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Does replication groups scoring reduce false positive rate in SNP interaction discovery?

BACKGROUND: Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in non-Mendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays wi...

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Autores principales: Toplak, Marko, Curk, Tomaz, Demsar, Janez, Zupan, Blaz
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2823693/
https://www.ncbi.nlm.nih.gov/pubmed/20092660
http://dx.doi.org/10.1186/1471-2164-11-58
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author Toplak, Marko
Curk, Tomaz
Demsar, Janez
Zupan, Blaz
author_facet Toplak, Marko
Curk, Tomaz
Demsar, Janez
Zupan, Blaz
author_sort Toplak, Marko
collection PubMed
description BACKGROUND: Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in non-Mendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays with hundreds of thousands of SNPs but record only hundreds of samples. Candidate SNP pairs inferred by interaction analysis may include a high proportion of false positives. Recently, Gayan et al. (2008) proposed to reduce the number of false positives by combining results of interaction analysis performed on subsets of data (replication groups), rather than analyzing the entire data set directly. If performing as hypothesized, replication groups scoring could improve interaction analysis and also any type of feature ranking and selection procedure in systems biology. Because Gayan et al. do not compare their approach to the standard interaction analysis techniques, we here investigate if replication groups indeed reduce the number of reported false positive interactions. RESULTS: A set of simulated and false interaction-imputed experimental SNP data sets were used to compare the inference of SNP-SNP interactions by means of replication groups to the standard approach where the entire data set was directly used to score all candidate SNP pairs. In all our experiments, the inference of interactions from the entire data set (e.g. without using the replication groups) reported fewer false positives. CONCLUSIONS: With respect to the direct scoring approach the utility of replication groups does not reduce false positive rates, and may, depending on the data set, often perform worse.
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spelling pubmed-28236932010-02-18 Does replication groups scoring reduce false positive rate in SNP interaction discovery? Toplak, Marko Curk, Tomaz Demsar, Janez Zupan, Blaz BMC Genomics Research Article BACKGROUND: Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in non-Mendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays with hundreds of thousands of SNPs but record only hundreds of samples. Candidate SNP pairs inferred by interaction analysis may include a high proportion of false positives. Recently, Gayan et al. (2008) proposed to reduce the number of false positives by combining results of interaction analysis performed on subsets of data (replication groups), rather than analyzing the entire data set directly. If performing as hypothesized, replication groups scoring could improve interaction analysis and also any type of feature ranking and selection procedure in systems biology. Because Gayan et al. do not compare their approach to the standard interaction analysis techniques, we here investigate if replication groups indeed reduce the number of reported false positive interactions. RESULTS: A set of simulated and false interaction-imputed experimental SNP data sets were used to compare the inference of SNP-SNP interactions by means of replication groups to the standard approach where the entire data set was directly used to score all candidate SNP pairs. In all our experiments, the inference of interactions from the entire data set (e.g. without using the replication groups) reported fewer false positives. CONCLUSIONS: With respect to the direct scoring approach the utility of replication groups does not reduce false positive rates, and may, depending on the data set, often perform worse. BioMed Central 2010-01-22 /pmc/articles/PMC2823693/ /pubmed/20092660 http://dx.doi.org/10.1186/1471-2164-11-58 Text en Copyright ©2010 Toplak et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Toplak, Marko
Curk, Tomaz
Demsar, Janez
Zupan, Blaz
Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title_full Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title_fullStr Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title_full_unstemmed Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title_short Does replication groups scoring reduce false positive rate in SNP interaction discovery?
title_sort does replication groups scoring reduce false positive rate in snp interaction discovery?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2823693/
https://www.ncbi.nlm.nih.gov/pubmed/20092660
http://dx.doi.org/10.1186/1471-2164-11-58
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