444 HUMAN MUTATION 28(5), 441^450, 2007 (k 5 8.874.2) per segment (average length of 18547329). Mean nucleotide diversity and haplotype diversity are p(%) 5 0.08270.056 and G 5 0.5870.23, respectively. The associated standard deviations manifest a large variance between individual loci. The extent of this variance may be due to: 1) stochastic effects resulting from different genealogical histories of each of these segments; 2) the effect of selection; or 3) heterogeneity in the mutation rate among loci [Chuang and Li, 2004; Matassi et al., 1999]. However, we did not notice any systematic correlation (r2 5 0.015) between S and nucleotide divergence d, measured as a proportion of fixed sites between humans and chimpanzees (Table 1). Thus, variation in the mutation rate alone cannot explain differences between the polymorphic content in the analyzed segments. It could possibly be due to differences in the underlying genealogies, reflecting either variation in demographic history or the effect of natural selection. Examination for these effects requires the analysis of diversity indices that capture different aspects of the data. In Table 1, we compared different estimators of the population mutation parameter Y 5 4Nm, where N denotes the effective population size and m, the mutation rate per segment per generation. Watterson’s estimator, YS [Watterson, 1975], and Tajima’s Yp [Tajima, 1989], based on the infinite sites model, can be derived from S and p, respectively. In turn, Yk [Ewens, 1972] and Yhom [Chakraborty, 1990], estimated from k and G, originate in the infinite alleles model (see Materials and Methods). Note that the term ‘‘allele’’ is reserved here to variants of a single segregating site as in the infinite sites model and, in the infinite alleles model, should be replaced by ‘‘haplotype’’ (i.e., variant of the whole locus). Two additional estimators, YF by Fu and Li [1993] and YH by Fay and Wu [2000], require the knowledge of the derived and the ancestral state at each of the segregating sites, here obtained by genotyping chimpanzee and other ape DNAs (see Materials and Methods). Knowing the ancestral allele at each polymorphic site, we introduced an additional descriptor of locus diversity, a haplotype allelic class describing the number of mutational steps separating each haplotype from the ancestral haplotype (i.e., entirely composed of ancestral alleles). Table 1 lists haplotype allelic class M for each of the major haplotypes. The major, i.e., most frequent, haplotype in 12 of these segments is the ancestral one (i.e., M 5 0). In contrast, we observed MZ5 in five of these (see the correlation with corresponding YHs). The four- gamete test indicated that recombinations contributed to haplotype diversity in 10 of the segments. Yet, as indicated in Table 1, in four of them the impact of these events was relatively small, with only one additional recombinant haplotype observed (see the corresponding haplotype networks in Supplementary Fig. S1). NeutralityTests We carried out neutrality tests that confront different estimates of Y and/or summary statistics, such as the number of haplotypes or their homozygosity (Table 1). Assuming a simple model of a population at constant size, mutational equilibrium, and neutrality, these different estimates are expected to be the same or to agree, given the associated variance (see Materials and Methods). An opposite result indicates departure from this model, suggesting selection or less simple demography, i.e., effects due to population growth, bottleneck, or population structure. Table 2 lists all segments highlighted by these tests; the results that remain significant after correcting for multiple testing [Storey, 2002] using a false discovery rate of 10% are shown in bold. Human Mutation DOI 10.1002/humu The testing of data in the framework of the infinite sites model can be illustrated by a histogram of allelic frequency classes that regroup sites with the same number of the derived allele, from i 5 1, 2, 3,yto i 5 n–1, where n is the number of chromosomes in the sample. The expected distribution is Si(i) 5 Y/i [Fan et al., 2002; Fu, 1997] where s Si 5 S, as illustrated in the left panels of Figure 1, where Yp estimates (Table 1) were used to trace the theoretical curve according to the above equation. The corre- sponding plots for other segments than the three shown in Figure 1, either highlighted by neutrality tests or singled out by Fst statistics, can be found in Supplementary Figure S2. The histogram of allelic frequency classes in Figure 1 shows an excess of low-frequency polymorphisms in the case of CDC25A, as revealed by the negative Tajima’s D in this segment (Table 2); it shows a good concordance between theoretical distribution and the data in the CX3XR1 segment and a marked excess of high- frequency-derived alleles in the case of GSTM3. The latter agrees with the result of the Fay and Wu test for this segment (Table 2). Middle histograms in Figure 1 illustrate the results of the haplotype-based tests. In the case of CX3CR1, as for allelic frequency classes, this plot shows an excellent fit between the theoretical distribution and the observed frequencies. In these representations illustrating the results of the neutrality test from Table 2, the CX3CR1 segment appears to conform to a simple neutral model. In contrast, in CDC25A given the number in the expected frequencies do not match with the data. There is an excess of the observed haplotypes, given their homozygosity (1–G). This discordant distribution, in the case of the CDC25A segment, reflects significant results of haplotype-based tests, including Fu’s Fs test, which, however, compares k with its estimate based on Yp rather than Yhom. After correcting for multiple testing, no segment remained significant for the Ewens- Watterson test as well as for the Fay and Wu test. Furthermore, the significant results of Chakraborty’s test for HTR2A and GPX2, as well as those of Fu’s Fs test for the GPX2, can likely be ascribed to the effect of recombinations. The latter, causing the number of the observed haplotypes to increase faster than they would simply due to mutation alone, can render the results of the above tests falsely significant. Yet, at the same time, the presence of recombinations renders other tests, such as Tajima’s or Fay and Wu’s, less conservative, i.e., ‘‘more significant’’ [Fay and Wu, 2000]. Indeed, considering the effect of recombinations (three- to six-fold genomic average) in six segments where more than one recombinant haplotype was observed (Table 1), GPX3 stayed significant for the Fay and Wu test after the correction for multiple testing. Haplotype Allelic Classes While the left and middle histograms in Figure 1 represented the allelic and haplotype configurations of the analyzed segments, the histograms shown in the right column combine the allelic and haplotypic information in a single plot to reveal additional characteristics of the data. The haplotype allelic classes A represent all haplotypes carrying the same number of new alleles m such that, for example, Am¼0 represents the number of the ancestral haplotypes in the sample; A1, the number of all haplotypes with one derived allele; A2, with two derived alleles, etc., such that SAi 5 n and not as ki opposite to SSi 5 S. Note that we have already introduced this notion to describe the allelic class m 5 M of the major haplotype (Table 1). In Figure 1, the theoretical distributions of the expected counts of haplotypes in each of their allelic classes m were computed using coalescent