The nuclear transcription factor PKNOX2 is a candidate gene for substance dependence in European-origin women

Exploring shared genetics between maximal oxygen uptake and disease: the HUNT study

Low cardiorespiratory fitness, measured as maximal oxygen uptake (V̇o2max), is associated with all-cause mortality and disease-specific morbidity and mortality and is estimated to have a large genetic component (∼60%). However, the underlying mechanisms explaining the associations are not known, and no association study has assessed shared genetics between directly measured V̇o2max and disease. We believe that identifying the mechanisms explaining how low V̇o2max is related to increased disease risk can contribute to prevention and therapy. We used a phenome-wide association study approach to test for shared genetics. A total of 64,479 participants from the Trøndelag Health Study (HUNT) were included. Genetic variants previously linked to V̇o2max were tested for association with diseases related to the cardiovascular system, diabetes, dementia, mental disorders, and cancer as well as clinical measurements and biomarkers from HUNT. In the total population, three single-nucleotide polymorphisms (SNPs) in and near the follicle-stimulating hormone receptor gene (FSHR) were found to be associated (false discovery rate < 0.05) with serum creatinine levels and one intronic SNP in the Rap-associating DIL domain gene (RADIL) with diabetes type 1 with neurological manifestations. In males, four intronic SNPs in the PBX/knotted homeobox 2 gene (PKNOX2) were found to be associated with endocarditis. None of the association tests in the female population reached overall statistical significance; the associations with the lowest P values included other cardiac conduction disorders, subdural hemorrhage, and myocarditis. The results might suggest shared genetics between V̇o2max and disease. However, further effort should be put into investigating the potential shared genetics between inborn V̇o2max and disease in larger cohorts to increase statistical power.NEW & NOTEWORTHY To our knowledge, this is the first genetic association study exploring how genes linked to cardiorespiratory fitness (CRF) relate to disease risk. By investigating shared genetics, we found indications that genetic variants linked to directly measured CRF also affect the level of blood creatinine, risk of diabetes, and endocarditis. Less certain findings showed that genetic variants of high CRF might cause lower body mass index, healthier HDL cholesterol, and lower resting heart rate.

Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies

BACKGROUND AND AIMS

Genomic and transcriptomic findings greatly broaden the biological knowledge regarding substance use. However, systematic convergence and comparison evidence of genome-wide findings is lacking for substance use. Here, we combined all the genome-wide findings from both substance use behavior and disorder (SUBD) and identified common and distinguishing genetic factors for different SUBDs.

METHODS

Systemic literature search for genome-wide association (GWAS) and RNA-seq studies of alcohol/nicotine/drug use behavior (partially meets or not reported diagnostic criteria) and alcohol use behavior and disorder (AUBD), nicotine use behavior and disorder (NUBD) and drug use behavior and disorder (DUBD) was performed using PubMed and the GWAS catalog. Drug use was focused upon cannabis, opioid, cocaine and methamphetamine use. GWAS studies required case-control or case/cohort samples. RNA-seq studies were based on brain tissues. The genes which contained significant single nucleotide polymorphism (P ≤ 1 × 10^-6 ) in GWAS and reported as significant in RNA-seq studies were extracted. Pathway enrichment was performed by using Metascape. Gene interaction networks were identified by using the Protein Interaction Network Analysis database.

RESULTS

Total SUBD-related 2910 genes were extracted from 75 GWAS studies (2 773 889 participants) and 17 RNA-seq studies. By overlapping the genes and pathways of AUBD, NUBD and DUBD, four shared genes (CACNB2, GRIN2B, PLXDC2 and PKNOX2), four shared pathways [two Gene Ontology (GO) terms of 'modulation of chemical synaptic transmission', 'regulation of trans-synaptic signaling', two Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 'dopaminergic synapse', 'cocaine addiction'] were identified (significantly higher than random, P < 1 × 10^-5 ). The top shared KEGG pathways (Benjamini-Hochberg-corrected P-value < 0.05) in the pairwise comparison of AUBD versus DUBD, NUBD versus DUBD, AUBD versus NUBD were 'Epstein-Barr virus infection', 'protein processing in endoplasmic reticulum' and 'neuroactive ligand-receptor interaction', respectively. We also identified substance-specific genetic factors: i.e. ADH1B and ALDH2 were unique for AUBD, while CHRNA3 and CHRNA4 were unique for NUBD.

CONCLUSIONS

This systematic review identifies the shared and unique genes and pathways for alcohol, nicotine and drug use behaviors and disorders at the genome-wide level and highlights critical biological processes for the common and distinguishing vulnerability of substance use behaviors and disorders.

Arc Regulates Transcription of Genes for Plasticity, Excitability and Alzheimer’s Disease

The immediate early gene Arc is a master regulator of synaptic function and a critical determinant of memory consolidation. Here, we show that Arc interacts with dynamic chromatin and closely associates with histone markers for active enhancers and transcription in cultured rat hippocampal neurons. Both these histone modifications, H3K27Ac and H3K9Ac, have recently been shown to be upregulated in late-onset Alzheimer’s disease (AD). When Arc induction by pharmacological network activation was prevented using a short hairpin RNA, the expression profile was altered for over 1900 genes, which included genes associated with synaptic function, neuronal plasticity, intrinsic excitability, and signalling pathways. Interestingly, about 100 Arc-dependent genes are associated with the pathophysiology of AD. When endogenous Arc expression was induced in HEK293T cells, the transcription of many neuronal genes was increased, suggesting that Arc can control expression in the absence of activated signalling pathways. Taken together, these data establish Arc as a master regulator of neuronal activity-dependent gene expression and suggest that it plays a significant role in the pathophysiology of AD.

Prospects for finding the mechanisms of sex differences in addiction with human and model organism genetic analysis

Despite substantial evidence for sex differences in addiction epidemiology, addiction-relevant behaviors and associated neurobiological phenomena, the mechanisms and implications of these differences remain unknown. Genetic analysis in model organism is a potentially powerful and effective means of discovering the mechanisms that underlie sex differences in addiction. Human genetic studies are beginning to show precise risk variants that influence the mechanisms of addiction but typically lack sufficient power or neurobiological mechanistic access, particularly for the discovery of the mechanisms that underlie sex differences. Our thesis in this review is that genetic variation in model organisms are a promising approach that can complement these investigations to show the biological mechanisms that underlie sex differences in addiction.

Genomic landscape of the signals of positive natural selection in populations of Northern Eurasia: A view from Northern Russia

Natural selection of beneficial genetic variants played a critical role in human adaptation to a wide range of environmental conditions. Northern Eurasia, despite its severe climate, is home to lots of ethnically diverse populations. The genetic variants associated with the survival of these populations have hardly been analyzed. We searched for the genomic signatures of positive selection in (1) the genome-wide microarray data of 432 people from eight different northern Russian populations and (2) the whole-genome sequences of 250 people from Northern Eurasia from a public repository through testing the extended haplotype homozigosity (EHH) and direct comparison of allele frequency, respectively. The 20 loci with the strongest selection signals were characterized in detail. Among the top EHH hits were the NRG3 and NBEA genes, which are involved in the development and functioning of the neural system, the PTPRM gene, which mediates cell-cell interactions and adhesion, and a region on chromosome 4 (chr4:28.7-28.9 Mb) that contained several loci affiliated with different classes of non-coding RNAs (RN7SL101P, MIR4275, MESTP3, and LINC02364). NBEA and the region on chromosome 4 were novel selection targets that were identified for the first time in Western Siberian populations. Cross-population comparisons of EHH profiles suggested a particular role for the chr4:28.7-28.9 Mb region in the local adaptation of Western Siberians. The strongest selection signal identified in Siberian sequenced genomes was formed by six SNPs on chromosome 11 (chr11:124.9-125.2 Mb). This region included well-known genes SLC37A2 and PKNOX2. SLC37A2 is most-highly expressed in the gut. Its expression is regulated by vitamin D, which is often deficient in northern regions. The PKNOX2 gene is a transcription factor of the homeobox family that is expressed in the brain and many other tissues. This gene is associated with alcohol addiction, which is widespread in many Northern Eurasian populations.

Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence

We propose a novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is a common phenomenon in mental health in which an individual suffers from multiple disorders simultaneously. For example, in the Study of Addiction: Genetics and Environment (SAGE), alcohol and nicotine addiction were recorded through multiple assessments that we refer to as hybrid traits. Statistical inference for studying the genetic basis of hybrid traits has not been well-developed. Recent rank-based methods have been utilized for conducting association analyses of hybrid traits but do not inform the strength or direction of effects. To overcome this limitation, a parametric modeling framework is imperative. Although such parametric frameworks have been proposed in theory, they are neither well-developed nor extensively used in practice due to their reliance on complicated likelihood functions that have high computational complexity. Many existing parametric frameworks tend to instead use pseudo-likelihoods to reduce computational burdens. Here, we develop a model fitting algorithm for the full likelihood. Our extensive simulation studies demonstrate that inference based on the full likelihood can control the type-I error rate, and gains power and improves the effect size estimation when compared with several existing methods for hybrid models. These advantages remain even if the distribution of the latent variables is misspecified. After analyzing the SAGE data, we identify three genetic variants (rs7672861, rs958331, rs879330) that are significantly associated with the comorbidity of alcohol and nicotine addiction at the chromosome-wide level. Moreover, our approach has greater power in this analysis than several existing methods for hybrid traits.Although the analysis of the SAGE data motivated us to develop the model, it can be broadly applied to analyze any hybrid responses.

SNP by SNP by environment interaction network of alcoholism

Background

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50–80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest.

Results

Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \times $$\end{document}×SNP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \times $$\end{document}×E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits. 

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0403-7) contains supplementary material, which is available to authorized users.

Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing

The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been examined and documented. Due to the difficulties of modeling multiple responses, nonparametric tests such as generalized Kendall's Tau have been developed to assess the association between multiple responses and risk factors. These procedures have been applied to genomewide association studies of multiple complex traits. Unfortunately, those nonparametric tests only provide the significance of the association but not the magnitude. We propose a Gaussian copula model with discrete margins for modeling multivariate binary responses. This model separates marginal effects from between-trait correlations. We use a bootstrapping margins approach to constructing Wald's statistic for the association test. Although our derivation is based on the fully parametric Gaussian copula framework for simplicity, the underlying assumptions to apply our method can be weakened. The bootstrapping margins approach only requires the correct specification of the model margins. Our simulation and real data analysis demonstrate that our proposed method not only increases power over some existing association tests, but also provides further insight into genetic association studies of multivariate traits.

Common single nucleotide variants underlying drug addiction: more than a decade of research

Drug-related phenotypes are common complex and highly heritable traits. In the last few years, candidate gene (CGAS) and genome-wide association studies (GWAS) have identified a huge number of single nucleotide polymorphisms (SNPs) associated with drug use, abuse or dependence, mainly related to alcohol or nicotine. Nevertheless, few of these associations have been replicated in independent studies. The aim of this study was to provide a review of the SNPs that have been most significantly associated with alcohol-, nicotine-, cannabis- and cocaine-related phenotypes in humans between the years of 2000 and 2012. To this end, we selected CGAS, GWAS, family-based association and case-only studies published in peer-reviewed international scientific journals (using the PubMed/MEDLINE and Addiction GWAS Resource databases) in which a significant association was reported. A total of 371 studies fit the search criteria. We then filtered SNPs with at least one replication study and performed meta-analysis of the significance of the associations. SNPs in the alcohol metabolizing genes, in the cholinergic gene cluster CHRNA5-CHRNA3-CHRNB4, and in the DRD2 and ANNK1 genes, are, to date, the most replicated and significant gene variants associated with alcohol- and nicotine-related phenotypes. In the case of cannabis and cocaine, a far fewer number of studies and replications have been reported, indicating either a need for further investigation or that the genetics of cannabis/cocaine addiction are more elusive. This review brings a global state-of-the-art vision of the behavioral genetics of addiction and collaborates on formulation of new hypothesis to guide future work.

Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction

Background

Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction.

Results

Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling.

Conlusions

Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0167-x) contains supplementary material, which is available to authorized users.

The DYX2 locus and neurochemical signaling genes contribute to speech sound disorder and related neurocognitive domains

A major milestone of child development is the acquisition and use of speech and language. Communication disorders, including speech sound disorder (SSD), can impair a child's academic, social and behavioral development. Speech sound disorder is a complex, polygenic trait with a substantial genetic component. However, specific genes that contribute to SSD remain largely unknown. To identify associated genes, we assessed the association of the DYX2 dyslexia risk locus and markers in neurochemical signaling genes (e.g., nicotinic and dopaminergic) with SSD and related endophenotypes. We first performed separate primary associations in two independent samples - Cleveland SSD (210 affected and 257 unaffected individuals in 127 families) and Denver SSD (113 affected individuals and 106 unaffected individuals in 85 families) - and then combined results by meta-analysis. DYX2 markers, specifically those in the 3' untranslated region of DCDC2 (P = 1.43 × 10(-4) ), showed the strongest associations with phonological awareness. We also observed suggestive associations of dopaminergic-related genes ANKK1 (P = 1.02 × 10(-2) ) and DRD2 (P = 9.22 × 10(-3) ) and nicotinic-related genes CHRNA3 (P = 2.51 × 10(-3) ) and BDNF (P = 8.14 × 10(-3) ) with case-control status and articulation. Our results further implicate variation in putative regulatory regions in the DYX2 locus, particularly in DCDC2, influencing language and cognitive traits. The results also support previous studies implicating variation in dopaminergic and nicotinic neural signaling influencing human communication and cognitive development. Our findings expand the literature showing genetic factors (e.g., DYX2) contributing to multiple related, yet distinct neurocognitive domains (e.g., dyslexia, language impairment, and SSD). How these factors interactively yield different neurocognitive and language-related outcomes remains to be elucidated.

Genetics and epigenetics of circadian rhythms and their potential roles in neuropsychiatric disorders

Circadian rhythm alterations have been implicated in multiple neuropsychiatric disorders, particularly those of sleep, addiction, anxiety, and mood. Circadian rhythms are known to be maintained by a set of classic clock genes that form complex mutual and self-regulatory loops. While many other genes showing rhythmic expression have been identified by genome-wide studies, their roles in circadian regulation remain largely unknown. In attempts to directly connect circadian rhythms with neuropsychiatric disorders, genetic studies have identified gene mutations associated with several rare sleep disorders or sleep-related traits. Other than that, genetic studies of circadian genes in psychiatric disorders have had limited success. As an important mediator of environmental factors and regulators of circadian rhythms, the epigenetic system may hold the key to the etiology or pathology of psychiatric disorders, their subtypes or endophenotypes. Epigenomic regulation of the circadian system and the related changes have not been thoroughly explored in the context of neuropsychiatric disorders. We argue for systematic investigation of the circadian system, particularly epigenetic regulation, and its involvement in neuropsychiatric disorders to improve our understanding of human behavior and disease etiology.

Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease

Genomic research is one of the tools for elucidating the pathogenesis of diseases of global health relevance and paving the research dimension to clinical and public health translation. Recent advances in genomic research and technologies have increased our understanding of human diseases, genes associated with these disorders, and the relevant mechanisms. Genome-wide association studies (GWAS) have proliferated since the first studies were published several years ago and have become an important tool in helping researchers comprehend human variation and the role genetic variants play in disease. However, the need to expand the diversity of populations in GWAS has become increasingly apparent as new knowledge is gained about genetic variation. Inclusion of diverse populations in genomic studies is critical to a more complete understanding of human variation and elucidation of the underpinnings of complex diseases. In this review, we summarize the available data on GWAS in recent African ancestry populations within the western hemisphere (i.e. African Americans and peoples of the Caribbean) and continental African populations. Furthermore, we highlight ways in which genomic studies in populations of recent African ancestry have led to advances in the areas of malaria, HIV, prostate cancer, and other diseases. Finally, we discuss the advantages of conducting GWAS in recent African ancestry populations in the context of addressing existing and emerging global health conditions.

Genome-wide association discoveries of alcohol dependence

OBJECTIVE

To report the genome-wide significant and/or replicable risk variants for alcohol dependence and explore their potential biological functions.

METHODS

We searched in PubMed for all genome-wide association studies (GWASs) of alcohol dependence. The following three types of the results were extracted: genome-wide significant associations in an individual sample, the combined samples, or the meta-analysis (p < 5 × 10(-8) ); top-ranked associations in an individual sample (p < 10(-5) ) that were nominally replicated in other samples (p < .05); and nominally replicable associations across at least three independent GWAS samples (p < .05). These results were meta-analyzed. cis-eQTLs in human, RNA expression in rat and mouse brains and bioinformatics properties of all of these risk variants were analyzed.

RESULTS

The variants located within the alcohol dehydrogenase (ADH) cluster were significantly associated with alcohol dependence at the genome-wide level (p < 5 × 10(-8) ) in at least one sample. Some associations with the ADH cluster were replicable across six independent GWAS samples. The variants located within or near SERINC2, KIAA0040, MREG-PECR or PKNOX2 were significantly associated with alcohol dependence at the genome-wide level (p < 5 × 10(-8) ) in meta-analysis or combined samples, and these associations were replicable across at least one sample. The associations with the variants within NRD1, GPD1L-CMTM8 or MAP3K9-PCNX were suggestive (5 × 10(-8)  < p < 10(-5) ) in some samples, and nominally replicable in other samples. The associations with the variants at HTR7 and OPA3 were nominally replicable across at least three independent GWAS samples (10(-5)  < p < .05). Some risk variants at the ADH cluster, SERINC2, KIAA0040, NRD1, and HTR7 had potential biological functions.

CONCLUSION

The most robust risk locus was the ADH cluster. SERINC2, KIAA0040, NRD1, and HTR7 were also likely to play important roles in alcohol dependence. PKNOX2, MREG, PECR, GPD1L, CMTM8, MAP3K9, PCNX, and OPA3 might play less important roles in risk for alcohol dependence based on the function analysis. This conclusion will significantly contribute to the post-GWAS follow-up studies on alcohol dependence.

Genetics and genomics of alcohol sensitivity

Alcohol abuse and alcoholism incur a heavy socioeconomic cost in many countries. Both genetic and environmental factors contribute to variation in the inebriating effects of alcohol and alcohol addiction among individuals within and across populations. From a genetics perspective, alcohol sensitivity is a quantitative trait determined by the cumulative effects of multiple segregating genes and their interactions with the environment. This review summarizes insights from model organisms as well as human populations that represent our current understanding of the genetic and genomic underpinnings that govern alcohol metabolism and the sedative and addictive effects of alcohol on the nervous system.

Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall's Tau

Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies (GWAS), environmental factors are known to be critical to addiction, and comorbidity is widely observed. Despite the importance of environmental factors and comorbidity for addiction study, few GWAS analyses adequately considered them due to the limitations of the existing statistical methods. Although parametric methods have been developed to adjust for covariates in association analysis, difficulties arise when the traits are multivariate because there is no ready-to-use model for them. Recent nonparametric development includes U-statistics to measure the phenotype-genotype association weighted by a similarity score of covariates. However, it is not clear how to optimize the similarity score. Therefore, we propose a semiparametric method to measure the association adjusted by covariates. In our approach, the nonparametric U-statistic is adjusted by parametric estimates of propensity scores using the idea of inverse probability weighting. The new measurement is shown to be asymptotically unbiased under our null hypothesis while the previous non-weighted and weighted ones are not. Simulation results show that our test improves power as opposed to the non-weighted and two other weighted U-statistic methods, and it is particularly powerful for detecting gene-environment interactions. Finally, we apply our proposed test to the Study of Addiction: Genetics and Environment (SAGE) to identify genetic variants for addiction. Novel genetic variants are found from our analysis, which warrant further investigation in the future.

NKAIN1-SERINC2 is a functional, replicable and genome-wide significant risk gene region specific for alcohol dependence in subjects of European descent

OBJECTIVE

We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs).

METHODS

A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis.

RESULTS

A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10(-7)≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10(-7)) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence.

CONCLUSION

NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.

NCK2 is significantly associated with opiates addiction in African-origin men

Substance dependence is a complex environmental and genetic disorder with significant social and medical concerns. Understanding the etiology of substance dependence is imperative to the development of effective treatment and prevention strategies. To this end, substantial effort has been made to identify genes underlying substance dependence, and in recent years, genome-wide association studies (GWASs) have led to discoveries of numerous genetic variants for complex diseases including substance dependence. Most of the GWAS discoveries were only based on single nucleotide polymorphisms (SNPs) and a single dichotomized outcome. By employing both SNP- and gene-based methods of analysis, we identified a strong (odds ratio = 13.87) and significant (P value = 1.33E - 11) association of an SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Codependence analysis also identified a genome-wide significant association between NCK2 and comorbidity of substance dependence (P value = 3.65E - 08) in African-origin men. Furthermore, we observed that the association between the NCK2 gene (P value = 3.12E - 10) and opiates addiction reached the gene-based genome-wide significant level. In summary, our findings provided the first evidence for the involvement of NCK2 in the susceptibility to opiates addiction and further revealed the racial and gender specificities of its impact.

Genetic association test for multiple traits at gene level

Genome-wide association studies (GWASs) at the gene level are commonly used to understand biological mechanisms underlying complex diseases. In general, one response or outcome is used to present a disease of interest in such studies. In this study, we consider a multiple traits association test from the gene level. We propose and examine a class of test statistics that summarizes the association information between single nucleotide polymorphisms (SNPs) and each of the traits. Our simulation studies demonstrate the advantage of gene-based multiple traits association tests when multiple traits share common genes. Using our proposed tests, we reanalyze the dataset from the Study of Addiction: Genetics and Environment (SAGE). Our result validates previous findings while presenting stronger evidence for consideration of multiple traits.

Large scale association analysis for drug addiction: results from SNP to gene

Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.

A novel, functional and replicable risk gene region for alcohol dependence identified by genome-wide association study

Several genome-wide association studies (GWASs) reported tens of risk genes for alcohol dependence, but most of them have not been replicated or confirmed by functional studies. The present study used a GWAS to search for novel, functional and replicable risk gene regions for alcohol dependence. Associations of all top-ranked SNPs identified in a discovery sample of 681 African-American (AA) cases with alcohol dependence and 508 AA controls were retested in a primary replication sample of 1,409 European-American (EA) cases and 1,518 EA controls. The replicable associations were then subjected to secondary replication in a sample of 6,438 Australian family subjects. A functional expression quantitative trait locus (eQTL) analysis of these replicable risk SNPs was followed-up in order to explore their cis-acting regulatory effects on gene expression. We found that within a 90 Mb region around PHF3-PTP4A1 locus in AAs, a linkage disequilibrium (LD) block in PHF3-PTP4A1 formed the only peak associated with alcohol dependence at p<10(-4). Within this block, 30 SNPs associated with alcohol dependence in AAs (1.6×10(-5)≤p≤0.050) were replicated in EAs (1.3×10(-3)≤p≤0.038), and 18 of them were also replicated in Australians (1.8×10(-3)≤p≤0.048). Most of these risk SNPs had strong cis-acting regulatory effects on PHF3-PTP4A1 mRNA expression across three HapMap samples. The distributions of -log(p) values for association and functional signals throughout this LD block were highly consistent across AAs, EAs, Australians and three HapMap samples. We conclude that the PHF3-PTP4A1 region appears to harbor a causal locus for alcohol dependence, and proteins encoded by PHF3 and/or PTP4A1 might play a functional role in the disorder.

A meta-analysis of two genome-wide association studies identifies 3 new loci for alcohol dependence

Family, twin and adoption studies have clearly demonstrated that genetic factors are important in modulating the vulnerability to alcohol dependence. Several genome-wide association (GWA) studies of alcohol dependence have been conducted; however, few loci have been replicated. A meta-analysis was performed on two GWA studies of 1283 cases of alcohol dependence and 1416 controls in Caucasian populations. Through meta-analysis we identified 131 SNPs associated with alcohol dependence with p<10(-4). The best novel signal was rs6701037 (p=1.86 × 10(-7)) at 1q24-q25 within KIAA0040 gene while the second best novel hit was rs1869324 (p=4.71 × 10(-7)) at 2q22.1 within THSD7B. The third novel locus was NRD1 at 1p32.2 (the top SNP was rs2842576 with p=7.90 × 10(-6)). We confirmed the association of PKNOX2 at 11q24.4 with alcohol dependence. The top hit of PKNOX2 (rs750338 with p=1.47 × 10(-6)) in the meta-analysis was replicated with the Australian Twin-Family Study of 778 families (p=1.39 × 10(-2)) Furthermore, several flanking SNPs of the top hits in the meta-analysis demonstrated borderline associations with alcohol dependence in the family sample (top SNPs were rs2269655, rs856613, and rs10496768 with p=4.58 × 10(-3), 2.1 × 10(-4), and 2.86 × 10(-3) for KIAA0040, NRD1 and THSD7B, respectively). In addition, ALK, CASC4, and SEMA5A were strongly associated with alcohol dependence (p<2 × 10(-5)) in the meta-analysis. In conclusion, we identified three new loci (KIAA0040, THSD7B and NRD1) and confirmed the previous association of PKNOX2 with alcohol dependence. These findings offer the potential for new insights into the pathogenesis of alcohol dependence.

Statistical Analysis in Genetic Studies of Mental Illnesses

Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.