A Machine Learning Classifier to Identify and Prioritise Genes Associated with Cardiac Development

Congenital heart disease (CHD) is a major cause of infant mortality and presents life-long challenges to individuals living with these conditions. Genetic causes are known for only a minority of types of CHD. Discovering further genetic causes is limited by challenges in prioritising candidate CHD genes. We examined a wide range of features of mouse genes, including sequence characteristics, protein localisation and interaction data, developmental expression data and gene ontology annotations. Many features differ between cardiac development and non-cardiac genes, suggesting that these two gene types can be distinguished by their attributes. Therefore, we developed a supervised machine learning (ML) method to identify Mus musculus genes with a high probability of being involved in cardiac development. These genes, when mutated, are candidates for causing human CHD. Our classifier showed a cross-validation accuracy of 81% in detecting cardiac and non-cardiac genes. From our classifier we generated predictions of the cardiac development association status for all protein-coding genes in the mouse genome. We also cross-referenced our predictions with datasets of known human CHD genes, determining which are orthologues of predicted mouse cardiac genes. Our predicted cardiac genes have a high overlap with human CHD genes. Thus, our predictions could inform the prioritisation of genes when evaluating CHD patient sequence data for genetic diagnosis. Knowledge of cardiac developmental genes may speed up reaching a genetic diagnosis for patients born with CHD.