Huff et al. BMC Genomics 2010, 11:8 http://www.biomedcentral.com/1471-2164/11/8 surprisingly important force in recent human evolution [4-7]. However, beneath the broad picture lie curious differences in the results of the two approaches. In 2006, Wang et al. identified 1799 candidate genes as potential targets of recent positive selection in a scan of the human genome based on the FRC statistic. Later that year, Voight et al. [5] identified 431 candidate genes in a similar genome scan using the iHS statistic, which is based on EHH. Both groups calculated a summary statistic at each site that measured the LD associated with that site, and then aggregated those statistics to identify candidate loci from the outliers of the empirical distribution, with Voight et al. including 1% of the dis- tribution and Wang et al. including 1.6%. So although the Wang et al. study was only slightly less restrictive, it identified over four times the number of candidate loci. One possible explanation is that FRC is a more powerful statistic for detecting recent positive selection. However, Voight et al. estimated that the power of iHS to detect recent positive selection was approximately 33% for the range of allele frequencies considered in Wang et al. If their estimate is accurate, even if the power of the FRC test is 100%, the discrepancy between the two tests can- not fully explained. Additional genome scans have demonstrated that the differences in these results are not artifacts, and instead represent stable differences between the two statistics [6,8]. While several studies have estimated the power of EHH statistics to infer positive selection, the statistical power of FRC has not yet been explored. To address this gap, we use simulated data to compare the proper- ties of FRC and EHH statistics. We first examine the power of the single-site statistics of each method under explicit models of neutrality and alternative models of selection. We then estimate the false positive rate, power, and false discovery rate of each test when applied to an empirical distribution of its respective sta- tistic based on a combination of neutral and selected loci. The available computational methods for simulating genealogies cannot easily model complex demographic scenarios combined with the presence of positive selec- tion. Most methods require a single population of con- stant size. This is problematic when evaluating the statistical power of LD-based tests in the presence of positive selection, as population bottlenecks and subdivi- sion can create LD that mimics that generated by selec- tion. Here, we introduce a new approach for simulating positive selection in complex population histories with subdivision, migration, bottlenecks, and expansions in a coalescent framework. With this approach, we first gen- erate a set of potential allele trajectories for the favored allele using forward-in-time simulations. Then for each backwards-in-time simulation, we select an allele Page 2 of 9 trajectory at random and condition the coalescent simu- lation on the population sizes and migration history of the favored allele as specified by the allele trajectory (see Methods). Results In our analysis, we considered one test derived from the FRC statistic, ALnLH, and two tests derived from the EHH statistic, LRH and iHS [3-5]. To evaluate the effects of population history on the power of each of these statistics, we considered four demographic models: constant population size, expansion, expansion with migration, and bottleneck with migration. For the final three models, we obtained parameter values from the best fitting model in [9]. From this model, we used Africa to represent the expanding population and Eur- ope to represent the bottlenecked population. For his- tories with migration, we allowed low levels of migration between Europe, Asia, and Africa as specified in their model [9]. For selection models, we used the estimate for the average strength of recent positive selection in humans of s = 0.022 from [8]. We set the origin generation of the favored allele for each model to produce an average allele frequency of approximately 0.5, which met our goal of providing coverage for allele frequencies between 0.2 and 0.8. Throughout the analysis, we calculated two versions of the FRC statistic. As originally presented by Wang et al., FRC is calculated from unphased data using the indivi- duals homozygous for each allele at the focal site [4]. However, the two EHH tests we evaluated, LRH and iHS, are calculated from phased data [3,5]. This intro- duces a complication when directly comparing the sta- tistics with simulated data, since ALnLH will have lower power than it would otherwise because it ignores infor- mation about phase that is available to the other two tests. To account for this, we calculated both a phased and unphased version of the statistic, ALnLHp and ALnLHu, with the phased statistic using information from both homozygotes and heterozygotes to infer FRC. As shown in Figure 1a, the power of the unphased sta- tistic was much lower than the phased statistic when one of the alleles is relatively rare, but as the allele fre- quency approaches 0.5 the two statistics were essentially equivalent. Throughout our analysis, we make the sim- plifying assumption that gametic phase is known, when in practice it can only be estimated. While this assump- tion may bias our evaluation of ALnLHu, the effect should be small given the accuracy of current phase estimation technology and that ALnLHu ignores infor- mation from all heterozygote comparisons [4,10,11]. In general, the properties of iHS and ALnLHp were similar when the recombination rate was constant (Fig- ure 1). The power of both tests increased substantially