Shihab Hashem A, Rogers Mark F, Gough Julian, Mort Matthew, Cooper David N, Day Ian N M, Gaunt Tom R, Campbell Colin
MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK, Bristol Centre for Systems Biomedicine, University of Bristol, Bristol BS8 2BN, UK, Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK, Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK and Institute of Medical Genetics, Cardiff University, Cardiff CF14 4XN, UK MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK, Bristol Centre for Systems Biomedicine, University of Bristol, Bristol BS8 2BN, UK, Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK, Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK and Institute of Medical Genetics, Cardiff University, Cardiff CF14 4XN, UK.
MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK, Bristol Centre for Systems Biomedicine, University of Bristol, Bristol BS8 2BN, UK, Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK, Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK and Institute of Medical Genetics, Cardiff University, Cardiff CF14 4XN, UK.
Bioinformatics. 2015 May 15;31(10):1536-43. doi: 10.1093/bioinformatics/btv009. Epub 2015 Jan 11.
MOTIVATION: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. RESULTS: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions.
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