Gayán et al. BMC Genomics 2010, 11:403 http://www.biomedcentral.com/1471-2164/11/403 select results consistent across groups. Genomewide studies of large datasets are showing that, for reasons not clear yet, but probably due to sampling, technical or genotyping differences, the top signals are not consis- tently replicating in independent samples (the winner's curse). On the contrary, signals down the top rankings are the ones finally replicating. Because in the recent history of genetic studies of complex traits it has been hard to find consistent results [8], and the level of noise to true signals is large [10], a replication group strategy seems useful for this sort of studies. List of abbreviations HFCC: hypothesis free clinical cloning; MAF: minor allele frequency; OR: Odds-ratio. Competing interests All authors in the paper are employees and/or shareholders in Neocodex. Neo- codex owns a patent on the HFCC algorithm described in this paper. Authors' contributions JG wrote the paper. JG, AGP and AR have discussed the topic, read, revised and approved the final manuscript. Acknowledgements This work was supported in part by Agencia IDEA, Consejería de Innovación, Ciencia y Empresa (830882); Corporación Tecnológica de Andalucía (07/124); and Ministerio de Educación y Ciencia (PCT-A41502790-2006 and PCT-010000- 2006-1). Response Marko Toplak, Tomaz Curk, Janez Demsar, Blaz Zupan Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia E-mail: blaz.zupan@fri.uni-lj.si We agree with the response by Gayan et al. that replication of experiments is an important and widely used technique, but would like to warn that it cannot be achieved by repetitive resampling of the same data. Gayan et al. use replication groups to "eliminate the tail of false positive results", which is the same as selection of "top best signals". For either, the quality of selection is directly related to appropriate ranking of the poten- tial candidates (e.g., SNP pairs). We have shown [5] that this ranking is of higher quality if the candidates are scored directly, that is, not using arbitrarily sam- pled replication groups. Replication group scoring is inferior to direct scoring regardless of the data processing stage. In their reply, Gayan et al. agree with this statement, yet highlight a special case when replication groups are com- ing from heterogeneous data sets. The task there is to find which candidates are common across different experiments. This is different from scoring candi- dates from a single data set, the type of analysis that appeared in the reference work of Gayan et al. [4] and was the focus of our study. Acknowledgements Authors are supported by project and program grants from Slovenian Research Agency (L2-1112, J2-9699 and P2-0209). Author Details Neocodex, Avda. Charles Darwin 6, 41092 Sevilla, Spain Received: 23 February 2010 Accepted: 24 June 2010 Published: 24 June 2010 TB©hM2isC0ia1Gsr0etaiGncolaeOmyipáscneasve2Aat0icl1a;0be,islce11fanr:o4st0emic3el:BhditospMt:r/eibdwuCwtewnd.tburainoldmLterdt.hcenetraml.csomft/h1e47C1re-2a1ti6v4e/C11o/m40m3onsAtributionLicense(htp:/creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited. References 1. Moore JH: A global view of epistasis. Nature Genetics 2005, 37(1):13-14. 2. Frankel WN, Schork NJ: Who's afraid of epistasis? Nat Genet 1996, 14(4):371-3. 3. Cordell HJ: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 2009, 10(6):392-404. 4. Gayan J, Gonzalez-Perez A, Bermudo F, Saez M, Royo J, Quintas A, Galan J, Moron F, Ramirez-Lorca R, Real L, Ruiz A: A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. BMC Genomics 2008, 9:360. 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NCI-NHGRI Working Group on Replication in Association Studies, Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF Jr, Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS: Replicating genotype-phenotype associations. Nature 2007, 447(7145):655-60. 9. Edwards TL, Bush WS, Turner SD, Dudek SM, Torstenson ES, Schmidt M, Martin E, Ritchie MD: Generating Linkage Disequilibrium Patterns in Data Simulations using genomeSIMLA. Lect Notes Comput Sci 2008, 4973:24-35. 10. The International Schizophrenia Consortium: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009, 460:748-752. doi: 10.1186/1471-2164-11-403 Cite this article as: Gayán et al., "Does replication groups scoring reduce false positive rate in SNP interaction discovery?: Response" BMC Genomics 2010, 11:403