Gayán et al. BMC Genomics 2010, 11:403 http://www.biomedcentral.com/1471-2164/11/403 already mentioned in our original paper, and therefore is preferable for selecting the top signals of a study. None- theless, when lack of replicability of results is an issue, using a multiple-group strategy is an useful tool to select a large subset of results that are consistent across groups, and that may replicate in future independent replication studies. As we stated in our original article [4], the replication groups strategy is used to select a large subset of prelimi- nary results, which are then subjected to post-hoc analy- sis for prioritization. We used the replication groups to eliminate the tail of false positive results, not to choose the top best signals (scoring or ranking), which is done in a post-hoc analysis. This is a key misinterpretation of our strategy in the Toplak et al. article, and, although we will not mention them all here, there are several others. For example, they claim we did not compare in our original publication a multiple-group strategy versus a combined group strategy [[5], page 1], but we compared them and stated that the analysis of the Parkinson Disease dataset, in a single combined group, yielded 784,506 pairs of SNPs at a p-value of 10-6. The same analysis, with 3 replication groups each at p-value of 10-2, yielded only 418,535 pairs [[4], page 12]. Moreover, when the direction filter was applied to this 3-group strategy, then the analysis only yielded 320,265 pairs [[4], Table 2], a significant decrease. When the multiple group strategy is used, it is impor- tant to use the direction filter, to select only those signals significant in each group but also consistent in the direc- tion of effect. Our original simulations suggested this fil- ter may eliminate about 24% of the signals selected by the 3-group analysis [[4], pages 7-8]. It is not clear from their article whether Toplak et al. have applied a direction fil- ter. It seems they have selected signals above a statistical threshold, but may have failed to select only those results with the same direction of effect in all groups. If this is true, then the results published in their article [5] are compromised, because it seems straightforward that selecting only consistent results, in both strength and direction, will reduce the number of false positives selected in the multiple-group analysis. Indeed, the results presented in Table 1 and in the Results section show that applying a replication group analysis with a direction filter lowers the false positive rate relative to a combined group analysis. Toplak et al. also argue that instead of using multiple replication groups, a decrease in false positives can be achieved by simply raising the significance level in a com- bined group analysis [[5], page 7], a statement with which we obviously agree. However, if this new significance level is applied with multiple replication groups, the false positive rate would be lower again than for the combined analysis. Page 4 of 5 We have also shown that when the replication groups are heterogeneous, a combined group analysis may detect the signal that is due to a subset of the sample, but a mul- tiple-group analysis with direction filter protects against this potential source of bias. Obviously, if there is hetero- geneity across samples and the effect is real only in some subsets, the combined analysis is more powerful to detect a potentially true effect, even though the effect could be erroneously generalised to all three groups. However, if the goal is to find results consistent across replication groups, and protect against false positive results, then the replication-group strategy provides this added value. We also want to note that our multiple group strategy has its roots in the ability to analyze simultaneously mul- tiple related phenotypes (such as comorbid or related dis- eases). This is another advantage of HFCC, which is inherently designed to allow this type of analysis. More- over, some gene factors may have a risk effect on a dis- ease, and a protective effect on another one, a possibility that can only be addressed with multiple-groups and flex- ible direction filters. Conclusion Replication of results in independent samples is generally used in scientific studies to establish credibility in a find- ing [8]. It protects against bias from unmeasured sources of noise (stratification, sampling or selection bias, techni- cal artefacts or non-random genotype errors, heterogene- ity across samples). Replication across samples has become the standard strategy in recent years with the proliferation of genome-wide studies of many diseases. Nonetheless, the combined analysis of several datasets is known to be a preferable and more powerful strategy [6,7] for the selection of top signals. HFCC [4] is a flexible analysis tool, and one of its analysis options combines these two strategies: A preliminary multiple replication group analysis to eliminate inconsistent false positive results, and a post-hoc combined-group analysis to select the top signals. HFCC, however, is a more complete soft- ware which includes other analysis options and method- ologies. This article explains these concepts and shows that using replication groups with direction of effect low- ers the false positive rate. As a summary of our results, if the goal of an analysis is to select the best top signals, a combined group analysis provides the most powerful approach. Subdividing a large homogeneous sample into several smaller subsets is not generally recommended in this case, unless there is some evidence of heterogeneity. When there exist several inde- pendent and heterogeneous samples, different in case selection, geographical location, race, genotyping, etc., or even sample size, applying a multiple replication group strategy may help eliminate false positive signals and