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Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus
Patients with systemic lupus erythematosus (SLE) present varied clinical manifestations, posing a diagnostic challenge for physicians. Genetic factors substantially contribute to SLE development. A polygenic risk scoring (PRS) model has been used to estimate the genetic risk of SLE in individuals. H...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421562/ https://www.ncbi.nlm.nih.gov/pubmed/36046232 http://dx.doi.org/10.3389/fgene.2022.902793 |
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author | Ma, Wen Lau, Yu-Lung Yang, Wanling Wang, Yong-Fei |
author_facet | Ma, Wen Lau, Yu-Lung Yang, Wanling Wang, Yong-Fei |
author_sort | Ma, Wen |
collection | PubMed |
description | Patients with systemic lupus erythematosus (SLE) present varied clinical manifestations, posing a diagnostic challenge for physicians. Genetic factors substantially contribute to SLE development. A polygenic risk scoring (PRS) model has been used to estimate the genetic risk of SLE in individuals. However, this approach assumes independent and additive contribution of genetic variants to disease development. We aimed to improve the accuracy of SLE prediction using machine-learning algorithms. We applied random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to classify SLE cases and controls using the data from our previous genome-wide association studies (GWAS) conducted in either Chinese or European populations, including a total of 19,208 participants. The overall performances of these predictors were assessed by the value of area under the receiver-operator curve (AUC). The analyses in the Chinese GWAS showed that the RF model significantly outperformed other predictors, achieving a mean AUC value of 0.84, a 13% improvement upon the PRS model (AUC = 0.74). At the optimal cut-off, the RF predictor reached a sensitivity of 84% with a specificity of 68% in SLE classification. To validate these results, similar analyses were repeated in the European GWAS, and the RF model consistently outperformed other algorithms. Our study suggests that the RF model could be an additional and powerful predictor for SLE early diagnosis. |
format | Online Article Text |
id | pubmed-9421562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94215622022-08-30 Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus Ma, Wen Lau, Yu-Lung Yang, Wanling Wang, Yong-Fei Front Genet Genetics Patients with systemic lupus erythematosus (SLE) present varied clinical manifestations, posing a diagnostic challenge for physicians. Genetic factors substantially contribute to SLE development. A polygenic risk scoring (PRS) model has been used to estimate the genetic risk of SLE in individuals. However, this approach assumes independent and additive contribution of genetic variants to disease development. We aimed to improve the accuracy of SLE prediction using machine-learning algorithms. We applied random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to classify SLE cases and controls using the data from our previous genome-wide association studies (GWAS) conducted in either Chinese or European populations, including a total of 19,208 participants. The overall performances of these predictors were assessed by the value of area under the receiver-operator curve (AUC). The analyses in the Chinese GWAS showed that the RF model significantly outperformed other predictors, achieving a mean AUC value of 0.84, a 13% improvement upon the PRS model (AUC = 0.74). At the optimal cut-off, the RF predictor reached a sensitivity of 84% with a specificity of 68% in SLE classification. To validate these results, similar analyses were repeated in the European GWAS, and the RF model consistently outperformed other algorithms. Our study suggests that the RF model could be an additional and powerful predictor for SLE early diagnosis. Frontiers Media S.A. 2022-08-15 /pmc/articles/PMC9421562/ /pubmed/36046232 http://dx.doi.org/10.3389/fgene.2022.902793 Text en Copyright © 2022 Ma, Lau, Yang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Ma, Wen Lau, Yu-Lung Yang, Wanling Wang, Yong-Fei Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title | Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title_full | Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title_fullStr | Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title_full_unstemmed | Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title_short | Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
title_sort | random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421562/ https://www.ncbi.nlm.nih.gov/pubmed/36046232 http://dx.doi.org/10.3389/fgene.2022.902793 |
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