Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence

Effects of adolescent substance use disorders on central cholinergic function

Adolescence is a transitional period between childhood and adulthood, in which the individual undergoes significant cognitive, behavioral, physical, emotional, and social developmental changes. During this period, adolescents engage in experimentation and risky behaviors such as licit and illicit drug use. Adolescents' high vulnerability to abuse drugs and natural reinforcers leads to greater risk for developing substance use disorders (SUDs) during adulthood. Accumulating evidence indicates that the use and abuse of licit and illicit drugs during adolescence and emerging adulthood can disrupt the cholinergic system and its processes. This review will focus on the effects of peri-adolescent nicotine and/or alcohol use, or exposure, on the cholinergic system during adulthood from preclinical and clinical studies. This review further explores potential cholinergic agents and pharmacological manipulations to counteract peri-adolescent nicotine and/or alcohol abuse.

Traumatic events during childhood and its risks to substance use in adulthood: an observational and genome-wide by environment interaction study in UK Biobank

We aimed to explore the underlying genetic mechanisms of traumatic events during childhood affecting the risks of adult substance use in present study. Using UK Biobank cohort, linear regression model was first applied to assess the relationships between cigarette smoking and alcohol drinking in adults with traumatic events during childhood, including felt hated by family member (41,648-111,465), felt loved (46,394-124,481) and sexually molested (47,598-127,766). Using traumatic events as exposure variables, genome-wide by environment interaction study was then performed by PLINK 2.0 to identify cigarette smoking and alcohol drinking associated genes interacting with traumatic events during childhood. We found that the frequency of cigarette smoking was significantly associated with felt hated by family member (coefficient = 0.42, P < 1.0 × 10^-9), felt loved (coefficient = -0.31, P < 1.0 × 10^-9) and sexually molested (coefficient = 0.46, P < 1.0 × 10^-9). We also observed weaker associations of alcohol drinking with felt hated by family member (coefficient = 0.08, P = 3.10 × 10^-6) and felt loved (coefficient = -0.06, P = 3.15 × 10^-7). GWEIS identified multiple candidate loci interacting with traumatic events, such as CTNNA3 (rs189142060, P = 4.23 × 10^-8) between felt hated by family member and the frequency of cigarette smoking, GABRG3 (rs117020886, P = 2.77 × 10^-8) between felt hated by family member and the frequency of alcohol drinking. Our results suggested the significant impact of traumatic events during childhood on the risk of cigarette smoking and alcohol drinking.

Pharmacogenetics factors influencing smoking cessation success; the importance of nicotine metabolism

Introduction: Smoking remains a worldwide epidemic, and despite an increase in public acceptance of the harms of tobacco use, it remains the leading cause of preventable death. It is estimated that up to 70% of all smokers express a desire to quit, but only 3-5% of them are successful.Areas covered: The goal of this review was to evaluate the current status of smoking cessation treatments and the feasibility of implementing personalized-medicine approaches to these pharmacotherapies. We evaluated the genetics associated with higher levels of nicotine addiction and follow with an analysis of the genetic variants that affect the nicotine metabolic ratio (NMR) and the FDA approved treatments for smoking cessation. We also highlighted the gaps in the process of translating current laboratory understanding into clinical practice, and the benefits of personalized treatment approaches for a successful smoking cessation strategy.Expert opinion: Evidence supports the use of tailored therapies to ensure that the most efficient treatments are utilized in an individual's smoking cessation efforts. An understanding of the genetic effects on the efficacy of individualized smoking cessation pharmacotherapies is key to smoking cessation, ideally utilizing a polygenetic risk score that considers all genetic variation.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples.

Genetic scores of smoking behaviour in a Chinese population

This study sought to structure a genetic score for smoking behaviour in a Chinese population. Single-nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) were evaluated in a community-representative sample (N = 3,553) of Beijing, China. The candidate SNPs were tested in four genetic models (dominance model, recessive model, heterogeneous codominant model and additive model), and 7 SNPs were selected to structure a genetic score. A total of 3,553 participants (1,477 males and 2,076 females) completed the survey. Using the unweighted score, we found that participants with a high genetic score had a 34% higher risk of trying smoking and a 43% higher risk of SI at ≤18 years of age after adjusting for age, gender, education, occupation, ethnicity, body mass index (BMI) and sports activity time. The unweighted genetic scores were chosen to best extrapolate and understand these results. Importantly, genetic score was significantly associated with smoking behaviour (smoking status and SI at ≤18 years of age). These results have the potential to guide relevant health education for individuals with high genetic scores and promote the process of smoking control to improve the health of the population.

nAChR dysfunction as a common substrate for schizophrenia and comorbid nicotine addiction: Current trends and perspectives

INTRODUCTION

The prevalence of tobacco use in the population with schizophrenia is enormously high. Moreover, nicotine dependence is found to be associated with symptom severity and poor outcome in patients with schizophrenia. The neurobiological mechanisms that explain schizophrenia-nicotine dependence comorbidity are not known. This study systematically reviews the evidence highlighting the contribution of nicotinic acetylcholine receptors (nAChRs) to nicotine abuse in schizophrenia.

METHODS

Electronic data bases (Medline, Google Scholar, and Web of Science) were searched using the selected key words that match the aims set forth for this review. A total of 276 articles were used for the qualitative synthesis of this review.

RESULTS

Substantial evidence from preclinical and clinical studies indicated that dysregulation of α7 and β2-subunit containing nAChRs account for the cognitive and affective symptoms of schizophrenia and nicotine use may represent a strategy to remediate these symptoms. Additionally, recent meta-analyses proposed that early tobacco use may itself increase the risk of developing schizophrenia. Genetic studies demonstrating that nAChR dysfunction that may act as a shared vulnerability factor for comorbid tobacco dependence and schizophrenia were found to support this view. The development of nAChR modulators was considered an effective therapeutic strategy to ameliorate psychiatric symptoms and to promote smoking cessation in schizophrenia patients.

CONCLUSIONS

The relationship between schizophrenia and smoking is complex. While the debate for the self-medication versus addiction vulnerability hypothesis continues, it is widely accepted that a dysfunction in the central nAChRs represent a common substrate for various symptoms of schizophrenia and comorbid nicotine dependence.

Smokescreen: a targeted genotyping array for addiction research

BACKGROUND

Addictive disorders are a class of chronic, relapsing mental disorders that are responsible for increased risk of mental and medical disorders and represent the largest, potentially modifiable cause of death. Tobacco dependence is associated with increased risk of disease and premature death. While tobacco control efforts and therapeutic interventions have made good progress in reducing smoking prevalence, challenges remain in optimizing their effectiveness based on patient characteristics, including genetic variation. In order to maximize collaborative efforts to advance addiction research, we have developed a genotyping array called Smokescreen. This custom array builds upon previous work in the analyses of human genetic variation, the genetics of addiction, drug metabolism, and response to therapy, with an emphasis on smoking and nicotine addiction.

RESULTS

The Smokescreen genotyping array includes 646,247 markers in 23 categories. The array design covers genome-wide common variation (65.67, 82.37, and 90.72% in African (YRI), East Asian (ASN), and European (EUR) respectively); most of the variation with a minor allele frequency ≥ 0.01 in 1014 addiction genes (85.16, 89.51, and 90.49% for YRI, ASN, and EUR respectively); and nearly all variation from the 1000 Genomes Project Phase 1, NHLBI GO Exome Sequencing Project and HapMap databases in the regions related to smoking behavior and nicotine metabolism: CHRNA5-CHRNA3-CHRNB4 and CYP2A6-CYP2B6. Of the 636 pilot DNA samples derived from blood or cell line biospecimens that were genotyped on the array, 622 (97.80%) passed quality control. In passing samples, 90.08% of markers passed quality control. The genotype reproducibility in 25 replicate pairs was 99.94%. For 137 samples that overlapped with HapMap2 release 24, the genotype concordance was 99.76%. In a genome-wide association analysis of the nicotine metabolite ratio in 315 individuals participating in nicotine metabolism laboratory studies, we identified genome-wide significant variants in the CYP2A6 region (min p = 9.10E-15).

CONCLUSIONS

We developed a comprehensive genotyping array for addiction research and demonstrated its analytic validity and utility through pilot genotyping of HapMap and study samples. This array allows researchers to perform genome-wide, candidate gene, and pathway-based association analyses of addiction, tobacco-use, treatment response, comorbidities, and associated diseases in a standardized, high-throughput platform.

Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease

BACKGROUND

There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease?

RESULTS

Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed.

CONCLUSIONS

The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.

Prioritizing Genes Related to Nicotine Addiction Via a Multi-source-Based Approach

Nicotine has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unknown. Under such situation, prioritizing the candidate genes for further investigation is becoming increasingly important. In this study, we presented a multi-source-based gene prioritization approach for nicotine addiction by utilizing the vast amounts of information generated from for nicotine addiction study during the past years. In this approach, we first collected and curated genes from studies in four categories, i.e., genetic association analysis, genetic linkage analysis, high-throughput gene/protein expression analysis, and literature search of single gene/protein-based studies. Based on these resources, the genes were scored and a weight value was determined for each category. Finally, the genes were ranked by their combined scores, and 220 genes were selected as the prioritized nicotine addiction-related genes. Evaluation suggested the prioritized genes were promising targets for further analysis and replication study.

Bayesian systems-based genetic association analysis with effect strength estimation and omic wide interpretation: a case study in rheumatoid arthritis

Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex intermediate clinical phenotypes are typically used in follow-up candidate gene association studies, e.g., after genome-wide analysis, to deepen the understanding of the associations and to estimate effect strength. This chapter discusses a solid methodology, which is useful in such a scenario, by using probabilistic graphical models, namely, Bayesian networks in the Bayesian statistical framework. This method offers systematically scalable, comprehensive hierarchical hypotheses about multivariate relevance. We discuss its workflow: from data engineering to semantic publication of the results. We overview the construction, visualization, and interpretation of complex hypotheses related to the structural analysis of relevance. Furthermore, we illustrate the use of a dependency model-based relevance measure, which takes into account the structural properties of the model, for quantifying the effect strength. Finally, we discuss the "interpretational" or translational challenge of a genetic association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate the results into a genome-wide context.

Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands

Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA.

Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies

Although Genome Wide Association Studies (GWAS) have identified many susceptibility loci for common diseases, they only explain a small portion of heritability. It is challenging to identify the remaining disease loci because their association signals are likely weak and difficult to identify among millions of candidates. One potentially useful direction to increase statistical power is to incorporate functional genomics information, especially gene expression networks, to prioritize GWAS signals. Most current methods utilizing network information to prioritize disease genes are based on the 'guilt by association' principle, in which networks are treated as static, and disease-associated genes are assumed to locate closer with each other than random pairs in the network. In contrast, we propose a novel 'guilt by rewiring' principle. Studying the dynamics of gene networks between controls and patients, this principle assumes that disease genes more likely undergo rewiring in patients, whereas most of the network remains unaffected in disease condition. To demonstrate this principle, we consider the changes of co-expression networks in Crohn's disease patients and controls, and how network dynamics reveals information on disease associations. Our results demonstrate that network rewiring is abundant in the immune system, and disease-associated genes are more likely to be rewired in patients. To integrate this network rewiring feature and GWAS signals, we propose to use the Markov random field framework to integrate network information to prioritize genes. Applications in Crohn's disease and Parkinson's disease show that this framework leads to more replicable results, and implicates potentially disease-associated pathways.

Scaffold ranking and positional scanning utilized in the discovery of nAChR-selective compounds suitable for optimization studies

Nicotine binds to nicotinic acetylcholine receptors (nAChR), which can exist as many different subtypes. The α4β2 nAChR is the most prevalent subtype in the brain and possesses the most evidence linking it to nicotine seeking behavior. Herein we report the use of mixture based combinatorial libraries for the rapid discovery of a series of α4β2 nAChR selective compounds. Further chemistry optimization provided compound 301, which was characterized as a selective α4β2 nAChR antagonist. This compound displayed no agonist activity but blocked nicotine-induced depolarization of HEK cells with an IC50 of approximately 430 nM. 301 demonstrated nearly 500-fold selectivity for binding and 40-fold functional selectivity for α4β2 over α3β4 nAChR. In total over 5 million compounds were assessed through the use of just 170 samples in order to identify a series of structural analogues suitable for future optimization toward the goal of developing clinically relevant smoking cessation medications.

A review of post-GWAS prioritization approaches

In the recent decade, high-throughput genotyping and next-generation sequencing platforms have enabled genome-wide association studies (GWAS) of many complex human diseases. These studies have discovered many disease susceptible loci, and unveiled unexpected disease mechanisms. Despite these successes, these identified variants only explain a small proportion of the genetic contributions to these diseases and many more remain to be found. This is largely due to the small effect sizes of most disease-associated variants and limited sample size. As a result, it is critical to leverage other information to more effectively prioritize GWAS signals to increase replication rates and better understand disease mechanisms. In this review, we introduce the biological/genomic features that have been found to be informative for post-GWAS prioritization, and discuss available tools to utilize these features for prioritization

Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region

Genome-wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p-values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease-associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000.

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.

[¹²⁵I]AT-1012, a new high affinity radioligand for the α3β4 nicotinic acetylcholine receptors

Recent genetic and pharmacological studies have implicated the α3, β4 and α5 subunits of the nicotinic acetylcholine receptor (nAChR) in dependence to nicotine and other abused drugs and nicotine withdrawal. The α3β4* nAChR subtype has been shown to co-assemble with the α5 or β3 nAChR subunits, and is found mainly in the autonomic ganglia and select brain regions. It has been difficult to study the α3β4 nAChR because there have been no selective nonpeptidic ligands available to independently examine its pharmacology. We recently reported the synthesis of a [(125)I]-radiolabeled analog of a high affinity, selective small-molecule α3β4 nAChR ligand, AT-1012. We report here the vitro characterization of this radioligand in receptor binding and in vitro autoradiographic studies targeting the α3β4* nAChR. Binding of [(125)I]AT-1012 was characterized at the rat α3β4 and α4β2 nAChR transfected into HEK cells, as well as at the human α3β4α5 nAChR in HEK cells. Binding affinity of [(125)I]AT-1012 at the rat α3β4 nAChR was 1.4 nM, with a B(max) of 10.3 pmol/mg protein, similar to what was determined for unlabeled AT-1012 using [(3)H]epibatidine. Saturation isotherms suggested that [(125)I]AT-1012 binds to a single site on the α3β4 nAChR. Similar high binding affinity was also observed for [(125)I]AT-1012 at the human α3β4α5 nAChR transfected into HEK cells. [(125)I]AT-1012 did not bind with high affinity to membranes from α4β2 nAChR-transfected HEK cells. Binding studies with [(3)H]epibatidine further confirmed that AT-1012 had over 100-fold binding selectivity for α3β4 over α4β2 nAChR. K(i) values determined for known nAChR compounds using [(125)I]AT-1012 as radioligand were comparable to those obtained with [(3)H]epibatidine. [(125)I]AT-1012 was also used to label α3β4 nAChR in rat brain slices in vitro using autoradiography, which showed highly localized binding of the radioligand in brain regions consistent with the discreet localization of the α3β4 nAChR. We demonstrate that [(125)I]AT-1012 is an excellent tool for labeling the α3β4 nAChR in the presence of other nAChR subtypes.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

Genome-wide association study of obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common, debilitating neuropsychiatric illness with complex genetic etiology. The International OCD Foundation Genetics Collaborative (IOCDF-GC) is a multi-national collaboration established to discover the genetic variation predisposing to OCD. A set of individuals affected with DSM-IV OCD, a subset of their parents, and unselected controls, were genotyped with several different Illumina SNP microarrays. After extensive data cleaning, 1465 cases, 5557 ancestry-matched controls and 400 complete trios remained, with a common set of 469,410 autosomal and 9657 X-chromosome single nucleotide polymorphisms (SNPs). Ancestry-stratified case-control association analyses were conducted for three genetically-defined subpopulations and combined in two meta-analyses, with and without the trio-based analysis. In the case-control analysis, the lowest two P-values were located within DLGAP1 (P=2.49 × 10(-6) and P=3.44 × 10(-6)), a member of the neuronal postsynaptic density complex. In the trio analysis, rs6131295, near BTBD3, exceeded the genome-wide significance threshold with a P-value=3.84 × 10(-8). However, when trios were meta-analyzed with the case-control samples, the P-value for this variant was 3.62 × 10(-5), losing genome-wide significance. Although no SNPs were identified to be associated with OCD at a genome-wide significant level in the combined trio-case-control sample, a significant enrichment of methylation QTLs (P<0.001) and frontal lobe expression quantitative trait loci (eQTLs) (P=0.001) was observed within the top-ranked SNPs (P<0.01) from the trio-case-control analysis, suggesting these top signals may have a broad role in gene expression in the brain, and possibly in the etiology of OCD.

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.

SNP prioritization using a Bayesian probability of association

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.

Exome sequencing of only seven Qataris identifies potentially deleterious variants in the Qatari population

The Qatari population, located at the Arabian migration crossroads of African and Eurasia, is comprised of Bedouin, Persian and African genetic subgroups. By deep exome sequencing of only 7 Qataris, including individuals in each subgroup, we identified 2,750 nonsynonymous SNPs predicted to be deleterious, many of which are linked to human health, or are in genes linked to human health. Many of these SNPs were at significantly elevated deleterious allele frequency in Qataris compared to other populations worldwide. Despite the small sample size, SNP allele frequency was highly correlated with a larger Qatari sample. Together, the data demonstrate that exome sequencing of only a small number of individuals can reveal genetic variations with potential health consequences in understudied populations.

GPSy: a cross-species gene prioritization system for conserved biological processes--application in male gamete development

We present gene prioritization system (GPSy), a cross-species gene prioritization system that facilitates the arduous but critical task of prioritizing genes for follow-up functional analyses. GPSy’s modular design with regard to species, data sets and scoring strategies enables users to formulate queries in a highly flexible manner. Currently, the system encompasses 20 topics related to conserved biological processes including male gamete development discussed in this article. The web server-based tool is freely available at http://gpsy.genouest.org.

AT-1001: a high affinity and selective α3β4 nicotinic acetylcholine receptor antagonist blocks nicotine self-administration in rats

Genomic and pharmacologic data have suggested the involvement of the α3β4 subtype of nicotinic acetylcholine receptors (nAChRs) in drug seeking to nicotine and other drugs of abuse. In order to better examine this receptor subtype, we have identified and characterized the first high affinity and selective α3β4 nAChR antagonist, AT-1001, both in vitro and in vivo. This is the first reported compound with a Ki below 10 nM at α3β4 nAChR and >90-fold selectivity over the other major subtypes, the α4β2 and α7 nAChR. AT-1001 competes with epibatidine, allowing for [³H]epibatidine binding to be used for structure-activity studies, however, both receptor binding and ligand-induced Ca²⁺ flux are not strictly competitive because increasing ligand concentration produces an apparent decrease in receptor number and maximal Ca²⁺ fluorescence. AT-1001 also potently and reversibly blocks epibatidine-induced inward currents in HEK cells transfected with α3β4 nAChR. Importantly, AT-1001 potently and dose-dependently blocks nicotine self-administration in rats, without affecting food responding. When tested in a nucleus accumbens (NAcs) synaptosomal preparation, AT-1001 inhibits nicotine-induced [³H]dopamine release poorly and at significantly higher concentrations compared with mecamylamine and conotoxin MII. These results suggest that its inhibition of nicotine self-administration in rats is not directly due to a decrease in dopamine release from the NAc, and most likely involves an indirect pathway requiring α3β4 nAChR. In conclusion, our studies provide further evidence for the involvement of α3β4 nAChR in nicotine self-administration. These findings suggest the utility of this receptor as a target for smoking cessation medications, and highlight the potential of AT-1001 and congeners as clinically useful compounds.

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.

Multifactor dimensionality reduction as a filter-based approach for genome wide association studies

Advances in genotyping technology and the multitude of genetic data available now provide a vast amount of data that is proving to be useful in the quest for a better understanding of human genetic diseases through the study of genetic variation. This has led to the development of approaches such as genome wide association studies (GWAS) designed specifically for interrogating variants across the genome for association with disease, typically by testing single locus, univariate associations. More recently it has been accepted that epistatic (interaction) effects may also be great contributors to these genetic effects, and GWAS methods are now being applied to find epistatic effects. The challenge for these methods still remain in prioritization and interpretation of results, as it has also become standard for initial findings to be independently investigated in replication cohorts or functional studies. This is motivating the development and implementation of filter-based approaches to prioritize variants found to be significant in a discovery stage for follow-up for replication. Such filters must be able to detect both univariate and interactive effects. In the current study we present and evaluate the use of multifactor dimensionality reduction (MDR) as such a filter, with simulated data and a wide range of effect sizes. Additionally, we compare the performance of the MDR filter to a similar filter approach using logistic regression (LR), the more traditional approach used in GWAS analysis, as well as evaporative cooling (EC)-another prominent machine learning filtering method. The results of our simulation study show that MDR is an effective method for such prioritization, and that it can detect main effects, and interactions with or without marginal effects. Importantly, it performed as well as EC and LR for main effect models. It also significantly outperforms LR for various two-locus epistatic models, while it has equivalent results as EC for the epistatic models. The results of this study demonstrate the potential of MDR as a filter to detect gene-gene interactions in GWAS studies.

Physiology and Endocrinology Symposium: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females

The promise of genomic selection is accurate prediction of the genetic potential of animals from their genotypes. Simple DNA tests might replace low-accuracy predictions for expensive or lowly heritable measures of puberty and fertility based on performance and pedigree. Knowing with some certainty which DNA variants (e.g., SNP) affect puberty and fertility is the best way to fulfill the promise. Several SNP from the BovineSNP50 assay have tentatively been associated with reproductive traits including age at puberty, antral follicle count, and pregnancy observed on different sets of heifers. However, sample sizes are too small and SNP density is too sparse to definitively determine genomic regions harboring causal variants affecting reproductive success. Additionally, associations between individual SNP and similar phenotypes are inconsistent across data sets, and genomic predictions do not appear to be globally applicable to cattle of different breeds. Discrepancies may be a result of different QTL segregating in the sampled populations, differences in linkage disequilibrium (LD) patterns such that the same SNP are not correlated with the same QTL, and spurious correlations with phenotype. Several approaches can be used independently or in combination to improve detection of genomic factors affecting heifer puberty and fertility. Larger samples and denser SNP will increase power to detect real associations with SNP having more consistent LD with underlying QTL. Meta-analysis combining results from different studies can also be used to effectively increase sample size. High-density genotyping with heifers pooled by pregnancy status or early and late puberty can be a cost-effective means to sample large numbers. Networks of genes, implicated by associations with multiple traits correlated with puberty and fertility, could provide insight into the complex nature of these traits, especially if corroborated by functional annotation, established gene interaction pathways, and transcript expression. Example analyses are provided to demonstrate how integrating information about gene function and regulation with statistical associations from whole-genome SNP genotyping assays might enhance knowledge of genomic mechanisms affecting puberty and fertility, enabling reliable DNA tests to guide heifer selection decisions.

A new methodology to associate SNPs with human diseases according to their pathway related context

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.

A network-based approach to prioritize results from genome-wide association studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

Genetics of COPD

Previous family studies suggested that genetic variation contributes to COPD susceptibility. The only gene proven to influence COPD susceptibility is SERPINA1, encoding α1-antitrypsin. Most studies on COPD candidate genes except SERPINA1, have not been consistently replicated. However, longitudinal studies of decline in lung function, meta-analyses of candidate gene studies, and family-based linkage analyses suggested that variants in EPHX1, GST, MMP12, TGFB1, and SERPINE2 were associated with susceptibility to COPD. A genome-wide association (GWA) study has recently demonstrated that CHRNA3/5 in 15q25 was associated with COPD compared with control smokers. It was of interest that the CHRNA3/5 locus was associated with nicotine dependence and lung cancer as well. The associations of HHIP on 4q31 and FAM13A on 4q22 with COPD were also suggested in GWA studies. Another GWA study has shown that BICD1 in 12p11 was associated with the presence or absence of emphysema. Although every genetic study on COPD has some limitations including heterogeneity in smoking behaviors and comorbidities, it has contributed to the progress in elucidating the pathogenesis of COPD. Future studies will make us understand the mechanisms underlying the polygenic disease, leading to the development of a specific treatment for each phenotype.

A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility

Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis (MS) trios to discover gene-gene interactions within established biological contexts. We identify heterogeneous signals, including a gene-gene interaction between CHRM3 (muscarinic cholinergic receptor 3) and MYLK (myosin light-chain kinase) (joint P=0.0002), an interaction between two phospholipase C-β isoforms, PLCβ1 and PLCβ4 (joint P=0.0098), and a modest interaction between ACTN1 (actinin alpha 1) and MYH9 (myosin heavy chain 9) (joint P=0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint P=5.2E-5) previously unidentified by single-locus analysis within another related gene, SCIN (scinderin), a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene-gene interactions and non-immune susceptibility loci for MS. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to MS.

Prioritizing candidate disease genes by network-based boosting of genome-wide association data

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

Using functional annotation for the empirical determination of Bayes Factors for genome-wide association study analysis

A genome wide association study (GWAS) typically results in a few highly significant ‘hits’ and a much larger set of suggestive signals (‘near-hits’). The latter group are expected to be a mixture of true and false associations. One promising strategy to help separate these is to use functional annotations for prioritisation of variants for follow-up. A key task is to determine which annotations might prove most valuable. We address this question by examining the functional annotations of previously published GWAS hits. We explore three annotation categories: non-synonymous SNPs (nsSNPs), promoter SNPs and cis expression quantitative trait loci (eQTLs) in open chromatin regions. We demonstrate that GWAS hit SNPs are enriched for these three functional categories, and that it would be appropriate to provide a higher weighting for such SNPs when performing Bayesian association analyses. For GWAS studies, our analyses suggest the use of a Bayes Factor of about 4 for cis eQTL SNPs within regions of open chromatin, 3 for nsSNPs and 2 for promoter SNPs.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Incorporating biological pathways via a Markov random field model in genome-wide association studies

Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene–based method. We also illustrate the usefulness of our approach through its applications to a real data example.

Statistical methods used in most GWAS are based on the analysis of single markers. Prior biological information about markers, genes, and pathways is not commonly incorporated in the detection of associated disease loci. Recently a number of methods have been developed to incorporate such information, and it has been shown that they may make use of prior biological knowledge in association analysis. However, most of these methods ignore the regulatory relationships and functional interactions among genes. In this article, we propose a statistical method that can explicitly model the interactions of genes in a neighborhood defined by the topology of a pathway. Simulation studies and a real data example show that the proposed method can improve the power of identifying associated genes when they are in the neighborhood of other genes whose association has been firmly established in previous studies.

Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies

The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, I discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. I describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. I discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.

Use of pathway information in molecular epidemiology

Candidate gene studies are generally motivated by some form of pathway reasoning in the selection of genes to be studied, but seldom has the logic of the approach been carried through to the analysis. Marginal effects of polymorphisms in the selected genes, and occasionally pairwise gene-gene or gene-environment interactions, are often presented, but a unified approach to modelling the entire pathway has been lacking. In this review, a variety of approaches to this problem is considered, focusing on hypothesis-driven rather than purely exploratory methods. Empirical modelling strategies are based on hierarchical models that allow prior knowledge about the structure of the pathway and the various reactions to be included as 'prior covariates'. By contrast, mechanistic models aim to describe the reactions through a system of differential equations with rate parameters that can vary between individuals, based on their genotypes. Some ways of combining the two approaches are suggested and Bayesian model averaging methods for dealing with uncertainty about the true model form in either framework is discussed. Biomarker measurements can be incorporated into such analyses, and two-phase sampling designs stratified on some combination of disease, genes and exposures can be an efficient way of obtaining data that would be too expensive or difficult to obtain on a full candidate gene sample. The review concludes with some thoughts about potential uses of pathways in genome-wide association studies.

A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder

Background

Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania.

Results

Using microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes.

Conclusions

We offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near genes in the network based on functional annotation. We also developed a "concept signature" for the genes in the network and identified additional candidate genes that may influence the system because they are significantly associated with the signature.

New tools and methods for direct programmatic access to the dbSNP relational database

Genome-wide association studies often incorporate information from public biological databases in order to provide a biological reference for interpreting the results. The dbSNP database is an extensive source of information on single nucleotide polymorphisms (SNPs) for many different organisms, including humans. We have developed free software that will download and install a local MySQL implementation of the dbSNP relational database for a specified organism. We have also designed a system for classifying dbSNP tables in terms of common tasks we wish to accomplish using the database. For each task we have designed a small set of custom tables that facilitate task-related queries and provide entity-relationship diagrams for each task composed from the relevant dbSNP tables. In order to expose these concepts and methods to a wider audience we have developed web tools for querying the database and browsing documentation on the tables and columns to clarify the relevant relational structure. All web tools and software are freely available to the public at http://cgsmd.isi.edu/dbsnpq. Resources such as these for programmatically querying biological databases are essential for viably integrating biological information into genetic association experiments on a genome-wide scale.

SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study

SPOT (http://spot.cgsmd.isi.edu), the SNP prioritization online tool, is a web site for integrating biological databases into the prioritization of single nucleotide polymorphisms (SNPs) for further study after a genome-wide association study (GWAS). Typically, the next step after a GWAS is to genotype the top signals in an independent replication sample. Investigators will often incorporate information from biological databases so that biologically relevant SNPs, such as those in genes related to the phenotype or with potentially non-neutral effects on gene expression such as a splice sites, are given higher priority. We recently introduced the genomic information network (GIN) method for systematically implementing this kind of strategy. The SPOT web site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the GIN method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.

A genomewide association study of nicotine and alcohol dependence in Australian and Dutch populations

Persistent tobacco use and excessive alcohol consumption are major public health concerns worldwide. Both alcohol and nicotine dependence (AD, ND) are genetically influenced complex disorders that exhibit a high degree of comorbidity. To identify gene variants contributing to one or both of these addictions, we first conducted a pooling-based genomewide association study (GWAS) in an Australian population, using Illumina Infinium 1M arrays. Allele frequency differences were compared between pooled DNA from case and control groups for: (1) AD, 1224 cases and 1162 controls; (2) ND, 1273 cases and 1113 controls; and (3) comorbid AD and ND, 599 cases and 488 controls. Secondly, we carried out a GWAS in independent samples from the Netherlands for AD and for ND. Thirdly, we performed a meta-analysis of the 10,000 most significant AD- and ND-related SNPs from the Australian and Dutch samples. In the Australian GWAS, one SNP achieved genomewide significance (p < 5 x 10(-8)) for ND (rs964170 in ARHGAP10 on chromosome 4, p = 4.43 x 10(-8)) and three others for comorbid AD/ND (rs7530302 near MARK1 on chromosome 1 (p = 1.90 x 10(-9)), rs1784300 near DDX6 on chromosome 11 (p = 2.60 x 10(-9)) and rs12882384 in KIAA1409 on chromosome 14 (p = 4.86 x 10(-8))). None of the SNPs achieved genomewide significance in the Australian/Dutch meta-analysis, but a gene network diagram based on the top-results revealed overrepresentation of genes coding for ion-channels and cell adhesion molecules. Further studies will be required before the detailed causes of comorbidity between AD and ND are understood.

Gene-environment interaction and children's health and development

PURPOSE OF REVIEW

A systematic approach to studying gene-environment interaction can have immediate impact on our understanding of how environmental factors induce developmental disease and toxicity and will provide biological insight for potential treatment and prevention measures.

RECENT FINDINGS

Because DNA sequence is static, genetic studies typically are not conducted prospectively. This limits the ability to incorporate environmental data into an analysis, as such data is usually collected cross-sectionally. Prospective environmental data collection could account for the role of critical windows of susceptibility that likely correspond to the expression of specific genes and gene pathways. The use of large-scale genomic platforms to discover genetic variants that modify environmental exposure in conjunction with a-priori planned replication studies would reduce the number of false positive results.

SUMMARY

Using a genome-wide approach, combined with prospective longitudinal measures of environmental exposure at critical developmental windows, is the optimal design for gene-environment interaction research. This approach would discover susceptibility variants, and then validate the findings in an independent sample of children. Designs that combine the strengths and methodologies of each field will yield data that can account for both genetic variability and the role of critical developmental windows in the etiology of childhood disease and development.

Role for protein-protein interaction databases in human genetics

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

A Genomewide Association Study of Nicotine and Alcohol Dependence in Australian and Dutch Populations

Persistent tobacco use and excessive alcohol consumption are major public health concerns worldwide. Both alcohol and nicotine dependence (AD, ND) are genetically influenced complex disorders that exhibit a high degree of comorbidity. To identify gene variants contributing to one or both of these addictions, we first conducted a pooling-based genomewide association study (GWAS) in an Australian population, using Illumina Infinium 1M arrays. Allele frequency differences were compared between pooled DNA from case and control groups for: (1) AD, 1224 cases and 1162 controls; (2) ND, 1273 cases and 1113 controls; and (3) comorbid AD and ND, 599 cases and 488 controls. Secondly, we carried out a GWAS in independent samples from the Netherlands for AD and for ND. Thirdly, we performed a meta-analysis of the 10, 000 most significant AD- and ND-related SNPs from the Australian and Dutch samples. In the Australian GWAS, one SNP achieved genomewide significance (p < 5 x 10-8) for ND (rs964170 in ARHGAPlOon chromosome 4, p = 4.43 x 10”8) and three others for comorbid AD/ND (rs7530302 near MARK1 on chromosome 1 (p = 1.90 x 10-9), rs1784300 near DDX6 on chromosome 11 (p = 2.60 x 10-9) and rs12882384 in KIAA1409 on chromosome 14 (p = 4.86 x 10-8)). None of the SNPs achieved genomewide significance in the Australian/Dutch meta-analysis, but a gene network diagram based on the top-results revealed overrepre-sentation of genes coding for ion-channels and cell adhesion molecules. Further studies will be requirec before the detailed causes of comorbidity between AC and ND are understood.

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder

Background

Comorbidity of psychiatric and substance use disorders represents a significant complication in the clinical course of both disorders. Bipolar Disorder (BD) is a psychiatric disorder characterized by severe mood swings, ranging from mania to depression, and up to a 70% rate of comorbid Tobacco Use Disorder (TUD). We found epidemiological evidence consistent with a common underlying etiology for BD and TUD, as well as evidence of both genetic and environmental influences on BD and TUD. Therefore, we hypothesized a common underlying genetic etiology, interacting with nicotine exposure, influencing susceptibility to both BD and TUD.

Methods

Using meta-analysis, we compared TUD rates for BD patients and the general population. We identified candidate genes showing statistically significant, replicated, evidence of association with both BD and TUD. We assessed commonality among these candidate genes and hypothesized broader, multi-gene network influences on the comorbidity. Using Fisher Exact tests we tested our hypothesized genetic networks for association with the comorbidity, then compared the inferences drawn with those derived from the commonality assessment. Finally, we prioritized candidate SNPs for validation.

Results

We estimate risk for TUD among BD patients at 2.4 times that of the general population. We found three candidate genes associated with both BD and TUD (COMT, SLC6A3, and SLC6A4) and commonality analysis suggests that these genes interact in predisposing psychiatric and substance use disorders. We identified a 69 gene network that influences neurotransmitter signaling and shows significant over-representation of genes associated with BD and TUD, as well as genes differentially expressed with exposure to tobacco smoke. Twenty four of these genes are known drug targets.

Conclusions

This work highlights novel bioinformatics resources and demonstrates the effectiveness of using an integrated bioinformatics approach to improve our understanding of complex disease etiology. We illustrate the development and testing of hypotheses for a comorbidity predisposed by both genetic and environmental influences. Consistent with our hypothesis, the selected network models multiple interacting genetic influences on comorbid BD with TUD, as well as the environmental influence of nicotine. This network nominates candidate genes for validation and drug testing, and we offer a panel of SNPs prioritized for follow-up.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu