Computational identification and analysis of functional polymorphisms involved in the activation and detoxification genes implicated in endometriosis

• Computational tools were used to study 16 genes implicated in endometriosis.
• Combining multiple tools increases power of the SNP function prediction.
• Bioinformatic approaches were found to mirror epidemiological findings.
• In silico techniques can be used as screening tools.

Endometriosis is a complex disorder of the female reproductive system where endometrial tissue embeds and grows at extrauterine location leading to inflammation and pain. Hundreds of polymorphisms in several genes have been studied as probable risk factors of this debilitating disease. Bioinformatics tools have come a long way in augmenting the search for putative functional polymorphisms in human diseases. In this study we have explored 16 genes involved in the detoxification of xenobiotic chemicals that are implicated in endometriosis by utilising publically available programs like SIFT, Polyphen, Panther, FastSNP, SNPeffect and PhosSNP. The variations among different ethnic populations of the SNPs were studied. We then calculated the extent to which bioinformatics based predictions are concurrent with real world epidemiological, genotyping studies using a set of SNPs that have been studied in endometriosis case–control studies. Our study shows that there is a significant positive correlation (r = 0.569, p < 0.005) between the summary of the predicted scores taken from 4 different servers and the odds ratio found from epidemiological studies. This report has identified and catalogued various deleterious SNPs that could be important in endometriosis and could aid in further analysis by in vitro and in vivo methods for the better understanding of the disease pathophysiology.