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

BMC Genomics

Research Article

Marko Toplak¹  Tomaz Curk¹  Janez Demsar¹  Blaz Zupan² 

[1] Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, SI-1000, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, SI-1000, Ljubljana, Slovenia;Department of Molecular and Human Genetics, Baylor College of Medicine, 1 Baylor Plaza, 77030, Houston, TX, USA;

关键词: Single Nucleotide Polymorphism;    Autism Spectrum Disorder;    Interaction Score;    Single Nucleotide Polymorphism Chip;    Replication Group;   

DOI  :  10.1186/1471-2164-11-58

 received in 2009-08-24, accepted in 2010-01-22,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundComputational 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.ResultsA 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.ConclusionsWith 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.

【 授权许可】

Unknown   
© Toplak et al; licensee BioMed Central Ltd. 2010. 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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.