The collaborative study on the genetics of alcoholism: an update

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism (i.e., alcohol dependence) and alcohol-related characteristics and behaviors (i.e., phenotypes1). This collaborative project is funded by the National Institute on Alcohol Abuse and Alcoholism. Data collection, analysis, and/or storage for this study take place at nine sites across the United States. Because alcoholism is a complex genetic disorder, the COGA researchers expected that multiple genes would contribute to the risk. In other words, there will be no single “gene for alcoholism” but rather variations in many different genes that together, interacting with the environment, place some people at significantly higher risk for the disease. This genetic and environmental variability (i.e., heterogeneity) makes the task of identifying individual genes difficult. However, the COGA project was designed with these difficulties in mind and incorporated strategies to meet the challenges. This article briefly reviews these strategies and summarizes some of the results already obtained in the ongoing COGA study

Single-nucleotide polymorphisms interact to affect ADH7 transcription

BACKGROUND: The class IV alcohol dehydrogenase (ADH7, μ-ADH, σ-ADH) is important in the metabolism of ethanol and retinol. ADH7 is the only ADH not expressed in liver, instead being expressed mainly in the upper gastrointestinal tract. Genome-wide studies have identified significant associations between single-nucleotide polymorphisms in ADH7 and alcoholism and cancer, but the causative variants have not been identified. METHODS: In vitro studies of gene expression by transient transfection into cell lines that express endogenous ADH7 (CP-A cells) and that do not (HepG2 cells). RESULTS: We have identified transcriptional regulatory elements of ADH7 and observed differences in the effects of variants on gene expression in CP-A cells and HepG2 cells. Two haplotypes of the proximal promoter that differ in a single nucleotide at rs2851028, A7P-G and A7P-A, have different transcriptional activities. There is an interaction between variants farther upstream and these proximal variants: Upstream regulatory sequences generally showed a greater increase or smaller reduction in activity when combined with the A7P-A promoter than with the A7P-G promoter. A sequence located 12.5-kb upstream (7P10) can function as an enhancer. In CP-A cells, both haplotypes of 7P10 increased A7P-A activity by 2.5-fold while having only 1.2-fold effect on A7P-G. In HepG2 cells, the 7P10-TTT haplotype had no effect on the A7P-A promoter but decreased A7P-G promoter activity by 50%, whereas the CTT haplotype increased A7P-A activity by 50%, but had no effect on A7P-G. CONCLUSIONS: These complex interactions indicate that the effects of variants in the ADH7 regulatory elements depend on both sequence and cellular context and should be considered in interpretation of the association of variants with alcoholism and cancer

An enhancer- blocking element regulates the cell-specific expression of alcohol dehydrogenase 7

The class IV alcohol dehydrogenase gene ADH7 encodes an enzyme that is involved in ethanol and retinol metabolism. ADH7 is expressed mainly in the upper gastrointestinal tract and not in the liver, the major site of expression of the other closely related ADHs. We identified an intergenic sequence (iA1C), located between ADH7 and ADH1C, that has enhancer-blocking activity in liver-derived HepG2 cells that do not express their endogenous ADH7. This enhancer blocking function was cell- and position-dependent, with no activity seen in CP-A esophageal cells that express ADH7 endogenously. iA1C function was not specific to the ADH enhancers; it had a similar cell-specific effect on the SV40 enhancer. The CCCTC-binding factor (CTCF), an insulator binding protein, bound iA1C in HepG2 cells but not in CP-A cells. Our results suggest that in liver-derived cells, iA1C blocks the effects of ADH enhancers and thereby contributes to the cell specificity of ADH7 expression

Effects of filtering by Present call on analysis of microarray experiments

BACKGROUND: Affymetrix GeneChips(® )are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression. RESULTS: Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ≤ 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3–5 samples per treatment group) benefit from more restrictive filtering (≥50% Present). CONCLUSION: Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives

Alcohol Dehydrogenases, Aldehyde Dehydrogenases, and Alcohol Use Disorders: A Critical Review

Alcohol use disorders (AUD) are complex traits, meaning that variations in many genes contribute to the risk, as does the environment. Although the total genetic contribution to risk is substantial, most individual variations make only very small contributions. By far the strongest contributors are functional variations in two genes involved in alcohol (ethanol) metabolism. A functional variant in alcohol dehydrogenase 1B (ADH1B) is protective in people of European and Asian descent, and a different functional variant in the same gene is protective in those of African descent. A strongly protective variant in aldehyde dehydrogenase 2 (ALDH2) is essentially only found in Asians. This highlights the need to study a wide range of populations. The likely mechanism of protection against heavy drinking and AUD in both cases is alteration in the rate of metabolism of ethanol that at least transiently elevates acetaldehyde. Other ADH and ALDH variants, including functional variations in ADH1C, have also been implicated in affecting drinking behavior and risk for alcoholism. The pattern of linkage disequilibrium in the ADH region, and the differences among populations, complicate analyses, particularly of regulatory variants. This critical review focuses upon the ADH and ALDH genes as they affect AUDs

Genetics of Alcoholism

Purpose of Review We review the search for genetic variants that affect the risk for alcohol dependence and alcohol consumption. Recent Findings Variations in genes affecting alcohol metabolism (ADH1B, ALDH2) are protective against both alcohol dependence and excessive consumption, but different variants are found in different populations. There are different patterns of risk variants for alcohol dependence vs. consumption. Variants for alcohol dependence, but not consumption, are associated with risk for other psychiatric illnesses. Summary ADH1B and ALDH2 strongly affect both consumption and dependence. Variations in many other genes affect both consumption and dependence—or one or the other of these traits—but individual effect sizes are small. Evidence for other specific genes that affect dependence is not yet strong. Most current knowledge derives from studies of European-ancestry populations, and large studies of carefully phenotyped subjects from different populations are needed to understand the genetic contributions to alcohol consumption and alcohol use disorders

Mapping of trans-acting regulatory factors from microarray data

To explore the mapping of factors regulating gene expression, we have carried out linkage studies using expression data from individual transcripts (from Affymetrix microarrays; Genetic Analysis Workshop 15 Problem 1) and composite data on correlated groups of transcripts. Quality measures for the arrays were used to remove outliers, and arrays with sex mismatches were also removed. Data likely to represent noise were removed by setting a minimum threshold of present calls among the non-redundant set of 190 arrays. SOLAR was used for genetic analysis, with MAS5 signal as the measure of expression. Probe sets with larger CVs generated more linkages (LOD > 2.0). While trans linkages predominated, linkages with the largest LOD scores (>4) were mostly cis. Hierarchical clustering was used to generate correlated groups of genes. We tested four composite measures of expression for the clusters. The average signal, average normalized signal, and the first principal component of the data behaved similarly; in 8/19 clusters tested, the composite measures linked to a region to which some individual probe sets within the cluster also linked. The second principal component only produced one linkage with LOD > 2. One cluster based upon chromosomal location, containing histone genes, linked to two trans regions. This work demonstrates that composite measures for genes with correlated expression can be used to identify loci that affect multiple co-expressed genes

Isolation of mRNA from specific tissues of Drosophila by mRNA tagging

To study the function of specific cells or tissues using genomic tools like microarray analyses, it is highly desirable to obtain mRNA from a homogeneous source. However, this is particularly challenging for small organisms, like Caenorhabditis elegans and Drosophila melanogaster. We have optimized and applied a new technique, mRNA tagging, to isolate mRNA from specific tissues of D.melanogaster. A FLAG-tagged poly(A)-binding protein (PABP) is expressed in a specific tissue and mRNA from that tissue is thus tagged by the recombinant PABP and separated from mRNA in other tissues by co-immunoprecipitation with a FLAG-tag specific antibody. The fractionated mRNA is then amplified and used as probe in microarray experiments. As a test system, we employed the procedures to identify genes expressed in Drosophila photoreceptor cells. We found that most known photoreceptor cell-specific mRNAs were identified by mRNA tagging. Furthermore, at least 11 novel genes have been identified as enriched in photoreceptor cells. mRNA tagging is a powerful general method for profiling gene expression in specific tissues and for identifying tissue-specific genes

Expand your research: Next-Gen Sequencing, Genotyping, Gene Expression, and Epigenetics at the Center for Medical Genomics at IUSM

poster abstractThe Center for Medical Genomics (CMG) provides Indiana researchers with next-generation sequencing, SNP genotyping, gene expression and epigenetics. We provide expertise in experimental design, carry out the procedures, and assist with analyses and interpretation. These state-of-the-art technologies have enabled a large number of grants to be funded over the years, and have resulted in a very large number of publications. Our next-generation sequencing technology includes SOLiD5500xl, Ion Proton and Ion Torrent PGM (Personal Genome Machine). This set of instruments covers a wide range of nextgeneration sequencing capabilities from small bacterial genomes to the whole human genome, transcriptome (total RNA), small RNA, targeted DNA fragments, exome, ChIP-seq, and methylseq, with high sequencing accuracy. We have generated sequencing data for 74 projects over the past two-three years. Our SNP genotyping facility, using the Sequenom MassArray platform, specializes in targeted genotyping of 20-30 SNPs per assay and is an excellent choice for candidate gene studies and for following up results from GWAS and next-generation sequencing. It has been a central part of several large, multi-site collaborative genetic studies, including Genetics of Alcoholism (COGA), bipolar disorder, osteoporosis and hypertension, as well as many smaller projects; it is most efficient for sets of approximately 370 samples. We have produced more than 20 million targeted SNP genotypes to date. This platform is also capable of measuring allele-specific gene expression, and targeted quantitative DNA methylation for epigenetics study. For many projects, microarrays offer a good alternative to next-generation sequencing for measuring gene expression. We use Affymetrix GeneChip microarrays, capable of measuring expression of nearly all genes in humans (and all exons within them), rats, mice and most model organisms, and can measure expression of miRNAs. We can also use RNA extracted from FFPE samples. We have carried out more than 6,700 GeneChip hybridizations to date in support of many different projects. The CMG partners with the Center for Computational Biology and Bioinformatics for data analysis. We are recognized as a Core Facility of the Indiana CTSI and available to faculty not only from IU and IUPUI, but also from Purdue and Notre Dame Universities

Psychiatric genetics and the structure of psychopathology

For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation