Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782177665079312384 |
---|---|
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. |
format | Text |
id | pubmed-2823693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT toplakmarko doesreplicationgroupsscoringreducefalsepositiverateinsnpinteractiondiscovery AT curktomaz doesreplicationgroupsscoringreducefalsepositiverateinsnpinteractiondiscovery AT demsarjanez doesreplicationgroupsscoringreducefalsepositiverateinsnpinteractiondiscovery AT zupanblaz doesreplicationgroupsscoringreducefalsepositiverateinsnpinteractiondiscovery |