SNPs selection using support vector regression and genetic algorithms in GWAS
Introduction This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic alg...
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| Published in | BMC genomics Vol. 15; no. Suppl 7; p. S4 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
27.10.2014
BioMed Central Ltd Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2164 1471-2164 |
| DOI | 10.1186/1471-2164-15-S7-S4 |
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| Abstract | Introduction
This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.
Results
The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.
Conclusions
The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. |
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| AbstractList | This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. Introduction This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. Keywords: Single nucleotide polymorphisms, GWAS, support vector regression, wrapper, genetic algorithms, Pearson Universal kernel Introduction This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. Introduction: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results: The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions: The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. Doc number: S4 Abstract Introduction: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results: The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions: The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.INTRODUCTIONThis paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.RESULTSThe suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.CONCLUSIONSThe method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. |
| ArticleNumber | S4 |
| Audience | Academic |
| Author | Borges, Carlos Cristiano Hasenclever e Silva, Fabyano Fonseca Arbex, Wagner da Silva, Marcos Vinicius GB de Oliveira, Fabrízzio Condé Almeida, Fernanda Nascimento da Silva Verneque, Rui |
| AuthorAffiliation | 3 Federal University of Viçosa - UFV, Viçosa, Minas Gerais, Brasil 2 State of Minas Gerais Research Support Agency - FAPEMIG, Brasil 4 Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais, Brasil 1 Federal University of Juiz de Fora - UFJF, Juiz de Fora, Minas Gerais, Brasil |
| AuthorAffiliation_xml | – name: 2 State of Minas Gerais Research Support Agency - FAPEMIG, Brasil – name: 3 Federal University of Viçosa - UFV, Viçosa, Minas Gerais, Brasil – name: 4 Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais, Brasil – name: 1 Federal University of Juiz de Fora - UFJF, Juiz de Fora, Minas Gerais, Brasil |
| Author_xml | – sequence: 1 givenname: Fabrízzio Condé surname: de Oliveira fullname: de Oliveira, Fabrízzio Condé organization: Federal University of Juiz de Fora - UFJF, Juiz de Fora, Minas Gerais – sequence: 2 givenname: Carlos Cristiano Hasenclever surname: Borges fullname: Borges, Carlos Cristiano Hasenclever organization: Federal University of Juiz de Fora - UFJF, Juiz de Fora, Minas Gerais – sequence: 3 givenname: Fernanda Nascimento surname: Almeida fullname: Almeida, Fernanda Nascimento organization: State of Minas Gerais Research Support Agency - FAPEMIG, Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais – sequence: 4 givenname: Fabyano Fonseca surname: e Silva fullname: e Silva, Fabyano Fonseca organization: Federal University of Viçosa - UFV, Viçosa, Minas Gerais – sequence: 5 givenname: Rui surname: da Silva Verneque fullname: da Silva Verneque, Rui organization: Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais – sequence: 6 givenname: Marcos Vinicius GB surname: da Silva fullname: da Silva, Marcos Vinicius GB organization: Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais – sequence: 7 givenname: Wagner surname: Arbex fullname: Arbex, Wagner email: wagner.arbex@ufjf.edu.br organization: Federal University of Juiz de Fora - UFJF, Juiz de Fora, Minas Gerais, Brazilian Agricultural Research Corporation - Embrapa, Juiz de Fora, Minas Gerais |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25573332$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fgene_2019_00189 crossref_primary_10_4137_BBI_S29469 crossref_primary_10_1088_1755_1315_31_1_012015 crossref_primary_10_1002_gepi_22293 crossref_primary_10_1093_bib_bbaa263 crossref_primary_10_3390_plants12142659 |
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| Copyright | de Oliveira et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. COPYRIGHT 2014 BioMed Central Ltd. 2014 de Oliveira et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Copyright © 2014 de Oliveira et al.; licensee BioMed Central Ltd. 2014 de Oliveira et al.; licensee BioMed Central Ltd. |
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| Keywords | support vector regression Pearson Universal kernel wrapper GWAS Single nucleotide polymorphisms genetic algorithms |
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| Snippet | Introduction
This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype... This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by... Introduction This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype... Doc number: S4 Abstract Introduction: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of... Introduction: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable... |
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| SubjectTerms | Algorithms Analysis Animal Genetics and Genomics Animals Artificial Intelligence Bioinformatics Biology Biomedical and Life Sciences Cattle - genetics Computational Biology Computer science Computer Simulation Databases, Nucleic Acid Female Genetic Markers Genetic Techniques Genetic vectors Genome-Wide Association Study - methods Genomics Genotype & phenotype Life Sciences Male Meetings Microarrays Microbial Genetics and Genomics Models, Statistical Peer review Phenotype Plant Genetics and Genomics Polymorphism, Single Nucleotide Proteomics Quality control Software Statistical methods Studies |
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| Title | SNPs selection using support vector regression and genetic algorithms in GWAS |
| URI | https://link.springer.com/article/10.1186/1471-2164-15-S7-S4 https://www.ncbi.nlm.nih.gov/pubmed/25573332 https://www.proquest.com/docview/1617563657 https://www.proquest.com/docview/1622604527 https://www.proquest.com/docview/1645773145 https://pubmed.ncbi.nlm.nih.gov/PMC4243330 https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-15-S7-S4 |
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