# A machine learning classifier to identify and prioritise genes associated with murine cardiac development

## TL;DR

This paper introduces a machine learning model that predicts mouse genes involved in heart development, which can help identify genetic causes of congenital heart disease in humans.

## Contribution

A supervised machine learning classifier was developed to prioritize mouse genes likely involved in cardiac development.

## Key findings

- The classifier achieved 81% cross-validation accuracy in distinguishing cardiac from non-cardiac genes.
- Predicted mouse cardiac genes showed high overlap with known human CHD genes.
- Mutations in predicted cardiac genes often cause heart defects, validating the model's predictions.

## Abstract

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 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 genes needed for cardiac development and non-cardiac genes, suggesting that these two gene types can be distinguished by their attributes. We therefore 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.

Congenital heart disease arises during pregnancy when the heart has formed incorrectly. These malformations affect ~1% of newborns. Yet, despite their frequency, the underlying causes are still not known in many cases. Genetic factors are a significant cause of CHD, and increasingly DNA sequencing is used to attempt to determine if an individual has a genetic change causative of their condition. However, analysis of patient sequence data often reveals changes that are difficult to interpret due to a lack of knowledge of the function of the gene harbouring a sequence change. We aimed to facilitate the process of sequence evaluation by predicting which genes of unknown function are likely involved in heart formation. Our predictions agree well with novel experimental evidence about genes needed for heart development, as we found that when mutated, a high proportion of the predicted cardiac genes do indeed cause heart defects. This result suggests that our predictions may be informative for expanding our understanding of the genetic basis of congenital heart disease.