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Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Interpreting the molecular mechanism of genomic variations and their causal relationship with diseases/traits are important and challenging problems in the human genetic study. To provide comprehensive and context-specific variant annotations for biologists and clinicians, here, by systematically integrating over 4TB genomic/epigenomic profiles and frequently-used annotation databases from various biological domains, we develop a variant annotation database, called VannoPortal. In general, the database has following major features: (i) systematically integrates 40 genome-wide variant annotations and prediction scores regarding allele frequency, linkage disequilibrium, evolutionary signature, disease/trait association, tissue/cell type-specific epigenome, base-wise functional prediction, allelic imbalance and pathogenicity; (ii) equips with our recent novel index system and parallel random-sweep searching algorithms for efficient management of backend databases and information extraction; (iii) greatly expands context-dependent variant annotation to incorporate large-scale epigenomic maps and regulatory profiles (such as EpiMap) across over 33 tissue/cell types; (iv) compiles many genome-scale base-wise prediction scores for regulatory/pathogenic variant classification beyond protein-coding region; (v) enables fast retrieval and direct comparison of functional evidence among linked variants using highly interactive web panel in addition to plain table; (vi) introduces many visualization functions for more efficient identification and interpretation of functional variants in single web page. VannoPortal is freely available at http://mulinlab.org/vportal.

PurposeAge-related macular degeneration (AMD), cataract, and glaucoma are leading causes of blindness worldwide. Previous genome-wide association studies (GWASs) have revealed a variety of susceptible loci associated with age-related ocular disorders, yet the genetic pleiotropy and causal genes across these diseases remain poorly understood. This study aims to identify genetic pleiotropic genes among AMD, cataract, and glaucoma.MethodsWe leveraged large-scale genetic and observational data from ocular disease GWASs and UK Biobank (UKBB) to investigate correlations among these ocular disorders. We undertook meta-analyses with the largest GWAS summary statistics of these ocular disorders to identify pleiotropic loci. We then comprehensively integrated eye-specific gene expression quantitative loci (eQTLs), epigenomic profiling, and 3D genome data to prioritize causal pleiotropic genes. Pathway enrichment analysis and drug repurposing analysis were also conducted.ResultsWe found significant pairwise genetic correlations and consistent epidemiological associations among AMD, cataract, and glaucoma. Cross-disease meta-analysis uncovered seven pleiotropic loci, three of which were replicated in an additional cohort. Integration of variants in pleiotropic loci and multiple single-cell omics data identified that Müller cells and astrocytes were likely causal cell types underlying ocular comorbidity. After the integration with multi-omics data, 15 causal genes were identified. We found that pleiotropic genes were essential in nerve development and eye pigmentation, and targetable by existing drugs for the treatment of single ocular disorder.ConclusionsThese findings will not only facilitate the mechanistic research of ocular comorbidities but also benefit the therapeutic optimization of age-related ocular diseases.

Graphical abstract, Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs.

Summary Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin., Graphical abstract, Highlights • Computational inference of transcriptional regulator (TR) cooperation in 3D chromatin • A landscape of 3D TR cooperation across 40 human tissue/cell types • TR cooperation can better interpret the disease-causal variants identified by GWAS • Cooperation of certain TRs shapes context-specific gene regulation in cell development, Genetics; Molecular genetics; Bioinformatics

Recent advances in genome sequencing and functional genomic profiling have promoted many large-scale quantitative trait locus (QTL) studies, which connect genotypes with tissue/cell type-specific cellular functions from transcriptional to post-translational level. However, no comprehensive resource can perform QTL lookup across multiple molecular phenotypes and investigate the potential cascade effect of functional variants. We developed a versatile resource, named QTLbase, for interpreting the possible molecular functions of genetic variants, as well as their tissue/cell-type specificity. Overall, QTLbase has five key functions: (i) curating and compiling genome-wide QTL summary statistics for 13 human molecular traits from 233 independent studies; (ii) mapping QTL-relevant tissue/cell types to 78 unified terms according to a standard anatomogram; (iii) normalizing variant and trait information uniformly, yielding >170 million significant QTLs; (iv) providing a rich web client that enables phenome- and tissue-wise visualization; and (v) integrating the most comprehensive genomic features and functional predictions to annotate the potential QTL mechanisms. QTLbase provides a one-stop shop for QTL retrieval and comparison across multiple tissues and multiple layers of molecular complexity, and will greatly help researchers interrogate the biological mechanism of causal variants and guide the direction of functional validation. QTLbase is freely available at http://mulinlab.org/qtlbase.

Enhancers are critical cis-regulatory elements that regulate spatiotemporal gene expression and control cell fates. However, the identification of enhancers in native cellular contexts still remains a challenge. Here, we develop an inducible CRISPR activation (CRISPRa) system by transgenic expression of doxycycline (Dox)-inducible dCas9-VPR in mouse embryonic stem cells (iVPR ESC). With this line, a simple introduction of specific guide RNAs targeting promoters or enhancers allows us to realize the effect of CRISPRa in an inducible, reversible, and Dox concentration-dependent manner. Taking advantage of this system, we induce tiled CRISPRa across genomic regions (105 kilobases) surrounding T (Brachyury), one of the key mesodermal development regulator genes. Moreover, we identify several CRISPRa-responsive elements with chromatin features of putative enhancers, including a region the homologous sequence in which humans harbors a body height risk variant. Genetic deletion of this region in ESC does affect subsequent T gene activation and osteogenic differentiation. Therefore, our inducible CRISPRa ESC line provides a convenient platform for high-throughput screens of putative enhancers.

Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD., Author summary The hippocampus is a potential neuroimaging endophenotype for Alzheimer’s disease (AD). This study identifies genes whose expression in hippocampal tissue is associated with AD and establishes the pathways from hippocampal gene expression to hippocampal volume to AD. We demonstrate that 24 genes are associated with AD in hippocampal tissue, and these genes are enriched for AD-related biological processes of amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. We further show that hippocampal volume mediates the effects of the hippocampal gene expression of QPCTL and ERCC2 on AD. These findings improve our understanding of the genetic and neural mechanisms of AD.

The advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.

Functional crosstalk between histone modifications and chromatin remodeling has emerged as a key regulatory mode of transcriptional control during cell fate decisions, but the underlying mechanisms are not fully understood. Here we discover an HRP2–DPF3a–BAF epigenetic pathway that coordinates methylated histone H3 lysine 36 (H3K36me) and ATP-dependent chromatin remodeling to regulate chromatin dynamics and gene transcription during myogenic differentiation. Using siRNA screening targeting epigenetic modifiers, we identify hepatoma-derived growth factor-related protein 2 (HRP2) as a key regulator of myogenesis. Knockout of HRP2 in mice leads to impaired muscle regeneration. Mechanistically, through its HIV integrase binding domain (IBD), HRP2 associates with the BRG1/BRM-associated factor (BAF) chromatin remodeling complex by interacting directly with the BAF45c (DPF3a) subunit. Through its Pro-Trp-Trp-Pro (PWWP) domain, HRP2 preferentially binds to H3K36me2. Consistent with the biochemical studies, ChIP-seq analyses show that HRP2 colocalizes with DPF3a across the genome and that the recruitment of HRP2/DPF3a to chromatin is dependent on H3K36me2. Integrative transcriptomic and cistromic analyses, coupled with ATAC-seq, reveal that HRP2 and DPF3a activate myogenic genes by increasing chromatin accessibility through recruitment of BRG1, the ATPase subunit of the BAF complex. Taken together, these results illuminate a key role for the HRP2-DPF3a-BAF complex in the epigenetic coordination of gene transcription during myogenic differentiation.

Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.

Identifying rare variants that contribute to complex diseases is challenging because of the low statistical power in current tests comparing cases with controls. Here, we propose a novel and powerful rare variants association test based on the deviation of the observed mutation burden of a gene in cases from a baseline predicted by a weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association tests and has reasonable type 1 error rates even for stratified populations or in small samples. Applied to real case-control data, RUNNER recapitulates known genes of Hirschsprung disease and Alzheimer's disease missed by current methods and detects promising new candidate genes for both disorders. In a case-only study, RUNNER successfully detected a known causal gene of amyotrophic lateral sclerosis. The present study provides a powerful and robust method to identify susceptibility genes with rare risk variants for complex diseases.

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.

Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

Allele-specific effects of cancer driver mutations influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal allele. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations significantly influenced drug responses, and observed that diversity was generally increased in advanced tumors. Finally, a new protocol was designed to predict cancer genes based on the diversity spectrum. Overall, our diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

As high throughput methods, such as whole genome genotyping arrays, whole exome sequencing (WES) and whole genome sequencing (WGS), have detected huge amounts of genetic variants associated with human diseases, function annotation of these variants is an indispensable step in understanding disease etiology. Large-scale functional genomics projects, such as The ENCODE Project and Roadmap Epigenomics Project, provide genome-wide profiling of functional elements across different human cell types and tissues. With the urgent demands for identification of disease-causal variants, comprehensive and easy-to-use annotation tool is highly in demand. Here we review and discuss current progress and trend of the variant annotation field. Furthermore, we introduce a comprehensive web portal for annotating human genetic variants. We use gene-based features and the latest functional genomics datasets to annotate single nucleotide variation (SNVs) in human, at whole genome scale. We further apply several function prediction algorithms to annotate SNVs that might affect different biological processes, including transcriptional gene regulation, alternative splicing, post-transcriptional regulation, translation and post-translational modifications. The SNVrap web portal is freely available at http://jjwanglab.org/snvrap.

With the rapid advances in high-throughput sequencing technologies, exome sequencing and targeted region sequencing have become routine approaches for identifying mutations of inherited disorders in both genetics research and molecular diagnosis. There is an imminent need for comprehensive and easy-to-use downstream analysis tools to isolate causal mutations in exome sequencing studies. We have developed a user-friendly online framework, wKGGSeq, to provide systematic annotation, filtration, prioritization, and visualization functions for characterizing causal mutation(s) in exome sequencing studies of inherited disorders. wKGGSeq provides: (1) a novel strategy-based procedure for downstream analysis of a large amount of exome sequencing data and (2) a disease-targeted analysis procedure to facilitate clinical diagnosis of well-studied genetic diseases. In addition, it is also equipped with abundant online annotation functions for sequence variants. We demonstrate that wKGGSeq either outperforms or is comparable to two popular tools in several real exome sequencing samples. This tool will greatly facilitate the downstream analysis of exome sequencing data and can play a useful role for researchers and clinicians in identifying causal mutations of inherited disorders. The wKGGSeq is freely available at http://statgenpro.psychiatry.hku.hk/wkggseq or http://jjwanglab.org/wkggseq, and will be updated frequently.

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant's regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test., published_or_final_version

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

Interpreting the genetic variants located in the regulatory regions, such as enhancers and promoters, is an indispensable step to understand molecular mechanism of complex traits. Recent studies show that genetic variants detected by genome-wide association study (GWAS) are significantly enriched in the regulatory regions. Therefore, detecting, annotating and prioritizing of genetic variants affecting gene regulation are critical to our understanding of genotype-phenotype relationships. Here, we developed a web server GWAS3D to systematically analyze the genetic variants that could affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation. The regulatory elements are inferred from the genome-wide chromosome interaction data, chromatin marks in 16 different cell types and 73 regulatory factors motifs from the Encyclopedia of DNA Element project. Furthermore, we used these function elements, as well as risk haplotype, binding affinity, conservation and P-values reported from the original GWAS to reprioritize the genetic variants. Using studies from low-density lipoprotein cholesterol, we demonstrated that our reprioritizing approach was effective and cell type specific. In conclusion, GWAS3D provides a comprehensive annotation and visualization tool to help users interpreting their results. The web server is freely available at http://jjwanglab.org/gwas3d.

We conducted a meta-analysis of three endometrial cancer genome-wide association studies (GWAS) and two follow-up phases totaling 7,737 endometrial cancer cases and 37,144 controls of European ancestry. Genome-wide imputation and meta-analysis identified five new risk loci of genome-wide significance at likely regulatory regions on chromosomes 13q22.1 (rs11841589, near KLF5), 6q22.31 (rs13328298, in LOC643623 and near HEY2 and NCOA7), 8q24.21 (rs4733613, telomeric to MYC), 15q15.1 (rs937213, in EIF2AK4, near BMF) and 14q32.33 (rs2498796, in AKT1, near SIVA1). We also found a second independent 8q24.21 signal (rs17232730). Functional studies of the 13q22.1 locus showed that rs9600103 (pairwise r2 = 0.98 with rs11841589) is located in a region of active chromatin that interacts with the KLF5 promoter region. The rs9600103[T] allele that is protective in endometrial cancer suppressed gene expression in vitro, suggesting that regulation of the expression of KLF5, a gene linked to uterine development, is implicated in tumorigenesis. These findings provide enhanced insight into the genetic and biological basis of endometrial cancer.

Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0 × 10(-3)) from each original publication, and generated a total of 252,530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.

The advances of high throughput profiling methods, such as microarray gene profiling and RNA-seq, have enabled researchers to identify thousands of differentially expressed genes under a certain perturbation. Much work has been done to understand the genetic factors that contribute to the expression changes by searching the over-represented regulatory motifs in the promoter regions of these genes. However, the changes could also be caused by epigenetic regulation, especially histone modifications, and no web server has been constructed to study the epigenetic factors responsible for gene expression changes. Here, we pre-sent a web tool for this purpose. Provided with different categories of genes (e.g., up-regulated, down-regulated or unchanged genes), the server will find epigenetic factors responsible for the difference among the categories and construct an epigenetic regulatory network. Furthermore, it will perform co-localization analyses between these epigenetic factors and transcription factors, which were collected from large scale experimental ChIP-seq or computational predicted data. In addition, for users who want to analyze dynamic change of a histone modification mark under different cell conditions, the server will find direct and indirect target genes of this mark by integrative analysis of experimental data and computational prediction, and present a regulatory network around this mark. Both networks can be visualized by a user friendly interface and the data are downloadable in batch. The server currently supports 12 cell types in human, including ESC and CD4+ T cells, and will expand as more public data are available. It also allows user to create a self-defined cell type, upload and analyze multiple ChIP-seq data. It is freely available to academic users at http://jjwanglab.org/EpiRegNet.

Transcription factors (TFs) play an important role in gene regulation. The interconnections among TFs, chromatin interactions, epigenetic marks and cis-regulatory elements form a complex gene transcription apparatus. Our previous work, ChIP-Array, combined TF binding and transcriptome data to construct gene regulatory networks (GRNs). Here we present an enhanced version, ChIP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components. Moreover, we substantially extended our motif database for human, mouse, rat, fruit fly, worm, yeast and Arabidopsis, and curated large amount of omics data for users to select as input or backend support. With ChIP-Array 2, we compiled a library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC). The web server and the mESC library are publicly free and accessible athttp://jjwanglab.org/chip-array.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Understanding the genetic basis of human traits/diseases and the underlying mechanisms of how these traits/diseases are affected by genetic variations is critical for public health. Current genome-wide functional genomics data uncovered a large number of functional elements in the noncoding regions of human genome, providing new opportunities to study regulatory variants (RVs). RVs play important roles in transcription factor bindings, chromatin states and epigenetic modifications. Here, we systematically review an array of methods currently used to map RVs as well as the computational approaches in annotating and interpreting their regulatory effects, with emphasis on regulatory single-nucleotide polymorphism. We also briefly introduce experimental methods to validate these functional RVs.

The dbPSHP database (http://jjwanglab.org/dbpshp) aims to help researchers to efficiently identify, validate and visualize putative positively selected loci in human evolution and further discover the mechanism governing these natural selections. Recent evolution of human populations at the genomic level reflects the adaptations to the living environments, including climate change and availability and stability of nutrients. Many genetic regions under positive selection have been identified, which assist us to understand how natural selection has shaped population differences. Here, we manually collect recent positive selections in different human populations, consisting of 15,472 loci from 132 publications. We further compiled a database that used 15 statistical terms of different evolutionary attributes for single nucleotide variant sites from the HapMap 3 and 1000 Genomes Project to identify putative regions under positive selection. These attributes include variant allele/genotype properties, variant heterozygosity, within population diversity, long-range haplotypes, pairwise population differentiation and evolutionary conservation. We also provide interactive pages for visualization and annotation of different selective signals. The database is freely available to the public and will be frequently updated., published_or_final_version

MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein level and therefore can reveal the direct effects of miRNAs' translational repression. Here, we present a web server, ProteoMir- Express, that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore, ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We used several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available online, with free access to academic users. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc., link_to_OA_fulltext

Recent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10 -3) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently. © The Author(s) 2011., published_or_final_version

Chromatin immunoprecipitation (ChIP) coupled with high-throughput techniques (ChIP-X), such as next generation sequencing (ChIP-Seq) and microarray (ChIP-chip), has been successfully used to map active transcription factor binding sites (TFBS) of a transcription factor (TF). The targeted genes can be activated or suppressed by the TF, or are unresponsive to the TF. Microarray technology has been used to measure the actual expression changes of thousands of genes under the perturbation of a TF, but is unable to determine if the affected genes are direct or indirect targets of the TF. Furthermore, both ChIP-X and microarray methods produce a large number of false positives. Combining microarray expression profiling and ChIP-X data allows more effective TFBS analysis for studying the function of a TF. However, current web servers only provide tools to analyze either ChIP-X or expression data, but not both. Here, we present ChIP-Array, a web server that integrates ChIP-X and expression data from human, mouse, yeast, fruit fly and Arabidopsis. This server will assist biologists to detect direct and indirect target genes regulated by a TF of interest and to aid in the functional characterization of the TF. ChIP-Array is available at http://jjwanglab.hku.hk/ChIP-Array, with free access to academic users. © 2011 The Author(s)., published_or_final_version