Collective judgment predicts disease-associated single nucleotide variants

By Emidio Capriotti, Russ B. Altman, Yana Bromberg. 2013

296 citations recorded by the source corpus.

BackgroundIn recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.ResultsHere we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor.ConclusionsHere we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Methods such as PredictSNP [48], Condel [49], and MetaSNP [50] have been explicitly optimized to combine different methods, mostly to annotate dis-ease-variant relationships (as opposed to functional changes).”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “Recent examples of consensus tools are CONDEL [27], PON-P [28] and Meta-SNP [29], all of which reported improved performance over individual integrated tools.”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, methodology): “This is particularly true when evaluating combinations of tool scores, as etal.,2013;Lietal.,2013;Bendletal.,2014].”