Predicting functional regulatory polymorphisms.

Abstract

Limited availability of data has hindered the development of algorithms that can identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given the large number of common polymorphisms known to reside in the human genome, the identification of functional rSNPs via laboratory assays will be costly and time-consuming. Therefore appropriate bioinformatics strategies for predicting functional rSNPs are necessary. Recent data from the Encyclopedia of DNA Elements (ENCODE) Project has significantly expanded the amount of available functional information relevant to non-coding regions of the genome, and, importantly, led to the conclusion that many functional elements in the human genome are not conserved. In this article we describe how ENCODE data can be leveraged to probabilistically determine the functional and phenotypic significance of non-coding SNPs (ncSNPs). The method achieves excellent sensitivity ( approximately 80%) and speci.city ( approximately 99%) based on a set of known phenotypically relevant and non-functional SNPs. In addition, we show that our method is not overtrained through the use of cross-validation analyses. The software platforms used in our analyses are freely available (http://www.cs.waikato.ac.nz/ml/weka/). In addition, we provide the training dataset (Supplementary Table 3), and our predictions (Supplementary Table 6), in the Supplementary Material. Supplementary data are available at Bioinformatics online.