Study Of Addiction : Genetics And Environment Research Articles

Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression.

Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways.

Expectile Neural Networks for Genetic Data Analysis of Complex Diseases.

The genetic etiologies of common diseases are highly complex and heterogeneous. Classic methods, such as linear regression, have successfully identified numerous variants associated with complex diseases. Nonetheless, for most diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides complete information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties, it has rarely been used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes, which can be used to discover variants predisposing to sub-populations. We further integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex genotype-phenotype relationships. We also applied the proposed method to the data from the Study of Addiction: Genetics and Environment (SAGE), investigating the relationships of candidate genes with smoking quantity.

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.

A hybrid method of the sequential Monte Carlo and the Edgeworth expansion for computation of very small p-values in permutation tests.

Permutation tests are very useful when parametric assumptions are violated or distributions of test statistics are mathematically intractable. The major advantage of permutation tests is that the procedure is so general that it is applicable to most test statistics. The computational expense is, however, impractical in high-dimensional settings such as genomewide association studies. This study provides a comprehensive review of existing methods that can compute very small p-values efficiently. A common issue with existing methods is that they can only be applied to a specific test statistic. To fill in the knowledge gap, we propose a hybrid method of the sequential Monte Carlo and the Edgeworth expansion approximation for a studentized statistic, which is applicable to a variety of test statistics. The simulation results show that the proposed method performs better than competing methods. Furthermore, applications of the proposed method are demonstrated by statistical analysis on the genomewide association studies data from the Study of Addiction: Genetics and Environment (SAGE).

Evaluation of previous substance dependence genome-wide significant findings in a Spanish sample

BackgroundSubstance dependence is a chronic and relapsing disorder explained by genetic and environmental risk factors. The aim of our study is to replicate previous genome-wide significant (GWS) hits identified in substance dependence in general or in cocaine dependence in particular using an independent sample from Spain. MethodsWe evaluated, in a Spanish sample of 1711 subjects with substance dependence (1011 of them cocaine dependent) and 1719 control individuals, three SNPs identified as GWS in previous studies: rs1868152 and rs2952621 (located near LINC02052 and LINC01854, respectively), associated with substance dependence, and rs2629540 (in the first intron of FAM53B), associated with cocaine dependence. ResultsWe replicated the association between rs2952621 and substance dependence under the dominant model (P = 0.020), with the risk allele (T) being the same in our sample and in those two reported previously. We then performed a meta-analysis of the two samples used in the original study that reported the association of rs2952621 with substance dependence (Collaborative Studies on Genetics of Alcoholism (COGA) and Study of Addiction: Genetics and Environment (SAGE)) together with our Spanish sample. The meta-analysis of 3747 cases and 4043 controls confirmed the association (OR = 1.26, 95% CI = 1.15–1.39). ConclusionsThe rs2952621 variant, located downstream from the yet uncharacterized gene LINC01854, is associated with substance dependence in our Spanish sample. Further research is needed to understand its contribution to the susceptibility to substance dependence.

Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans

BackgroundOpioid dependence (OD) is at epidemic levels in the United States. Genetic studies can provide insight into its biology. MethodsWe completed an OD genome-wide association study in 3058 opioid-exposed European Americans, 1290 of whom met criteria for a DSM-IV diagnosis of OD. Analysis used DSM-IV criterion count. ResultsBy meta-analysis of four cohorts, Yale-Penn 1 (n = 1388), Yale-Penn 2 (n = 996), Yale-Penn 3 (n = 98), and SAGE (Study of Addiction: Genetics and Environment) (n = 576), we identified a variant on chromosome 15, rs12442183, near RGMA, associated with OD (p = 1.3 × 10−8). The association was also genome-wide significant in Yale-Penn 1 taken individually and nominally significant in two of the other three samples. The finding was further supported in a meta-analysis of all available opioid-exposed African Americans (n = 2014 [1106 meeting DSM-IV OD criteria]; p = 3.0 × 10−3) from three cohorts; there was nominal significance in two of these samples. Thus, of seven subsamples examined in two populations, one was genome-wide significant, and four of six were nominally (or nearly) significant. RGMA encodes repulsive guidance molecule A, which is a central nervous system axon guidance protein. Risk allele rs12442183*T was correlated with higher expression of a specific RGMA transcript variant in frontal cortex (p = 2 × 10−3). After chronic morphine injection, the homologous mouse gene (Rgma) was upregulated in C57BL/6J striatum. Coexpression analysis of 1301 brain samples revealed that RGMA messenger RNA expression was associated with that of four genes implicated in other psychiatric disorders, including GRIN1. ConclusionsThis is the first study to demonstrate an association of RGMA with OD. It provides a new lead into our understanding of OD pathophysiology.

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function

BackgroundA multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic.ResultsThe simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes.ConclusionsThis study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales.

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies.

Polymorphisms in PDLIM5 gene are associated with alcohol dependence, type 2 diabetes, and hypertension

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in alcohol dependence (AD), bipolar disorder, and major depressive disorder; however, no study has identified shared genetic variants within PDLIM5 gene among AD, type 2 diabetes (T2D), and hypertension. This study investigated the association of 72 single nucleotide polymorphism (SNPs) with AD (1066 AD cases and 1278 controls) in the Study of Addiction - Genetics and Environment (SAGE) sample and 47 SNPs with T2D (878 cases and 2686 non-diabetic) and hypertension (825 cases and 2739 non-hypertensive) in the Marshfield sample. Multiple logistic regression models in PLINK software were used to examine the associations of genetic variants with AD, T2D, and hypertension and SNP x alcohol consumption interactions for T2D and hypertension. Twenty-five SNPs were associated with AD in the SAGE sample (p < 0.05); rs1048627 showed the strongest association with AD (p = 5.53 × 10−4). Of the 25 SNPs, 5 SNPs showed associations with both AD in the SAGE sample and T2D in the Marshfield sample (top SNP rs11097432 with p = 0.00107 for T2D and p = 0.0483 for AD) while 6 SNPs showed associations with both AD in the SAGE sample and hypertension in the Marshfield sample (top SNP rs12500426 with p = 0.0119 for hypertension and p = 1.51 × 10−3 for AD). SNP (rs6532496) showed significant interaction with alcohol consumption for hypertension. Our results showed that several genetic variants in PDLIM5 gene influence AD, T2D and hypertension. These findings offer the potential for new insights into the pathogenesis of AD, T2D, and hypertension.

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

BackgroundIn genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data.ResultsOur simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct.ConclusionsThe proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

Genetic variants in the CPNE5 gene are associated with alcohol dependence and obesity in Caucasian populations

Alcohol addiction may increase the risk of obesity due to shared genetic components. The Copine V (CPNE5) gene is involved in Ca2+ binding and may play an important role in the development of the central nervous system. This study tested the genetic associations of 77 single-nucleotide polymorphisms (SNPs) within the CPNE5 gene with alcohol dependence (AD) and obesity using a Caucasian sample – The Study of Addiction – Genetics and Environment (SAGE) sample (1066 AD cases and 1278 non-AD controls, 422 obese cases and 1395 non-obese controls). The Marshfield sample (1442 obese cases and 2122 non-obese controls) was used for replication of obesity. Multiple logistic regression analysis was performed using the PLINK software. In the SAGE sample, we identified 10 SNPs associated with AD and 17 SNPs associated with obesity (p < 0.05). Interestingly, 6 SNPs (rs9986517, rs9470387, rs3213534, rs10456444, rs3752482, and rs9470386) were associated with both AD (OR = 0.77, 0.77, 0.83, 0.84, 0.79 and 1.14, respectively; p = 9.72 × 10−5, 1.1 × 10−4, 4.09 × 10−3, 5.26 × 10−3, 1.59 × 10−2, and 3.81 × 10−2, respectively) and obesity (OR = 0.77, 0.77, 0.78, 0.77, 0.68 and 1.18, respectively; p = 2.74 × 10−3, 2.69 × 10−3, 2.45 × 10−3, 1.01 × 10−3, 5.18 × 10−3 and 3.85 × 10−2, respectively). In the Marshfield sample, rs3752480 was associated with obesity (p = 0.0379). In addition, four SNPs (rs9986517, rs10456444, rs7763347 and rs4714010) showed associations with obesity in the meta-analysis using both samples (p = 0.00493, 0.0274, 0.00346, and 0.0141, respectively). These findings provide the first evidence of common genetic variants in the CPNE5 gene influencing both the AD and obesity; and will serve as a resource for replication in other populations.

Genomewide Association Study for Maximum Number of Alcoholic Drinks in European Americans and African Americans.

We conducted a genomewide association study (GWAS) for maximum number of alcoholic drinks consumed in a 24-hour period ("MaxDrinks"), in 2 independent samples comprised of over 9,500 subjects, following up on our GWAS for alcohol dependence (AD) in European Americans (EAs) and African Americans (AAs). The samples included our GWAS samples (Yale-UPenn) recruited for studies of the genetics of drug or AD, and a publicly available sample: the Study of Addiction: Genetics and Environment (SAGE). Genomewide association analysis was performed for ~890,000 single nucleotide polymorphisms (SNPs) using linear association random effects models. EAs and AAs were separately analyzed. The results confirmed significant associations of the well-known functional loci at ADH1B with MaxDrinks in EAs (rs1229984 Arg48His p=5.96×10(-15) ) and AAs (rs2066702 Arg370Cys, p=2.50×10(-10) ). The region of significant association on chromosome 4 was extended to LOC100507053 in AAs but not EAs. We also identified potentially novel significant common SNPs for MaxDrinks in EAs in the Yale-UPenn sample: rs1799876 at SERPINC1 on chromosome 1 (4.00×10(-8) ) and rs2309169 close to ANKRD36 on chromosome 2 (p=5.58×10(-9) ). After adjusting for the peak SNP rs1229984 on ADH1B, rs1799876 was nearly significant (p=1.99×10(-7) ) and rs2309169 remained highly significant (2.12×10(-9) ). The results provide further support that ADH1B modulates alcohol consumption. Future replications of potential novel loci are warranted. This is the largest MaxDrinks GWAS to date, the first in AAs.

The glucagon-like peptide-1 receptor as a potential treatment target in alcohol use disorder: evidence from human genetic association studies and a mouse model of alcohol dependence.

The hormone glucagon-like peptide-1 (GLP-1) regulates appetite and food intake. GLP-1 receptor (GLP-1R) activation also attenuates the reinforcing properties of alcohol in rodents. The present translational study is based on four human genetic association studies and one preclinical study providing data that support the hypothesis that GLP-1R may have a role in the pathophysiology of alcohol use disorder (AUD). Case–control analysis (N=908) was performed on a sample of individuals enrolled in the National Institute on Alcohol Abuse and Alcoholism (NIAAA) intramural research program. The Study of Addiction: Genetics and Environment (SAGE) sample (N=3803) was used for confirmation purposes. Post hoc analyses were carried out on data from a human laboratory study of intravenous alcohol self-administration (IV-ASA; N=81) in social drinkers and from a functional magnetic resonance imaging study in alcohol-dependent individuals (N=22) subjected to a Monetary Incentive Delay task. In the preclinical study, a GLP-1R agonist was evaluated in a mouse model of alcohol dependence to demonstrate the role of GLP-1R for alcohol consumption. The previously reported functional allele 168Ser (rs6923761) was nominally associated with AUD (P=0.004) in the NIAAA sample, which was partially replicated in males of the SAGE sample (P=0.033). The 168Ser/Ser genotype was further associated with increased alcohol administration and breath alcohol measures in the IV-ASA experiment and with higher BOLD response in the right globus pallidus when receiving notification of outcome for high monetary reward. Finally, GLP-1R agonism significantly reduced alcohol consumption in a mouse model of alcohol dependence. These convergent findings suggest that the GLP-1R may be an attractive target for personalized pharmacotherapy treatment of AUD.

TARV: tree-based analysis of rare variants identifying risk modifying variants in CTNNA2 and CNTNAP2 for alcohol addiction.

Since the development of next generation sequencing (NGS) technology, researchers have been extending their efforts on genome-wide association studies (GWAS) from common variants to rare variants to find the missing inheritance. Although various statistical methods have been proposed to analyze rare variants data, they generally face difficulties for complex disease models involving multiple genes. In this paper, we propose a tree-based analysis of rare variants (TARV) that adopts a nonparametric disease model and is capable of exploring gene-gene interactions. We found that TARV outperforms the sequence kernel association test (SKAT) in most of our simulation scenarios, and by notable margins in some cases. By applying TARV to the study of addiction: genetics and environment (SAGE) data, we successfully detected gene CTNNA2 and its 43 specific variants that increase the risk of alcoholism in women, with an odds ratio (OR) of 1.94. This gene has not been detected in the SAGE data. Post hoc literature search also supports the role of CTNNA2 as a likely risk gene for alcohol addiction. In addition, we also detected a plausible protective gene CNTNAP2, whose 97 rare variants can reduce the risk of alcoholism in women, with an OR of 0.55. These findings suggest that TARV can be effective in dissecting genetic variants for complex diseases using rare variants data.

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 nonweighted and weighted ones are not. Simulation results show that our test improves power as opposed to the nonweighted 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.

Variants near CHRNB3-CHRNA6 are associated with DSM-5 cocaine use disorder: evidence for pleiotropy

In the U.S.A., cocaine is the second most abused illicit drug. Variants within the CHRNB3-A6 gene cluster have been associated with cigarette consumption in several GWAS. These receptors represent intriguing candidates for the study of cocaine dependence because nicotinic receptors are thought to be involved in generalized addiction pathways. Using genotypic data from a GWAS of the Study of Addiction: Genetics and Environment (SAGE) dataset, we tested for association of CHRNB3-A6 SNPs with DSM-5 cocaine use disorder. Multiple SNPs in the region were significantly associated with increased risk of cocaine use disorder. Inclusion of the most significant SNP as a covariate in a linear regression model provided evidence for an additional independent signal within this locus for cocaine use disorder. These results suggest that the CHRNB3-A6 locus contains multiple variants affecting risk for vulnerability to cocaine and nicotine dependence as well as bipolar disorder, suggesting that they have pleiotropic effects.

Association and interaction analyses of 5-HT3 receptor and serotonin transporter genes with alcohol, cocaine, and nicotine dependence using the SAGE data.

Previous studies have implicated genes encoding the 5-HT3AB receptors (HTR3A and HTR3B) and the serotonin transporter (SLC6A4), both independently and interactively, in alcohol (AD), cocaine (CD), and nicotine dependence (ND). However, whether these genetic effects also exist in subjects with comorbidities remains largely unknown. We used 1,136 African-American (AA) and 2,428 European-American (EA) subjects from the Study of Addiction: Genetics and Environment (SAGE) to determine associations between 88 genotyped or imputed variants within HTR3A, HTR3B, and SLC6A4 and three types of addictions, which were measured by DSM-IV diagnoses of AD, CD, and ND and the Fagerström Test for Nicotine Dependence (FTND), an independent measure of ND commonly used in tobacco research. Individual SNP-based association analysis revealed a significant association of rs2066713 in SLC6A4 with FTND in AA (β = -1.39; P = 1.6E - 04). Haplotype-based association analysis found one major haplotype formed by SNPs rs3891484 and rs3758987 in HTR3B that was significantly associated with AD in the AA sample, and another major haplotype T-T-G, formed by SNPs rs7118530, rs12221649, and rs2085421 in HTR3A, which showed significant association with FTND in the EA sample. Considering the biologic roles of the three genes and their functional relations, we used the GPU-based Generalized Multifactor Dimensionality Reduction (GMDR-GPU) program to test SNP-by-SNP interactions within the three genes and discovered two- to five-variant models that have significant impacts on AD, CD, ND, or FTND. Interestingly, most of the SNPs included in the genetic interaction model(s) for each addictive phenotype are either overlapped or in high linkage disequilibrium for both AA and EA samples, suggesting these detected variants in HTR3A, HTR3B, and SLC6A4 are interactively contributing to etiology of the three addictive phenotypes examined in this study.

Biomedical Informatics and Computational Biology for High-Throughput Data Analysis

The last few years have witnessed the emergence of several high-throughput (HTP) platforms that are based on the evolution of omic science through transcriptomics, proteomics, and metabolomics. With the huge data accumulation, the informatics and computational methods have become essential to understand the complexity of biomedical data. We lunched the special issue to address the demand for statistical models, biological omic data analyses, and meta-analysis of biomarker for complex diseases. Statistical models are always important for the understanding of complex data; in this issue, Y. Liang et al. proposed a new adaptive L1/2 shooting regularization method for variable selection based on the Cox's proportional hazards mode. Simulation and the real gene expression dataset analysis showed that the method is more accurate for variable selection than Lasso and adaptive Lasso methods. L. Tian et al. employed a nonlinear model to analyze time course gene expression data. They firstly developed a method for estimating the parameters in the nonlinear model and then utilized the model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The simulation and real-life biological data analyses showed that their methods outperform some existing methods. For the application of bioinformatics tools, Z. E et al. analyzed the gene expression in rice leaf blades at different temperatures. They analyzed the next-generation sequencing (NGS) datasets and characterized the transcription profiles of the samples in rice seedling leaf blades at 25°C and 30°C. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were applied to study the effect of the temperature. Finally, they observed that temperature markedly regulates several superfamilies of transcription factors, including bZIP, MYB, and WRKY. Chronobiology is a field of biology that studies biological rhythms. R. Lopes et al. reviewed the bioinformatics or computational systems biology methods, tools, and databases which are very helpful to the understanding of the patterns and biological rhythms found in living organisms and the review will benefit the community for the application of bioinformatics in their future researches. For the understanding of protein structure and structure-function relationship, two papers are selected in this issue. M. Shambhu et al. studied the surface accessibility of hydrophobic residues by considering their conservation score and knowledge of flanking regions. The accuracy of prediction is therefore improved. In the other work, D. Lu et al. applied molecular dynamics to investigate the effects of X-linked agammaglobulinaemia associated with amino acid mutations in pleckstrin homology (PH) domain on the binding of the domain with inositol 1,3,4,5-tetrakisphosphate (Ins(1,3,4,5)P4). The mutations were then classified as “functional mutations”, and “folding mutations.” The result provided new insights into the biological function of the Btk-PH domain and related mutation-causing diseases. Finally, two works in this special issue are medical studies. The first is the work by B. Lv et al. They analyzed the 347 studies for the diagnostic value of tumor necrosis factor-α (TNF-α) and found that the northern hemisphere group in the TNF-α test has higher sensitivity and specificity than those of southern hemisphere group. The second medical study is an association study. Z. Liu et al. reported the significantly association of NCK2 with opiates addiction in African-origin men by a comprehensive analysis of a dataset from the Study of Addiction: Genetics and Environment (SAGE). They employed both SNP and gene based methods of analysis and identified a strong and significant association between a SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Bairong  Shen Jian  Ma Jiajun  Wang Junbai  Wang

Integrating GWASs and Human Protein Interaction Networks Identifies a Gene Subnetwork Underlying Alcohol Dependence

Despite a significant genetic contribution to alcohol dependence (AD), few AD-risk genes have been identified to date. In the current study, we aimed to integrate genome-wide association studies (GWASs) and human protein interaction networks to investigate whether a subnetwork of genes whose protein products interact with one another might collectively contribute to AD. By using two discovery GWAS data sets of the Study of Addiction: Genetics and Environment (SAGE) and the Collaborative Study on the Genetics of Alcoholism (COGA), we identified a subnetwork of 39 genes that not only was enriched for genes associated with AD, but also collectively associated with AD in both European Americans (p < 0.0001) and African Americans (p = 0.0008). We replicated the association of the gene subnetwork with AD in three independent samples, including two samples of European descent (p = 0.001 and p = 0.006) and one sample of African descent (p = 0.0069). To evaluate whether the significant associations are likely to be false-positive findings and to ascertain their specificity, we examined the same gene subnetwork in three other human complex disorders (bipolar disorder, major depressive disorder, and type 2 diabetes) and found no significant associations. Functional enrichment analysis revealed that the gene subnetwork was enriched for genes involved in cation transport, synaptic transmission, and transmission of nerve impulses, all of which are biologically meaningful processes that may underlie the risk for AD. In conclusion, we identified a gene subnetwork underlying AD that is biologically meaningful and highly reproducible, providing important clues for future research into AD etiology and treatment.

Association of Gamma-Aminobutyric Acid A Receptor α2 Gene (GABRA2) with Alcohol Use Disorder

Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter in mammalian brain. GABA receptor are involved in a number of complex disorders, including substance abuse. No variants of the commonly studied GABA receptor genes that have been associated with substance dependence have been determined to be functional or pathogenic. To reconcile the conflicting associations with substance dependence traits, we performed a meta-analysis of variants in the GABAA receptor genes (GABRB2, GABRA6, GABRA1, and GABRG2 on chromosome 5q and GABRA2 on chromosome 4p12) using genotype data from 4739 cases of alcohol, opioid, or methamphetamine dependence and 4924 controls. Then, we combined the data from candidate gene association studies in the literature with two alcohol dependence (AD) samples, including 1691 cases and 1712 controls from the Study of Addiction: Genetics and Environment (SAGE), and 2644 cases and 494 controls from our own study. Using a Bonferroni-corrected threshold of 0.007, we found strong associations between GABRA2 and AD (P=9 × 10(-6) and odds ratio (OR) 95% confidence interval (CI)=1.27 (1.15, 1.4) for rs567926, P=4 × 10(-5) and OR=1.21 (1.1, 1.32) for rs279858), and between GABRG2 and both dependence on alcohol and dependence on heroin (P=0.0005 and OR=1.22 (1.09, 1.37) for rs211014). Significant association was also observed between GABRA6 rs3219151 and AD. The GABRA2 rs279858 association was observed in the SAGE data sets with a combined P of 9 × 10(-6) (OR=1.17 (1.09, 1.26)). When all of these data sets, including our samples, were meta-analyzed, associations of both GABRA2 single-nucleotide polymorphisms remained (for rs567926, P=7 × 10(-5) (OR=1.18 (1.09, 1.29)) in all the studies, and P=8 × 10(-6) (OR=1.25 (1.13, 1.38)) in subjects of European ancestry and for rs279858, P=5 × 10(-6) (OR=1.18 (1.1, 1.26)) in subjects of European ancestry. Findings from this extensive meta-analysis of five GABAA receptor genes and substance abuse support their involvement (with the best evidence for GABRA2) in the pathogenesis of AD. Further replications with larger samples are warranted.