Genome-wide Association Studies Datasets Research Articles

HiGwas: how to compute longitudinal GWAS data in population designs.

Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package. https://github.com/wzhy2000/higwas. rwu@phs.psu.edu. Supplementary data are available at Bioinformatics online.

Sex hormone-binding globulin and arthritis: a Mendelian randomization study

BackgroundSex hormone-binding globulin (SHBG) has been reported to be a risk factor associated with the development of arthritis by previous observational studies more so of three common forms of arthritis: osteoarthritis (OA), rheumatoid arthritis (RA), and ankylosing spondylitis (AS). This study aimed to determine whether the concentrations of circulating SHBG are causally associated with the risk of OA, RA, and AS.MethodsThe two-sample Mendelian randomization (MR) approach was used for this study. The inverse-variance-weighted (IVW) method was used for the main analysis. Single-nucleotide polymorphisms (SNPs) associated with SHBG were selected from a large genome-wide association study (GWAS) of 28,837 European individuals. The summary statistics for OA, RA, and AS were extracted from the UK Biobank Resource (n = 361,141) and a GWAS dataset (n = 455,221).ResultsPositive causal associations were found between circulating SHBG concentrations and OA (effect = 1.086; 95% CI, 1.009 to 1.168; P = 0.027) and RA (effect = 1.003; 95% CI, 1.000 to 1.007; P = 0.047) in overall analyses. However, there was no evidence of association between SHBG levels and AS. Based on the stratification of skeletal sites, SHBG levels were found to be significantly associated with hip OA (effect = 1.423; 95% CI, 1.219 to 1.660; P = 7.753 × 10−6). However, this was not the case with knee OA.ConclusionsThere were positive causal effects of circulating SHBG on the development of OA and RA. Moreover, there was a site-specific association between SHBG and hip OA. Evidently, measurement of SHBG in serum could be valuable in the clinical assessment of arthritis especially in early screening and prevention of OA and RA. However, the mechanisms by which SHBG plays causal roles in the development of arthritis require further investigations.

T173. POLYGENETIC RISK SCORES FOR MAJOR PSYCHIATRIC DISORDERS AMONG SCHIZOPHRENIA PATIENTS, THEIR FIRST-DEGREE RELATIVES AND HEALTHY SUBJECTS

BackgroundThe genetic etiology of schizophrenia (SCZ) overlaps with that of other major psychiatric disorders in samples of European ancestry. On the other hand, these major psychiatric disorders are distinct diagnoses that have disorder-specific genetic factors. Recently, the bipolar disorder (BIP) and SCZ Working Group of the PGC identified two genome-wide significant loci differentiating the two disorders in individuals of European descent. We hypothesized that genetic variants differentiating SCZ from BIP in Europeans as well as genetic variants related to psychiatric disorders in Europeans would overlap with genetic risk variants in Japanese SCZ patients and unaffected first-degree relatives (FRs), i.e., individuals at high risk of developing SCZ. The present study investigated transethnic polygenetic features shared between Japanese SCZ or their unaffected FRs and European patients with major psychiatric disorders by conducting polygenic risk score (PRS) analyses.MethodsTo calculate PRSs for five psychiatric disorders [SCZ, BIP, major depressive disorder (MDD), autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD)] and PRSs differentiating SCZ from BIP, we utilized large-scale European genome-wide association study (GWAS) datasets as discovery samples. PRSs derived from these GWASs were calculated for 335 Japanese target subjects [131 SCZ patients, 57 of their unaffected FRs and 147 healthy controls (HCs)]. We took these PRSs based on GWASs of European psychiatric disorders (SCZ, BIP, SCZ vs BIP, MDD, ASD and ADHD) and investigated their effect on risk in Japanese SCZ patients [(i) SCZ vs FRs vs HCs, (ii) SCZ vs HCs and (iii) SCZ vs FRs] or unaffected FRs [(iv) FRs vs HCs] by PRS analyses.ResultsThe PRSs obtained from European SCZ samples were significantly higher in Japanese patients with SCZ than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤1.0: adjusted R2=0.028, p=1.30×10–3; (ii) SCZ vs HCs, a maximum at PT≤1.0: Nagelkerke’s R2=0.049, p=1.66×10–3]. In addition, the PRSs related to European BIP were nominally higher in Japanese patients with SCZ than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤0.5: adjusted R2=0.016, p=0.012; (ii) SCZ vs HCs, a maximum at PT≤0.5: Nagelkerke’s R2=0.029, p=0.015]. Furthermore, PRSs differentiating SCZ patients from European BIP patients were marginally higher in Japanese SCZ patients than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤0.05: adjusted R2=0.010, p=0.043; (ii) SCZ vs HCs, a maximum at PT≤0.05: Nagelkerke’s R2=0.020, p=0.046]. Interestingly, the PRSs obtained from European ASD were marginally lower in Japanese FRs compared with HCs [(iv) FRs vs HCs, a maximum at PT≤0.01: Nagelkerke’s R2=0.045, p=0.013] and patients with SCZ [(iii) SCZ vs FRs, a maximum at PT≤0.2: Nagelkerke’s R2=0.023, p=0.084]. As childhood-onset patients with SCZ have showed higher PRSs for both SCZ and ASD than their unaffected siblings, we further investigated the correlation between age at onset and PRSs for both SCZ and ASD in our SCZ samples. Lower age at onset of SCZ was significantly associated with higher PRSs for ASD (PT≤0.05: beta=-0.20, p=7.13×10–3) but not PRSs for SCZ (p>0.05).DiscussionThese findings suggest that polygenic factors related to European SCZ and BIP and the polygenic components differentiating SCZ from BIP can transethnically contribute to SCZ risk in Japanese people. Furthermore, we suggest that reduced levels of an ASD-related genetic factor in unaffected FRs may help protect against SCZ development.

Analysis of GWAS-Derived Schizophrenia Genes for Links to Ischemia-Hypoxia Response of the Brain.

Obstetric complications (OCs) can induce major adverse conditions for early brain development and predispose to mental disorders, including schizophrenia (SCZ). We previously hypothesized that SCZ candidate genes respond to ischemia-hypoxia as part of OCs which impacts neurodevelopment. We here tested for an overlap between SCZ genes from genome-wide association study (GWAS) (n=458 genes from 145 loci of the most recent GWAS dataset in SCZ) and gene sets for ischemia-hypoxia response. Subsets of SCZ genes were related to (a) mutation-intolerant genes (LoF database), (b) role in monogenic disorders of the nervous system (OMIM, manual annotations), and (c) synaptic function (SynGO). Ischemia-hypoxia response genes of the brain (IHR genes, n=1,629), a gene set from RNAseq in focal brain ischemia (BH, n=2,449) and genes from HypoxiaDB (HDB, n=2,289) were overlapped with the subset of SCZ genes and tested for enrichment with Chi-square tests (p < 0.017). The SCZ GWAS dataset was enriched for LoF (n=112; p=0.0001), and the LoF subset was enriched for IHR genes (n=25; p=0.0002), BH genes (n=35; p=0.0001), and HDB genes (n=23; p=0.0005). N=96 genes of the SCZ GWAS dataset (21%) could be linked to a monogenic disorder of the nervous system whereby IHR genes (n=19, p=0.008) and BH genes (n=23; p=0.002) were found enriched. N=46 synaptic genes were found in the SCZ GWAS gene set (p=0.0095) whereby enrichments for IHR genes (n=20; p=0.0001) and BH genes (n=13; p=0.0064) were found. In parallel, detailed annotations of SCZ genes for a role of the hypoxia-inducible factors (HIFs) identified n=33 genes of high interest. Genes from SCZ GWAS were enriched for mutation-intolerant genes which in turn were strongly enriched for three sets of genes for the ischemia-hypoxia response that may be invoked by OCs. A subset of one fifth of SCZ genes has established roles in monogenic disorders of the nervous system which was enriched for two gene sets related to ischemia-hypoxia. SCZ genes related to synaptic functions were also related to ischemia-hypoxia. Variants of SCZ genes interacting with ischemia-hypoxia provide a specific starting point for functional and genomic studies related to OCs.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

BackgroundHeterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.ResultsHere, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.ConclusionPheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Genetic variants of the peroxisome proliferator-activated receptor (PPAR) signaling pathway genes and risk of pancreatic cancer.

Because the peroxisome proliferator-activated receptor (PPAR) signaling pathway is involved in development and progression of pancreatic cancer, we investigated associations between genetic variants of the PPAR pathway genes and pancreatic cancer risk by using three published genome-wide association study datasets including 8477 cases and 6946 controls of European ancestry. Expression quantitative trait loci (eQTL) analysis was also performed for correlations between genotypes of the identified genetic variants and messenger RNA (mRNA) expression levels of their genes by using available databases of the 1000 Genomes, TCGA, and GTEx projects. In the single-locus logistic regression analysis, we identified 1141 out of 17 532 significant single-nucleotide polymorphisms (SNPs) in 112 PPAR pathway genes. Further multivariate logistic regression analysis identified three independent, potentially functional loci (rs12947620 in MED1, rs11079651 in PRKCA, and rs34367566 in PRKCB) for pancreatic cancer risk (odds ratio [OR] = 1.11, 95% confidence interval [CI], [1.06-1.17], P = 5.46 × 10-5 ; OR = 1.10, 95% CI, [1.04-1.15], P = 1.99 × 10-4 ; and OR = 1.09, 95% CI, [1.04-1.14], P = 3.16 × 10-4 , respectively) among 65 SNPs that passed multiple comparison correction by false discovery rate (< 0.2). When risk genotypes of these three SNPs were combined, carriers with 2 to 3 unfavorable genotypes (NUGs) had a higher risk of pancreatic cancer than those with 0 to 1 NUGs. The eQTL analysis showed that rs34367566 A>AG was associated with decreased expression levels of PRKCB mRNA in 373 lymphoblastoid cell lines. Our findings indicate that genetic variants of the PPAR pathway genes, particularly MED1, PRKCA, and PRKCB, may contribute to susceptibility to pancreatic cancer.

Integrating Genome-Wide Association Studies and Gene Expression Profiles With Chemical-Genes Interaction Networks to Identify Chemicals Associated With Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer and has the second highest mortality rate in global cancer. Exploring the associations between chemicals and CRC has great significance in prophylaxis and therapy of tumor diseases. This study aims to explore the relationships between CRC and environmental chemicals on genetic basis by bioinformatics analysis. The genome-wide association study (GWAS) datasets for CRC were obtained from the UK Biobank. The GWAS data for colon cancer (category C18) includes 2,581 individuals and 449,683 controls, while that of rectal cancer (category C20) includes 1,244 individuals and 451,020 controls. In addition, we derived CRC gene expression datasets from the NCBI-GEO (GSE106582). The chemicals related gene sets were acquired from the comparative toxicogenomics database (CTD). Transcriptome-wide association study (TWAS) analysis was applied to CRC GWAS summary data and calculated the expression association testing statistics by FUSION software. We performed chemicals related gene set enrichment analysis (GSEA) by integrating GWAS summary data, mRNA expression profiles of CRC and the CTD chemical-gene interaction networks to identify relationships between chemicals and genes of CRC. We observed several significant correlations between chemicals and CRC. Meanwhile, we also detected 5 common chemicals between colon and rectal cancer, including methylnitronitrosoguanidine, isoniazid, PD 0325901, sulindac sulfide, and importazole. Our study performed TWAS and GSEA analysis, linked prior knowledge to newly generated data and thereby helped identifying chemicals related to tumor genes, which provides new clues for revealing the associations between environmental chemicals and cancer.

Identifying 5 Common Psychiatric Disorders Associated Chemicals Through Integrative Analysis of Genome-Wide Association Study and Chemical-Gene Interaction Datasets.

Psychiatric disorders are a group of complex psychological syndromes whose etiology remains unknown. Previous study suggested that various chemicals contributed to the development of psychiatric diseases through affecting gene expression. This study aims to systematically explore the potential relationships between 5 major psychiatric disorders and more than 11 000 chemicals. The genome-wide association studies (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depression disorder (MDD), and schizophrenia (SCZ) were driven from the Psychiatric GWAS Consortium and iPSYCH website. The chemicals related gene sets were obtained from the comparative toxicogenomics database (CTD). First, transcriptome-wide association studies (TWAS) were performed by FUSION to calculate the expression association testing statistics utilizing GWAS summary statistics of the 5 common psychiatric disorders. Chemical-related gene set enrichment analysis (GSEA) was then conducted to explore the relationships between chemicals and each of the psychiatric diseases. We observed several significant correlations between chemicals and each of the psychiatric disorders. We also detected common chemicals between every 4 of the 5 major psychiatric disorders, such as androgen antagonists for ADHD (P value = .0098), ASD (P value = .0330), BD (P value = .0238), and SCZ (P value = .0062), and imipramine for ADHD (P value = .0054), ASD (P value = .0386), MDD (P value = .0438), and SCZ (P value = .0008). Our study results provide new clues for revealing the roles of environmental chemicals in the development of psychiatric disorders.

Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.

Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary data are available at Bioinformatics online.

Dense module searching for gene networks associated with multiple sclerosis

BackgroundMultiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility.ResultsApproximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as “regulation of glial cell differentiation” (adjusted p-value = 2.58 × 10− 3), “T-cell costimulation” (adjusted p-value = 2.11 × 10− 6) and “virus receptor activity” (adjusted p-value = 1.67 × 10− 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA,ConclusionsOur dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

Characterisation of genetic regulatory effects for osteoporosis risk variants in human osteoclasts

BackgroundOsteoporosis is a complex disease with a strong genetic contribution. A recently published genome-wide association study (GWAS) for estimated bone mineral density (eBMD) identified 1103 independent genome-wide significant association signals. Most of these variants are non-coding, suggesting that regulatory effects may drive many of the associations. To identify genes with a role in osteoporosis, we integrate the eBMD GWAS association results with those from our previous osteoclast expression quantitative trait locus (eQTL) dataset.ResultsWe identify sixty-nine significant cis-eQTL effects for eBMD GWAS variants after correction for multiple testing. We detect co-localisation of eBMD GWAS and osteoclast eQTL association signals for 21 of the 69 loci, implicating a number of genes including CCR5, ZBTB38, CPE, GNA12, RIPK3, IQGAP1 and FLCN. Summary-data-based Mendelian Randomisation analysis of the eBMD GWAS and osteoclast eQTL datasets identifies significant associations for 53 genes, with TULP4 presenting as a strong candidate for pleiotropic effects on eBMD and gene expression in osteoclasts. By performing analysis using the GARFIELD software, we demonstrate significant enrichment of osteoporosis risk variants among high-confidence osteoclast eQTL across multiple GWAS P value thresholds. Mice lacking one of the genes of interest, the apoptosis/necroptosis gene RIPK3, show disturbed bone micro-architecture and increased osteoclast number, highlighting a new biological pathway relevant to osteoporosis.ConclusionWe utilise a unique osteoclast eQTL dataset to identify a number of potential effector genes for osteoporosis risk variants, which will help focus functional studies in this area.

Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism

Identifying the causal gene(s) that connects genetic variation to a phenotype is a challenging problem in genome-wide association studies (GWASs). Here, we develop a systematic approach that integrates mouse liver co-expression networks with human lipid GWAS data to identify regulators of cholesterol and lipid metabolism. Through our approach, we identified 48 genes showing replication in mice and associated with plasma lipid traits in humans and six genes on the X chromosome. Among these 54 genes, 25 have no previously identified role in lipid metabolism. Based on functional studies and integration with additional human lipid GWAS datasets, we pinpoint Sestrin1 as a causal gene associated with plasma cholesterol levels in humans. Our validation studies demonstrate that Sestrin1 influences plasma cholesterol in multiple mouse models and regulates cholesterol biosynthesis. Our results highlight the power of combining mouse and human datasets for prioritization of human lipid GWAS loci and discovery of lipid genes.

Integrated GWAS and mRNA Microarray Analysis Identified IFNG and CD40L as the Central Upstream Regulators in Primary Biliary Cholangitis.

Genome‐wide association studies (GWASs) in European and East Asian populations have identified more than 40 disease‐susceptibility genes in primary biliary cholangitis (PBC). The aim of this study is to computationally identify disease pathways, upstream regulators, and therapeutic targets in PBC through integrated GWAS and messenger RNA (mRNA) microarray analysis. Disease pathways and upstream regulators were analyzed with ingenuity pathway analysis in data set 1 for GWASs (1,920 patients with PBC and 1,770 controls), which included 261 annotated genes derived from 6,760 single‐nucleotide polymorphisms (P < 0.00001), and data set 2 for mRNA microarray analysis of liver biopsy specimens (36 patients with PBC and 5 normal controls), which included 1,574 genes with fold change >2 versus controls (P < 0.05). Hierarchical cluster analysis and categorization of cell type–specific genes were performed for data set 2. There were 27 genes, 10 pathways, and 149 upstream regulators that overlapped between data sets 1 and 2. All 10 pathways were immune‐related. The most significant common upstream regulators associated with PBC disease susceptibility identified were interferon‐gamma (IFNG) and CD40 ligand (CD40L). Hierarchical cluster analysis of data set 2 revealed two distinct groups of patients with PBC by disease activity. The most significant upstream regulators associated with disease activity were IFNG and CD40L. Several molecules expressed in B cells, T cells, Kupffer cells, and natural killer–like cells were identified as potential therapeutic targets in PBC with reference to a recently reported list of cell type–specific gene expression in the liver. Conclusion: Our integrated analysis using GWAS and mRNA microarray data sets predicted that IFNG and CD40L are the central upstream regulators in both disease susceptibility and activity of PBC and identified potential downstream therapeutic targets.

Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity.

Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e−05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS (Pcohort1 = 2.44e−08; Pcohort2 = 0.00205) and a significant negative correlation with age of SLE onset (Pcohort1 = 1.76e−12; Pcohort2 = 0.00384). We found that the GRS performed better in the prediction of renal disease in the ‘later onset’ compared with the ‘earlier onset’ group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.

Alveolar Macrophage Chromatin Is Modified to Orchestrate Host Response to Mycobacterium bovis Infection.

Bovine tuberculosis is caused by infection with Mycobacterium bovis, which can also cause disease in a range of other mammals, including humans. Alveolar macrophages are the key immune effector cells that first encounter M. bovis and how the macrophage epigenome responds to mycobacterial pathogens is currently not well understood. Here, we have used chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq to examine the effect of M. bovis infection on the bovine alveolar macrophage (bAM) epigenome. We show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin from M. bovis-infected bAM compared to control non-infected bAM; this was particularly evident at the transcriptional start sites of genes that determine programmed macrophage responses to mycobacterial infection (e.g. M1/M2 macrophage polarisation). This pattern was also supported by the distribution of RNA Polymerase II (Pol II) ChIP-seq results, which highlighted significantly increased transcriptional activity at genes demarcated by permissive chromatin. Identification of these genes enabled integration of high-density genome-wide association study (GWAS) data, which revealed genomic regions associated with resilience to infection with M. bovis in cattle. Through integration of these data, we show that bAM transcriptional reprogramming occurs through differential distribution of H3K4me3 and Pol II at key immune genes. Furthermore, this subset of genes can be used to prioritise genomic variants from a relevant GWAS data set.

An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder

BackgroundBipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown.MethodsWe conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Firstly, GWAS dataset 1 of BD (including 20,352 cases and 31,358 controls) and GWAS dataset 2 of BD (including 7481 BD patients and 9250 controls) were integrated with rSNP annotation database to obtain BD associated SNP regulatory elements and SNP regulatory element-target gene (E–G) pairs, respectively. Secondly, a comparative analysis of the two datasets results was conducted to identify the common rSNPs and also their target genes. Then, gene sets enrichment analysis (FUMA GWAS) and HumanNet-XC analysis were conducted to explore the functional relevance of identified target genes with BD.ResultsAfter the integrative analysis, we identified 52 TFBRs target genes, 44 TADs target genes, 55 CIRs target genes and 21 lncRNAs target genes for BD, such as ITIH4 (Pdataset1 = 6.68 × 10−8, Pdataset2 = 6.64 × 10−7), ITIH3 (Pdataset1 = 1.09 × 10−8, Pdataset2 = 2.00 × 10−7), SYNE1 (Pdataset1 = 1.80 × 10−6, Pdataset2 = 4.33 × 10−9) and OPRM1 (Pdataset1 = 1.80 × 10−6, Pdataset2 = 4.33 × 10−9).ConclusionWe conducted a large-scale integrative analysis of GWAS and 6 common rSNP information datasets to explore the potential roles of rSNPs in the genetic mechanism of BD. We identified multiple candidate genes for BD, supporting the importance of rSNP in the development of BD.

Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis.

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10−6) and 1,044 pleotropic genes (P < 4.34 × 10−6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.

Meta-Analysis of Genome-Wide Association and Gene Expression Studies Implicates Donor T Cell Function and Cytokine Pathways in Acute GvHD.

Graft-vs.-host disease (GvHD) is a major complication after allogeneic hematopoietic stem cell transplantation that causes mortality and severe morbidity. Genetic disparities in human leukocyte antigens between the recipient and donor are known contributors to the risk of the disease. However, the overall impact of genetic component is complex, and consistent findings across different populations and studies remain sparse. To gain a comprehensive understanding of the genes responsible for GvHD, we combined genome-wide association studies (GWAS) from two distinct populations with previously published gene expression studies on GvHD in a single gene-level meta-analysis. We hypothesized that genes driving GvHD should be associated in both data modalities and therefore could be detected more readily through their combined effects in the integrated analysis rather than in separate analyses. The meta-analysis yielded a total of 51 acute GvHD-associated genes (false detection rate [FDR] <0.1). In support of our hypothesis, this number was significantly higher than that in a permutation meta-analysis involving the whole data set, as well as in separate meta-analyses on the GWAS and gene expression data sets. The genes indicated by the meta-analysis were significantly enriched in 277 Gene Ontology terms (FDR < 0.05), such as T cell function and cytokine-mediated signaling pathways, and the results highlighted several established immune mediators, such as interleukins and JAK-STAT signaling, and presented TRAF6 and TERT as potential effector candidates. Altogether, the results support the chosen methodological approach, implicate a role of gene-level variation in donors' key immunological regulators predisposing patients to acute GVHD, and present potential targets for therapeutic intervention.

Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms.

Stroke ranks the second leading cause of death among people over the age of 60 in the world. Stroke is widely regarded as a complex disease that is affected by genetic and environmental factors. Evidence from twin and family studies suggests that genetic factors may play an important role in its pathogenesis. Therefore, research on the genetic association of susceptibility genes can help understand the mechanism of stroke. Genome-wide association study (GWAS) has found a large number of stroke-related loci, but their mechanism is unknown. In order to explore the function of single-nucleotide polymorphisms (SNPs) at the molecular level, in this paper, we integrated 8 GWAS datasets with brain expression quantitative trait loci (eQTL) dataset to identify SNPs and genes which are related to four types of stroke (ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke). Thirty-eight SNPs which can affect 14 genes expression are found to be associated with stroke. Among these 14 genes, 10 genes expression are associated with ischemic stroke, one gene for large artery stroke, six genes for cardioembolic stroke and eight genes for small vessel stroke. To explore the effects of environmental factors on stroke, we identified methylation susceptibility loci associated with stroke using methylation quantitative trait loci (MQTL). Thirty-one of these 38 SNPs are at greater risk of methylation and can significantly change gene expression level. Overall, the genetic pathogenesis of stroke is explored from locus to gene, gene to gene expression and gene expression to phenotype.

Circulating Vitamin D Levels and Alzheimer's Disease: A Mendelian Randomization Study in the IGAP and UK Biobank.

Observational studies strongly supported the association of low levels of circulating 25-hydroxyvitamin D (25OHD) and cognitive impairment or dementia in aging populations. However, randomized controlled trials have not shown clear evidence that vitamin D supplementation could improve cognitive outcomes. In fact, some studies reported the association between vitamin D and cognitive impairment based on individuals aged 60 years and over. However, it is still unclear that whether vitamin D levels are causally associated with Alzheimer's disease (AD) risk in individuals aged 60 years and over. Here, we performed a Mendelian randomization (MR) study to investigate the causal association between vitamin D levels and AD using a large-scale vitamin D genome-wide association study (GWAS) dataset and two large-scale AD GWAS datasets from the IGAP and UK Biobank with individuals aged 60 years and over. Our results showed that genetically increased 25OHD levels were significantly associated with reduced AD risk in individuals aged 60 years and over. Hence, our findings in combination with previous literature indicate that maintaining adequate vitamin D status in older people especially aged 60 years and over, may contribute to slow down cognitive decline and forestall AD. Long-term randomized controlled trials are required to test whether vitamin D supplementation may prevent AD in older people especially those aged 60 years and may be recommended as preventive agents.