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Article: Pathway-based identification of SNPs predictive of survival

TitlePathway-based identification of SNPs predictive of survival
Authors
Issue Date2011
Citation
European Journal of Human Genetics, 2011, v. 19 n. 6, p. 704-709 How to Cite?
AbstractIn recent years, several association analysis methods for case-control studies have been developed. However, as we turn towards the identification of single nucleotide polymorphisms (SNPs) for prognosis, there is a need to develop methods for the identification of SNPs in high dimensional data with survival outcomes. Traditional methods for the identification of SNPs have some drawbacks. First, the majority of the approaches for case-control studies are based on single SNPs. Second, SNPs that are identified without incorporating biological knowledge are more difficult to interpret. Random forests has been found to perform well in gene expression analysis with survival outcomes. In this paper we present the first pathway-based method to correlate SNP with survival outcomes using a machine learning algorithm. We illustrate the application of pathway-based analysis of SNPs predictive of survival with a data set of 192 multiple myeloma patients genotyped for 500 000 SNPs. We also present simulation studies that show that the random forests technique with log-rank score split criterion outperforms several other machine learning algorithms. Thus, pathway-based survival analysis using machine learning tools represents a promising approach for the identification of biologically meaningful SNPs associated with disease. © 2011 Macmillan Publishers Limited All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/194412
ISSN
2015 Impact Factor: 4.58
2015 SCImago Journal Rankings: 2.077
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPang, H-
dc.contributor.authorHauser, M-
dc.contributor.authorMinvielle, S-
dc.date.accessioned2014-01-30T03:32:33Z-
dc.date.available2014-01-30T03:32:33Z-
dc.date.issued2011-
dc.identifier.citationEuropean Journal of Human Genetics, 2011, v. 19 n. 6, p. 704-709-
dc.identifier.issn1018-4813-
dc.identifier.urihttp://hdl.handle.net/10722/194412-
dc.description.abstractIn recent years, several association analysis methods for case-control studies have been developed. However, as we turn towards the identification of single nucleotide polymorphisms (SNPs) for prognosis, there is a need to develop methods for the identification of SNPs in high dimensional data with survival outcomes. Traditional methods for the identification of SNPs have some drawbacks. First, the majority of the approaches for case-control studies are based on single SNPs. Second, SNPs that are identified without incorporating biological knowledge are more difficult to interpret. Random forests has been found to perform well in gene expression analysis with survival outcomes. In this paper we present the first pathway-based method to correlate SNP with survival outcomes using a machine learning algorithm. We illustrate the application of pathway-based analysis of SNPs predictive of survival with a data set of 192 multiple myeloma patients genotyped for 500 000 SNPs. We also present simulation studies that show that the random forests technique with log-rank score split criterion outperforms several other machine learning algorithms. Thus, pathway-based survival analysis using machine learning tools represents a promising approach for the identification of biologically meaningful SNPs associated with disease. © 2011 Macmillan Publishers Limited All rights reserved.-
dc.languageeng-
dc.relation.ispartofEuropean Journal of Human Genetics-
dc.titlePathway-based identification of SNPs predictive of survival-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1038/ejhg.2011.3-
dc.identifier.pmid21368918-
dc.identifier.scopuseid_2-s2.0-79956304186-
dc.identifier.volume19-
dc.identifier.issue6-
dc.identifier.spage704-
dc.identifier.epage709-
dc.identifier.isiWOS:000290765700014-

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