Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission

Identification of genomic regions and genes associated with subclinical ketosis in periparturient dairy cows

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

New insights into the physiology and pathophysiology of the atypical sodium leak channel NALCN

Cell excitability and its modulation by hormones and neurotransmitters involve the concerted action of a large repertoire of membrane proteins, especially ion channels. Unique complements of coexpressed ion channels are exquisitely balanced against each other in different excitable cell types, establishing distinct electrical properties that are tailored for diverse physiological contributions, and dysfunction of any component may induce a disease state. A crucial parameter controlling cell excitability is the resting membrane potential (RMP) set by extra- and intracellular concentrations of ions, mainly Na+, K+, and Cl-, and their passive permeation across the cell membrane through leak ion channels. Indeed, dysregulation of RMP causes significant effects on cellular excitability. This review describes the molecular and physiological properties of the Na+ leak channel NALCN, which associates with its accessory subunits UNC-79, UNC-80, and NLF-1/FAM155 to conduct depolarizing background Na+ currents in various excitable cell types, especially neurons. Studies of animal models clearly demonstrate that NALCN contributes to fundamental physiological processes in the nervous system including the control of respiratory rhythm, circadian rhythm, sleep, and locomotor behavior. Furthermore, dysfunction of NALCN and its subunits is associated with severe pathological states in humans. The critical involvement of NALCN in physiology is now well established, but its study has been hampered by the lack of specific drugs that can block or agonize NALCN currents in vitro and in vivo. Molecular tools and animal models are now available to accelerate our understanding of how NALCN contributes to key physiological functions and the development of novel therapies for NALCN channelopathies.

Bipolar disorder-iPSC derived neural progenitor cells exhibit dysregulation of store-operated Ca2+ entry and accelerated differentiation

While most of the efforts to uncover mechanisms contributing to bipolar disorder (BD) focused on phenotypes at the mature neuron stage, little research has considered events that may occur during earlier timepoints of neurodevelopment. Further, although aberrant calcium (Ca2+) signaling has been implicated in the etiology of this condition, the possible contribution of store-operated Ca2+ entry (SOCE) is not well understood. Here, we report Ca2+ and developmental dysregulations related to SOCE in BD patient induced pluripotent stem cell (iPSC)-derived neural progenitor cells (BD-NPCs) and cortical-like glutamatergic neurons. First, using a Ca2+ re-addition assay we found that BD-NPCs and neurons had attenuated SOCE. Intrigued by this finding, we then performed RNA-sequencing and uncovered a unique transcriptome profile in BD-NPCs suggesting accelerated neurodifferentiation. Consistent with these results, we measured a slower rate of proliferation, increased neurite outgrowth, and decreased size in neurosphere formations with BD-NPCs. Also, we observed decreased subventricular areas in developing BD cerebral organoids. Finally, BD NPCs demonstrated high expression of the let-7 family while BD neurons had increased miR-34a, both being microRNAs previously implicated in neurodevelopmental deviations and BD etiology. In summary, we present evidence supporting an accelerated transition towards the neuronal stage in BD-NPCs that may be indicative of early pathophysiological features of the disorder.

Long noncoding RNA HOTAIR polymorphisms and susceptibility to bipolar disorder: a preliminary case-control study

Recent studies have shown that long noncoding RNAs contribute to the pathogenesis of bipolar disorder (BD). In this study, we genotyped four HOX Transcript Antisense Intergenic RNA (HOTAIR) gene polymorphisms to investigate if these variations could affect the risk of BD and its clinical subtypes. A total of 357 subjects, comprised of 194 BD patients and 163 age-matched healthy controls, were enrolled. Genotyping was carried out using PCR-RFLP and ARMS-PCR methods. We detected significant associations between the HOTAIR gene rs1899663 G/T, rs12826786 C/T, rs4759314 A/G, and rs920778 C/T polymorphism and the risk of BD under allelic, recessive, dominant, and codominant contrasted genetic models. The CT genotype of rs920778 C/T, GT genotype of rs1899663 G/T, and CT genotype of rs12826786 C/T polymorphisms enhanced the risk of BD type II (BDII). In contrast, the GG genotype of rs4759314 A/G polymorphism significantly diminished BDII risk by 83%. A positive association was noticed between CTTA and CTCG haplotypes of rs920778/rs1899663/rs12826786/rs4759314 and BD risk. Our findings reveal an interactive effect of HOTAIR polymorphisms on the development of BD and its subtypes. Further functional studies are needed to elucidate the role of these variations on HOTAIR expression and epigenetic status.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease

Background: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. Objective: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. Methods: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. Results: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. Conclusion: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer’s disease.

[Comorbidity in migraine]

Migraine is a common and often disabling disease with a prominent genetic basis. There are many comorbidities associated with migraine which have been identified as risk factors for progression to chronic migraine. Each of these has its own genetic load and shares some common characteristics with migraine. The identification of migraine comorbidities may help clarify common underlying genetic and biological mechanisms of diseases. The treatment of migraine should involve a multifaceted approach, aimed at identifying and reducing possible risk and comorbidity factors. This may prevent the evolution toward a chronic form and then toward pharmacological resistance.

Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic pathway to AD, with an emphasis on common loci. However, the contribution of rare variants in this pathway to AD has not been thoroughly investigated. In this study, we focused on the effect of rare variants on AD by first applying a rare-variant gene-set burden analysis using genes in the endocytic pathway on over 3,000 individuals with European ancestry from three large whole-genome sequencing (WGS) studies. We identified significant associations of rare-variant burden within the endocytic pathway with AD, which were successfully replicated in independent datasets. We further demonstrated that this endocytic rare-variant enrichment is associated with neurofibrillary tangles (NFTs) and age-related phenotypes, increasing the risk of obtaining severer brain damage, earlier age-at-onset, and earlier age-of-death. Next, by aggregating rare variants within each gene, we sought to identify single endocytic genes associated with AD and NFTs. Careful examination using NFTs revealed one significantly associated gene, ANKRD13D. To identify functional associations, we integrated bulk RNA-Seq data from over 600 brain tissues and found two endocytic expression genes (eGenes), HLA-A and SLC26A7, that displayed significant influences on their gene expressions. Differential expressions between AD patients and controls of these three identified genes were further examined by incorporating scRNA-Seq data from 48 post-mortem brain samples and demonstrated distinct expression patterns across cell types. Taken together, our results demonstrated strong rare-variant effect in the endocytic pathway on AD risk and progression and functional effect of gene expression alteration in both bulk and single-cell resolution, which may bring more insight and serve as valuable resources for future AD genetic studies, clinical research, and therapeutic targeting.

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left-Right Atrial Appendages

More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue: p = 0.0076; yellow: p = 0.023). Five AF-associated biomarkers were identified (ERBB2, HERC4, MYH7, MYPN, and PBXIP1), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene LOXL1 was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of ERBB2, MYH7, and MYPN were observed (p < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes ERBB2 and MYPN via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Pathophysiological Bases of Comorbidity in Migraine

Despite that it is commonly accepted that migraine is a disorder of the nervous system with a prominent genetic basis, it is comorbid with a plethora of medical conditions. Several studies have found bidirectional comorbidity between migraine and different disorders including neurological, psychiatric, cardio- and cerebrovascular, gastrointestinal, metaboloendocrine, and immunological conditions. Each of these has its own genetic load and shares some common characteristics with migraine. The bidirectional mechanisms that are likely to underlie this extensive comorbidity between migraine and other diseases are manifold. Comorbid pathologies can induce and promote thalamocortical network dysexcitability, multi-organ transient or persistent pro-inflammatory state, and disproportionate energetic needs in a variable combination, which in turn may be causative mechanisms of the activation of an ample defensive system with includes the trigeminovascular system in conjunction with the neuroendocrine hypothalamic system. This strategy is designed to maintain brain homeostasis by regulating homeostatic needs, such as normal subcortico-cortical excitability, energy balance, osmoregulation, and emotional response. In this light, the treatment of migraine should always involves a multidisciplinary approach, aimed at identifying and, if necessary, eliminating possible risk and comorbidity factors.

Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder

Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.

Structure of the human sodium leak channel NALCN in complex with FAM155A

NALCN, a sodium leak channel expressed mainly in the central nervous system, is responsible for the resting Na+ permeability that controls neuronal excitability. Dysfunctions of the NALCN channelosome, NALCN with several auxiliary subunits, are associated with a variety of human diseases. Here, we report the cryo-EM structure of human NALCN in complex with FAM155A at an overall resolution of 3.1 angstroms. FAM155A forms extensive interactions with the extracellular loops of NALCN that may help stabilize NALCN in the membrane. A Na+ ion-binding site, reminiscent of a Ca2+ binding site in Cav channels, is identified in the unique EEKE selectivity filter. Despite its 'leaky' nature, the channel is closed and the intracellular gate is sealed by S6I, II-III linker and III-IV linker. Our study establishes the molecular basis of Na+ permeation and voltage sensitivity, and provides important clues to the mechanistic understanding of NALCN regulation and NALCN channelosome-related diseases.

Gene-set Enrichment with Mathematical Biology (GEMB)

BACKGROUND

Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

RESULTS

We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

CONCLUSIONS

Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways

Supplemental Digital Content is available in the text.

Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease.

Understanding the nature of psychiatric comorbidity in migraine: a systematic review focused on interactions and treatment implications

BACKGROUND

Migraine is a highly prevalent and disabling neurological disorder which is commonly linked with a broad range of psychiatric comorbidities, especially among subjects with migraine with aura or chronic migraine. Defining the exact nature of the association between migraine and psychiatric disorders and bringing out the pathophysiological mechanisms underlying the comorbidity with psychiatric conditions are relevant issues in the clinical practice.

METHODS

A systematic review of the most relevant studies about migraine and psychiatric comorbidity was performed using "PubMed", "Scopus", and "ScienceDirect" electronic databases from 1 January 1998 to 15 July 2018. Overall, 178 studies met our inclusion criteria and were included in the current review.

RESULTS

According to the most relevant findings of our overview, the associations with psychiatric comorbidities are complex, with a bidirectional association of major depression and panic disorder with migraine. Importantly, optimizing the pharmacological and non-pharmacological treatment of either migraine or its psychiatric comorbidities might help clinicians to attenuate the burden of both these conditions.

CONCLUSIONS

The available data highlight the need for a comprehensive evaluation of psychiatric disorders in migraine in order to promote an integrated model of care and carefully address the burden and psychosocial impairment related to psychiatric comorbidities in migraine.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Neurons derived from patients with bipolar disorder divide into intrinsically different sub-populations of neurons, predicting the patients' responsiveness to lithium

Bipolar disorder (BD) is a progressive psychiatric disorder with more than 3% prevalence worldwide. Affected individuals experience recurrent episodes of depression and mania, disrupting normal life and increasing the risk of suicide greatly. The complexity and genetic heterogeneity of psychiatric disorders have challenged the development of animal and cellular models. We recently reported that hippocampal dentate gyrus (DG) neurons differentiated from induced pluripotent stem cell (iPSC)-derived fibroblasts of BD patients are electrophysiologically hyperexcitable. Here we used iPSCs derived from Epstein-Barr virus-immortalized B-lymphocytes to verify that the hyperexcitability of DG-like neurons is reproduced in this different cohort of patients and cells. Lymphocytes are readily available for research with a large number of banked lines with associated patient clinical description. We used whole-cell patch-clamp recordings of over 460 neurons to characterize neurons derived from control individuals and BD patients. Extensive functional analysis showed that intrinsic cell parameters are very different between the two groups of BD neurons, those derived from lithium (Li)-responsive (LR) patients and those derived from Li-non-responsive (NR) patients, which led us to partition our BD neurons into two sub-populations of cells and suggested two different subdisorders. Training a Naïve Bayes classifier with the electrophysiological features of patients whose responses to Li are known allows for accurate classification with more than 92% success rate for a new patient whose response to Li is unknown. Despite their very different functional profiles, both populations of neurons share a large, fast after-hyperpolarization (AHP). We therefore suggest that the large, fast AHP is a key feature of BD and a main contributor to the fast, sustained spiking abilities of BD neurons. Confirming our previous report with fibroblast-derived DG neurons, chronic Li treatment reduced the hyperexcitability in the lymphoblast-derived LR group but not in the NR group, strengthening the validity and utility of this new human cellular model of BD.

Treatment with TREK1 and TRPC3/6 ion channel inhibitors upregulates microRNA expression in a mouse model of chronic mild stress

Depression is a common mental disorder characterized by the mood of deep sadness. Recent studies have demonstrated that microRNAs and ion channels have significant roles in the etiopathogenesis of depression. Therefore, we investigated the effects of the TREK1 ion channel inhibitor anandamide and the TRPC3/6 inhibitor norgestimate on microRNA expression and antidepressant effect in the mouse chronic mild stress (CMS) model of depression. Male BALB/c mice were divided into groups as control, CMS, CMS+sertraline, CMS+anandamide, CMS+sertraline+anandamide, CMS+norgestimate and CMS+sertraline+norgestimate. Forced swim test (FST) and Sucrose Preference Test (SPT) were utilized to assess depression levels. Anandamide and norgestimate were administered subcutaneously (5mg/kg/day), and sertraline was applied intraperitoneally (10mg/kg/day) for two days during FST. miRNA and ion channel gene expression levels in the prefrontal cortex were assessed with qRT-PCR. qRT-PCR results demonstrated that there was a significant increase in miR-9-5p, miR-128-1-5p, and miR-382-5p, and a significant decrease in miR-16-5p, miR-129-5p, and miR-219a-5p in the CMS group compared with the control group. Generally, anandamide and norgestimate significantly increased all miRNA expression. It was also determined that anandamide and norgestimate had an antidepressant action in FST when used alone and especially when used in conjunction with sertraline. Based on the study results, it could be argued that an increase in miR-9-5p and miR-128-1-5p, consistent with the literature, could play significant roles in the etiopathogenesis of depression. The antidepressant action of anandamide and norgesimate in FST showed for the first time that these inhibitors could be used as in conjuction with sertraline in depression treatment.

The statistical properties of gene-set analysis

The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.

Potassium channel gene associations with joint processing speed and white matter impairments in schizophrenia

Patients with schizophrenia show decreased processing speed on neuropsychological testing and decreased white matter integrity as measured by diffusion tensor imaging, two traits shown to be both heritable and genetically associated indicating that there may be genes that influence both traits as well as schizophrenia disease risk. The potassium channel gene family is a reasonable candidate to harbor such a gene given the prominent role potassium channels play in the central nervous system in signal transduction, particularly in myelinated axons. We genotyped members of the large potassium channel gene family focusing on putatively functional single nucleotide polymorphisms (SNPs) in a population of 363 controls, 194 patients with schizophrenia spectrum disorder (SSD) and 28 patients with affective disorders with psychotic features who completed imaging and neuropsychological testing. We then performed three association analyses using three phenotypes - processing speed, whole-brain white matter fractional anisotropy (FA) and schizophrenia spectrum diagnosis. We extracted SNPs showing an association at a nominal P value of <0.05 with all three phenotypes in the expected direction: decreased processing speed, decreased FA and increased risk of SSD. A single SNP, rs8234, in the 3' untranslated region of voltage-gated potassium channel subfamily Q member 1 (KCNQ1) was identified. Rs8234 has been shown to affect KCNQ1 expression levels, and KCNQ1 levels have been shown to affect neuronal action potentials. This exploratory analysis provides preliminary data suggesting that KCNQ1 may contribute to the shared risk for diminished processing speed, diminished white mater integrity and increased risk of schizophrenia.

A combined analysis of genome-wide expression profiling of bipolar disorder in human prefrontal cortex

Numbers of gene expression profiling studies of bipolar disorder have been published. Besides different array chips and tissues, variety of the data processes in different cohorts aggravated the inconsistency of results of these genome-wide gene expression profiling studies. By searching the gene expression databases, we obtained six data sets for prefrontal cortex (PFC) of bipolar disorder with raw data and combinable platforms. We used standardized pre-processing and quality control procedures to analyze each data set separately and then combined them into a large gene expression matrix with 101 bipolar disorder subjects and 106 controls. A standard linear mixed-effects model was used to calculate the differentially expressed genes (DEGs). Multiple levels of sensitivity analyses and cross validation with genetic data were conducted. Functional and network analyses were carried out on basis of the DEGs. In the result, we identified 198 unique differentially expressed genes in the PFC of bipolar disorder and control. Among them, 115 DEGs were robust to at least three leave-one-out tests or different pre-processing methods; 51 DEGs were validated with genetic association signals. Pathway enrichment analysis showed these DEGs were related with regulation of neurological system, cell death and apoptosis, and several basic binding processes. Protein-protein interaction network further identified one key hub gene. We have contributed the most comprehensive integrated analysis of bipolar disorder expression profiling studies in PFC to date. The DEGs, especially those with multiple validations, may denote a common signature of bipolar disorder and contribute to the pathogenesis of disease.

Pathway-based approach using hierarchical components of collapsed rare variants

MOTIVATION

To address 'missing heritability' issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem.

RESULTS

Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study.

AVAILABILITY AND IMPLEMENTATION

An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/

CONTACT

tspark@stats.snu.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

DNA methylation and expression of KCNQ3 in bipolar disorder

OBJECTIVES

Accumulating evidence implicates the potassium voltage-gated channel, KQT-like subfamily, member 2 and 3 (KCNQ2 and KCNQ3) genes in the etiology of bipolar disorder (BPD). Reduced KCNQ2 or KCNQ3 gene expression might lead to a loss of inhibitory M-current and an increase in neuronal hyperexcitability in disease. The goal of the present study was to evaluate epigenetic and gene expression associations of the KCNQ2 and KCNQ3 genes with BPD.

METHODS

DNA methylation and gene expression levels of alternative transcripts of KCNQ2 and KCNQ3 capable of binding the ankyrin G (ANK3) gene were evaluated using bisulfite pyrosequencing and the quantitative real-time polymerase chain reaction in the postmortem prefrontal cortex of subjects with BPD and matched controls from the McLean Hospital. Replication analyses of DNA methylation findings were performed using prefrontal cortical DNA obtained from the Stanley Medical Research Institute.

RESULTS

Significantly lower expression was observed in KCNQ3, but not KCNQ2. DNA methylation analysis of CpGs within an alternative exonic region of KCNQ3 exon 11 demonstrated significantly lower methylation in BPD, and correlated significantly with KCNQ3 mRNA levels. Lower KCNQ3 exon 11 DNA methylation was observed in the Stanley Medical Research Institute replication cohort, although only after correcting for mood stabilizer status. Mood stabilizer treatment in rats resulted in a slight DNA methylation increase at the syntenic KCNQ3 exon 11 region, which subsequent analyses suggested could be the result of alterations in neuronal proportion.

CONCLUSION

The results of the present study suggest that epigenetic alterations in the KCNQ3 gene may be important in the etiopathogenesis of BPD and highlight the importance of controlling for medication and cellular composition-induced heterogeneity in psychiatric studies of the brain.

Variants in Ion Channel Genes Link Phenotypic Features of Bipolar Illness to Specific Neurobiological Process Domains

Recent advances in genome-wide association studies are pointing towards a major role for voltage-gated ion channels in neuropsychiatric disorders and, in particular, bipolar disorder (BD). The phenotype of BD is complex, with symptoms during mood episodes and deficits persisting between episodes. We have tried to elucidate the common neurobiological mechanisms associated with ion channel signaling in order to provide a new perspective on the clinical symptoms and possible endophenotypes seen in BD patients. We propose a model in which the multiple variants in genes coding for ion channel proteins would perturb motivational circuits, synaptic plasticity, myelination, hypothalamic-pituitary-adrenal axis function, circadian neuronal rhythms, and energy regulation. These changes in neurobiological mechanisms would manifest in endophenotypes of aberrant reward processing, white matter hyperintensities, deficits in executive function, altered frontolimbic connectivity, increased amygdala activity, increased melatonin suppression, decreased REM latency, and aberrant myo-inositol/ATP shuttling. The endophenotypes result in behaviors of poor impulse control, motivational changes, cognitive deficits, abnormal stress response, sleep disturbances, and energy changes involving different neurobiological process domains. The hypothesis is that these disturbances start with altered neural circuitry during development, following which multiple environmental triggers may disrupt the neuronal excitability balance through an activity-dependent molecular process, resulting in clinical mood episodes.

Genome-wide pathway-based association study implicates complement system in the development of Kashin-Beck disease in Han Chinese

Kashin-Beck disease (KBD) is a chronic osteochondropathy. The pathogenesis of KBD remains unknown. To identify relevant biological pathways for KBD, we conducted a genome-wide pathway-based association study (GWPAS) following by replication analysis, totally using 2743 Chinese Han adults. A modified gene set enrichment algorithm was used to detect association between KBD and 963 biological pathways. Cartilage gene expression analysis and serum complement measurement were performed to evaluate the functional relevance of identified pathway with KBD. We found that the Complement and Coagulation Cascades (CACC) pathway was significantly associated with KBD (P value=3.09×10(-5), false-discovery rate=0.042). Within the CACC pathway, the most significant association was observed at rs1656966 (P value=1.97×10(-4)) of KNG1 gene. Further replication study observed that rs1656966 (P value=0.037) was significantly associated with KBD in an independent validation sample of 1026 subjects. Gene expression analysis observed that CFD (ratio=3.39±2.68), A2M (ratio=3.67±5.63), C5 (ratio=2.65±2.52) and CD46 (ratio=2.29±137) genes of the CACC pathway were up-regulated in KBD articular cartilage compared to healthy articular cartilage. The serum level of complement C5 in KBD patients were significantly higher than that in healthy controls (P value=0.038). Our study is the first to suggest that complement system-related CACC pathway contributed to the development of KBD.

Meta-analysis based variable selection for gene expression data

Recent advance in biotechnology and its wide applications have led to the generation of many high-dimensional gene expression data sets that can be used to address similar biological questions. Meta-analysis plays an important role in summarizing and synthesizing scientific evidence from multiple studies. When the dimensions of datasets are high, it is desirable to incorporate variable selection into meta-analysis to improve model interpretation and prediction. According to our knowledge, all existing methods conduct variable selection with meta-analyzed data in an "all-in-or-all-out" fashion, that is, a gene is either selected in all of studies or not selected in any study. However, due to data heterogeneity commonly exist in meta-analyzed data, including choices of biospecimens, study population, and measurement sensitivity, it is possible that a gene is important in some studies while unimportant in others. In this article, we propose a novel method called meta-lasso for variable selection with high-dimensional meta-analyzed data. Through a hierarchical decomposition on regression coefficients, our method not only borrows strength across multiple data sets to boost the power to identify important genes, but also keeps the selection flexibility among data sets to take into account data heterogeneity. We show that our method possesses the gene selection consistency, that is, when sample size of each data set is large, with high probability, our method can identify all important genes and remove all unimportant genes. Simulation studies demonstrate a good performance of our method. We applied our meta-lasso method to a meta-analysis of five cardiovascular studies. The analysis results are clinically meaningful.

Systematic annotation of celiac disease loci refines pathological pathways and suggests a genetic explanation for increased interferon-gamma levels

Although genome-wide association studies and fine mapping have identified 39 non-HLA loci associated with celiac disease (CD), it is difficult to pinpoint the functional variants and susceptibility genes in these loci. We applied integrative approaches to annotate and prioritize functional single nucleotide polymorphisms (SNPs), genes and pathways affected in CD. CD-associated SNPs were intersected with regulatory elements categorized by the ENCODE project to prioritize functional variants, while results from cis-expression quantitative trait loci (eQTL) mapping in 1469 blood samples were combined with co-expression analyses to prioritize causative genes. To identify the key cell types involved in CD, we performed pathway analysis on RNA-sequencing data from different immune cell populations and on publicly available expression data on non-immune tissues. We discovered that CD SNPs are significantly enriched in B-cell-specific enhancer regions, suggesting that, besides T-cell processes, B-cell responses play a major role in CD. By combining eQTL and co-expression analyses, we prioritized 43 susceptibility genes in 36 loci. Pathway and tissue-specific expression analyses on these genes suggested enrichment of CD genes in the Th1, Th2 and Th17 pathways, but also predicted a role for four genes in the intestinal barrier function. We also discovered an intricate transcriptional connectivity between CD susceptibility genes and interferon-γ, a key effector in CD, despite the absence of CD-associated SNPs in the IFNG locus. Using systems biology, we prioritized the CD-associated functional SNPs and genes. By highlighting a role for B cells in CD, which classically has been described as a T-cell-driven disease, we offer new insights into the mechanisms and pathways underlying CD.

Pathway analysis of cervical cancer genome-wide association study highlights the MHC region and pathways involved in response to infection

Cervical cancer is caused by infection with human papillomavirus (HPV). A genome-wide association study (GWAS) has identified several susceptibility loci for cervical cancer, but they explain only a small fraction of cervical cancer heritability. Other variants with weaker effect may be missed due to the stringent significance threshold. To identify important pathways in cervical carcinogenesis, we performed a two-stage pathway analysis in two independent GWASs in the Swedish population, using the single-nucleotide polymorphism (SNP) ratio test. The 565 predefined pathways from Kyoto Encyclopedia of Genes and Genomes and BioCarta databases were systematically evaluated in the discovery stage (1034 cases and 3948 controls with 632,668 SNPs) and the suggestive pathways were further validated in the replication stage (616 cases and 506 controls with 341,358 SNPs). We found 12 pathways that were significant in both stages, and these were further validated using set-based analysis. For 10 of these pathways, the effect was mainly due to genetic variation within the major histocompatibility complex (MHC) region. In addition, we identified a set of novel candidate genes outside the MHC region in the pathways denoted 'Staphylococcus aureus infection' and 'herpes simplex infection' that influenced susceptibility to cervical cancer (empirical P = 4.99 × 10(-5) and 4.99 × 10(-5) in the discovery study; empirical P = 8.98 × 10(-5) and 0.009 in the replication study, respectively). Staphylococcus aureus infection may evoke an inflammatory response that inadvertently enhances malignant progression caused by HPV infection, and Herpes simplex virus-2 infection may act in conjunction with HPV infection to increase the risk of cervical carcinoma development. These findings provide new insights into the etiology of cervical cancer.

Identification of gene ontologies linked to prefrontal-hippocampal functional coupling in the human brain

Functional interactions between the dorsolateral prefrontal cortex and hippocampus during working memory have been studied extensively as an intermediate phenotype for schizophrenia. Coupling abnormalities have been found in patients, their unaffected siblings, and carriers of common genetic variants associated with schizophrenia, but the global genetic architecture of this imaging phenotype is unclear. To achieve genome-wide hypothesis-free identification of genes and pathways associated with prefrontal-hippocampal interactions, we combined gene set enrichment analysis with whole-genome genotyping and functional magnetic resonance imaging data from 269 healthy German volunteers. We found significant enrichment of the synapse organization and biogenesis gene set. This gene set included known schizophrenia risk genes, such as neural cell adhesion molecule (NRCAM) and calcium channel, voltage-dependent, beta 2 subunit (CACNB2), as well as genes with well-defined roles in neurodevelopmental and plasticity processes that are dysfunctional in schizophrenia and have mechanistic links to prefrontal-hippocampal functional interactions. Our results demonstrate a readily generalizable approach that can be used to identify the neurogenetic basis of systems-level phenotypes. Moreover, our findings identify gene sets in which genetic variation may contribute to disease risk through altered prefrontal-hippocampal functional interactions and suggest a link to both ongoing and developmental synaptic plasticity.

Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia

In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

The sodium leak channel, NALCN, in health and disease

Ion channels are crucial components of cellular excitability and are involved in many neurological diseases. This review focuses on the sodium leak, G protein-coupled receptors (GPCRs)-activated NALCN channel that is predominantly expressed in neurons where it regulates the resting membrane potential and neuronal excitability. NALCN is part of a complex that includes not only GPCRs, but also UNC-79, UNC-80, NLF-1 and src family of Tyrosine kinases (SFKs). There is growing evidence that the NALCN channelosome critically regulates its ion conduction. Both in mammals and invertebrates, animal models revealed an involvement in many processes such as locomotor behaviors, sensitivity to volatile anesthetics, and respiratory rhythms. There is also evidence that alteration in this NALCN channelosome can cause a wide variety of diseases. Indeed, mutations in the NALCN gene were identified in Infantile Neuroaxonal Dystrophy (INAD) patients, as well as in patients with an Autosomal Recessive Syndrome with severe hypotonia, speech impairment, and cognitive delay. Deletions in NALCN gene were also reported in diseases such as 13q syndrome. In addition, genes encoding NALCN, NLF- 1, UNC-79, and UNC-80 proteins may be susceptibility loci for several diseases including bipolar disorder, schizophrenia, Alzheimer's disease, autism, epilepsy, alcoholism, cardiac diseases and cancer. Although the physiological role of the NALCN channelosome is poorly understood, its involvement in human diseases should foster interest for drug development in the near future. Toward this goal, we review here the current knowledge on the NALCN channelosome in physiology and diseases.

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

Genome-wide association study combining pathway analysis for typical sporadic amyotrophic lateral sclerosis in Chinese Han populations

Sporadic amyotrophic lateral sclerosis (sALS) is a severe neurodegenerative disease that causes progressive motor neuron death. Although the etiology of sALS remains unknown, genetic variants are thought to predispose individuals to the disease. Several recent genome-wide association studies have identified a number of loci that increase sALS susceptibility, but these only explain a small proportion of the disease. To extend the current genetic evidence and to identify novel candidates of sALS, we performed a pooling genome-wide association study by 859,311 autosomal single-nucleotide polymorphisms of IlluminaHumanOmniZhongHua-8 combining pathway analysis in 250 typical sALS cases precluding age, clinical course, and phenotype interference and 250 control subjects from Chinese Han populations (CHP). The results revealed that 8 novel loci of 1p34.3, 3p21.1, 3p22.2, 10p15.2, 22q12.1, 3q13.11, 11q25, 12q24.33, and 5 previously reported loci of CNTN4 (kgp11325216), ATXN1 (kgp8327591), C9orf72 (kgp6016770), ITPR2 (kgp3041552), and SOD1 (kgp10760302) were associated with sALS from CHP. Furthermore, the pathway analysis based on the Gene Set Analysis Toolkit V2 showed that 10 top pathways were strongly associated with sALS from CHP, and among them, the 7 most potentially candidate pathways were phosphatidylinositol signaling system, Wnt signaling pathway, axon guidance, MAPK signaling pathway, neurotrophin signaling pathway, arachidonic acid metabolism, and T-cell receptor signaling pathway, a total of 39 significantly associate genes in 7 candidate pathways was suggested to involve in the pathogenesis of sALS from CHP. In conclusion, our results revealed several new loci and pathways related to sALS from CHP and extend the association evidence for partial loci, genes, and pathways, which were previously identified in other populations. Thus, our data provided new clues for exploring the pathogenesis of sALS.

Pathways systematically associated to Hirschsprung's disease

Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.

BDgene: a genetic database for bipolar disorder and its overlap with schizophrenia and major depressive disorder

BACKGROUND

Bipolar disorder (BD) is a common psychiatric disorder with complex genetic architecture. It shares overlapping genetic influences with schizophrenia (SZ) and major depressive disorder (MDD). Large numbers of genetic studies of BD and cross-disorder studies between BD and SZ/MDD have accumulated numerous genetic data. There is a growing need to integrate the data to provide a comprehensive data set to facilitate the genetic study of BD and its highly relevant diseases.

METHODS

BDgene database was developed to integrate BD-related genetic factors and shared ones with SZ/MDD from profound literature reading. On the basis of data from the literature, in-depth analyses were performed for further understanding of the data, including gene prioritization, pathway-based analysis, intersection analysis of multidisease candidate genes, and pathway enrichment analysis.

RESULTS

BDgene includes multiple types of literature-reported genetic factors of BD with both positive and negative results, including 797 genes, 3119 single nucleotide polymorphisms, and 789 regions. Shared genetic factors such as single nucleotide polymorphisms, genes, and regions from published cross-disorder studies among BD and SZ/MDD were also presented. In-depth data analyses identified 43 BD core genes; 70 BD candidate pathways; and 127, 79, and 107 new potential cross-disorder genes for BD-SZ, BD-MDD, and BD-SZ-MDD, respectively.

CONCLUSIONS

As a central genetic database for BD and the first cross-disorder database for BD and SZ/MDD, BDgene provides not only a comprehensive review of current genetic research but also high-confidence candidate genes and pathways for understanding of BD mechanism and shared etiology among its relevant diseases. BDgene is freely available at http://bdgene.psych.ac.cn.

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.

A review of potassium channels in bipolar disorder

Although bipolar disorder (BP) is one of the most heritable psychiatric conditions, susceptibility genes for the disorder have yet to be conclusively identified. It is likely that variants in multiple genes across multiple pathways contribute to the genotype–phenotype relationship in the affected population. Recent evidence from genome-wide association studies implicates an entire class of genes related to the structure and regulation of ion channels, suggesting that the etiology of BP may arise from channelopathies. In this review, we examine the evidence for this hypothesis, with a focus on the potential role of voltage-gated potassium channels. We consider evidence from genetic and expression studies, and discuss the potential underlying biology. We consider animal models and treatment implications of the involvement of potassium ion channelopathy in BP. Finally, we explore intriguing parallels between BP and epilepsy, the signature channelopathy of the central nervous system.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.

White matter integrity as an intermediate phenotype: exploratory genome-wide association analysis in individuals at high risk of bipolar disorder

White matter integrity, as measured using diffusion tensor imaging (DTI), is reduced in individuals with bipolar disorder (BD), their unaffected relatives and carriers of specific risk-alleles. Fractional anisotropy (FA), an index of white matter integrity, is highly heritable but the genetic architecture of this trait has received little investigation. In this study we performed a genome-wide association study with FA as quantitative phenotype, in unaffected relatives of patients with BD (N=70) and a matched control group (N=80). Amongst our top results were SNPs located in genes involved in cell adhesion, white matter development and neuronal plasticity. Pathway analysis of the top associated polymorphisms and genes confirmed the enrichment of processes relevant to BD and white matter development, including axon guidance, ErbB-signalling neurotrophin signalling, phosphatidylinositol signalling, and cell adhesion. The majority of genes implicated in these pathways were differentially associated with FA in individuals at high familial risk, suggesting interactions with genetic background or environmental factors secondary to familial risk for BD. Although the present findings require independent replication, the results encourage the use of global FA as a quantitative phenotype in future large-scale studies which may help to identify the biological processes underlying reduced FA in BD and other psychiatric disorders.

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

Identifying SNP targeted pathways in partial epilepsies with genome-wide association study data

PURPOSE

In a recent genome-wide association study for partial epilepsies in the European population, a common genetic variation has been reported to affect partial epilepsy only modestly. However, in complex diseases such as partial epilepsy, multiple factors (e.g. single nucleotide polymorphisms, microRNAs, metabolic and epigenetic factors) may target different sets of genes in the same pathway, affecting its function and thus causing the disease development. In this regard, we hypothesize that the pathways are critical for elucidating the mechanisms underlying partial epilepsy.

METHODS

Previously we had developed a novel methodology with the aim of identifying the disease-related pathways. We had combined evidence of genetic association with current knowledge of (i) biochemical pathways, (ii) protein-protein interaction networks, and (iii) the functional information of selected single nucleotide polymorphisms. In our present study, we apply this methodology to a data set on partial epilepsy, including 3445 cases and 6935 controls of European ancestry.

RESULTS

We have identified 30 overrepresented pathways with corrected p-values smaller than 10(-12). These pathways include complement and coagulation cascades, cell cycle, focal adhesion, extra cellular matrix-receptor interaction, JAK-STAT signaling pathway, MAPK signaling pathway, proteasome, ribosome, calcium signaling and regulation of actin cytoskeleton pathways. Most of these pathways have growing scientific support in the literature as being associated with partial epilepsy. We also demonstrate that different factors affect distinct parts of the pathways, as shown here on complement and coagulation cascades pathway with a comparison of gene expression vs. genome-wide association study.

CONCLUSIONS

Traditional studies on genome-wide association have not revealed strong associations in epilepsies, since these single nucleotide polymorphisms are not shared by most of the patients. Our results suggest that it is more effective to incorporate the functional effect of a single nucleotide polymorphism on the gene product, protein-protein interaction networks and functional enrichment tools into genome-wide association studies. These can then be used to determine leading molecular pathways, which cannot be detected through traditional analyses. We hope that this type of analysis brings the research community one step closer to unraveling the complex genetic structure of epilepsies.

The identification of pathway markers in intracranial aneurysm using genome-wide association data from two different populations

The identification of significant individual factors causing complex diseases is challenging in genome-wide association studies (GWAS) since each factor has only a modest effect on the disease development mechanism. In this study, we hypothesize that the biological pathways that are targeted by these individual factors show higher conservation within and across populations. To test this hypothesis, we searched for the disease related pathways on two intracranial aneurysm GWAS in European and Japanese case-control cohorts. Even though there were a few significantly conserved SNPs within and between populations, seven of the top ten affected pathways were found significant in both populations. The probability of random occurrence of such an event is 2.44E-36. We therefore claim that even though each individual has a unique combination of factors involved in the mechanism of disease development, most targeted pathways that need to be altered by these factors are, for the most part, the same. These pathways can serve as disease markers. Individuals, for example, can be scanned for factors affecting the genes in marker pathways. Hence, individual factors of disease development can be determined; and this knowledge can be exploited for drug development and personalized therapeutic applications. Here, we discuss the potential avenues of pathway markers in medicine and their translation to preventive and individualized health care.

Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives

A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future.

Genome-wide association study of cardiac structure and systolic function in African Americans: the Candidate Gene Association Resource (CARe) study

BACKGROUND

Using data from 4 community-based cohorts of African Americans, we tested the association between genome-wide markers (single-nucleotide polymorphisms) and cardiac phenotypes in the Candidate-gene Association Resource study.

METHODS AND RESULTS

Among 6765 African Americans, we related age, sex, height, and weight-adjusted residuals for 9 cardiac phenotypes (assessed by echocardiogram or magnetic resonance imaging) to 2.5 million single-nucleotide polymorphisms genotyped using Genome-wide Affymetrix Human SNP Array 6.0 (Affy6.0) and the remainder imputed. Within the cohort, genome-wide association analysis was conducted, followed by meta-analysis across cohorts using inverse variance weights (genome-wide significance threshold=4.0 ×10(-7)). Supplementary pathway analysis was performed. We attempted replication in 3 smaller cohorts of African ancestry and tested lookups in 1 consortium of European ancestry (EchoGEN). Across the 9 phenotypes, variants in 4 genetic loci reached genome-wide significance: rs4552931 in UBE2V2 (P=1.43×10(-7)) for left ventricular mass, rs7213314 in WIPI1 (P=1.68×10(-7)) for left ventricular internal diastolic diameter, rs1571099 in PPAPDC1A (P=2.57×10(-8)) for interventricular septal wall thickness, and rs9530176 in KLF5 (P=4.02×10(-7)) for ejection fraction. Associated variants were enriched in 3 signaling pathways involved in cardiac remodeling. None of the 4 loci replicated in cohorts of African ancestry was confirmed in lookups in EchoGEN.

CONCLUSIONS

In the largest genome-wide association study of cardiac structure and function to date in African Americans, we identified 4 genetic loci related to left ventricular mass, interventricular septal wall thickness, left ventricular internal diastolic diameter, and ejection fraction, which reached genome-wide significance. Replication results suggest that these loci may be unique to individuals of African ancestry. Additional large-scale studies are warranted for these complex phenotypes.

Pathway analysis using information from allele-specific gene methylation in genome-wide association studies for bipolar disorder

Bipolar disorder (BPD) is a complex psychiatric trait with high heritability. Despite efforts through conducting genome-wide association (GWA) studies, the success of identifying susceptibility loci for BPD has been limited, which is partially attributed to the complex nature of its pathogenesis. Pathway-based analytic strategy is a powerful tool to explore joint effects of gene sets within specific biological pathways. Additionally, to incorporate other aspects of genomic data into pathway analysis may further enhance our understanding for the underlying mechanisms for BPD. Patterns of DNA methylation play important roles in regulating gene expression and function. A commonly observed phenomenon, allele-specific methylation (ASM) describes the associations between genetic variants and DNA methylation patterns. The present study aimed to identify biological pathways that are involve in the pathogenesis of BPD while incorporating brain specific ASM information in pathway analysis using two large-scale GWA datasets in Caucasian populations. A weighting scheme was adopted to take ASM information into consideration for each pathway. After multiple testing corrections, we identified 88 and 15 enriched pathways for their biological relevance for BPD in the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium dataset, respectively. Many of these pathways were significant only when applying the weighting scheme. Three ion channel related pathways were consistently identified in both datasets. Results in the GAIN dataset also suggest for the roles of extracellular matrix in brain for BPD. Findings from Gene Ontology (GO) analysis exhibited functional enrichment among genes of non-GO pathways in activity of gated channel, transporter, and neurotransmitter receptor. We demonstrated that integrating different data sources with pathway analysis provides an avenue to identify promising and novel biological pathways for exploring the underlying molecular mechanisms for bipolar disorder. Further basic research can be conducted to target the biological mechanisms for the identified genes and pathways.