One way to infer evidence of past selection is to compare variation in allele frequency at different loci among different populations. This assumes that geographically variable selective forces favor different variants in different regions. Hence, between-population allele frequency differences may be more extreme in genome portions harboring such variants. An established approach to detecting such genome regions is that of comparing F_ST values among loci; F_ST provides an estimate of how much genetic variability partitions between, rather than within, populations.

A particularly promising approach is that of population genomics. Here, the testing of large numbers of loci enables the compiling of an empirical distribution for a summary statistic, such as F_ST, from which outlying values may be identified as biologically interesting 910. An empirical approach may confer an additional level of rigor as model-based statistical tests may be sensitive to demographic effects that can produce allele frequency patterns similar to those seen under the presence of selection 111213141516. Importantly, general population demographic history will affect the whole genome but selection will only act on specific loci and these will show unusual deviation from genomic patterns. Human population-specific skews in these distributions have been previously demonstrated. For example, Tajima's D values in humans tend to be skewed towards negative values and this can be attributed to population expansion that occurred to humans post migration from Africa 17.

In an early genome-level analysis, analyzed 26,530 single nucleotide polymorphisms (SNPs) from 'The SNP Consortium' (TSC) allele frequency project in three populations (African-American, East Asian and European-American). From this three-way comparison, they identified 174 genes associated with SNPs whose extreme deviations in F_ST values suggested histories of selection. Interestingly, two of these had been implicated in previous studies but another 18 were putative bioinformatically predicted candidate genes. The markers used in this work had been initially discovered by assaying a small number of chromosomes, leading to possible bias toward more common polymorphisms. It should also be noted that these and other results from genome scans are subject to problems arising from multiple testing and the 'winners curse' phenomenon 9. Therefore, there is a clear and acknowledged need for follow-up analyses to verify the preliminary signatures of selection within this first generation selection map of the human genome.

Here we select, using an analysis of locus-specific branch lengths generated from the data of Akey and colleagues 12, eight SNPs that are good candidates to have undergone selection within European populations. We identify microsatellite markers flanking these loci, and genotype these in an Irish population sample that has previously been genotyped at 372 micrsosatellites throughout the whole genome. This allows a comparison between our test markers and an empirical distribution of the Ewans-Watterson statistic, a summary of within-population allele frequency spectra that is a test for selection. This demonstrates that seven of the SNPs are in the proximity of microsatellite markers with extreme frequency spectra, allowing stronger inference of selective history in north-western Europe at three biologically interesting genes.

In the present study, we first selected SNPs with extreme F_ST values from Akey and colleagues' 12 original data set of 26,530 (among all populations; F_ST > 0.45; 812 SNPs selected from the upper 3% tail). The diversity at each SNP may be summarized in more detail by a simple three-branch phylogeny constructed from pairwise F_ST distances. We focused on the locus-specific branch length (LSBL) from the European sample node to the central node in the network; an indication of differentiation that may be peculiar to Europe (Figure 1). This is an approach that has also been used by Shriver and colleagues 18 on a separate but similar data set. Values from the high tail of this statistic are considered in Figure 2, where the locus-specific European branch length (the absolute value) is plotted versus this statistic scaled by dividing it by the total locus-specific branch length. From this, 23 outlying SNPs showing a locus-specific branch length > 0.8 for the European population were examined. Among these, six were in known genes, six were in genes of unknown function and 11 were not in coding sequences. However, three of the latter were in proximity (within 50 kb) to a gene. A subset of eight SNPs (from the above 23), all the six SNPs in known genes, one in proximity to a gene and one not in coding sequences, were further investigated.

Loci within a genomic region containing a selected variant may share the population genetic effects of its selection because of genetic hitchhiking. Thus, in an effort to verify the evidence for European-centerd selection we identified and investigated the population genetics of microsatellite markers flanking the eight SNPs described above. We genotyped these in an Irish test population and compared their diversity to that of 372 microsatellite markers for which there is genotype data from the same subjects. Fourteen microsatellites markers were developed during this project while two were taken from previously published literature (IVS8CA 19; IVS17bTA 20) (Table 1).

Two microsatellites proved impossible to genotype due to poor amplification and/or high levels of stuttering. All but one of the microsatellites genotyped did not deviate significantly from Hardy-Weinberg expectations (1 result of p = 0.04 in 15 tests; Table 1), indicating a population in equilibrium and supporting genotyping accuracy 21.

All microsatellites flanking the same SNP (Table 1) were tested for linkage disequilibrium. With the exception of microsatellites TPSG-1 versus TPSG-2, IVS8CA versus IVS17bTA and IVS17bTA versus CFTR-3, all the other linked markers showed significant values (CFTR-3 versus IVS8CA, p < 0.001; TOX-1 versus TOX-2, p = 0.0017; ng-1 versus ng-2, p = 0.045; SYT9-1 versus SYT9-2, p = 0.015; PRKCH-1 versus PRKCH-2, p < 0.001)

In the present analysis putative signatures of selection were assessed in two different ways. Firstly, all the genotypes from the microsatellites flanking the eight outlier SNPs were used for a single population statistical test based on simulated values; a development of the Ewens-Watterson test implemented using the program BOTTLENECK. 22. Secondly, divergences from the heterozygosity values expected under Ewens-Watterson were assessed against an empirical distribution of the same statistic calculated from 372 microsatellites, spread through the entire genome, genotyped on the same panel of individuals.

According to Ewens 23, under neutrality an expected configuration of allele counts can be calculated from the sample size and the observed number of alleles. Subsequently, a test (Ewens-Watterson) that compares the observed and expected allele configuration to determine departure from neutrality was developed 24. Changes in allele proportions can be an indication of selection pressure. The differences in allele frequency spectra between that expected under neutrality and that observed can be quantified by the difference between observed gene diversity (equivalent to the Hardy-Weinberg expected heterozygosity) and expected Ewens-Watterson heterozygosity (a DH value) 22. Each DH value is divided by the standard deviation (sd) of the gene diversity to standardize differences between microsatellite loci (DH/sd) and a significance value is assigned to DH/sd using simulations 22.

Nine of the fourteen markers genotyped gave significant deviation from expected heterozygosity as assessed using the simulation test (Table 1). When the values (DH/sd) from the Ewens-Watterson test were compared against the genome-wide empirical distribution (Figure 3), all the microsatellites that had a significant DH/sd from simulation (P_S, Table 1) were found in the tails (8%) of the distribution. Eight were in the negative and one in the positive tail; perhaps indicating different modes of selection. Only four microsatellites in the positive tail and one in the negative one had a significant DH/sd when more stringent significance values were calculated from the empirical distribution (P_E, Table 1). The negative tail could indicate that the deviation between observed gene diversity and expected Ewens-Watterson heterozygosity is due to positive selection. This is because a selective sweep will lead to a high frequency of the selected allele, a reduction of variability and an excess of rare variants 10. Therefore, a lower number of heterozygotes than expected are observed. On the other hand, the positive tail could indicate the presence of balancing selection where two or more alleles are maintained at higher frequency, leading to a higher number of observed heterozygotes. Furthermore, the Irish genome-wide empirical distribution is skewed towards negative values (Figure 3), a result consistent with the distribution across 5,257 microsatellites in individuals of European ancestry 13.

We also examined the possibility that the tendency of the microsatellite test markers' DH/sd values to occur in the negative tail of the empirical distribution could be a consequence of bias due to their proximity to coding sequences and, thus, the effects of purifying selection. We constructed a sub-distribution of the empirical null by selecting only those markers that were within genes (introns or exons, n = 174) (Figure 3b). Clearly, a subset of the test markers remain in the tail of this more conservative distribution. A further indication of the neutrality of the gene-associated marker scores from the empirical distribution was that DH/sd values for these microsatellites did not correlate with their distances to the nearest gene (Spearman's rho = 0.08; Pearson correlation = 0.085).

Many methods of assessing non-neutrality using population genetic data are known to be related and thus largely redundant. However, the two employed here belong to two separate approaches: locus-specific F_ST branch length (LBSL) is based on divergence between populations and DH/sd is an assessment of allele frequency spectra within a single group. To empirically assess the potential complementarity of these approaches, we took a data set composed of 377 microsatellite markers typed in French, Han Chinese and Nigerian Yoruba samples 25. We calculated two statistics analagous to those we used above: DH/sd in each sample plus the LSBL terminating with the same group. In the European (French) sample the two were negatively correlated (Spearman's Rank correlation: r_s = -0.233; p < 0.001). However, LSBL proves a poor predictor of DH/sd; for example, when one examines the top 5% of LSBL score outliers, only 1/19 is also a 5% outlier for DH/sd. Analysis of the rank correlation between LBSL and DH/sd in the Yoruba sample gave a weaker correlation result (r_s = -0.155; p < 0.003) and in the Han sample a somewhat stronger correspondence (r_s = 0.308; p < 0.001). In the former and the latter, respectively, 3/19 and 5/19 of the top 5% outliers coincided for the two approaches.

On the positive tail of the distribution, suggesting a history of balancing selection, there is 1 of 2 microsatellites (TPSG-1; Table 1 and Figure 3) within the TPSG1 (tryptase gamma 1; MIM:*609341) gene with four common alleles ranging in frequency from 27% to 23% (Additional data file 2). Tryptases have been implicated as mediators in the pathogenesis of asthma and other allergic and inflammatory disorders 26. The suggestion of balancing selection in this gene is consistent with the expression of multiple tryptase isoforms, some of which are allelic variants; a common feature in genes involved in the immune response, for example, as seen in the major histocompatibility complex (MHC) of vertebrates 27.

Correlation, although limited, between LSBL and the Ewens-Watterson based statistic used here suggests that there is not complete independence between the two approaches, despite their examining different aspects of allele diversity. However, the TPSG1 outlying result is at the opposite tail to that expected under the correlation, giving stronger inference of underlying adaptive biology. Outlying results at the other tail retain a measure of complementarity given that: the correlation is weak and gives a poor empirical correspondence for outliers; the approaches draw on different aspects of population biology; and the results come from a fresh test population. We discuss two such results below.

Two microsatellites, PRKCH-1 and PRKCH-2 (Table 1 and Figure 3), within the gene PKCη (protein kinase C, eta, MIM *605437), show the most extreme negative DH/sd values. Each of these markers has one predominant allele with frequencies of 50% and 70%, respectively (Additional data file 2), consistent with an allelic configuration expected under positive selection. The majority of the other alleles have a frequency of < 10%. This shared signal was despite a distance between the markers of 79 kb (Table 1 and Figure 3); they were also in significant linkage disequilibrium (p < 0.001). PKC family members phosphorylate a wide variety of protein targets, are known to be involved in diverse cellular signaling pathways 28 and the protein transcribed by PKCη is involved in processes associated with several medical conditions 29303132. Interestingly, this protein is highly expressed in the epidermis and inhibits UV-induced apoptosis of keratinocytes. It has been suggested that this mechanism prevents the excessive elimination of differentiated epidermic keratinocytes, cells that are involved in defending the skin from chemical stimulation and UV exposure 33. A variant in this gene may have featured as part of the selected transition to light skin in the populations ancestral to Europe, although corroborating evidence would be required to assert this. Selective histories at genes related to human pigmentation have been inferred previously 34.

The next outlying marker in the negative tail of the distribution in a known gene is IVS8CA (Figure 3). This marker is associated with the gene CFTR (cystic fibrosis transmembrane conductance regulator; MIM:*602421), which has been well studied as mutations in it cause the autosomal recessive disorder cystic fibrosis (CF). The CFTR gene product functions as a chloride channel and controls the regulation of other transport pathways. It is also a receptor for bacterial pathogens 3536. There have been several hypotheses as to why CF is present at such a high level in Europe, including that of heterozygote advantage, where a single copy of the main western European disease-causing mutation (ΔF508) increases resistance to typhoid 36. In this study, only one microsatellite, from the three examined in the gene, showed a significant deviation in the Ewens-Watterson test. Interestingly, this was also the closest to the gene region that encodes the receptor for bacterial pathogens (residues 108 to 117) 36. The results from our study might be from selection acting on this region with an immune response function, but are unlikely to be associated with CF. The frequency of the disease in Ireland is 1/1,461 37. Thus, our 72 Irish samples should have about four carriers, a number too low to profoundly influence the test statistic. Interestingly, a recent analysis has stated evidence of recent selection associated with a variation (V470) that is in linkage disequilibrium with haplotypes that are not associated with the disease 38.

The remaining microsatellites (LRRC, ABCD3-1, SYT9-1), which have significant but less extreme values in the Ewens-Watterson test (P_S, Table 1 and Figure 3), are associated with poorly characterized genes. However, they each appear to encode trans-membrane proteins 394041.