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A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Verzilli, C.J., Whittaker, J.C., Stallard, N. and Chasman, D. (2005) A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms. Applied Statistics, 54 (1). pp. 191-206. ISSN 0266-4763

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To link to this article DOI: 10.1111/j.1467-9876.2005.00478.x

Abstract/Summary

Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:9442
Uncontrolled Keywords:Bayesian inference , Hierarchical model , Multivariate adaptive regression splines , Protein site-directed mutagenesis , Supervised learning

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