Bioinformatic analyses to select phenotype affecting polymorphisms in HTR2C gene

Objective Single nucleotide polymorphisms (SNPs) in serotonin related genes influence mental disorders, responses to pharmacological and psychotherapeutic treatments. In planning association studies, researchers that want to investigate new SNPs have to select some among a large number of candidates. Our aim is to guide researchers in the selection of the most likely phenotype affecting polymorphisms. Here, we studied serotonin receptor 2C (HTR2C) SNPs because, till now, only relatively few of about 2000 are investigated. Methods We used the most updated and assessed bioinformatic tools to predict which variations can give rise to biological effects among 2450 HTR2C SNPs. Results We suggest 48 SNPs that are worth considering in future association studies in the field of psychiatry, psychology and pharmacogenomics. Moreover, our analyses point out the biological level probably affected, such as transcription, splicing, miRNA regulation and protein structure, thus allowing to suggest future molecular investigations. Conclusions Although few association studies are available in literature, their results are in agreement with our predictions, showing that our selection methods can help to guide future association studies. Copyright © 2011 John Wiley & Sons, Ltd. key words—computational biology; serotonin; SNP selection; association studies; RNA splicing