Huff et al. BMC Genomics 2010, 11:8 http://www.biomedcentral.com/1471-2164/11/8 Page 3 of 9 Figure 1 Power to detect selection from single-site statistics with a constant recombination rate. For all figures, the power was averaged across 4 population histories of constant size, expansion, expansion with migration, and bottleneck with migration. Both ALnLHp and iHS performed quite well in most models. The power of LRH was consistently lower than the other statistics. Neutral simulations for each set of simulation parameters provided the critical values for each statistic. a. Power to detect selection for allele frequencies between 0.2 and 0.8 with a simulated region of 1 Mb at a significance level of 0.01. ALnLHp and ALnLHu were equivalent when allele frequencies were close to 0.5, but the power of ALnLHu drops by 40% with allele frequencies of 0.2 and 0.8. b. Power to detect selection in simulated regions of 0.1 Mb to 1 Mb. The power was calculated from an equal proportion of allele frequencies 0.2, 0.4, 0.6, and 0.8 for the favored allele at a significance level of 0.01. The average power increased substantially for ALnLH and iHS out to nucleotide lengths of 400 Kb, beyond which there was little improvement. c. Power to detect selection for significance levels of 0.005 to 0.05 with simulated region of 1 Mb and an equal proportion of allele frequencies 0.2, 0.4, 0.6, and 0.8 for the favored allele. The average power of ALnLHp and iHS was over 0.9 for significance levels of 0.01 or greater. with the size of the simulated region out to 400 Kb, beyond which there was little improvement, as shown in Figure 1b. Both statistics performed very well even at low critical levels, with an average power of over 0.9 at the 0.01 level (Figure 1c). The statistics also perform well across the range of allele frequencies we tested, with an average power of over 0.8 at the 0.01 level for allele frequencies between 0.2 and 0.8 (Figure 1a). Both statistics maintained high power across all of our demo- graphic models, though iHS was more sensitive to expansions, bottlenecks, and migration (Figure 2). The performance of the statistics diverged when we intro- duced variable recombination rates to the models. While both tests were negatively impacted, the effect on ALnLHp was much greater, as shown in Figure 3. On average, the power of ALnLHp dropped by 46%, while iHS dropped by only 8% for (Figure 3). This directly reflects the strength of the internal controls for local recombination rates within each test. For iHS, there is no measure of global recombination rate, and the mea- surement of LD is based solely on the relative difference in LD between the two alleles at each site [5]. For ALnLHp, the global recombination rate is based on the observed decay of LD at G6PD and the genome devia- tion from the G6PD model [4]. The test controls for local recombination rate by ignoring all sites where the observed LD is greater than 1 standard deviation above the mean for both alleles. Therefore, positive selection is difficult to detect in regions with high recombination rates, as discussed by Wang et al.[4] in their analysis of positive selection at the DRD4 gene. For the results presented above, we calculated a statis- tic for each SNP and evaluated the power to detect selection, with the hypothesis of neutrality and the alternative hypothesis of strong positive selection acting on the SNP in question. This is an appropriate test for positive selection when the investigator has a prior hypothesis about the potential influence of natural selec- tion and when there are a small number of candidate loci. However, as we demonstrate below, when this sim- ple strategy is applied to an uninformed scan across the genome, it introduces a multiple testing problem that heavily weights the significant results toward false posi- tives. The testing methodology that Voight et al. [5] employed for iHS addresses this problem by binning the genome into 100 Kb segments, and then calculating the fraction of SNPs in each segment with |iHS| greater than 2.0 as their test statistic. This approach takes advantage of the tight linkage of genetic hitchhikers near the favored locus to reduce the number of tests from the number of SNPs in the study to the number of 100 Kb regions in the genome. Their candidate genes were those in regions with the highest fraction of signifi- cant iHS scores, taking the top 1% of the empirical dis- tribution. By lowering the criteria for a significant iHS score and considering the total fraction of significant results, they were able to test each 100 Kb region one time at the 0.01 level. In contrast, Wang et al. set a higher threshold for a significant result and tested each SNP individually, taking the top 1.6% of the distribution. All genes within 100 Kb of a significant result were included as candidate genes, which resulted in poten- tially hundreds of tests at the 0.016 level for each 200 Kb region [4]. Figure 4 illustrates the different effects of the two approaches. For these results, we follow Teshima et al. [12] in combining data from neutral simulations with selection simulations to evaluate the performance of each empirical test. Since both methods depends heavily on the fraction of the genome that has been affected by positive selection, we allowed this fraction to vary between 0 and 0.1, reporting the corresponding range of