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Congenital renal tract malformations (RTMs) are the major cause of end stage renal disease (ESRD) in children. In the last three decades, progress has been made in discovering monogenic causes for human RTMs. Nevertheless, studies to date have identified defined genetic causes for only a minority of human RTMs. Unfortunately, the speed of progress in discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants or implicated by genomic association or linkage studies.
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In this study, we exploited the computer-based machine learning methodology to identify genes with a high probability of being involved in renal development from their atrributes. These genes, when mutated, would therefore be promising candidates for causing RTMs. The accuracy of this RTM gene classifier is 89%. This classifier provides predictions of the RTM association status for all protein-coding genes in the mouse genome. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.
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The training dataset used in developing the classifier can be downloded from here (Tables S1-S2, manuscript submitted): Feature dataset (.xlsx)
Kids Kidney Research has now become a part of Kidney Research UK.
