Association between common alcohol dehydrogenase gene (ADH) variants and schizophrenia and autism
Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06516, USA, .Human Genetics (Impact Factor: 4.82). 03/2013; 132(7). DOI: 10.1007/s00439-013-1277-4
Humans express at least seven alcohol dehydrogenase (ADH) isoforms that are encoded by ADH gene cluster (ADH7-ADH1C-ADH1B-ADH1A-ADH6-ADH4-ADH5) at chromosome 4. ADHs are key catabolic enzymes for retinol and ethanol. The functional ADH variants (mostly rare) have been implicated in alcoholism risk. In addition to catalyzing the oxidation of retinol and ethanol, ADHs may be involved in the metabolic pathways of several neurotransmitters that are implicated in the neurobiology of neuropsychiatric disorders. In the present study, we comprehensively examined the associations between common ADH variants [minor allele frequency (MAF) >0.05] and 11 neuropsychiatric and neurological disorders. A total of 50,063 subjects in 25 independent cohorts were analyzed. The entire ADH gene cluster was imputed across these 25 cohorts using the same reference panels. Association analyses were conducted, adjusting for multiple comparisons. We found 28 and 15 single nucleotide polymorphisms (SNPs), respectively, that were significantly associated with schizophrenia in African-Americans and autism in European-Americans after correction by false discovery rate (FDR) (q < 0.05); and 19 and 6 SNPs, respectively, that were significantly associated with these two disorders after region-wide correction by SNPSpD (8.9 × 10-5 ≤ p ≤ 0.0003 and 2.4 × 10-5 ≤ p ≤ 0.0003, respectively). No variants were significantly associated with the other nine neuropsychiatric disorders, including alcohol dependence. We concluded that common ADH variants conferred risk for both schizophrenia in African-Americans and autism in European-Americans.
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ABSTRACT: Increased alcohol consumption has been associated with depression and alcoholism, but whether these associations are causal remains unclear. We tested whether alcohol consumption is causally associated with depression and alcoholism. We included 78 154 men and women aged 20-100 years randomly selected in 1991-2010 from the general population of Copenhagen, Denmark, and genotyped 68 486 participants for two genetic variants in two alcohol dehydrogenase (ADH) genes, ADH-1B (rs1229984) and ADH-1C (rs698). We performed observational and causal analyses using a Mendelian randomization design with antidepressant medication use and hospitalization/death, with depression and alcoholism as outcomes. In prospective analyses, the multifactorially adjusted hazard ratio for participants reporting >6 drinks/day vs participants reporting 0.1-1 drinks/day was 1.28 (95% confidence interval, 1.00-1.65) for prescription antidepressant use, with a corresponding hazard ratio of 0.80 (0.45-1.45) for hospitalization/death with depression and of 11.7 (8.77-15.6) for hospitalization/death with alcoholism. For hospitalization/death with alcoholism, instrumental variable analysis yielded a causal odds ratio of 28.6 (95 % confidence interval 6.47-126) for an increase of 1 drink/day estimated from the combined genotype combination, whereas the corresponding multifactorially adjusted observational odds ratio was 1.28 (1.25-1.31). Corresponding odds ratios were 1.11 (0.67-1.83) causal and 1.04 (1.03-1.06) observational for prescription antidepressant use, and 4.52 (0.99-20.5) causal and 0.98 (0.94-1.03) observational for hospitalization/death with depression. These data indicate that the association between increased alcohol consumption and alcoholism is causal, without similar strong evidence for depression. © The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
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ABSTRACT: Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS.
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