Applying the new genomics to alcohol dependence

SP Farris, AZ Pietrzykowski, MF Miles, MA O'Brien… - Alcohol, 2015 - Elsevier
SP Farris, AZ Pietrzykowski, MF Miles, MA O'Brien, PP Sanna, S Zakhari, RD Mayfield…
Alcohol, 2015Elsevier
This review summarizes the proceedings of a symposium presented at the “Alcoholism and
Stress: A Framework for Future Treatment Strategies” conference held in Volterra, Italy on
May 6–9, 2014. The overall goal of the symposium titled “Applying the New Genomics to
Alcohol Dependence”, chaired by Dr. Adron Harris, was to highlight recent genomic
discoveries and applications for profiling alcohol use disorder (AUD). Dr. Sean Farris
discussed the gene expression networks related to lifetime consumption of alcohol within …
Abstract
This review summarizes the proceedings of a symposium presented at the “Alcoholism and Stress: A Framework for Future Treatment Strategies” conference held in Volterra, Italy on May 6–9, 2014. The overall goal of the symposium titled “Applying the New Genomics to Alcohol Dependence”, chaired by Dr. Adron Harris, was to highlight recent genomic discoveries and applications for profiling alcohol use disorder (AUD). Dr. Sean Farris discussed the gene expression networks related to lifetime consumption of alcohol within human prefrontal cortex. Dr. Andrzej Pietrzykowski presented the effects of alcohol on microRNAs in humans and animal models. Alcohol-induced alterations in the synaptic transcriptome were discussed by Dr. Michael Miles. Dr. Pietro Sanna examined methods to probe the gene regulatory networks that drive excessive alcohol drinking, and Dr. Samir Zakhari served as a panel discussant and summarized the proceedings. Collectively, the presentations emphasized the power of integrating multiple levels of genetics and transcriptomics with convergent biological processes and phenotypic behaviors to determine causal factors of AUD. The combined use of diverse data types demonstrates how unique approaches and applications can help categorize genetic complexities into relevant biological networks using a systems-level model of disease.
Elsevier