Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students.

Background: Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk. Methods: We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants (h2SNP) was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults. Results: Two genome-wide significant markers were observed: rs11201929 in GRID1 for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in SAMD12 (alcohol problems), tested only in the African ancestry group. The h2SNP estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample. Conclusions: These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in GRID1 impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification.

Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-Response Behaviors in Model Organisms.

BACKGROUND: Alcohol dependence (AD) shows evidence for genetic liability, but genes influencing risk remain largely unidentified. METHODS: We conducted a genomewide association study in 706 related AD cases and 1,748 unscreened population controls from Ireland. We sought replication in 15,496 samples of European descent. We used model organisms (MOs) to assess the role of orthologous genes in ethanol (EtOH)-response behaviors. We tested 1 primate-specific gene for expression differences in case/control postmortem brain tissue. RESULTS: We detected significant association in COL6A3 and suggestive association in 2 previously implicated loci, KLF12 and RYR3. None of these signals are significant in replication. A suggestive signal in the long noncoding RNA LOC339975 is significant in case:control meta-analysis, but not in a population sample. Knockdown of a COL6A3 ortholog in Caenorhabditis elegans reduced EtOH sensitivity. Col6a3 expression correlated with handling-induced convulsions in mice. Loss of function of the KLF12 ortholog in C. elegans impaired development of acute functional tolerance (AFT). Klf12 expression correlated with locomotor activation following EtOH injection in mice. Loss of function of the RYR3 ortholog reduced EtOH sensitivity in C. elegans and rapid tolerance in Drosophila. The ryanodine receptor antagonist dantrolene reduced motivation to self-administer EtOH in rats. Expression of LOC339975 does not differ between cases and controls but is reduced in carriers of the associated rs11726136 allele in nucleus accumbens (NAc). CONCLUSIONS: We detect association between AD and COL6A3, KLF12, RYR3, and LOC339975. Despite nonreplication of COL6A3, KLF12, and RYR3 signals, orthologs of these genes influence behavioral response to EtOH in MOs, suggesting potential involvement in human EtOH response and AD liability. The associated LOC339975 allele may influence gene expression in human NAc. Although the functions of long noncoding RNAs are poorly understood, there is mounting evidence implicating these genes in multiple brain functions and disorders.

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

MOTIVATION: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts. RESULTS: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia. AVAILABILITY AND IMPLEMENTATION: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/. CONTACT: donghyung.lee@vcuhealth.org SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.

MOTIVATION: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. RESULTS: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. AVAILABILITY AND IMPLEMENTATION: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.

MOTIVATION: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared with the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by (i) not measuring all eQTLs in a gene and/or (ii) lack of access to individual genotypes. RESULTS: We propose joint effect on phenotype of eQTL/functional SNPs associated with a gene (JEPEG), a novel software tool which uses only GWAS summary statistics to (i) impute the summary statistics at unmeasured eQTLs and (ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium Stage 1 and the Genetic Consortium for Anorexia Nervosa. CONCLUSIONS: Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: (i) increasing signal detection power via uncovering (a) novel genes or (b) known associated genes in smaller cohorts and (ii) assisting in fine-mapping of challenging regions, e.g. major histocompatibility complex for schizophrenia. AVAILABILITY AND IMPLEMENTATION: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at http://code.google.com/p/jepeg/. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence.

Family-based and genome-wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared with family history information has not been reported. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we examined the aggregate impact of multiple single nucleotide polymorphisms (SNPs) on risk prediction. We created genetic sum scores by adding risk alleles associated in discovery samples, and then tested the scores for their ability to discriminate between cases and controls in validation samples. Genetic sum scores were assessed separately for SNPs associated with AD in candidate gene studies and SNPs from GWAS analyses that met varying P-value thresholds. Candidate gene sum scores did not exhibit significant predictive accuracy. Family history was a better classifier of case-control status, with a significant area under the receiver operating characteristic curve (AUC) of 0.686 in COGA and 0.614 in SAGE. SNPs that met less stringent P-value thresholds of 0.01-0.50 in GWAS analyses yielded significant AUC estimates, ranging from mean estimates of 0.549 for SNPs with P < 0.01 to 0.565 for SNPs with P < 0.50. This study suggests that SNPs currently have limited clinical utility, but there is potential for enhanced predictive ability with better understanding of the large number of variants that might contribute to risk.

Variants in nicotinic acetylcholine receptors alpha5 and alpha3 increase risks to nicotine dependence.

Nicotinic acetylcholine receptors bind to nicotine and initiate the physiological and pharmacological responses to tobacco smoking. In this report, we studied the association of alpha5 and alpha3 subunits with nicotine dependence and with the symptoms of alcohol and cannabis abuse and dependence in two independent epidemiological samples (n = 815 and 1,121, respectively). In this study, seven single nucleotide polymorphisms were genotyped in the CHRNA5 and CHRNA3 genes. In both samples, we found that the same alleles of rs16969968 (P = 0.0068 and 0.0028) and rs1051730 (P = 0.0237 and 0.0039) were significantly associated with the scores of Fagerström test for nicotine dependence (FTND). In the analyses of the symptoms of abuse/dependence of alcohol and cannabis, we found that rs16969968 and rs1051730 were significantly associated with the symptoms of alcohol abuse or dependence (P = 0.0072 and 0.0057) in the combined sample, but the associated alleles were the opposite of that of FTND. No association with cannabis abuse/dependence was found. These results suggested that the alpha5 and alpha3 subunits play a significant role in both nicotine dependence and alcohol abuse/dependence. However, the opposite effects with nicotine dependence and alcohol abuse/dependence were puzzling and future studies are necessary to resolve this issue.

Cannabinoid receptor 1 gene association with nicotine dependence.

CONTEXT: The endogenous cannabinoid system has been implicated in drug addiction in animal models. The cannabinoid receptor 1 (CNR1) gene is 1 of the 2 receptors expressed in the brain. It has been reported to be associated with alcoholism and multiple drug abuse and dependence. OBJECTIVE: To test the hypothesis that the CNR1 gene is associated with nicotine dependence. DESIGN: Genotype-phenotype association study. Ten single-nucleotide polymorphisms were genotyped in the CNR1 gene in 2 independent samples. For the first sample (n = 688), a 3-group case-control design was used to test allele association with smoking initiation and nicotine dependence. For the second sample (n = 961), association was assessed with scores from the Fagerström Test for Nicotine Dependence (FTND). Settings Population samples selected from the Mid-Atlantic Twin Registry. PARTICIPANTS: White patients aged 18 to 65 years who met the criteria of inclusion. MAIN OUTCOME MEASURES: Fagerström Tolerance Questionnaire and FTND scores. RESULTS: Significant single-marker and haplotype associations were found in both samples, and the associations were female specific. Haplotype 1-1-2 of markers rs2023239-rs12720071-rs806368 was associated with nicotine dependence and FTND score in the 2 samples (P < .001 and P = .009, respectively). CONCLUSION: Variants and haplotypes in the CNR1 gene may alter the risk for nicotine dependence, and the associations are likely sex specific.