Predicting congenital renal tract malformation genes using machine learning

Abstract Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis...

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Main Authors: Mitra Kabir, Helen M. Stuart, Filipa M. Lopes, Elisavet Fotiou, Bernard Keavney, Andrew J. Doig, Adrian S. Woolf, Kathryn E. Hentges
Format: Article
Language:English
Published: Nature Portfolio 2023-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-38110-z
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author Mitra Kabir
Helen M. Stuart
Filipa M. Lopes
Elisavet Fotiou
Bernard Keavney
Andrew J. Doig
Adrian S. Woolf
Kathryn E. Hentges
author_facet Mitra Kabir
Helen M. Stuart
Filipa M. Lopes
Elisavet Fotiou
Bernard Keavney
Andrew J. Doig
Adrian S. Woolf
Kathryn E. Hentges
author_sort Mitra Kabir
collection DOAJ
description Abstract Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.
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spelling doaj.art-f53fdbd76f954105b41d5e7e7e9932562023-11-26T13:06:06ZengNature PortfolioScientific Reports2045-23222023-08-0113111310.1038/s41598-023-38110-zPredicting congenital renal tract malformation genes using machine learningMitra Kabir0Helen M. Stuart1Filipa M. Lopes2Elisavet Fotiou3Bernard Keavney4Andrew J. Doig5Adrian S. Woolf6Kathryn E. Hentges7CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of ManchesterCentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of ManchesterDivision of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of ManchesterDivision of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of ManchesterDivision of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of ManchesterDivision of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDivision of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of ManchesterCentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of ManchesterAbstract Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.https://doi.org/10.1038/s41598-023-38110-z
spellingShingle Mitra Kabir
Helen M. Stuart
Filipa M. Lopes
Elisavet Fotiou
Bernard Keavney
Andrew J. Doig
Adrian S. Woolf
Kathryn E. Hentges
Predicting congenital renal tract malformation genes using machine learning
Scientific Reports
title Predicting congenital renal tract malformation genes using machine learning
title_full Predicting congenital renal tract malformation genes using machine learning
title_fullStr Predicting congenital renal tract malformation genes using machine learning
title_full_unstemmed Predicting congenital renal tract malformation genes using machine learning
title_short Predicting congenital renal tract malformation genes using machine learning
title_sort predicting congenital renal tract malformation genes using machine learning
url https://doi.org/10.1038/s41598-023-38110-z
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