The genetics of alcoholism
Article Feb 18, 2015
Alcoholism is a multifaceted disease, affecting almost every system in the body, including neurological, physiological/hormonal, and cardiovascular systems. Scientists have known for a long time that alcoholism is partly inherited: based on twin and family studies, 50% of the risk comes from the genes someone receives from their parents.1 In the past two decades, government-funded COGA (Collaborative Study on the Genetics of Alcoholism)2,3 and SAGE (Study of Addiction: Genetics and Environment)4 studies have identified specific genes that are associated with the risk of alcoholism. For instance, two genes that help metabolize alcohol, alcohol dehydrogenase (ADH1B) and aldehyde dehydrogenase (ALDH2), have the strongest effect on the risk of developing an alcohol use disorder. However, scientists continue to find many other genes associated with alcoholism. Alcoholism is far from a so-called “one gene, one disease” disease. It is complex, where many genes each make a small contribution.
COGA and SAGE rely largely on genome-wide association studies (or GWAS), which compare healthy and “diseased” genomes for point mutations called single nucleotide polymorphisms, or SNPs. Unfortunately, it is not possible to determine how a SNP will affect gene expression without further experimentation. Discovering what these alcoholism-related genes do—when and where they’re expressed, and how this affects an individual’s health—could bridge the gap between the lab and the clinic. This is where gene expression studies can be extremely useful.
Recently, Dr. Dayne Mayfield at the University of Texas-Austin (UT-Austin)’s Waggoner Center for Alcohol and Addiction Research led research that looked at gene expression changes in the brains of alcoholics. He found that not only did expression levels of known susceptibility genes increase, but also that these changes were seen across a group of similar, co-expressed genes.5 The study was published in the journal Molecular Psychiatry.
Variation in gene expression is a driving force in cellular function, and can be implicated in many diseases. Differential gene expression is dependent on many variables, including genetic mutations and variations like SNPs and structural changes, as well as environmental stressors. In the case of complex diseases like alcoholism, because individual gene expression changes are small, it is hard to attach any biological significance to them, says Dr. Candice Contet, an assistant professor at The Scripps Research Institute who was not involved in the study. “However, coordinated changes in the expression of genes belonging to the same functional network could have synergistic consequences and a meaningful impact on the biology of the cell [or] neuronal circuit,” she says. “The focus [of research] has therefore shifted from pinpointing discrete candidate genes to identifying large modules of co-regulated genes.”
Essentially, the UT scientists used RNA-seq—applying next-generation sequencing to mRNA molecules—to identify gene expression changes in the prefrontal cortex of postmortem alcoholics’ brain tissue compared to matched controls. The prefrontal cortex is an area of the brain known to be highly affected by addictive drinking. The investigators used bioinformatics to define “gene modules,” or sets of genes that are both co-expressed and “share highly similar expression patterns,” Dr. Mayfield says. The main genes that were enriched (expressed more in alcoholics’ brains) were those involved in synaptic plasticity and ion channel function as well as transmembrane transporters and intracellular signaling molecules; and specifically, glutamatergic and GABAergic receptors and the SCN4B (voltage-gated sodium channel type-IV beta subunit) ion channels. “We were able to identify a lot of novelties,” Dr. Mayfield says, calling it a much more sensitive technique than gene expression microarrays. He compared the module expression patterns to existing data from COGA and SAGE and found that the gene module linked to a “lifetime consumption of alcohol” was the only set that also shared SNPs known to be associated with alcohol dependence.
GWAS and gene expression: together toward the clinic
Gene expression studies are good pairs to genome-wide association studies because they put function to form, as it were. Generally speaking, a GWAS finds risk factors. And while identifying risk factors is critical to improving prevention of alcohol dependence, there is no way to know how these mutations cause the gene expression changes that lead to alcoholism unless functional studies are also done.
“Importantly, gene expression studies are not only helpful to identify predisposing or protective factors, but also to understand the effect of alcohol exposure on the brain,” Dr. Contet says. Alcoholism is a progressive disorder that takes years to develop—most alcoholics become increasingly dependent on alcohol the longer they drink. “Changes in gene expression are not only part of the adaptive response to chronic alcohol abuse, but also contribute to the motivational state driving excessive drinking,” she says. “Capturing the dynamics of gene expression changes over time can therefore lead to the identification of genes causing the transition into alcohol dependence.”
These genes—and the proteins that they make—might be the future targets of improved therapeutics. Right now, there are effectively only three medications approved by the US Food and Drug Administration (FDA) to treat alcohol use disorders, and the most prevalent, naltrexone, works moderately if at all.6 Pharmacogenomics—matching individual genetic variation to how efficacious a medicine will be for that person—is at the forefront of personalized medicine. However, Dr. Henry Kranzler, a professor of psychiatry and the director of the Center for Studies of Addiction at the University of Pennsylvania, believes that it is going to take at least five to ten years before doctors begin applying this knowledge to patient care. Newer targets would expedite the process. Dr. Mayfield believes doctors can use his “transcriptional signatures” to compare select target genes to existing databases of FDA-approved drugs. Maybe the target already has a medication that acts on it.
Aside from addressing the challenges of working with post-mortem tissue (Mayfield is presently conducting blood analyses) and the problems of small sample size and lack of ethnic diversity, Dr. Mayfield realizes the ultimate challenge lies in using highly variable gene expression changes to build better therapies. “We are currently collaborating with the COGA group to answer this exact question: What is the relationship between individual genetics and gene expression? Hopefully the answers will start to come in soon.”
- 1. Foroud T, Edenberg HJ, Crabbe JC. (2010) Who Is At Risk for Alcoholism? Alcohol Res Health 33(1-2):64–75.
- 2. Bierut LJ, Saccone NL, Rice JP, Goate A, Foroud T, Edenberg H, Almasy L, Conneally PM, Crowe R, Hesselbrock V, Li TK, Nurnberger J Jr, Porjesz B, Schuckit MA, Tischfield J, Begleiter H, Reich T. (2002) Defining alcohol-related phenotypes in humans. The Collaborative Study on the Genetics of Alcoholism. Alcohol Res Health 26(3):208–213.
- 3. Edenberg HJ. (2002) The collaborative study on the genetics of alcoholism: an update. Alcohol Res Health 26(3):214–218.
- 4. Study of Addiction: Genetics and Environment (SAGE) http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000092.v1.p1
- 5. Farris SP, Arasappan D, Hunicke-Smith S, Harris RA, Mayfield RD. (2014) Transcriptome organization for chronic alcohol abuse in human brain. Mol Psychiatry doi: 10.1038/mp.2014.159 [Epub ahead of print]
- 6. Rösner S, Hackl-Herrwerth A, Leucht S, Vecchi S, Srisurapanont M, Soyka M. (2010) Opioid antagonists for alcohol dependence. Cochrane Database Syst Rev (12):CD001867.
To pick apart the differences between individual cells in complex multicellular organisms, we need to look at cells one-by-one. This article takes a look at how several scientists in North America are using single cell proteomics (SCP) technologies to discern disease pathogenesis and enhance directed stem-cell differentiation.READ MORE
As many people spent the summer trying to keep the flies away from their fruit-bowls, an international group of scientists published one of the biggest and most important datasets in the field of connectomics to date, the complete 3D electron micrograph volume of the fruit-fly (Drosophila melanogaster) brain.READ MORE