Huff et al. BMC Genomics 2010, 11:8 http://www.biomedcentral.com/1471-2164/11/8 Page 5 of 9 Figure 4 Power, false positive rates, and false discovery rates for a) iHS and b) ALnLHu. To obtain critical values, we combined loci from neutral simulations with loci from selection simulations in proportion to the fraction of the genome influenced by positive selection [12]. Our implementation of each test followed the methodology employed in the empirical genome scans from [5] (iHS) and Wang et al., 2006 (ALnLH). The false discovery rate was between 0 and 0.53 for iHS, and between 0.74 and 0.96 for ALnLHu. To approximate the histories of the populations in the original studies, we combined results from simulations of African and European populations based on the best fitting model in [9]. The locus recombination rate for each simulation was set to an exponential random variate with mean equal to 1 cm/Mb. For other simulation parameters, see Figure 2. rate for each gene in Voight et al. was between 0 and 0.53. Despite the higher false positive rate, we estimate that the power was approximately 25% lower in the Wang et al. test due to the issues with uncertain phase and variable recombination rates discussed above. Discussion From our evaluation of false discovery rates, we can esti- mate the number of false discoveries for each genomic scan. Of the 1799 candidate genes identified by Wang et al. [4], we estimate that 1331 to 1727 of those were false discoveries. For Voight et al. [5], we estimate that there were 0 to 231 false discoveries over 431 candidate genes. The estimates for true discoveries are then 72 to 468 for Wang et al. [4], and 200 to 431 for Voight et al. [5]. So after adjusting for false discoveries, the two stu- dies are in close agreement. Given our true discovery and power estimates for iHS, we estimate that there are between 600 and 1000 variants with an allele frequency of at least 0.2 that have been influenced by strong recent positive selection in the HapMap phase 2 populations. While the single-site statistics used in these studies perform equally well under simulations with constant recombination rates, several factors inhibited the perfor- mance of ALnLH. These factors primarily involve imple- mentation details of the test and not the properties of the FRC statistic itself. Since both ALnLH and iHS methods measure the long range LD for each allele at each focal site, it may be possible to design a test based on the FRC statistic that matches or exceeds the perfor- mance of iHS using the Voight et al. implementation as a template [5]. Five features that should be included in such a test are local controls for recombination rate, standardization for allele frequency, population specific critical regions, external inference of gametic phase, and the aggregation of results at nearby loci to mitigate multiple testing problems. While a future FRC test may prove more valuable, the false positive and false discov- ery rates are too high in the current ALnLH implemen- tation to provide a useful set of candidate genes in genomic scans. Throughout our analysis of EHH statistics, iHS consis- tently outperformed LRH. Since specific guidelines are not available for determining the core haplotype region and level of EHH decay for LRH, we may have underes- timated the power of LRH. However, we tested 4 sets of parameter values using examples in Sabeti et al. as a guide [3], and none of the tests were able to match the performance of iHS in any of our scenarios. Our estimates for the power of the iHS test were con- sistently higher than those reported in Voight et al. [5], but it is important to distinguish between our single-site analysis vs. our site-aggregation analysis when compar- ing the two results. Figures 1, 2, and 3 report the power of the single-site statistic, which is based on one iHS value measuring the decay of LD surrounding one SNP. This is not directly comparable to the power analysis in Voight et al., which was based on the aggregation of 51 iHS scores for SNPs near the favored allele [5]. This aggregation strategy successfully mitigates the multiple testing problem inherit in a full genome scan by incor- porating information from potential genetic hitchhikers near the favored allele. However, as demonstrated in the comparisons between Figures 3 and 4, the power of the site-aggregation test is appreciably lower than the sin- gle-site test. This tradeoff is worthwhile for uniformed genome scans involving large numbers of SNPs, since it reduces the number of tests by one or more orders of magnitude. However, candidate gene studies that involve only a few potential targets of selection do not suffer from the same multiple testing problems as full genome scans. For these studies, the single-site iHS test is a