Linking disease associations with regulatory information in the human genome

Genome recognition by MYC

MYC dimerizes with MAX to bind DNA, with a preference for the E-box consensus CACGTG and several variant motifs. In cells, MYC binds DNA preferentially within transcriptionally active promoter regions. Although several thousand promoters are bound under physiological (low MYC) conditions, these represent only a fraction of all accessible, active promoters. MYC overexpression-as commonly observed in cancer cells-leads to invasion of virtually all active promoters, as well as of distal enhancer elements. We summarize here what is currently known about the mechanisms that may guide this process. We propose that binding site recognition is determined by low-affinity protein-protein interactions between MYC/MAX dimers and components of the basal transcriptional machinery, other chromatin-associated protein complexes, and/or DNA-bound transcription factors. DNA binding occurs subsequently, without an obligate requirement for sequence recognition. Local DNA scanning then leads to preferential stabilization of the MYC/MAX dimer on high-affinity DNA elements. This model is consistent with the invasion of all active promoters that occurs at elevated MYC levels, but posits that important differences in affinity persist between physiological target sites and the newly invaded elements, which may not all be bound in a productive regulatory mode. The implications of this model for transcriptional control by MYC in normal and cancer cells are discussed in the light of the latest literature.

Lessons and Implications from Genome-Wide Association Studies (GWAS) Findings of Blood Cell Phenotypes

Genome-wide association studies (GWAS) have identified reproducible genetic associations with hundreds of human diseases and traits. The vast majority of these associated single nucleotide polymorphisms (SNPs) are non-coding, highlighting the challenge in moving from genetic findings to mechanistic and functional insights. Nevertheless, large-scale (epi)genomic studies and bioinformatic analyses strongly suggest that GWAS hits are not randomly distributed in the genome but rather pinpoint specific biological pathways important for disease development or phenotypic variation. In this review, we focus on GWAS discoveries for the three main blood cell types: red blood cells, white blood cells and platelets. We summarize the knowledge gained from GWAS of these phenotypes and discuss their possible clinical implications for common (e.g., anemia) and rare (e.g., myeloproliferative neoplasms) human blood-related diseases. Finally, we argue that blood phenotypes are ideal to study the genetics of complex human traits because they are fully amenable to experimental testing.

Atrial Fibrillation associated chromosome 4q25 variants are not associated with PITX2c expression in human adult left atrial appendages

Atrial Fibrillation (AF), the most common sustained arrhythmia, has a strong genetic component, but the mechanism by which common genetic variants lead to increased AF susceptibility is unknown. Genome-wide association studies (GWAS) have identified that the single nucleotide polymorphisms (SNPs) most strongly associated with AF are located on chromosome 4q25 in an intergenic region distal to the PITX2 gene. Our objective was to determine whether the AF-associated SNPs on chromosome 4q25 were associated with PITX2c expression in adult human left atrial appendages. Analysis of a lone AF GWAS identified four independent AF risk SNPs at chromosome 4q25. Human adult left atrial appendage tissue was obtained from 239 subjects of European Ancestry and used for SNP analysis of genomic DNA and determination of PITX2c RNA expression levels by quantitative PCR. Subjects were divided into three groups based on their history of AF and pre-operative rhythm. AF rhythm subjects had higher PITX2c expression than those with history of AF but in sinus rhythm. PITX2c expression was not associated with the AF risk SNPs in human adult left atrial appendages in all subjects combined or in each of the three subgroups. However, we identified seven SNPs modestly associated with PITX2c expression located in the introns of the ENPEP gene, ∼54 kb proximal to PITX2. PITX2c expression in human adult left atrial appendages is not associated with the chromosome 4q25 AF risk SNPs; thus, the mechanism by which these SNPs are associated with AF remains enigmatic.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

rSNPBase: a database for curated regulatory SNPs

In recent years, human regulatory SNPs (rSNPs) have been widely studied. Here, we present database rSNPBase, freely available at http://rsnp.psych.ac.cn/, to provide curated rSNPs that analyses the regulatory features of all SNPs in the human genome with reference to experimentally supported regulatory elements. In contrast with previous SNP functional annotation databases, rSNPBase is characterized by several unique features. (i) To improve reliability, all SNPs in rSNPBase are annotated with reference to experimentally supported regulatory elements. (ii) rSNPBase focuses on rSNPs involved in a wide range of regulation types, including proximal and distal transcriptional regulation and post-transcriptional regulation, and identifies their potentially regulated genes. (iii) Linkage disequilibrium (LD) correlations between SNPs were analysed so that the regulatory feature is annotated to SNP-set rather than a single SNP. (iv) rSNPBase provides the spatio-temporal labels and experimental eQTL labels for SNPs. In summary, rSNPBase provides more reliable, comprehensive and user-friendly regulatory annotations on rSNPs and will assist researchers in selecting candidate SNPs for further genetic studies and in exploring causal SNPs for in-depth molecular mechanisms of complex phenotypes.

A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects

Platelets are enucleated cell fragments derived from megakaryocytes that play key roles in hemostasis and in the pathogenesis of atherothrombosis and cancer. Platelet traits are highly heritable and identification of genetic variants associated with platelet traits and assessing their pleiotropic effects may help to understand the role of underlying biological pathways. We conducted an electronic medical record (EMR)-based study to identify common variants that influence inter-individual variation in the number of circulating platelets (PLT) and mean platelet volume (MPV), by performing a genome-wide association study (GWAS). We characterized genetic variants associated with MPV and PLT using functional, pathway and disease enrichment analyses; we assessed pleiotropic effects of such variants by performing a phenome-wide association study (PheWAS) with a wide range of EMR-derived phenotypes. A total of 13,582 participants in the electronic MEdical Records and GEnomic network had data for PLT and 6,291 participants had data for MPV. We identified five chromosomal regions associated with PLT and eight associated with MPV at genome-wide significance (P < 5E-8). In addition, we replicated 20 SNPs [out of 56 SNPs (α: 0.05/56 = 9E-4)] influencing PLT and 22 SNPs [out of 29 SNPs (α: 0.05/29 = 2E-3)] influencing MPV in a published meta-analysis of GWAS of PLT and MPV. While our GWAS did not find any new associations, our functional analyses revealed that genes in these regions influence thrombopoiesis and encode kinases, membrane proteins, proteins involved in cellular trafficking, transcription factors, proteasome complex subunits, proteins of signal transduction pathways, proteins involved in megakaryocyte development, and platelet production and hemostasis. PheWAS using a single-SNP Bonferroni correction for 1,368 diagnoses (0.05/1368 = 3.6E-5) revealed that several variants in these genes have pleiotropic associations with myocardial infarction, autoimmune, and hematologic disorders. We conclude that multiple genetic loci influence interindividual variation in platelet traits and also have significant pleiotropic effects; the related genes are in multiple functional pathways including those relevant to thrombopoiesis.

Identifying proteins controlling key disease signaling pathways

MOTIVATION

Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.

RESULTS

We developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins' likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection.

AVAILABILITY

SDREM is available from http://sb.cs.cmu.edu/sdrem.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Non-coding RNAs in homeostasis, disease and stress responses: an evolutionary perspective

Cells and organisms are subject to challenges and perturbations in their environment and physiology in all stages of life. The molecular response to such changes, including insulting conditions such as pathogen infections, involves coordinated modulation of gene expression programmes and has not only homeostatic but also ecological and evolutionary importance. Although attention has been primarily focused on signalling pathways and protein networks, non-coding RNAs (ncRNAs), which comprise a significant output of the genomes of prokaryotes and especially eukaryotes, are increasingly implicated in the molecular mechanisms of these responses. Long and short ncRNAs not only regulate development and cell physiology, they are also involved in disease states, including cancers, in host-pathogen interactions, and in a variety of stress responses. Indeed, regulatory RNAs are part of genetically encoded response networks and also underpin epigenetic processes, which are emerging as key mechanisms of adaptation and transgenerational inheritance. Here we present the growing evidence that ncRNAs are intrinsically involved in cellular and organismal adaptation processes, in both robustness and protection to stresses, as well as in mechanisms generating evolutionary change.

Genetic correction using engineered nucleases for gene therapy applications

Genetic mutations in humans are associated with congenital disorders and phenotypic traits. Gene therapy holds the promise to cure such genetic disorders, although it has suffered from several technical limitations for decades. Recent progress in gene editing technology using tailor-made nucleases, such as meganucleases (MNs), zinc finger nucleases (ZFNs), TAL effector nucleases (TALENs) and, more recently, CRISPR/Cas9, has significantly broadened our ability to precisely modify target sites in the human genome. In this review, we summarize recent progress in gene correction approaches of the human genome, with a particular emphasis on the clinical applications of gene therapy.

Unmasking risk loci: DNA methylation illuminates the biology of cancer predisposition: analyzing DNA methylation of transcriptional enhancers reveals missed regulatory links between cancer risk loci and genes

Paradoxically, DNA sequence polymorphisms in cancer risk loci rarely correlate with the expression of cancer genes. Therefore, the molecular mechanism underlying an individual's susceptibility to cancer has remained largely unknown. However, recent evaluations of the correlations between DNA methylation and gene expression levels across healthy and cancerous genomes have revealed enrichment of disease-related DNA methylation variations within disease-associated risk loci. Moreover, it appears that transcriptional enhancers embedded in cancer risk loci often contain DNA methylation sites that closely define the expression of prominent cancer genes, despite the lack of significant correlations between gene expression levels and the surrounding disease-associated polymorphic sequences. We suggest that DNA methylation variations may obscure the effect of co-residing risk sequence alleles. Analysis of enhancer methylation data may help to reveal the regulatory circuits underlying predisposition to cancers and other common diseases.

Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases

While studies to associate genomic variants to complex traits have gradually become increasingly productive, the molecular mechanisms that underlie these associations are rarely understood. Because only a small fraction of trait-associated variants can be linked to coding sequences, investigators have speculated that many of the underlying causal alleles influence non-coding gene regulatory sites. Recent studies have successfully identified examples of mechanisms for non-coding alleles at individual loci. Now, genome-wide chromatin assays have resulted in maps of dozens of genomic annotations of the non-coding genome across multiple different tissues, cell types and cell lines. This gives a tremendous opportunity to integrate these annotations with complex trait signals to globally interpret associated variants, and prioritize likely causal alleles. Here, we review the examples of mechanisms by which non-coding, common alleles result in phenotypes. We discuss the efforts to integrate common trait-associated variants with genomic annotations. Finally, we highlight some caveats of these approaches and outline future directions for improvement.

The mystery of extreme non-coding conservation

Regions of several dozen to several hundred base pairs of extreme conservation have been found in non-coding regions in all metazoan genomes. The distribution of these elements within and across genomes has suggested that many have roles as transcriptional regulatory elements in multi-cellular organization, differentiation and development. Currently, there is no known mechanism or function that would account for this level of conservation at the observed evolutionary distances. Previous studies have found that, while these regions are under strong purifying selection, and not mutational coldspots, deletion of entire regions in mice does not necessarily lead to identifiable changes in phenotype during development. These opposing findings lead to several questions regarding their functional importance and why they are under strong selection in the first place. In this perspective, we discuss the methods and techniques used in identifying and dissecting these regions, their observed patterns of conservation, and review the current hypotheses on their functional significance.

Extensive variation in chromatin states across humans

The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.

Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits

DNA variants (SNPs) that predispose to common traits often localize within noncoding regulatory elements such as enhancers. Moreover, loci identified by genome-wide association studies (GWAS) often contain multiple SNPs in linkage disequilibrium (LD), any of which may be causal. Thus, determining the effect of these multiple variant SNPs on target transcript levels has been a major challenge. Here, we provide evidence that for six common autoimmune disorders (rheumatoid arthritis, Crohn's disease, celiac disease, multiple sclerosis, lupus, and ulcerative colitis), the GWAS association arises from multiple polymorphisms in LD that map to clusters of enhancer elements active in the same cell type. This finding suggests a “multiple enhancer variant” hypothesis for common traits, where several variants in LD impact multiple enhancers and cooperatively affect gene expression. Using a novel method to delineate enhancer–gene interactions, we show that multiple enhancer variants within a given locus typically target the same gene. Using available data from HapMap and B lymphoblasts as a model system, we provide evidence at numerous loci that multiple enhancer variants cooperatively contribute to altered expression of their gene targets. The effects on target transcript levels tend to be modest and can be either gain- or loss-of-function. Additionally, the genes associated with multiple enhancer variants encode proteins that are often functionally related and enriched in common pathways. Overall, the multiple enhancer variant hypothesis offers a new paradigm by which noncoding variants can confer susceptibility to common traits.

Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Detection of regulatory SNPs in human genome using ChIP-seq ENCODE data

A vast amount of SNPs derived from genome-wide association studies are represented by non-coding ones, therefore exacerbating the need for effective identification of regulatory SNPs (rSNPs) among them. However, this task remains challenging since the regulatory part of the human genome is annotated much poorly as opposed to coding regions. Here we describe an approach aggregating the whole set of ENCODE ChIP-seq data in order to search for rSNPs, and provide the experimental evidence of its efficiency. Its algorithm is based on the assumption that the enrichment of a genomic region with transcription factor binding loci (ChIP-seq peaks) indicates its regulatory function, and thereby SNPs located in this region are more likely to influence transcription regulation. To ensure that the approach preferably selects functionally meaningful SNPs, we performed enrichment analysis of several human SNP datasets associated with phenotypic manifestations. It was shown that all samples are significantly enriched with SNPs falling into the regions of multiple ChIP-seq peaks as compared with the randomly selected SNPs. For experimental verification, 40 SNPs falling into overlapping regions of at least 7 TF binding loci were selected from OMIM. The effect of SNPs on the binding of the DNA fragments containing them to the nuclear proteins from four human cell lines (HepG2, HeLaS3, HCT-116, and K562) has been tested by EMSA. A radical change in the binding pattern has been observed for 29 SNPs, besides, 6 more SNPs also demonstrated less pronounced changes. Taken together, the results demonstrate the effective way to search for potential rSNPs with the aid of ChIP-seq data provided by ENCODE project.

Identification of genetic variants that affect histone modifications in human cells

Histone modifications are important markers of function and chromatin state, yet the DNA sequence elements that direct them to specific genomic locations are poorly understood. Here, we identify hundreds of quantitative trait loci, genome-wide, that affect histone modification or RNA polymerase II (Pol II) occupancy in Yoruba lymphoblastoid cell lines (LCLs). In many cases, the same variant is associated with quantitative changes in multiple histone marks and Pol II, as well as in deoxyribonuclease I sensitivity and nucleosome positioning. Transcription factor binding site polymorphisms are correlated overall with differences in local histone modification, and we identify specific transcription factors whose binding leads to histone modification in LCLs. Furthermore, variants that affect chromatin at distal regulatory sites frequently also direct changes in chromatin and gene expression at associated promoters.

Effect of natural genetic variation on enhancer selection and function

The mechanisms by which genetic variation affects transcription regulation and phenotypes at the nucleotide level are incompletely understood. Here, we use natural genetic variation as an in vivo mutagenesis screen to assess the genome-wide effects of sequence variation on lineage-determining and signal-specific transcription factor binding, epigenomics, and transcriptional outcomes in primary macrophages from different mouse strains. We find substantial genetic evidence supporting the concept that lineage-determining transcription factors (LDTFs) define epigenetic and transcriptomic states by selecting enhancer-like regions in the genome in a collaborative fashion and facilitating binding of signal-dependent factors. This hierarchical model of transcription factor function suggests that limited sets of genomic data for LDTFs and informative histone modifications can be used for prioritization of disease-associated regulatory variants.

Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation—by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and "Measles" pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.

Osteoarthritis year 2013 in review: genetics and genomics

Progress in genetic research has delivered important highlights in the last year. One of the widest impact is the publication of the Encyclopedia of DNA Elements (ENCODE) project showing the impressive complexity of the human genome and providing information useful for all areas of genetics. More specific of osteoarthritis (OA) has been the incorporation of DOT1-like, histone H3 methyltransferase (DOT1L) to the list of 11 OA loci with genome-wide significant association, the demonstration of significant overlap between OA genetics and height or body mass index (BMI) genetics, and the tentative prioritization of HMG-box transcription factor 1 (HBP1) in the 7q22 locus based on functional analysis. In addition, the first large scale analysis of DNA methylation has found modest differences between OA and normal cartilage, but has identified a subgroup of OA patients with a very differentiated phenotype. The role of DNA methylation in regulation of NOS2, SOX9, MMP13 and IL1B has been further clarified. MicroRNA expression studies in turn have shown some replication of differences between OA and control cartilage from previous profiling studies and have identified potential regulators of TGFβ signaling and of IL1β effects. In addition, non-coding RNAs showed promising results as serum biomarkers of cartilage damage. Gene expression microarray studies have found important differences between studies of hip or knee OA that reinforce the idea of joint specificity in OA. Expression differences between articular cartilage and other types of cartilage highlighted the WNT pathway whose regulation is proposed as critical for maintaining the articular cartilage phenotype. Many of these results need confirmation but they signal the exciting progress that is taking place in all areas of OA genetics, indicate questions requiring more study and augur further interesting discoveries.

Defining the contribution of SNPs identified in asthma GWAS to clinical variables in asthmatic children

Background

Asthma genome-wide association studies (GWAS) have identified several asthma susceptibility genes with confidence; however the relative contribution of these genetic variants or single nucleotide polymorphisms (SNPs) to clinical endpoints (as opposed to disease diagnosis) remains largely unknown. Thus the aim of this study was to firstly bridge this gap in knowledge and secondly investigate whether these SNPs or those that are in linkage disequilibrium are likely to be functional candidates with respect to regulation of gene expression, using reported data from the ENCODE project.

Methods

Eleven of the key SNPs identified in eight loci from recent asthma GWAS were evaluated for association with asthma and clinical outcomes, including percent predicted FEV₁, bronchial hyperresponsiveness (BHR) to methacholine, severity defined by British Thoracic Society steps and positive response to skin prick test, using the family based association test additive model in a well characterised UK cohort consisting of 370 families with at least two asthmatic children.

Results

GSDMB SNP rs2305480 (Ser311Pro) was associated with asthma diagnosis (p = 8.9×10^-4), BHR (p = 8.2×10^-4) and severity (p = 1.5×10^-4) with supporting evidence from a second GSDMB SNP rs11078927 (intronic). SNPs evaluated in IL33, IL18R1, IL1RL1, SMAD3, IL2RB, PDE4D, CRB1 and RAD50 did not show association with any phenotype tested when corrected for multiple testing. Analysis using ENCODE data provides further insight into the functional relevance of these SNPs.

Conclusions

Our results provide further support for the role of GSDMB SNPs in determining multiple asthma related phenotypes in childhood asthma including associations with lung function and disease severity.

A network-based method to assess the statistical significance of mild co-regulation effects

Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.

Next-generation sequencing in understanding complex neurological disease

Next-generation sequencing techniques have made vast quantities of data on human genomes and transcriptomes available to researchers. Huge progress has been made towards understanding the basis of many Mendelian neurological conditions, but progress has been considerably slower in complex neurological diseases (multiple sclerosis, migraine, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and so on). The authors review current next-generation sequencing methodologies and present selected studies illustrating how these have been used to cast light on the genetic etiology of complex neurological diseases with specific focus on multiple sclerosis. The authors highlight particular pitfalls in next-generation sequencing experiments and speculate on both clinical and research applications of these sequencing platforms for complex neurological disorders in the future.

Properties of local interactions and their potential value in complementing genome-wide association studies

Local interactions between neighbouring SNPs are hypothesized to be able to capture variants missing from genome-wide association studies (GWAS) via haplotype effects but have not been thoroughly explored. We have used a new high-throughput analysis tool to probe this underexplored area through full pair-wise genome scans and conventional GWAS in diastolic and systolic blood pressure and six metabolic traits in the Northern Finland Birth Cohort 1966 (NFBC1966) and the Atherosclerosis Risk in Communities study cohort (ARIC). Genome-wide significant interactions were detected in ARIC for systolic blood pressure between PLEKHA7 (a known GWAS locus for blood pressure) and GPR180 (which plays a role in vascular remodelling), and also for triglycerides as local interactions within the 11q23.3 region (replicated significantly in NFBC1966), which notably harbours several loci (BUD13, ZNF259 and APOA5) contributing to triglyceride levels. Tests of the local interactions within the 11q23.3 region conditional on the top GWAS signal suggested the presence of two independent functional variants, each with supportive evidence for their roles in gene regulation. Local interactions captured 9 additional GWAS loci identified in this study (3 significantly replicated) and 73 from previous GWAS (24 in the eight traits and 49 in related traits). We conclude that the detection of local interactions requires adequate SNP coverage of the genome and that such interactions are only likely to be detectable between SNPs in low linkage disequilibrium. Analysing local interactions is a potentially valuable complement to GWAS and can provide new insights into the biology underlying variation in complex traits.

Prioritization of genetic variants in the microRNA regulome as functional candidates in genome-wide association studies

Comprehensive analyses of results from genome-wide association studies (GWAS) have demonstrated that complex disease/trait-associated loci are enriched in gene regulatory regions of the genome. The search for causal regulatory variation has focused primarily on transcriptional elements, such as promoters and enhancers. microRNAs (miRNAs) are now widely appreciated as critical posttranscriptional regulators of gene expression and are thought to impart stability to biological systems. Naturally occurring genetic variation in the miRNA regulome is likely an important contributor to phenotypic variation in the human population. However, the extent to which polymorphic miRNA-mediated gene regulation underlies GWAS signals remains unclear. In this study, we have developed the most comprehensive bioinformatic analysis pipeline to date for cataloging and prioritizing variants in the miRNA regulome as functional candidates in GWAS. We highlight specific findings, including a variant in the promoter of the miRNA let-7 that may contribute to human height variation. We also provide a discussion of how our approach can be expanded in the future. Overall, we believe that the results of this study will be valuable for researchers interested in determining whether GWAS signals implicate the miRNA regulome in their disease/trait of interest.

Fine mapping of the Bmgr5 quantitative trait locus for allogeneic bone marrow engraftment in mice

To identify novel mechanisms regulating allogeneic hematopoietic cell engraftment, we used forward genetics and previously described identification, in mice, of a bone marrow (BM) engraftment quantitative trait locus (QTL), termed Bmgr5. This QTL confers dominant and large allele effects for engraftment susceptibility. It was localized to chromosome 16 by quantitative genetic techniques in a segregating backcross bred from susceptible BALB.K and resistant B10.BR mice. We now report verification of the Bmgr5 QTL using reciprocal chromosome 16 consomic strains. The BM engraftment phenotype in these consomic mice shows that Bmgr5 susceptibility alleles are not only sufficient but also indispensable for conferring permissiveness for allogeneic BM engraftment. Using panels of congenic mice, we resolved the Bmgr5 QTL into two separate subloci, termed Bmgr5a (Chr16:14.6-15.8 Mb) and Bmgr5b (Chr16:15.8-17.6 Mb), each conferring permissiveness for the engraftment phenotype and both fine mapped to an interval amenable to positional cloning. Candidate Bmgr5 genes were then prioritized using whole exome DNA sequencing and microarray gene expression data. Further studies are warranted to elucidate the genetic interaction between the Bmgr5a and Bmgr5b QTL and identify causative genes and underlying gene variants. This may lead to new approaches for overcoming the problem of graft rejection in clinical hematopoietic cell transplantation.

Translating the ENCyclopedia Of DNA Elements Project findings to the clinic: ENCODE's implications for eye disease

Approximately 10 years after the Human Genome Project unravelled the sequence of our DNA, the ENCyclopedia Of DNA Elements (ENCODE) Project sought to interpret it. Data from the recently completed project have shed new light on the proportion of biologically active human DNA, assigning a biochemical role to much of the sequence previously considered to be 'junk'. Many of these newly catalogued functional elements represent epigenetic mechanisms involved in regulation of gene expression. Analogous to an Ishihara plate, a gene-coding region of DNA (target dots) only comes into context when the non-coding DNA (surrounding dots) is appreciated. In this review we provide an overview of the ENCODE project, discussing the significance of these data for ophthalmic research and eye disease. The novel insights afforded by the ENCODE project will in time allow for the development of new therapeutic strategies in the management of common blinding disorders.

GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.

Identification of a KCNQ1 polymorphism acting as a protective modifier against arrhythmic risk in long-QT syndrome

BACKGROUND

Long-QT syndrome (LQTS) is characterized by such striking clinical heterogeneity that, even among family members carrying the same mutation, clinical outcome can range between sudden death and no symptoms. We investigated the role of genetic variants as modifiers of risk for cardiac events in patients with LQTS.

METHODS AND RESULTS

In a matched case-control study including 112 patient duos with LQTS from France, Italy, and Japan, 25 polymorphisms were genotyped based on either their association with QTc duration in healthy populations or on their role in adrenergic responses. The duos were composed of 2 relatives harboring the same heterozygous KCNQ1 or KCNH2 mutation: 1 with cardiac events and 1 asymptomatic and untreated. The findings were then validated in 2 independent founder populations totaling 174 symptomatic and 162 asymptomatic patients with LQTS, and a meta-analysis was performed. The KCNQ1 rs2074238 T-allele was significantly associated with a decreased risk of symptoms 0.34 (0.19-0.61; P<0.0002) and with shorter QTc (P<0.0001) in the combined discovery and replication cohorts.

CONCLUSIONS

We provide evidence that the KCNQ1 rs2074238 polymorphism is an independent risk modifier with the minor T-allele conferring protection against cardiac events in patients with LQTS. This finding is a step toward a novel approach for risk stratification in patients with LQTS.

De novo mutations in the genome organizer CTCF cause intellectual disability

An increasing number of genes involved in chromatin structure and epigenetic regulation has been implicated in a variety of developmental disorders, often including intellectual disability. By trio exome sequencing and subsequent mutational screening we now identified two de novo frameshift mutations and one de novo missense mutation in CTCF in individuals with intellectual disability, microcephaly, and growth retardation. Furthermore, an individual with a larger deletion including CTCF was identified. CTCF (CCCTC-binding factor) is one of the most important chromatin organizers in vertebrates and is involved in various chromatin regulation processes such as higher order of chromatin organization, enhancer function, and maintenance of three-dimensional chromatin structure. Transcriptome analyses in all three individuals with point mutations revealed deregulation of genes involved in signal transduction and emphasized the role of CTCF in enhancer-driven expression of genes. Our findings indicate that haploinsufficiency of CTCF affects genomic interaction of enhancers and their regulated gene promoters that drive developmental processes and cognition.

A contemporary view of genes and behavior: complex systems and interactions

Several large-scale searches for genes that influence complex human traits, such as intelligence and personality, in the normal range of variation have failed to identify even one gene that makes a significant difference. All previously published claims for genetic influences of this kind now appear to have been false positives. For more serious psychiatric and medical disorders such as schizophrenia and autism, several genes have been found where a rare mutation contributes to abnormal behavior, but in many instances they are de novo mutations not obtained from a parent. Despite the many disappointments in the search for genes influencing human behavior, the field of molecular genetics has made remarkable progress to the extent that several broadly applicable principles can now be affirmed. These principles show how development is regulated by networks of interacting genes that function in an environmental context. They invalidate several key assumptions of statistical genetic analysis that are made when estimating heritability. There is now a need to reform the teaching of genetics to our students and to restrict the funding of further searches for elusive genes that account for so little variance in normal behaviors.

Mapping of immune-mediated disease genes

Genetic studies in immune-mediated diseases have yielded a large number of disease-associated loci. Here we review the progress being made in 12 such diseases, for which 199 independently associated non-HLA loci have been identified by genome-wide association studies since 2007. It is striking that many of the loci are not unique to a single disease but shared between different immune-mediated diseases. The challenge now is to understand how the unique and shared genetic factors can provide insight into the underlying disease biology. We annotated disease-associated variants using the Encyclopedia of DNA Elements (ENCODE) database and demonstrate that, of the predisposing disease variants, the majority have the potential to be regulatory. We also demonstrate that many of these variants affect the expression of nearby genes. Furthermore, we summarize results from the Immunochip, a custom array, which allows a detailed comparison between five of the diseases that have so far been analyzed using this platform.

Current Genetics and Epigenetics of Smoking/Tobacco-Related Cardiovascular Disease

Genetic and epigenetic factors are of great importance in cardiovascular biology and disease. Tobacco-smoking, one of the most important cardiovascular risk factors, is itself partially determined by genetic background and is associated with altered epigenetic patterns. This could render the genetics and epigenetics of smoking-related cardiovascular disease a textbook example of environmental epigenetics and modern approaches to multimodal data analysis. A pronounced association of smoking-related methylation patterns in the F2RL3 gene with prognosis in patients with stable coronary heart disease has recently been described. Nonetheless, surprisingly little concrete knowledge on the role of specific genetic variants and epigenetic modifications in the development of cardiovascular diseases in people who smoke has been accumulated. Beyond the current knowledge, the present review briefly outlines some chief challenges and priorities for moving forward in this field.

GWAS3D: Detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications

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.

Pleiotropy in complex traits: challenges and strategies

Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

A brief review on the Human Encyclopedia of DNA Elements (ENCODE) project

The ENCyclopedia Of DNA Elements (ENCODE) project is an international research consortium that aims to identify all functional elements in the human genome sequence. The second phase of the project comprised 1640 datasets from 147 different cell types, yielding a set of 30 publications across several journals. These data revealed that 80.4% of the human genome displays some functionality in at least one cell type. Many of these regulatory elements are physically associated with one another and further form a network or three-dimensional conformation to affect gene expression. These elements are also related to sequence variants associated with diseases or traits. All these findings provide us new insights into the organization and regulation of genes and genome, and serve as an expansive resource for understanding human health and disease.

Comprehensive functional annotation of seventy-one breast cancer risk Loci

Breast Cancer (BCa) genome-wide association studies revealed allelic frequency differences between cases and controls at index single nucleotide polymorphisms (SNPs). To date, 71 loci have thus been identified and replicated. More than 320,000 SNPs at these loci define BCa risk due to linkage disequilibrium (LD). We propose that BCa risk resides in a subgroup of SNPs that functionally affects breast biology. Such a shortlist will aid in framing hypotheses to prioritize a manageable number of likely disease-causing SNPs. We extracted all the SNPs, residing in 1 Mb windows around breast cancer risk index SNP from the 1000 genomes project to find correlated SNPs. We used FunciSNP, an R/Bioconductor package developed in-house, to identify potentially functional SNPs at 71 risk loci by coinciding them with chromatin biofeatures. We identified 1,005 SNPs in LD with the index SNPs (r(2)≥0.5) in three categories; 21 in exons of 18 genes, 76 in transcription start site (TSS) regions of 25 genes, and 921 in enhancers. Thirteen SNPs were found in more than one category. We found two correlated and predicted non-benign coding variants (rs8100241 in exon 2 and rs8108174 in exon 3) of the gene, ANKLE1. Most putative functional LD SNPs, however, were found in either epigenetically defined enhancers or in gene TSS regions. Fifty-five percent of these non-coding SNPs are likely functional, since they affect response element (RE) sequences of transcription factors. Functionality of these SNPs was assessed by expression quantitative trait loci (eQTL) analysis and allele-specific enhancer assays. Unbiased analyses of SNPs at BCa risk loci revealed new and overlooked mechanisms that may affect risk of the disease, thereby providing a valuable resource for follow-up studies.

Systematic functional regulatory assessment of disease-associated variants

Genome-wide association studies have discovered many genetic loci associated with disease traits, but the functional molecular basis of these associations is often unresolved. Genome-wide regulatory and gene expression profiles measured across individuals and diseases reflect downstream effects of genetic variation and may allow for functional assessment of disease-associated loci. Here, we present a unique approach for systematic integration of genetic disease associations, transcription factor binding among individuals, and gene expression data to assess the functional consequences of variants associated with hundreds of human diseases. In an analysis of genome-wide binding profiles of NFκB, we find that disease-associated SNPs are enriched in NFκB binding regions overall, and specifically for inflammatory-mediated diseases, such as asthma, rheumatoid arthritis, and coronary artery disease. Using genome-wide variation in transcription factor-binding data, we find that NFκB binding is often correlated with disease-associated variants in a genotype-specific and allele-specific manner. Furthermore, we show that this binding variation is often related to expression of nearby genes, which are also found to have altered expression in independent profiling of the variant-associated disease condition. Thus, using this integrative approach, we provide a unique means to assign putative function to many disease-associated SNPs.

The ENCODE project: implications for psychiatric genetics

The ENCyclopedia Of DNA Elements (ENCODE) project is a public research consortium that aims to identify all functional elements of the human genome sequence. The project comprised 1640 data sets, from 147 different cell type and the findings were released in a coordinated set of 34 publications across several journals. The ENCODE publications report that 80.4% of the human genome displays some functionality. These data have important implications for interpreting results from large-scale genetics studies. We reviewed some of the key findings from the ENCODE publications and discuss how they can influence or inform further investigations into the genetic factors contributing to neuropsychiatric disorders.

Population genetic tools for dissecting innate immunity in humans

Innate immunity involves direct interactions between the host and microorganisms, both pathogenic and symbiotic, so natural selection is expected to strongly influence genes involved in these processes. Population genetics investigates the impact of past natural selection events on the genome of present-day human populations, and it complements immunological as well as clinical and epidemiological genetic studies. Recent data show that the impact of selection on the different families of innate immune receptors and their downstream signalling molecules varies considerably. This Review discusses these findings and highlights how they help to delineate the relative functional importance of innate immune pathways, which can range from being essential to being redundant.

A variant in FTO shows association with melanoma risk not due to BMI

We report the results of an association study of melanoma that is based on the genome-wide imputation of the genotypes of 1,353 cases and 3,566 controls of European origin conducted by the GenoMEL consortium. This revealed an association between several SNPs in intron 8 of the FTO gene, including rs16953002, which replicated using 12,313 cases and 55,667 controls of European ancestry from Europe, the USA and Australia (combined P = 3.6 × 10(-12), per-allele odds ratio for allele A = 1.16). In addition to identifying a new melanoma-susceptibility locus, this is to our knowledge the first study to identify and replicate an association with SNPs in FTO not related to body mass index (BMI). These SNPs are not in intron 1 (the BMI-related region) and exhibit no association with BMI. This suggests FTO's function may be broader than the existing paradigm that FTO variants influence multiple traits only through their associations with BMI and obesity.

ENCODE: A Sourcebook of Epigenomes and Chromatin Language

Until recently, since the Human Genome Project, the general view has been that the majority of the human genome is composed of junk DNA and has little or no selective advantage to the organism. Now we know that this conclusion is an oversimplification. In April 2003, the National Human Genome Research Institute (NHGRI) launched an international research consortium called Encyclopedia of DNA Elements (ENCODE) to uncover non-coding functional elements in the human genome. The result of this project has identified a set of new DNA regulatory elements, based on novel relationships among chromatin accessibility, histone modifications, nucleosome positioning, DNA methylation, transcription, and the occupancy of sequence-specific factors. The project gives us new insights into the organization and regulation of the human genome and epigenome. Here, we sought to summarize particular aspects of the ENCODE project and highlight the features and data that have recently been released. At the end of this review, we have summarized a case study we conducted using the ENCODE epigenome data.

From Identification to Characterization of the Multiple Sclerosis Susceptibility Gene CLEC16A

Multiple sclerosis (MS) is an inflammatory, demyelinating disorder of the central nervous system that develops in genetically susceptible individuals, probably triggered by common environmental factors. Human leukocyte antigen (HLA) loci were early shown to confer the strongest genetic associations in MS. Now, more than 50 non-HLA MS susceptibility loci are identified, of which the majority are located in immune-regulatory genes. Single nucleotide polymorphisms (SNPs) in the C-type lectin-like domain family 16A (CLEC16A) gene were among the first non-HLA genetic variants that were confirmed to be associated with MS. Fine-mapping has indicated a primary association in MS and also other autoimmune diseases to intronic CLEC16A SNPs. Here, we review the identification of MS susceptibility variants in the CLEC16A gene region, functional studies of the CLEC16A molecule and the recent progress in understanding the implications thereof for MS development. This may serve as an example of the importance for further molecular investigation of the loci identified in genetic studies, with the aim to translate this knowledge into the clinic.

Exploring the effects of polymorphisms on cis-regulatory signal transduction response

cis-Regulatory sequences (CRSs) direct cell-specific and inducible gene expression in response to signal transduction networks, and it is becoming apparent that many cases of disease susceptibility and drug response stratification are due to polymorphisms that alter CRS responses in a context-dependent manner. In the current review, we describe successful methods for identifying CRSs and analyzing the effects of allelic variation on their responses to signal transduction. The technologies described build on the successes of ENCODE (ENCyclopedia Of DNA Elements) by exploring the effects of polymorphisms on CRS context dependency. This understanding is essential to uncover the genomic basis of disease susceptibility and will play a major role in delivering on the promise of personalized medicine.