The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants.
High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7).



A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC = 0.61, Acc = 79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction.



Analyses were implemented in Perl and C and used the Java-based Weka machine learning environment. Please contact the authors for availability.



andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk



Supplementary data are available at Bioinformatics online.
Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
Nouf S
NS
Al-Numair
Institute of Cardiovascular Science, UCL, London, UK.
Luis
L
Lopes
Institute of Cardiovascular Science, UCL, London, UK.
Petros
P
Syrris
Complejo Hospitalario Universitario de A Coruña, Insituto de Investigación Biomédica, Coruña, Spain.
Lorenzo
L
Monserrat
Institute of Cardiovascular Science, UCL, London, UK.
Perry
P
Elliott
Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
Andrew C R
AC
Martin
D009202:Cardiomyopathies / Q000235:genetics / Q000503:physiopathology; D016000:Cluster Analysis; D006801:Humans; D009154:Mutation; D018995:Myosin Heavy Chains; D024723:Ventricular Myosins
D024723:Ventricular Myosins; D018995:Myosin Heavy Chains