Gayán et al. BMC Genomics 2010, 11:403 http://www.biomedcentral.com/1471-2164/11/403 arate additive multi-marker combinations from more epi- static interactions. Those genetic effects that are due to epistatic interactions are one of the priorities in our anal- ysis, because they complement those effects detectable by single-locus analysis. Because HFCC performs an exhaustive search of the entire data space, several optional tools have been imple- mented to overcome this multiple testing problem, such as multiple replication groups, the direction filter, the control filter, the tracking filter, etc. For example, the case-control sample can be simultaneously analyzed in replication groups, to select only significant results in each group. There is also a complementary direction fil- ter, which selects only those results which are consistent across groups, that is, they are significant and with the same direction of effect in each group. A recent article by Toplak et al. [5] has been inspired by the following statement in HFCC's article [[4], page 3]: "... a multi-group analysis strategy ... allows the replication of consistent results, and it also aids the elimination of false positive results, a very attractive quality for genome-wide analysis of large number of genetic markers." These authors have interpreted the above statement as claiming that using replication groups, by itself, reduces "the false positive rate" [[5], page 2], and can therefore "... improve ... any type of feature ranking and selection procedure ..." [[5], page 1]. Our approach to detecting multi-locus effects uses a two-stage analysis strategy. In a first step, a large subset of preliminary results that are associated with the disease are selected. Then, this liberal subset of results is sub- jected to a post-hoc analysis to select the most promising results [[4], page 7]. Using replication groups is only one of the possible analysis strategies of HFCC, aiming to reduce the number of selected signals, that is, it elimi- nates a larger amount of the tail of the distribution of the results, which are mostly false positives, together with some true effects that are undistinguishable from unasso- ciated variants [10]. Our original statement claims that using multiple repli- cation groups should reduce the number of signals selected with a liberal statistical threshold (mostly false positives), but does not claim to use this strategy to select the top results of the study. Indeed, to select the top find- ings, we analyze the combined sample [[4], page 6], which, as we state repeatedly across our article [[4], pages 2,3,4], is known to be the most powerful analysis strategy [6,7]. Therefore it seems Toplak et al. interpreted our article incorrectly, and applied this misinterpretation to test their own hypothesis (replication groups aids prioritiza- tion of signals), which was finally rejected by their simu- lations. In their paper, these authors provide evidence that the analysis of a combined sample is less prone to Page 2 of 5 false positives than the separate analysis of replication samples. However, it is not clear from their article whether they have selected signals consistent in strength and direction, as suggested in our paper and in the guide- lines for replication of association results [8], what may compromise to some extent their results. In this study, we have carried out HFCC analysis of several simulated data- sets to evaluate the power and Type I error rate of the combined-group and replication-groups analysis strate- gies. Methods We simulated several case-control datasets using a freely available software, genomeSIMLA [9]. Each dataset con- sists of 3000 cases and 3000 controls genotyped for 20 SNPs, with minor allele frequencies between 0.1 and 0.5. Different effect sizes were simulated, and each experi- mental dataset was simulated 100 times, so that we could estimate the power and Type I error rate under the defined parameters. The first experiment was a sim- ulation, where all 20 snps were simulated under the hypothesis of no association between the SNPs and case status. Next, we performed 3 experiments, each with 3000 cases and 3000 controls, 18 SNPs, and one pair of SNPs in epistatic association with case-status under different effect sizes. The SNP pair was designed to have a MAF = 0.3 for each SNP, having a larger penetrance (.08) for only one genotype, the double-homozygote (AAbb). A background penetrance of 0.05 (for all other genotypes) versus the AAbb penetrance of 0.08 resulted in a mean Odds-Ratio (OR) of 1.67 (Range = 1.34-2.25). Decreasing the background penetrance to 0.04, resulted in a mean OR of 2.10 (Range = 1.64-2.75). Finally, a back- ground penetrance of 0.03 resulted in a mean OR of 2.85 (Range = 1.94-3.87). In addition to these experiments, a mixed simulation was carried out, in wich 3 different case-control samples (ie, replication groups) were simulated, each with 1000 cases, 1000 controls, and 20 SNPs. Two replication groups were simulated under the hypothesis of no association. A third replication group was simulated with 18 SNPs, and a pair of SNPs in epistatic association with background penetrance of 0.03 and AAbb pene- trance of 0.08. These 3 replication groups were then merged for a combined group analysis with an average OR of 1.57. This experiment simulates an scenario to search for consistent association results, where one case- control sample shows an association which could be real or could be due to sampling or genotyping artifacts, and two other samples which do not exhibit this association. Results We analyzed each simulated dataset with the HFCC soft- ware, using two-strategies: A combined-group analysis