A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci
Abstract Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restricti...
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Nature Portfolio
2022-09-01
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Online Access: | https://doi.org/10.1038/s41598-022-19708-1 |
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author | Princess P. Silva Joverlyn D. Gaudillo Julianne A. Vilela Ranzivelle Marianne L. Roxas-Villanueva Beatrice J. Tiangco Mario R. Domingo Jason R. Albia |
author_facet | Princess P. Silva Joverlyn D. Gaudillo Julianne A. Vilela Ranzivelle Marianne L. Roxas-Villanueva Beatrice J. Tiangco Mario R. Domingo Jason R. Albia |
author_sort | Princess P. Silva |
collection | DOAJ |
description | Abstract Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients. |
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spelling | doaj.art-8a21439e06184c13bc9f9e1e6903e85d2022-12-22T03:18:05ZengNature PortfolioScientific Reports2045-23222022-09-0112111010.1038/s41598-022-19708-1A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility lociPrincess P. Silva0Joverlyn D. Gaudillo1Julianne A. Vilela2Ranzivelle Marianne L. Roxas-Villanueva3Beatrice J. Tiangco4Mario R. Domingo5Jason R. Albia6Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los BañosData-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los BañosPhilippine Genome Center Program for Agriculture, Office of the Vice Chancellor for Research and Extension, University of the Philippines Los BañosData-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los BañosNational Institute of Health, UP College of MedicineDomingo AI Research Center (DARC Labs)Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los BañosAbstract Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.https://doi.org/10.1038/s41598-022-19708-1 |
spellingShingle | Princess P. Silva Joverlyn D. Gaudillo Julianne A. Vilela Ranzivelle Marianne L. Roxas-Villanueva Beatrice J. Tiangco Mario R. Domingo Jason R. Albia A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci Scientific Reports |
title | A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci |
title_full | A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci |
title_fullStr | A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci |
title_full_unstemmed | A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci |
title_short | A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci |
title_sort | machine learning based snp set analysis approach for identifying disease associated susceptibility loci |
url | https://doi.org/10.1038/s41598-022-19708-1 |
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