Correlating observed odds ratios from lung cancer case–control studies to SNP functional scores predicted by bioinformatic tools

Abstract

Bioinformatic tools are widely utilized to predict functional single nucleotide polymorphisms (SNPs) for genotyping in molecular epidemiological studies. However, the extent to which these approaches are mirrored by epidemiological findings has not been fully explored. In this study, we first surveyed SNPs examined in case–control studies of lung cancer, the most extensively studied cancer type. We then computed SNP functional scores using four popular bioinformatics tools: SIFT, PolyPhen, SNPs3D, and PMut, and determined their predictive potential using the odds ratios (ORs) reported. Spearman's correlation coefficient ( r) for the association with SNP score from SIFT, PolyPhen, SNPs3D, and PMut, and the summary ORs were r = −0.36 ( p = 0.007), r = 0.25 ( p = 0.068), r = −0.20 ( p = 0.205), and r = −0.12 ( p = 0.370), respectively. By creating a combined score using information from all four tools we were able to achieve a correlation coefficient of r = 0.51 ( p < 0.001). These results indicate that scores of predicted functionality could explain a certain fraction of the lung cancer risk detected in genetic association studies and more accurate predictions may be obtained by combining information from a variety of tools. Our findings suggest that bioinformatic tools are useful in predicting SNP functionality and may facilitate future genetic epidemiological studies.