occurring in 35 genes. A total of 1,280 values for FPRP and BFDP were obtained. Seventy-five for FPRP (5.86%) and 95 for BFDP (14.79%) were noteworthy. In conclusion, the polymorphisms in CCND1, CTLA4, EGF, IL6, IL12A, KIF1B, MDM2, MICA, miR-499, MTHFR, PNPLA3, STAT4, TM6SF2, and XPD genes were considered as noteworthy biomarkers for HCC risk.

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

Immune-related conditions and cancer-specific mortality among older adults with cancer in the United States

Immunity may play a role in preventing cancer progression. We studied associations of immune-related conditions with cancer-specific mortality among older adults in the United States. We evaluated 1 229 443 patients diagnosed with 20 common cancer types (age 67-99, years 1993-2013) using Surveillance Epidemiology and End Results-Medicare data. With Medicare claims, we ascertained immune-related medical conditions diagnosed before cancer diagnosis (4 immunosuppressive conditions [n = 3380 affected cases], 32 autoimmune conditions [n = 155 766], 3 allergic conditions [n = 101 366]). For each cancer site, we estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for cancer-specific mortality associated with each condition, applying a Bonferroni cutoff for significance (P < 5.1 × 10-5 ). Bayesian metaanalysis methods were used to detect patterns across groups of conditions and cancers. We observed 21 associations with cancer-specific mortality at the Bonferroni threshold. Increased cancer-specific mortality was observed with rheumatoid arthritis for patients with melanoma (aHR 1.51, 95% CI 1.31-1.75) and breast cancer (1.24, 1.15-1.33)), and with hemolytic anemia for bladder cancer (2.54, 1.68-3.82). Significant inverse associations with cancer-specific mortality were observed for allergic rhinitis (range of aHRs: 0.84-0.94) and asthma (0.83-0.95) for cancers of the lung, breast, and prostate. Cancer-specific mortality was nominally elevated in patients with immunosuppressive conditions for eight cancer types (aHR range: 1.27-2.36; P-value range: 7.5 × 10-5 to 3.1 × 10-2 ) and was strongly associated with grouped immunosuppressive conditions using Bayesian metaanalyses methods. For older patients with several cancer types, certain immunosuppressive and autoimmune conditions were associated with increased cancer-specific mortality. In contrast, inverse associations with allergic conditions may reflect enhanced immune control of cancer.

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis

The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Benchmarking of univariate pleiotropy detection methods applied to epilepsy

Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, cFDR, CPBayes, and PLACO, by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published ILAE consortium dataset on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression and regulatory function analysis as well as Bayesian co-localization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies. This article is protected by copyright. All rights reserved.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

The effects of modified Buzhong Yiqi decoction combined with Gangtai ointment on the wound healing and anal function in circumferential mixed hemorrhoid patients

OBJECTIVE

To investigate the effects of modified Buzhong Yiqi decoction combined with Gangtai ointment on the wound healing and anal function of circumferential mixed hemorrhoid patients.

METHODS

Patients (n=120) with circumferential mixed hemorrhoids were recruited as the research cohort. All the patients underwent surgical treatment and were randomly divided into a control group and a research group. The control group was administered chitosan hydrogels for wound healing, once a day. The research group was administered modified Buzhong Yiqi decoction (1 dose a day, orally) combined with Kangtai ointment for external application (twice a day, for two consecutive weeks). We compared the two groups' effective rates, their pain indexes, their perianal edema scores, their quality of life scores, their wound healing times, their pain resolution times, their anal functions, their wound exudate scores, and their adverse reactions.

RESULTS

Compared with the control group, the research group had a higher effective rate (P<0.01), a lower pain index, and a lower perianal edema score (both P<0.001), a higher quality of life score (P<0.001), a shorter wound healing time, a shorter pain resolution time, and fewer adverse reactions than the control group (both P<0.001). The lengths of the anal canals in the research group were shorter than they were in the control group (P<0.01), and the resting pressure, maximum diastolic blood pressure, and maximum systolic blood pressure were higher than they were in the control group (all P<0.001). The wound exudate scores at 7 and 14 days after the treatment in the research group were lower than they were in the control group (all P<0.001). There were fewer adverse reactions in the research group than there were in the control group (P<0.05).

CONCLUSION

Modified Buzhong Yiqi decoction combined with Gangtai ointment for patients with circumferential mixed hemorrhoids has good short-term efficacy. It helps to promote wound healing, improves anal function, and does not increase the incidence of adverse reactions. It is worthy of promotion and application.

Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies

Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism (SNP). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda) and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this trade-off with multiple phenotype analyses of six quantitative electrocardiogram traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

Patterns of Human Leukocyte Antigen Class I and Class II Associations and Cancer

Human leukocyte antigen (HLA) gene variation is associated with risk of cancers, particularly those with infectious etiology or hematopoietic origin, given its role in immune presentation. Previous studies focused primarily on HLA allele/haplotype-specific associations. To answer whether associations are driven by HLA class I (essential for T-cell cytotoxicity) or class II (important for T-cell helper responses) genes, we analyzed GWAS from 24 case-control studies and consortia comprising 27 cancers (totaling >71,000 individuals). Associations for most cancers with infectious etiology or of hematopoietic origin were driven by multiple HLA regions, suggesting that both cytotoxic and helper T-cell responses are important. Notable exceptions were observed for nasopharyngeal carcinoma, an EBV-associated cancer, and CLL/SLL forms of non-Hodgkin lymphomas; these cancers were associated with HLA class I region only and HLA class II region only, implying the importance of cytotoxic T-cell responses for the former and CD4+ T-cell helper responses for the latter. Our findings suggest that increased understanding of the pattern of HLA associations for individual cancers could lead to better insights into specific mechanisms involved in cancer pathogenesis. SIGNIFICANCE: GWAS of >71,000 individuals across 27 cancer types suggest that patterns of HLA Class I and Class II associations may provide etiologic insights for cancer.

Clustering suicidal phenotypes and genetic associations with brain-derived neurotrophic factor in patients with substance use disorders

Suicide attempts (SA), especially recurrent SA or serious SA, are common in substance use disorders (SUD). However, the genetic component of SA in SUD samples remains unclear. Brain-derived neurotrophic factor (BDNF) alleles and levels have been repeatedly involved in stress-related psychopathology. This investigation uses a within-cases study of BDNF and associated factors in three suicidal phenotypes ('any', 'recurrent', and 'serious') of outpatients seeking treatment for opiate and/or cocaine use disorder. Phenotypic characterization was ascertained using a semi-structured interview. After thorough quality control, 98 SNPs of BDNF and associated factors (the BDNF pathway) were extracted from whole-genome data, leaving 411 patients of Caucasian ancestry, who had reliable data regarding their SA history. Binary and multinomial regression with the three suicidal phenotypes were further performed to adjust for possible confounders, along with hierarchical clustering and compared to controls (N = 2504). Bayesian analyses were conducted to detect pleiotropy across the suicidal phenotypes. Among 154 (37%) ever suicide attempters, 104 (68%) reported at least one serious SA and 96 (57%) two SA or more. The median number of non-tobacco SUDs was three. The BDNF gene remained associated with lifetime SA in SNP-based (rs7934165, rs10835210) and gene-based tests within the clinical sample. rs10835210 clustered with serious SA. Bayesian analysis identified genetic correlation between 'any' and 'serious' SA regarding rs7934165. Despite limitations, 'serious' SA was shown to share both clinical and genetic risk factors of SA-not otherwise specified, suggesting a shared BDNF-related pathophysiology of SA in this population with multiple SUDs.

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

MOTIVATION

While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes.

RESULTS

Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches.

AVAILABILITY

We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

BACKGROUND

Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging.

METHODS

Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension "basis" that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging.

RESULTS

We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations.

CONCLUSIONS

Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

Polymorphisms in the interleukin genes and chronic periodontitis: A field synopsis and revaluation by Bayesian approaches

Periodontitis is a high prevalent disease into the clinical dentistry. Genetic variations in interleukins (IL) genes were associated with chronic periodontitis (CP) and were focus of several meta-analyses. This study aimed to assess the noteworthiness in the meta-analyses by means of a Bayesian approach to determinate possible false report associations. A systematic search was performed for meta-analyses with associations between gene polymorphisms in interleukins and CP. The calculations for the False-Positive Rate Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) were performed to assess the noteworthiness with a statistical power of 1.2 and 1.5 of Odds Ratio at a prior probability of 10^-3 and 10^-6. As results, eight meta-analyses approaching the IL1A/rs1800587, IL1B/rs1143634, IL1RN/rs2234663, IL4/rs2243250, IL6/rs1800795/rs1800796, IL17A/rs2275913 and IL18/rs1946518/rs187238 polymorphisms have been identified. Twenty-two from 270 calculations (8.15%) were noteworthy. Herein, we have identified the IL1A and IL1B polymorphisms as noteworthy biomarkers for CP susceptibility.

Collagenous Colitis Is Associated With HLA Signature and Shares Genetic Risks With Other Immune-Mediated Diseases

BACKGROUND & AIMS

Collagenous colitis (CC) is an inflammatory bowel disorder with unknown etiopathogenesis involving HLA-related immune-mediated responses and environmental and genetic risk factors. We carried out an array-based genetic association study in a cohort of patients with CC and investigated the common genetic basis between CC and Crohn's disease (CD), ulcerative colitis (UC), and celiac disease.

METHODS

DNA from 804 CC formalin-fixed, paraffin-embedded tissue samples was genotyped with Illumina Immunochip. Matching genotype data on control samples and CD, UC, and celiac disease cases were provided by the respective consortia. A discovery association study followed by meta-analysis with an independent cohort, polygenic risk score calculation, and cross-phenotype analyses were performed. Enrichment of regulatory expression quantitative trait loci among the CC variants was assessed in hemopoietic and intestinal cells.

RESULTS

Three HLA alleles (HLA-B∗08:01, HLA-DRB1∗03:01, and HLA-DQB1∗02:01), related to the ancestral haplotype 8.1, were significantly associated with increased CC risk. We also identified an independent protective effect of HLA-DRB1∗04:01 on CC risk. Polygenic risk score quantifying the risk across multiple susceptibility loci was strongly associated with CC risk. An enrichment of expression quantitative trait loci was detected among the CC-susceptibility variants in various cell types. The cross-phenotype analysis identified a complex pattern of polygenic pleiotropy between CC and other immune-mediated diseases.

CONCLUSIONS

In this largest genetic study of CC to date with histologically confirmed diagnosis, we strongly implicated the HLA locus and proposed potential non-HLA mechanisms in disease pathogenesis. We also detected a shared genetic risk between CC, celiac disease, CD, and UC, which supports clinical observations of comorbidity.

Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer's and cancer

INTRODUCTION

We compared genetic variants between Alzheimer's disease (AD) and two age-related cancers-breast and prostate -to identify single-nucleotide polymorphisms (SNPs) that are associated with inverse comorbidity of AD and cancer.

METHODS

Bayesian multinomial regression was used to compare sex-stratified cases (AD and cancer) against controls in a two-stage study. A ±500 KB region around each replicated hit was imputed and analyzed after merging individuals from the two stages. The microRNAs (miRNAs) that target the genes involving these SNPs were analyzed for miRNA family enrichment.

RESULTS

We identified 137 variants with inverse odds ratios for AD and cancer located on chromosomes 19, 4, and 5. The mapped miRNAs within the network were enriched for miR-17 and miR-515 families.

DISCUSSION

The identified SNPs were rs4298154 (intergenic), within TOMM40/APOE/APOC1, MARK4, CLPTM1, and near the VDAC1/FSTL4 locus. The miRNAs identified in our network have been previously reported to have inverse expression in AD and cancer.

Genetic correlations of polygenic disease traits: from theory to practice

The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.

Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test

The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10^-6 . Further simulations under different models of association show that Meta-MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.

Subset testing and analysis of multiple phenotypes

Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region-based meta-analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between-study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated. Extensive simulations show that STAMP is more powerful than standard approaches for meta-analyses when the proportion of truly associated outcomes is between 25% and 50%. For other settings, the power of STAMP is similar to that of existing methods. We illustrate our method on two examples, the association of a region on chromosome 9p21 with the risk of 14 cancers, and the associations of expression of quantitative trait loci from two genetic regions with their cis-single-nucleotide polymorphisms measured in 17 tissue types using data from The Cancer Genome Atlas.

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Background

Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation.

Methods

We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107).

Results

Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10⁻⁹) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10⁻¹⁰) and variants in IRF4 (p=2.8×10⁻⁵⁷), SLC45A2 (p=2.2×10⁻¹³⁰), HERC2 (p=2.8×10⁻¹⁷⁶), OCA2 (p=2.4×10⁻¹²¹) and MC1R (p=7.7×10⁻²²) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10⁻⁹) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9.

Conclusion

Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.

Multi-polygenic score approach to trait prediction

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.