those changed in all three experiments included CD81 molecule nucleoporin like

Those changed in all three experiments included cd81

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(those changed in all three experiments) included CD81 molecule, nucleoporin like 1, phosphatidylethanolamine- binding protein and aldehyde dehydrogenase 6 family, member A1. The studies outlined in this section demonstrate that large data sets of gene expression data can be combined with behavioural and genetic data to identify genes or functional pathways that underlie ethanol-related phenotypes and other complex traits. Looking into the future Increasingly sophisticated genetic tools (haplotype and SNP maps, mapping arrays, expression arrays and so on) are being applied to complex diseases ranging from cancer to schizo- phrenia. What is the desired or imagined outcome for such studies? For most diseases, genetic or genomic assessment of risk or susceptibility is a goal. Breast cancer prediction is an area where genetics have been aggressively developed and marketed, although the accuracy of such markers remains controversial. For alcoholism, family history and personal history are strong predictors of risk and it is not clear that genetic markers of risk will be a practical or useful contribution. Areas of more importance for alcoholism are ‘genetic medicines’ and genomic/proteomic biomarkers for alcohol abuse. The success (or lack thereof) for naltrexone in the treatment of alcohol dependence depends in part on a polymorphism in the m opioid receptor, and this gives the possibility of genotype-based selection of pharmacotherapy for alcoholism (Oslin et al ., 2006). Another likely application of ‘omics’ to addiction medicine is selection of biomarkers for alcohol and drug dependence or abuse based on changes in gene expression or protein levels in blood samples. Sensitive and selective biomarkers can only be defined after measuring many different transcripts or proteins with array technologies. This review presents many ‘candidate’ genes for alcohol and drug dependence and a plethora of changes in gene expression that might, or might not, be responsible for development of dependence. When will we move past ‘candidates’ to ‘defined’ genes? The history of genetics of complex diseases brings great excitement about new techni- ques with large increases in genetic power (for example, selected lines, recombinant inbred strains, QTL analysis, gene expression arrays, SNP maps and so on). But application of these new approaches reveals that the complexity of the disease and the genetics of the organism are much greater than we appreciated. This leads to the development of new approaches, which reveal new complexities. The immediate future may bring the realization that we will not be able to define the genetics of dependence until we better under- stand how genes interact with environmental variables to influence drug responses and related behaviours.
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