Application of microarray in the study of drug addiction is hindered by
several factors, such as the heterogeneity of neuronal cell types in any
particular brain area and the relatively small amplitude (usually < 2 fold)
of the changes in mRNA levels. Most microarray experiments studied the
effect of drugs of abuse on brain gene expression identified less than ten
genes that are affected by the treatment. As an alternative approach of
identifying candidate genes involved in the effects nicotine, we utilized
data from multiple sources, including genetic study identifying the genomic
regions contributing to addiction in human, gene expression of dopamine
neurons in ventral tegmental area, GNF tissue expression atlas, WebQTL, and
a collection a genes known to be affected by drugs of abuse in the
literature. We compared genes among these lists and analyzed their
relationships using our text-mining softwares, Chilibot and SGO, to suggest
candidate genes for nicotine addiction. The involvment of each suggested
gene in nicotine addiction is supported by multiple lines of evidence.