Jan 23, 2010

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

BMC Genomics
Marko ToplakBlaz Zupan


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. 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 stan...Continue Reading

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Mentioned in this Paper

In Silico
Mentally Disabled Persons
Gene Expression
SYN2 gene
Data Interpretation, Statistical
TCL1B protein, human
False Positive Reactions
Disease Theory of Alcohol or Other Drug Use, Abuse and Dependence
Single Nucleotide Polymorphism

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