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In this approach, more than 100,000 (sometimes more than a million) SNPs are evaluated for large groups selleck chemical of individuals (often tens of thousands) composed of people with and without the characteristic being studied. A downside of simultaneously evaluating the relationship of so many SNPs with a characteristic (e.g., externalizing attributes, LR, or AUDs) is the level of statistical significance required for a finding to be considered as highly likely to be meaningful. Instead of the probability (p) that a single finding is meaningful of 5 in a 100 (i.e., p NK cell a few GWAS results have been impressive (e.g., the discovery of a gene variation related to a degeneration of the eye, macular degeneration; Klein et al., 2005), few results in the substance-related disorders field have been as impressive. One prominent finding from a GWAS reported an association of alcohol intake levels with a variation in the autism susceptibility candidate 2 (AUTS2) gene, with a p value of 10-8 (Schumann et al., 2011). Another large GWAS reported a finding on chromosome 2 in a region that other studies had suggested as potentially related to the LR to alcohol, some electrophysiological measures, and alcohol dependence (p GSI-IX order the other hand, this non-hypothesis-driven and broad brush approach has also highlighted some potentially promising genes that have not been identified in prior linkage or association work and that have little, if any, logical relationship to alcohol and other drug use disorders. A variation of the GWAS approach is to center the work on a limited set of brain pathways that might logically be related to the development of AUDs or SUDs. These approaches, referred to as Gene Set Analyses or Gene Set Enrichment Analyses, are more focused than the usual GWAS approach but still incorporate SNPs from a wide range of genes (Biernacka et al., 2013; Joslyn et al., 2010).

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