Genome-wide Association Studies Datasets Research Articles

Genetic Etiology Shared by Multiple Sclerosis and Ischemic Stroke.

Although dramatic progress has been achieved in the understanding and treatment of multiple sclerosis (MS) and ischemic stroke (IS), more precise and instructive support is required for further research. Recent large-scale genome-wide association studies (GWASs) have already revealed risk variants for IS and MS, but the common genetic etiology between MS and IS remains an unresolved issue. This research was designed to overlapping genes between MS and IS and unmask their transcriptional features. We designed a three-section analysis process. Firstly, we computed gene-based analyses of MS GWAS and IS GWAS data sets by VGEAS2. Secondly, overlapping genes of significance were identified in a meta-analysis using the Fisher’s procedure. Finally, we performed gene expression analyses to confirm transcriptional changes. We identified 24 shared genes with Bonferroni correction (Pcombined < 2.31E-04), and five (FOXP1, CAMK2G, CLEC2D, LBH, and SLC2A4RG) had significant expression differences in MS and IS gene expression omnibus data sets. These meaningful shared genes between IS and MS shed light on the underlying genetic etiologies shared by the diseases. Our results provide a basis for in-depth genomic studies of associations between MS and IS.

Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

SummaryThe genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.

Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis.

Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of ‘druggable’ genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein–protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.

Shared genetic etiology underlying late-onset Alzheimer's disease and posttraumatic stress syndrome.

Late-onset Alzheimer's disease (LOAD) manifests comorbid neuropsychiatric symptoms and posttraumatic stress disorder (PTSD) is associated with an increased risk for dementia in late life, suggesting the two disorders may share genetic etiologies. We performed genetic pleiotropy analysis using LOAD and PTSD genome-wide association study (GWAS) datasets from white and African-American populations, followed by functional-genomic analyses. We found an enrichment for LOAD across increasingly stringent levels of significance with the PTSD GWAS association (LOAD|PTSD) in the discovery and replication cohorts and a modest enrichment for the reverse conditional association (PTSD|LOAD). LOAD|PTSD association analysis identified and replicated the MS4A genes region. These genes showed similar expression pattern in brain regions affected in LOAD, and across-brain-tissue analysis identified a significant association for MS4A6A. The African-American samples showed moderate enrichment; however, no false discovery rate-significant associations. We demonstrated common genetic signatures for LOAD and PTSD and suggested immune response as a common pathway for these diseases.

Clinical and genetic relationships between the QTc interval and risk of a stroke among atrial fibrillation patients undergoing catheter ablation

Background and objectivesA prolonged QTc interval is associated with an increased risk of a stroke or atrial fibrillation (AF). However, its direct causal relationship with AF associated a stroke has not been proven yet. To examine whether QTc interval is causally linked with risk of stroke in AF patients, we used the Mendelian randomization analysis.Subjects and methodsAmong 2742 patients (73.6% male; 58.2 ± 11.0 years old; 69.5% with paroxysmal AF) who underwent AF catheter ablation, we analyzed 1766 patients who had preablation sinus rhythm electrocardiograms off antiarrhythmic drugs after excluding amiodarone users. Among them, 1213 subjects had genome-wide association study dataset analyzable for the Mendelian randomization. We explored the mechanistic relationships between QTc interval (ms) and the risk of a stroke by analyzing the Mendelian randomization (1213 subjects) after reviewing 35 genetic polymorphisms associated with the QTc in 31 European descent studies.ResultsAmong the patients in the higher quartile with a higher QTc, CHA2DS2-VASc score (p < 0.001), and age (p < 0.001), the proportions of a prior stroke (p < 0.001), females, heart failure, and persistent AF were significantly higher than in those in the lower quartile. The QTc was independently associated with the CHA2DS2-VASc score (β, 4.63E−5; 95% confidence interval, 3.57E−6–8.90E−5; p = 0.034) and ischemic strokes (odds ratio, 1.01; 95% confidence interval, 1.00–1.01; p = 0.027). However, there was no direct causal relationship between the QTc and CHA2DS2-VASc score or a prior stroke in either the one-sample or two-sample Mendelian randomizations.ConclusionThe QTc was independently associated with the CHA2DS2-VASc score and strokes among the patients with AF who underwent catheter ablation, despite no genetically direct causal relationship.

Epigenomes of Human Hearts Reveal New Genetic Variants Relevant for Cardiac Disease and Phenotype.

Identifying genetic markers for heterogeneous complex diseases such as heart failure is challenging and requires prohibitively large cohort sizes in genome-wide association studies to meet the stringent threshold of genome-wide statistical significance. On the other hand, chromatin quantitative trait loci, elucidated by direct epigenetic profiling of specific human tissues, may contribute toward prioritizing subthreshold variants for disease association. Here, we captured noncoding genetic variants by performing epigenetic profiling for enhancer H3K27ac chromatin immunoprecipitation followed by sequencing in 70 human control and end-stage failing hearts. We have mapped a comprehensive catalog of 47 321 putative human heart enhancers and promoters. Three thousand eight hundred ninety-seven differential acetylation peaks (FDR [false discovery rate], 5%) pointed to pathways altered in heart failure. To identify cardiac histone acetylation quantitative trait loci (haQTLs), we regressed out confounding factors including heart failure disease status and used the G-SCI (Genotype-independent Signal Correlation and Imbalance) test1 to call out 1680 haQTLs (FDR, 10%). RNA sequencing performed on the same heart samples proved a subset of haQTLs to have significant association also to gene expression (expression quantitative trait loci), either in cis (180) or through long-range interactions (81), identified by Hi-C (high-throughput chromatin conformation assay) and HiChIP (high-throughput protein centric chromatin) performed on a subset of hearts. Furthermore, a concordant relationship between the gain or disruption of TF (transcription factor)-binding motifs, inferred from alternative alleles at the haQTLs, implied a surprising direct association between these specific TF and local histone acetylation in human hearts. Finally, 62 unique loci were identified by colocalization of haQTLs with the subthreshold loci of heart-related genome-wide association studies datasets. Disease and phenotype association for 62 unique loci are now implicated. These loci may indeed mediate their effect through modification of enhancer H3K27 acetylation enrichment and their corresponding gene expression differences (bioRxiv: https://doi.org/10.1101/536763). Graphical Abstract: A graphical abstract is available for this article.

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.