Huff et al. BMC Genomics 2010, 11:8 http://www.biomedcentral.com/1471-2164/11/8 set of SNPs [3]. Sabeti et al. [3] introduced the long range haplotype test (LRH), which is calculated by divid- ing EHH on a core haplotype by EHH among all sam- ples not containing the core haplotype. Since explicit guidelines for identifying the core haplotype were not available, we tested two criteria. Our core haplotype region was then either a fixed 15 Kb region surrounding the focal site, or the first 8 SNPs nearest the focal site with minor allele frequencies greater than 0.05, includ- ing the focal site. The core haplotype was then the lar- gest haplotype in the core haplotype region. We based these criteria on the size of the G6PD region and the simulation methodology employed in [3]. We calculated LRH for both methods at the furthest distance where the EHH was greater than either 0.25 or 0.05 at core haplotype. In our tests, the 15 Kb regions with an EHH cutoff of 0.25 had the highest average power of the options we considered, so we only report those results. For both LRH and iHS, we measure EHH from the expected homozygosity given the allele frequencies of each haplotype rather than observed homozygosity. Voight et al. [5] introduced the integrated EHH (iHH), which is the integral of the observed decay of EHH away from a particular allele, summing over both directions until EHH is less than 0.05. To obtain their single-site statistic, iHS, they divide the value of iHH at the ances- tral allele by the value of at the derived allele and then take the natural log. Finally, they standardize iHS by subtracting the expectation and dividing by the standard deviation, which are conditioned on the frequency of the derived allele. This final step accounts for the fre- quency of the allele, since low frequency derived alleles are younger and as such will be associated with longer LD blocks. FRC is the fraction of inferred recombinant chromo- somes between two sites within a sample [4]. Through- out their analysis, the decay of FRC at G6PD is used as a model for recent positive selection, and each site is measured by how closely it matches the model. Their formula for this model was derived by fitting a sigmoid to the observed decay of FRC at G6PD: F(X)   2  64.4665e X7.47593E6 1 where X is the distance from the focal site. For a given allele at a focal site, Wang et al. calculate FRC separately for each site within 500 Kb of the focal site with a minor allele frequency greater than 0.1 [4]. They then input each array of FRC statistics into a pseudo-likelihood function to measure the goodness-of- fit to the G6PD model under the assumption that FRC values are normally distributed. This likelihood is adjusted for allele age, as described below. Page 8 of 9 Positively selected alleles that are much younger than G6PD will, in general, have larger LD blocks surround- ing the selected allele. If the likelihood calculation were left unadjusted, this would result in low likelihood scores for alleles with very low LD, since they would be a poor fit to the G6PD model. This is also an issue for alleles older than G6PD or in regions with higher rates of recombination. Since these are undesired properties, Wang et al. [4] set the likelihood equal to its maximum value for each FRC value that is between 0 and F(X) +0.1. Their test statistic is then the average log likeli- hood of selection, ALnLH, for each allele at the focal site given the model: ALnLH  ln   1   1  2     2N 2  i g(Yi , X i)2, where  0 Yi  F(X i)  0.1 g(Yi , X i)    Yi  F(X i), Yi  F(X i)  0.1 . Here, Yi is the FRC at site i, Xi is the distance from site i to the focal site, F is the expected value of FRC as a function of the distance from the focal site, N is the number of sites, and s2 is the variance of g over the entire empirical distribution. They calculate ALnLH for each allele at each site with a homozygote minor allele frequency of greater than 0.05. From the empirical distribution, they determine the average and standard deviation of ALnLH scores. Candidates for positive selection are those SNPs where one allele has an ALnLH score of 2.6 SD above the mean while the other allele has a score of less than 1 SD above the mean. In their 2006 study, these criteria included the top 1.6% of the empirical distribution [4]. We determined the details of this algorithm from source code provided by Eric Wang (personal communication). Acknowledgements We would like to thank Jon Seger for his help in designing the coalescent simulation algorithm we present here. We would also like to thank Eric Wang for providing the source code used to calculate ALnLH. This work was supported in part by the Primary Children’s Medical Center Foundation National Institute of Diabetes and Digestive and Kidney Diseases (DK069513). Author details 1Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA. 2Department of Anthropology, University of Utah, Salt Lake City, UT, USA. Authors’ contributions CH, HH, and AR designed the study and participated in the data analysis. CH and AR developed the coalescent simulation algorithm. CH implemented the coalescent algorithm and wrote the manuscript with extensive input and feedback from coauthors. All authors read and approved the final manuscript.