RFPRED: A RANDOM FOREST APPROACH FOR PREDICTION OF MISSENSE VARIANTS IN HUMAN EXOME

Exome sequencing is becoming a standard tool for gene mapping of genetic diseases. Given the vast amount of data generated by Next Generation Sequencing techniques, identification of disease causal variants is like finding a needle in a haystack. The impact assessment and the prioritization of potential pathogenic variants are expected to reduce work in biological validation, which is long and costly. One of the possible approaches to determine the most probable deleterious variants in individual exomes is to use protein function alteration prediction. We propose in this paper to use a machine learning approach, the random forest to build a new meta-score based on five previously described scores (SIFT, Polyphen2, LRT, PhyloP and MutationTaster) and compiled in the dbNSFP database. The functional meta-score was trained on a dataset of 61500 non-synonymous Single Nucleotide Polymorphisms (SNPs). The random forest method (rfPred) appears to be globally better than each of the classifiers separately or in combination in a logistic regression model, and better than a newly described score (CADD) on independent validation sets. RfPred scores have been pre-calculated for all the possible non-synonymous SNPs of human exome and are freely accessible at the web-server http://www.sbim.fr/rfPred/