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.

Single nucleotide variations: Biological impact and theoretical interpretation

“…A perfect prediction would result in a vertical line (infinite slope) at the origin and an AUC of 1, in contrast to a completely random prediction that would result in a line with a slope of 1 and an AUC of 0.5. Other measures to evaluate the ability of prediction methods to prioritize the impact of mutations include the balanced accuracy, which is the average of the sensitivity and specificity, 25 the F1 score, which is the harmonic mean of precision and recall, 122 the Matthews correlation coefficient (MMC), 93 the Spearman's rank correlation coefficient, 123 the Kendall tau rank correlation coefficient, 124 and the scaledependent metric root-mean-square deviation (RMSD). 61 It is important to be cautious when attempting to objectively compare methods, and only new, unpublished data should be included in a validation set in order to keep the methods on equal footing.…”

A bioinformatics strategy for detecting the complexity of Chronic Obstructive Pulmonary Disease in Northern Chinese Han Population

“…The highest agreement was observed between 1-SIFT and PolyPhen-2 with a relative ratio agreement of 2/3, followed by the agreement between VarioWatch and 1-SIFT with a relative ratio agreement of 3/5, and the lowest agreement was seen between VarioWatch and PolyPhen-2 with a relative ratio agreement of 1/4. While previous studies have investigated the correlations between the 1-SIFT scores and PolyPhen-2 scores with Spearman's rank correlation coefficients, and significant correlations were found between these two scores (Zhu et al, 2008). According to be called 'deleterious' or 'damaging' by two programs at least, rs1051740 (EPHX1), rs1138272 (GSTP1) and rs6712954 (SERPINE2) are more likely to be potentially damaging than other variants which are likely benign.…”

Leucine to proline substitution by SNP at position 197 in Caspase-9 gene expression leads to neuroblastoma: a bioinformatics analysis

“…The tools that were utilized for identifying the coding nsSNPs include Sorting Intolerant from Tolerant (SIFT) (Ng and Henikoff 2003; Shen et al 2006), Polymorphism Phenotyping (PolyPhen) (Johnson et al 2005; Zhu et al 2008), SNPeffect (Reumers et al 2005, 2006), large-scale annotation of coding nsSNPs (LS-SNP) (Karchin et al 2005; Ryan et al 2009) and SNPs3D (Yue et al 2006). …”