Multiple Advantageous Amino Acid Variants in the NAT2 Gene in Human Populations

Abstract

Background

Genetic variation at NAT2 has been long recognized as the cause of differential ability to metabolize a wide variety of drugs of therapeutic use. Here, we explore the pattern of genetic variation in 12 human populations that significantly extend the geographic range and resolution of previous surveys, to test the hypothesis that different dietary regimens and lifestyles may explain inter-population differences in NAT2 variation.

Methodology/Principal Findings

The entire coding region was resequenced in 98 subjects and six polymorphic positions were genotyped in 150 additional subjects. A single previously undescribed variant was found (34T>C; 12Y>H). Several aspects of the data do not fit the expectations of a neutral model, as assessed by coalescent simulations. Tajima's D is positive in all populations, indicating an excess of intermediate alleles. The level of between-population differentiation is low, and is mainly accounted for by the proportion of fast vs. slow acetylators. However, haplotype frequencies significantly differ across groups of populations with different subsistence.

Conclusions/Significance

Data on the structure of haplotypes and their frequencies are compatible with a model in which slow-causing variants were present in widely dispersed populations before major shifts to pastoralism and/or agriculture. In this model, slow-causing mutations gained a selective advantage in populations shifting from hunting-gathering to pastoralism/agriculture. We suggest the diminished dietary availability of folates resulting from the nutritional shift, as the possible cause of the fitness increase associated to haplotypes carrying mutations that reduce enzymatic activity.

About the Authors:

Francesca Luca

Current address: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America

Affiliation: Department of Cell Biology, University of Calabria, Rende, Italy

Giuseppina Bubba

Affiliation: Department of Cell Biology, University of Calabria, Rende, Italy

Massimo Basile

Affiliation: Department of Cell Biology, University of Calabria, Rende, Italy

Radim Brdicka

Affiliation: Institute for Haematology and Blood Transfusion, Prague, Czech Republic

Emmanuel Michalodimitrakis

Affiliation: Institute of Molecular Biology and Biotechnology, Heraklion, Greece

Olga Rickards

Affiliation: Department of Biology, University “Tor Vergata”, Rome, Italy

Galina Vershubsky

Affiliations Arct. An. C Innovative Laboratory, Moscow, Russia, Institute of Developmental Physiology, Russian Academy of Education, Moscow, Russia

Lluis Quintana-Murci

Affiliation: UP Human Evolutionary Genetics, CNRS-URA3012, Institut Pasteur, Paris, France

Andrey I. Kozlov

Affiliation: Arct. An. C Innovative Laboratory, Moscow, Russia

Andrea Novelletto

* E-mail: [email protected]

Affiliations Department of Cell Biology, University of Calabria, Rende, Italy, Department of Biology, University “Tor Vergata”, Rome, Italy

Introduction

Arylamine N-acetyltransferases (NATs) are drug-metabolizing enzymes that catalyze the conjugation of an acetyl group from acetyl CoA onto an amine, hydrazine or hydroxylamine moiety of an aromatic compound [1]. In humans, duplicated genes encode for two enzymes (NAT1 and NAT2) with distinct, but partially overlapping substrate specificities and with different tissue distribution: NAT1 is virtually ubiquitous, whereas NAT2 is expressed at high levels only in the intestine and liver [2]. NAT1 specific substrates include para-aminobenzoate (PABA), 4-aminosalicylic acid and para-aminobenzoylglutamate (pABGlu, a folate catabolite) [3]–[5]. NAT2 is active on a wide range of substrates, but particularly arylhydrazine compounds, thus providing a major route to detoxification.

Genetic variation at NAT2 has been long recognized as the cause of differential ability to metabolize a wide variety of drugs of therapeutic use in diseases with high prevalence such as tuberculosis, arrhythmia and hypertension [6]. In fact, three major phenotypes are observed, i.e. the fast, intermediate and slow acetylators, which are inherited as a codominant trait. It is also recognized that the different activity of the allelic isoenzymes results in altered activation/deactivation of many compounds that are commonly found in native food or are generated by cooking or other treatments. Additionally, depending on the metabolites that are generated and on the body compartments where they are dispatched, the products of NAT2 activity can exert carcinogenic or toxic effects [7].

Thanks to the well-established genotype-phenotype correlation, the overall population prevalence of the acetylator phenotype can be inferred from the population frequency of variants known to affect NAT2 function. Because of its medical interest, the geographic distribution of NAT2 variants has been well characterized. This distribution shows a belt of populations with high frequencies of the slow-acetylator allele(s) stretching from Europe to central and northern Africa to southern Asia; in constrast, populations from southern Africa, eastern Asia and the Americas are characterized by higher frequencies of the fast acetylator allele(s) (Fig. 1, data from [6], [8] and references therein). Wilson et al. [9] analysed NAT2 and other genes coding for drug metabolizing enzymes and found greater heterogeneity among genetically-defined than ethnically-defined populations. This finding suggests that a population's drug-metabolizing repertoire is likely to vary across local populations within broadly defined human groups. These findings raise the question of whether the current geographic distribution of NAT2 variants is the result of human adaptations to different and changing environments. It is implicit that such adaptations were induced by long-term exposure to environmental chemicals and dietary components rather than medical drugs, which were introduced too recently to result in any considerable selective effect.

[Figure omitted. See PDF.]

Figure 1. Map of the old world showing the sampling locations for the populations here examined.

Each pie represents the percentage of the slow (white), intermediate (grey), and fast (black) acetylators in the extended panel, inferred from genotype data as described in Materials and Methods. Inset: map showing the frequency of slow (white) and fast (black) haplotypes as inferred based on biochemical data [6].

https://doi.org/10.1371/journal.pone.0003136.g001

Here, we report on the pattern of genetic variation at NAT2 coding region in 12 human populations that significantly extend the geographic range (Fig. 1) and resolution of previous NAT2 surveys [8], [10]–[12]. In this study, populations were selected according to two criteria: i) they represent a wide spectrum of environmental and dietary regimens that may have shaped NAT2 variation and ii) they represent test samples of large national communities that may become exposed to drugs metabolized by NAT2 isoenzymes for the treatment of a number of diseases of major Public Health importance (e.g. tuberculosis). Moreover, the ongoing westernization of the life style and diet of these same communities e.g. [13] is likely to modify substantially their cancer risk, which is also a function of their NAT2 genotypic pool. Our results show that spatially and temporally varying selection reflecting different dietary regimens and lifestyles may explain inter-population differences in NAT2 variation.

Results

Twelve population samples were included in our study of NAT2 variation; these populations cover a geographic area extending from Africa North of the equator to South and East Europe and North East Asia to the Beringian coast. The extremes of the geographic range include three African populations (Dendi from Nigeria and the Amhara and Oromo from Ethiopia) and a Siberian population from the Chukotka peninsula. We selected small sub-samples (6–14 individuals) from 9 of the 12 above sampling locations for a full re-sequencing survey (referred to as “re-sequencing panel”, see Supplementary Table S1). Also, seven known polymorphisms were genotyped in additional 150 individuals from the same 12 populations, bringing the overall number of genotyped subjects to 248 (referred to as “extended panel”, see Supplementary Tables S1 and S2).

Re-sequencing survey

The NAT2 gene spans 9.9 kb (Chr 8 positions 18,293,034–18,302,961 in NCBI B35 assembly) and consists in a non-coding exon at the 5′ end, separated by a 9 kb intron from a uninterrupted coding region of 873 bp (including the stop codon). Because a previous study had reported a putative signal of non-neutral evolution for the NAT2 coding region only [8], we re-sequenced this region in a total of 98 subjects from nine populations (listed in Supplementary Table S2), thus significantly extending the geographic range and resolution of previous surveys. This allowed the search for possible, yet undetected, variants. A single new variant was found (34T>C); it results in a non-synonymous change (12Y>H) whose effects on NAT2 function are unknown. This variant was found in a Chukchee individual homozygous for two haplotypes otherwise identified as *5B. This polymorphism was only considered in the neutrality tests and when calculating Extended Haplotype Homozygosity (EHH).

The remaining 6 polymorphic sites are the ones most commonly reported in surveys of NAT2 variation in human populations (http://www.louisville.edu/medschool/pharmacology/NAT.html). Two are synonymous changes (C282T and C481T), whereas the remaining four variants are non-synonymous (T341C, G590A, A803G and G857A). Although G191A is described to be relatively common in Western Africa (up to 15%) [8], [12], [14], none of the subjects in our sample carried this variant.

The ancestral state at each variable position was inferred by comparison with the chimpanzee and macaque sequences; both outgroup sequences showed the same nucleotide at each polymorphic position in humans. We find that haplotype *4 carries the ancestral allele at all variable sites detected in our survey as well as at all the polymorphisms reported in the NAT2 literature, thus identifying this fast haplotype as the ancestral arrangement of human polymorphic alleles.

Extensive theoretical work [15]–[20] has shown that surveys of ascertained SNPs may introduce a bias in the distribution of allele frequencies, linkage disequilibrium and polymorphism levels and, therefore, affect population genetics inferences. In contrast, full re-sequencing surveys, in which the sequence of a genomic segment is determined in each individual in the sample, allow an unbiased characterization of multiple aspects of genetic variation and are suitable for standard methods of population genetics analyses. In this sense, the set of 196 (98×2) gene copies entirely sequenced for the NAT2 coding region provides an appropriate data set for performing statistical tests based on a variety of summaries of genetic variation data. For this survey, we focused on the portion of the gene for which a precise genotype/phenotype correlation has been established [7]; however, we cannot rule out that non-coding variants exist, which influence NAT2 gene expression and function. Polymorphism levels were summarized by the estimators Theta(pi) and Theta(s), which are based on average number of pairwise sequence differences and on the number of segregating sites, respectively. In the three continental pools, the estimator Theta(pi) exceeded the estimator Theta(s) (Table 1). Under the standard neutral model, the distributions of the two parameters have the same mean. While chance variation predicts that, in any given sample, one of the parameters may exceed the other, Theta(pi) also exceeded Theta(s) in all individual populations samples (Supplementary Table S3). Consequently, Tajima's D [21], a summary statistic of the frequency spectrum based on the difference between the Theta estimators, was invariably positive in the continental and in the individual population samples, indicating the excess of intermediate frequency alleles as a feature of NAT2 variation.

[Figure omitted. See PDF.]

Table 1. Summary statistics of NAT2 diversity in the continental samples of the resequencing panel.

https://doi.org/10.1371/journal.pone.0003136.t001

Table 1 also reports the statistical significance of the departure of summary statistics from null distributions obtained under three commonly considered demographic models (see Materials and Methods). When compared to the results of neutral simulations under the exponential growth model: Tajima's D values were significantly positive for the African and European samples (p<0.05) and reached borderline significance in the Asian sample; Fay and Wu's H reached borderline significance in the European and Asian samples. When compared to the results of neutral simulations under the Bottleneck model A: Tajima's D values were significantly positive for the African and European samples (p<0.05) and reached borderline significance in the Asian sample; Fay and Wu's H was significant in the Asian sample (p<0.01). When compared to the results of neutral simulations under the Bottleneck model B: Tajima's D values were significantly positive for the African and European samples (p<0.05); Fay and Wu's H was significant in the European (p<0.05) and Asian (p<0.01) samples.

The non-synonymous variants (T341C, G590A, A803G and G857A) make a substantial contribution to the overall diversity, as their Theta(pi) is also always greater than Theta(s) in all Continents (Table 1, cols. 6 vs 9). These results did not change when samples were considered analytically, indicating that the non-synonymous variable sites reaching intermediate frequencies are in excess, reproducibly across populations (Supplementary Table S3).

Additionally, both the haplotype count (not shown) and haplotype diversity (Table 1) in Asian and European populations significantly exceeded those obtained from the coalescent simulations under neutral scenarios, assuming the rho value estimated from the HapMap data for the NAT2 genic region (0.24).

The partitioning of sequence diversity according to lineages marked by slow-causing mutations was very informative. The average number of nucleotide differences between pairs of sequences (formally identical to Theta(pi) per gene) in the pool of fast haplotypes was by far the highest (0.75). Conversely, it turned out to be much lower for the *5, *6 and *7 series (0.13, 0.06 and 0.24, respectively).

We also investigated the haplotype structure in the NAT2 region by calculating the EHH [22] on both sides of each slow-causing mutation (Supplementary Fig. S2a, top line). In all cases, the derived allele at positions 341, 590 and 857 showed higher EHH than the ancestral allele. This difference turned out not to be significant when tested against simulations under a standard neutral model.

In order to verify this finding using data from a larger genomic region, we repeated the same analysis using the HapMap Phase II data for more than 200 SNPs spanning approximately 100 kb centered on the NAT2 coding region [23]. The results showed higher EHH for haplotypes harbouring the derived (slow-causing) allele at positions 341, 590 and 857 in all HapMap populations on both sides of these positions (Supplementary Fig. S2b–d, top lines). Similar results were obtained also for the G191A in Yorubans (Supplementary Fig. S2b). Qualitatively, EHH profiles were remarkably similar between Yoruba and the other populations, more so for fast haplotypes. Taken at face value, this indicates that non-African populations did not suffer any major bottleneck.

The majority of the common variants are non-synonymous (5 vs. 2 synonymous) and are known to reduce NAT2 activity to a similar extent. Though this proportion is not statistically significant in the McDonald-Kreitman test (9 vs. 3 fixed differences between human and chimpanzee), we notice that this test does not take into account the frequency of each variant. Instead, our data indicate that considering allele and haplotype frequencies is crucially important.

Thus, we provisionally conclude that multiple slow acetylator mutations contribute an excess polymorphism to the NAT2 coding sequence. Even though neutral evolution cannot be unequivocally excluded, the departure of the observed frequency spectrum from the simulated neutral scenarios, coupled with the presence of multiple non-synonymous variants with well documented phenotypic effects, suggests that positive natural selection drove these variants to intermediate frequencies at some point during their evolutionary history. Our resequencing data do not allow us to distinguish between different selective scenarios (e.g. directional selection on multiple standing variants vs. balancing selection).

Genotyping data

The 6 common polymorphic variants identified in the resequencing panel were also genotyped in an additional set of 150 individuals (for a total of 248 individuals) (Supplementary Tables S1 and S2). Table 2 displays the structure of haplotypes as well as their population frequencies.

[Figure omitted. See PDF.]

Table 2. Relative haplotype frequencies in 12 population samples.

https://doi.org/10.1371/journal.pone.0003136.t002

Five of the 6 genotyped SNPs are polymorphic in all samples, whereas position 857 was polymorphic in all samples but the Dendi and the Czech.

Only two haplotypes are present in all population samples and both are derived, i.e. *5B and *6A. The first one exhibits frequencies above 18% in all the examined populations. The ancestral haplotype *4 is the third most common, with frequencies ranging between 0% and 38%.

Figure 1 and the Supplementary Table S1 list the phenotypes in each population sample divided into fast, intermediate and slow acetylators, inferred as described above. The results are in agreement with previous observations, by showing a belt of populations characterized by the high prevalence of slow and intermediate acetylators stretching from Eastern Africa to North-Western Europe. In contrast, a higher prevalence of fast acetylators is observed in the single Western African sample examined here and in the Northern Eurasian samples.

The genealogical relationships among haplotypes inferred by network analysis (Fig. 2), reveal three main features. First, the haplotypes are connected in a very condensed and highly reticulated network (Fig. 2, top). It is worth noting that the two most divergent haplotypes (*5B with 34T>C vs. *6A) differ by 6 substitutions, i.e. only 2.5 fold the average pairwise distance (2.4). When large, this measure is taken as an indicator of deep-rooted gene genealogies, possibly maintained by long-term balancing selection [24]. Second, the networks obtained for each separate population are very similar. Third, all networks show relatively common haplotypes at nodes that are two (for *6A) and three (for *5B) steps apart from the ancestral *4 haplotype, while haplotypes at intermediate nodes are present in most populations, but occur at lower frequencies. These results show that, even though we surveyed geographically diverse populations, we did not find populations harbouring common haplotypes that are ancestral to those most represented in contemporary populations, and argue for a recent increase in frequency of the terminal haplotypes. It is worth noting that, based on the network reconstruction, the three key mutations determining the slow acetylator haplotypes (T341C, G590A and G857A) tend to be more recent than mutations that do not modify the acetylator status.

[Figure omitted. See PDF.]

Figure 2. Median-joining networks of NAT2 haplotypes in the 12 populations of the extended panel.

The relationships between the 17 haplotypes found in the pool of the 12 populations are displayed by the network on top, with nodes of fixed size. Vectors representing each mutation are oriented as shown on top right (e.g. the vector 341 connects haplotypes which differ only at the position 341). Nodes representing fast and slow haplotypes are color-coded in black and grey, respectively. Networks specific for each population are displayed using the same skeleton and omitting only unnecessary links. Node size is proportional to the number of observations. Note that three main features of the overall network are necessary to explain the haplotypic composition of all but two populations (the left-hand “cube” leading to *5B and the right-hand “planes” leading to *7B and *6A), and that in most cases the median vectors of these structures are actually found. In all graphs the ancestral haplotype *4 is identified by a lozenge.

https://doi.org/10.1371/journal.pone.0003136.g002

Pairwise LD (Supplementary Table S4) diplays strong D' but quite low r2 values. In fact, no two variants are uniquely associated in a single haplotype (condition to have r2 = 1) and several combinations show the presence of all 4 haploid arrangements in 2×2 comparisons (e.g. 590G>A vs. 803A>G). This data are consistent with a relevant role of recombination (or other inter-chromosomal mechanisms) in reshuffling alleles at different but very closely spaced positions.

We then sought to further test the consistency of our data with a scenario of a small population exiting and expanding out of Africa, the emergence of new variants during this process and the increase of their frequencies up to intermediate values by neutral processes. We reasoned that, under this hypothesis, increasing levels of resolution (i.e. when considering phenotypes, haplotypes and haplotypes with their molecular distances) should lead to increasing values of the corresponding fixation index, due to the ongoing molecular radiation paralleling populations splits and dispersals. We then measured the intra- and inter-population quotas of genetic diversity by considering different properties of NAT2 haplotypes. When analysed individually, all of the 6 variable positions revealed low FST values, with only A857G reaching 0.04 (Supplementary Table S5). At the haplotypic level, when only two groups were considered, i.e. fast and slow haplotypes (Table 3, col. 3), we obtained the lowest values for both intra- and inter-population variances. The corresponding fixation index, FST, was 1.96%, indicating a fairly modest differentiation among the set of widely dispersed populations. When all 17 haplotypes were considered, without taking into account their actual molecular diversity (number of mutations differentiating them), larger variance components were obtained, as expected. Nevertheless the FST index was only slightly increased (Table 3, col. 4). Finally, when molecular distances were considered in the calculation (i.e. using ΦST in the AMOVA framework [25]), variance components reached the highest values, but the fixation index was again not increased (Table 3, col. 5). In the three conditions, the comparison between African and Eurasian populations contributed 2/3 to 1/3 of the overall inter-population diversity (not shown).

[Figure omitted. See PDF.]

Table 3. Measures of intra- and inter-population diversity by using different inter-haplotypic distances.

https://doi.org/10.1371/journal.pone.0003136.t003

Nutritional data

Previous studies [8] proposed a selection in favour of 341(T), which leads to a slow acetylator phenotype, as a consequence of the shift in modes of subsistence and lifestyle in human populations. Based on the results presented here and the similarity of the phenotype produced by slow-causing haplotypes, this hypothesis can be extended to all of the three slow variants. To test this hypothesis more directly, we assigned 47 populations to one of the major subsistence modes, and performed tests for the equality of haplotype frequencies across subsistence modes (Table 4).

[Figure omitted. See PDF.]

Table 4. Test of equality of frequency of 5 NAT2 haplotype series among three major modes of subsistence.

https://doi.org/10.1371/journal.pone.0003136.t004

The pool of fast haplotypes showed a strong decreasing trend in the order hunter-gatherers/pastoralists/agriculturalist. Frequency heterogeneity among the categories was significant by the non-parametric Kruskal Wallis test. Though haplotype frequencies violate the assumption of independence (due to their summing to 1), it is important to note that both *5 and *6 show a definite increase among agriculturalists, with a trend significantly departing from equality.

The vast geographic distance between populations with the same subsistence modes (especially H-G, Supplementary Figure S3) makes it unlikely that these results are due to spatial clustering and preferential gene flow. The evidence regarding the *7 haplotype, which shows the highest frequency among pastoralists, must be considered cautionarily, due to the overrepresentation of the closely spaced Central Asians.

Discussion

Unusual features of NAT2 polymorphism

Our resequencing survey of NAT2 variation in geographically dispersed human populations showed that several aspects of the data do not fit the expectations of a neutral model. This finding, coupled with the presence of multiple amino acid polymorphisms that are widely distributed and that are known to have similar phenotypic effects, suggests that these variants were advantageous and that they increased in frequency in parallel in different human populations.

Several aspects of sequence variation data at the NAT2 gene are unusual when compared to neutral expectations for demographic models that were previously shown to fit genome-wide patterns of variation. For example, while Sub-Saharan African populations generally tend to have a genome-wide excess of rare variants which probably reflects a history of recent growth, the frequency spectrum at NAT2 shows a marked excess of intermediate frequency variants (positive Tajima's D), which departed significantly from a neutral model of a population at equilibrium followed by rapid exponential growth. A similar excess of intermediate frequency variants was observed in non-African populations, which tend to have positive Tajima's D values at the genome-wide level; yet, the Tajima's D value at NAT2 is unusual in the European and Asian samples (though only marginally significant in the latter) when compared to neutral expectations for two different bottleneck models. Significantly positive figures of Tajima's D underline the excess of high frequency polymorphisms and the paucity of singletons. As it is not computationally feasible to test all the space of possible demographic scenarios, this result may still be compatible with the hypothesis of a purely demographic affect, i.e. a bottleneck experienced by all of the populations here scored.

Moreover, our results depict a pattern of variation at NAT2 very discrepant from the average of 313 and 132 genes scored by Stephens et al. [26] and Akey et al. [27], respectively. The density of SNPs/kb is large (8.03 vs. 3.4), with a Theta(s) value of the same magnitude but a very different Theta(pi) (0.273% vs. 0.034%). Based on Theta(pi), NAT2 ranks among the most polymorphic genes as compared to other drug metabolizing genes [28]. Generalized and strongly enhanced mutation rates at NAT2 can be excluded as Theta(s), which is an estimator of 4Neμ (where Ne is the population effective size and μ the mutation rate), falls into the range reported by Stephens et al. [26] for all genic regions. However, our results cannot rule out site-specific mutation proneness.

Also, there is a preferential polymorphism at non-synonymous sites. Six variants have intermediate frequencies above 5% worldwide, four of which are non-synonymous (non-synonymous/synonymous ratio = 2), contrasting with the findings by Wong et al. [29] for SNPs with Minor Allele Frequency (MAF) above 6.1% (non-synonymous/synonymous ratio ∼0.5). The percentage of synonymous substitutions is only two thirds of that reported by Stephens et al. [26](2/7 not conditioned on frequency vs. 459/1033). We indeed measured the dN/dS ratio in a tripartite tree including human, chimp and macaque sequences (rodents as outgroup) and found this ratio largely increased in NAT2 (0.71) as compared to NAT1 (0.28). As a minimal interpretation, this is in agreement with a long-lasting relaxation of selection on NAT2. However, the observation that 3 out of the 4 human polymorphic non-synonymous substitutions are also responsible for NAT2 slow acetylation activity (slow-causing variants) support the hypothesis that this could be an adaptive trait subjected to some form of selection in the recent past or even today. Network analysis turned out to be a powerful tool to indicate that haplotypes *5B, *6A and *7B stem out from low-frequency haplotypes radiating from the ancestral *4, which is still today an intermediate frequency haplotype. This is a pattern representing an obvious departure from the shape expected under neutrality and is responsible for the excess of positive Tajima's D values and the maintenance of longer EHH on slow haplotypes. These features can be hardly perceived if examining only numerical data such as linkage disequilibrium (LD) estimators (Supplementary Table S4).

Finally, low fixation indexes over a transept crossing the Old World, place NAT2 at one edge of the genome-wide distribution [30]. Our results can be summarized and interpreted as follows: 1) the NAT2 coding region is poorly differentiated in the population samples examined (absolute values of the fixation indexes); 2) the major determinant of inter-population diversity are the phenotypic proportions (the first fixation index value in Table 3 is not lower than the others); 3) population dispersals were not accompanied by a concomitant accumulation of molecular diversity (similarity between the 2nd and 3rd fixation index); 4) the data fit the distribution obtained by Currat et al. [31] for neutral alleles already attaining polymorphic frequencies at the time of exit out of Africa [32].

From a phylogeographic perspective, the consistency of the networks across different populations means that all the major haplotypes had originated prior to the differentiation of the study populations. However, the out-of-Africa bottleneck predicts lower diversity levels in non-African compared to African populations, and this is not observed at the NAT2 locus. There is no marked tendency of any of the derived NAT2 haplotypes to be continent-specific. In addition, our data display an enhanced frequency of the ancestral *4 haplotype out of Africa, a feature exhibited by only a subset of genes reported in the literature [for reviews see 33], [34].

This indicates that heterogeneity of NAT2 haplotype frequencies cannot be appropriately predicted by geography alone. Thus, a simple model of variation pre-existent within Africa, spreading out of Africa and evolving by neutral drift is unsatisfactory. Rather, the finding of an independent categorizing variable (subsistence style), which is able to unveil frequency heterogeneity also on a restricted spatial scale [11], [12], and contrasting EHH patterns [8], suggest that NAT2 variation was shaped by environmental features.

Deciphering a selective scenario

Several features of the data generated by us and by others [8], [10]–[12] suggest that selection acted on multiple slow-causing variants. There are two main models of selection that may generate patterns of variation similar to those observed at the NAT2 gene: balancing selection and directional selection on multiple standing variants.

A markedly positive Tajima's D value, as observed at the NAT2 gene, is expected under balancing selection [35], [36], which can act in the form of heterozygote advantage, frequency-dependent selection or changes in the selective regime over time and/or space. This pattern in the frequency spectrum is a function of the equilibrium frequency of the balanced polymorphism and may be observed in the case of a bi-allelic or a multi-allelic polymorphism [37], [38]. An additional feature of balanced polymorphisms is an excess of diversity, but this is expected only in the case of long-standing selection, i.e. longer than 4Ne generations. Therefore, the marked positive Tajima's D value at NAT2 may be explained by a model of relatively recent balancing selection on NAT2 non-synonymous variation. The data cannot distinguish between a recent onset and a recent increase in the strength of balancing selection. In either case, the presence of multiple of non-synonymous variants with well documented phenotypic effects suggests selection maintained multiple alleles.

Alternatively, the findings at NAT2 may be explained by a model of directional selection acting on multiple standing variants, i.e. variants that segregated in the population prior to the onset of selection; these variants may have been completely neutral or slightly deleterious before they became advantageous. Due to the rapid environmental changes occurred during human evolution, a number of investigators have postulated that selection on standing variation (rather than selection on a new beneficial allele) played a major role in human adaptations, thus affording a more rapid adaptive response to the environmental change. A variety of scenarios of directional selection on standing variation have been modelled to determine the expected signature of selection (for reviews see [35], [39], [40]). These models may prove particularly useful to understand the pattern of variation at NAT2. Specifically, Pennings and Hermisson [41] showed that Tajima's D may take markedly positive values, as we observe in the NAT2 coding region, when directional selection acts on multiple existing variants. If multiple variants segregating in a population are concurrently driven to intermediate frequency by positive selection, the variation tightly linked to these variants will also tend to occur at intermediate frequencies, thus generating a skew in the frequency spectrum. In addition, high EHH around the putative advantageous variants may also be observed, though the power of this test for such a selection scenario has not been investigated.

The worldwide pattern of variation at NAT2 individual SNPs and haplotypes is barely compatible with molecular radiation after population dispersals out of Africa, because it would require later abundant gene flow back into Africa. Conversely, these findings can be easily explained by the presence of NAT2 polymorphism prior to the exit out of Africa. Within each of the allelic series the repertoire of NAT2 haplotypes fit the expectations worked out theoretically by Przeworski et al. [42] for a scenario of selection beginning to act on accumulated neutral variation. In this case, many recombination events occur when the allele(s) that will become selected sojourn(s) in the population as neutral variant(s).

However, in one aspect the models proposed by Pennings and Hermisson [41] and Przeworski et al. [42] do not fit the NAT2 case. In fact they assumed that the beneficial alleles are fixed in the population, thus focusing on a linked neutral locus. Instead, based on biochemical data on the effect of mutations on NAT2 acetylator activity, it is plausible that in the ancestral genetic background, all slow-causing mutations had an equivalent effect on phenotype and fitness. If two or more of these mutations were picked up by selection and simultaneously started to increase in frequency, at some point they started to interfere with each other, thus preventing the fixation of any allele and the sweep itself.

Under such a complex scenario current methods to detect the signature(s) of selection have little power. This may explain the failure of four genome-wide scans in detecting selection at NAT2. In the study by Bustamante et al. [43] NAT2 did not emerge and no genes were reported under positive selection in the GO categories to which it belongs (MF = acyltransferase; BP = other metabolism). No indicator of selection was significant in the study by Voight et al. [44], with peaks of marginal significance only at PSD3, a gene located 200 kb 3′ to NAT2. In the analysis by Wang et al. [45], which is based on data largely overlapping with the previous work, NAT2 falls in a chromosomal region devoid of signals. Finally, no evidence for selection in the NAT2 region was detected in HapMap phase II data [23].

In conclusion, our data favour the hypothesis that a selective pressure drove at least three slow-causing variants to the frequencies observed today, but do not allow us to discriminate between balancing selection and directional selection on multiple standing variants. While some aspects are in line with balancing selection, parallel directional positive selection favouring all slow causing mutations cannot be ruled out, as was first proposed by Patin et al. [8] when considering the *5 series alone.

A testable model for a putative selective factor

Our model of selection acting on the *5, *6 and *7 series is supported by the presence of all these haplotypes in widely dispersed populations as contrasted with the strong heterogeneity of their frequencies according to the subsistence style. As populations were sampled in geographically distant locations (see Supplementary Figure S3), co-inheritance of both subsistence style and composition of the gene pool due to shared ancestry is unlikely. Thus, some degree of selection depending on the main food source, or some yet unidentified covariate of it, is the most parsimonious explanation [6 p. 8], [44].

It is to be noted that the two competing selection models mentioned above both imply that the fast-acetylator phenotype began to suffer a selective disadvantage. This is straightforward in the case of positive selection but has to be assumed also in the case of balancing selection, as the frequency of the pooled slow-causing variants is now well above 50% worldwide.

Several studies have pointed out a possible role of NAT enzymes in the catabolism of folates, possibly via the acetylation of p-ABGlu [for reviews see 46], [47]. However, the specific question of the relative roles of enzymes encoded by NAT1 and NAT2 in maintaining the overall folate balance in humans has not been directly addressed. Kawamura et al. [3] showed that the affinity of both human NAT1 and NAT2 for p-ABGlu is reduced to a similar extent as compared to each enzyme's best substrate (PABA and hydralazine, respectively). The fact that the liver is the main organ where folate is stored and is also the main site of NAT2 expression, suggests a non negligible role of NAT2 as compared to NAT1.

Different observations indicate possible overlapping effects of folate levels and NATs activity. The overexpression of the orthologue of human NAT1 in mouse embryos causes developmental abnormalities which are reminiscent of spina bifida-like phenotype [46], [48]. Accordingly, reduced NAT1 activity in humans has been related to a reduced risk of spina bifida [49], a condition well known to be associated with low levels of folates in pregnant mothers.

It is likely that populations shifting from hunting-gathering (H-G) to agriculture became exposed to drastic fluctuations in the supply of folates and to long periods of deprivation. Folates are an essential constituent of the diet, abundant in green leaf vegetables and animal liver, i.e. regular components in the diet of H-G in the terrestrial and marine environment, and in many pastoralist cultures (e.g. the Yakuts). On the contrary, cereals and grains are poor sources of folates, which are further degraded upon storage and cooking [50]. The clinical effects of such a shift, on a short time frame, have been verified in a follow-up study of a !Kung San population [51].

Based on the above elements and the relevance of folates in the etiology of neural tube defects (e.g. OMIM #601634) and in a number of functions related to successful reproduction for reviews see [52], [53], we propose the following model for the increase in frequency of multiple NAT2 slow haplotypes under changing dietary regimens:

1. i) A fast acetylating phenotype is neutral (or even advantageous) only in the presence of a folate-rich diet, a situation fulfilled among H-G;

2. ii) Conversely, when the supply of folates becomes limiting (as might be in the case of a nutritional shift to agriculture), the fitness of fast acetylators drops, due to increased folate loss;

3. iii) In the same conditions, the slow (or possibly the intermediate) acetylator phenotype, by favouring folate retention, reduce the load caused by fetal loss, birth defects and sub-optimal maternal fertility. Any slow-causing haplotype thus acquires a fitness higher than the fast ones.

The above model relies on the assumption that NAT2 brings an extra contribution to the overall rate of folate catabolism, which is otherwise mainly determined by NAT1. This assumption can be experimentally tested biochemically, in vitro, and by correlating NATs phenotypes and folate levels in vivo. A necessary corollary is that, while NAT1 activity is strictly physiologically constrained, variation at NAT2 determines whether the overall rate of folate catabolism becomes critical in the presence of reduced folate supply.

The model easily allows for a small selective advantage of all multiple slow-causing NAT2 allelic variants, which may explain the growing phase in their frequency trajectory, independently of the underlying biochemical mechanism. The observation that this mechanism often is reduced protein stability [54] leads to the prediction that activity is equally reduced for all substrates, including folate catabolites.

Also, the putative reduction of fitness of fast NAT2 variants in the presence of low folate supply predicted by our model, applies to a condition that impacts directly on the reproductive success.

On a microgeographic scale, the frequency of NAT2 slow variants is not easily predictable based on the subsistence style. This is because even populations whose main caloric source are cereals and grains may have historically complemented their diets with valuable sources of folates. This might be the case for Eastern Asian populations, in which the highest frequencies of *4 are observed, despite a rice-based diet (rice being among the poorest sources of folate). For example, in this case, consumption of brown rather than white rice and/or large amounts of fish (as in the Japanese population) may have re-established adequate amounts of folates. Additional aspects to be considered are food diversity, regularity in feeding (hypocaloric stresses being more severe in H-G) and cooking habits (temperature). Finally, further dietary heterogeneity within the three broad categories might be relevant. For example Perry et al. [55] showed that selection pressure on the amylase gene number was related to the dietary input of starch, which is known to vary greatly among H-G's of the arid, rainforest and circum-arctic environments.

In our model, ancestral haplotypes (mainly *4 but also *11, *12 and *13) were compatible with pre-agricultural environmental conditions but begun to determine sub-optimal fitnesses as humans changed lifestyles with or without dispersing into new environmental niches. From this point of view they can be considered bona fide ancestral susceptibility alleles [56].

In conclusion, we propose that the present NAT2 diversity in human populations is the result of three distinct processes: i) presence of variation for slow-causing sites in widely dispersed populations (possibly as neutral variation) before major shifts to pastoralism and/or agriculture as the main modes of food production; ii) independent emergence of selective advantage for multiple slow-causing mutations in populations shifting from H-G to pastoralism/agriculture; iii) further introgression of slow-causing variants into populations anchored to H-G by later gene flow.

Materials and Methods

The subjects

Samples from Nigeria (Dendi, coded WAF in Fig. 1), Ethiopia (Amhara [AMH] and Oromo [ORO]), Egypt [EGY], Italy [ITA], Czech Republic [CZK] and Greece [GRE] were previously described [57]–[60], whereas the remaining population samples (i.e. Mordovians [MOE], Perm Russians [PER], Khanti and Mansi [K&M], Yakut [YKL], Chukchee [CHK]) are described here for the first time. All samples were collected personally by one or more of the authors during field campaigns, as consecutive series of unrelated subjects. Field work included the assessment of subsistence style for each population sample. The subjects to be collected were randomly chosen, without selecting for their clinical status or the presence/absence of digestive disturbances.

DNA was extracted from either buccal swab or dried blood adsorbed on paper, with commercially available kits (NucleoSpin, Macherey-Nagel GmbH, Duren, Germany) according to the manufacturer's instructions. Khanty and Mansi subjects were lumped in a single sample. All samples were collected upon written or oral informed consent. This study was approved by the University of Calabria intramural Ethical Committee.

All populations studied here and 35 additional ones from published NAT2 surveys were classified into the three main subsistence style categories: hunter-gatherers (H-G), pastoralists and agriculturalists as defined in Bromley [61] and references therein or in the original reference (see Supplementary Tables S1 and S6).

Nomenclature

In contrast with the pharmacogenetic literature, in which the term allele is used to indicate protein variants and corresponding DNA sequences regardless of the number of mutations, we use the term allele to indicate the variant at each nucleotide position and the term haplotype to indicate the overall sequence. Also, note that in the pharmacogenetic literature, protein variants are grouped into series, each of which is characterized by the presence of a specific DNA substitution (for details see http://www.louisville.edu/medschool/pharmacology/NAT.html). We refer to them by omitting the prefix “NAT2” (e.g. *4 instead of NAT2*4). Moreover, the pool of series *4, *11, *12 and *13 is referred to as fast acetylator haplotypes, whereas the pool of the remaining series is referred to as slow acetylator haplotypes [7]. Nucleotide positions are numbered according to the coding sequence (Acc. no. D90042.1).

The orthologous NAT2 sequences for the common chimpanzee (Pan troglodytes) and the rhesus macaque (Macaca mulatta) were obtained by BLAST using the human coding sequence. These sequences were unambiguously aligned and compared to the human sequence to determine the ancestral state at each polymorphic position.

Re-sequencing

DNA from all subjects was subjected to a two-step nested PCR protocol to isolate NAT2 from the paralogous NAT genes [8].

Resequencing was performed on the PCR product with standard protocols for ABI310 automatic Sequencer. Electropherograms were aligned with the reference sequence and visually inspected. Ambiguous polymorphisms were confirmed/dismissed by sequencing the opposite strand. The single instance of a novel mutation passing this step, i.e. 34T>C, was further confirmed by restriction analysis as it determines the gain of a HaeIII site.

SNP genotyping

The following genotyping protocol was adopted: C282T (rs1041983), C481T (rs1799929), G590A (rs1799930), A803G (rs1208) and G857A (rs1799931) were typed according to a restriction fragment length analysis protocol modified from Deitz et al. [62], on the nested PCR product. T341C (rs1801280) was assayed by an allele-specific PCR protocol modified from Bakayev et al. [63]. Since in compound heterozygotes the products obtained with this allele-specific PCR contain markers in a haploid form, the same assay was also used to determine the phase of specific variants with respect to position 341 by either digestion with restriction enzyme (Supplementary Fig. S1) or re-sequencing.

African samples were also genotyped at the G191A variant (rs1801279), which was previously found only in African populations [8], [14]. This was done by digesting the nested PCR product with HapII in all carriers of haplotypes with the ancestral state at all the positions 341, 590 and 857.

Data analysis

Haplotypes were inferred in each population sample using the program PHASE (ver. 2) [64]. Genotypes whose phase was reconstructed with posterior probability <0.70, or corresponding to haplotypes never described before (http://www.louisville.edu/medschool/pharmacology/NAT.html), were subjected to experimental validation of the inferred phase, as described above.

The acetylator phenotype for each individual was inferred by assuming that the homozygous or compound heterozygous genotype for two haplotypes of the series *4, *11, *12 or *13 results in the fast acetylator status, the occurrence of one of these haplotypes in combination with a haplotype of the series *5, *6 or *7 results in the intermediate acetylator status and the occurrence of two haplotypes of the series *5, *6 or *7 results in the slow acetylator phenotype [7], [14], [65].

EHH was calculated as described [22] using the program Sweep (http://www.broad.mit.edu/mpg/sweep/index.htm). Core haplotypes were defined for each SNP known to affect NAT2 activity. The EHH for each core haplotype carrying the derived (slow-acetylator) allele was compared to: 1) the EHH for all the remaining haplotypes; and 2) the EHH for the subset of haplotypes carrying the ancestral (fast acetylator) allele at all of the SNPs known to affect NAT2 activity. This analysis was performed on all the data collected in our population samples as well as in the HapMap Phase II data [23] for a region of 100 kb surrounding NAT2 (chromosome 8 positions 18,250,000 to 18,350,000).

Median joining networks were constructed using the program NETWORK, ver. 4.1.1.2 [66]. The DNAsp package [67] was used to calculate summary statistics of polymorphism data (Theta(pi), Theta(s), haplotype diversity, Tajima's D and Fay & Wu's H) [21], [68], and to perform the McDonald and Kreitman [69] test.

Measures of intra- and inter-population diversity were obtained using the Arlequin 3.01 package [70]. When haplotypes were considered, the fixation indexes were computed with and without information about inter-haplotype molecular distances.

The equality of frequencies across subsistence styles was tested by the non-parametric Kruskal Wallis test as implemented in SPSS.

For all tests of neutrality, coalescent neutral simulations were run under different demographic models to assess the significance of the resequencing data. For all continental samples (i.e. Africa, Europe, Asia), 10,000 replicates were generated using the program MS [71], under three demographic models: i) an exponential growth model with exponential growth rate of 0.183, typical for the African population; ii) bottleneck model A, with a bottleneck of severity 0.1 starting 1200 generations before present and lasting 400 generations, typical for the European population; iii) bottleneck model B, with a bottleneck of severity 0.3 starting 1200 generations before present and lasting 400 generations, typical for the Asian population [72]. The numbers of segregating sites were fixed at the values obtained from the re-sequencing data, the population recombination rate parameter rho ( = 4Nr, where N is the population effective size and r the recombination rate between adjacent sites per generation) was assumed to be 0.24, based on published estimates [73] using the HapMap data for the genomic segment spanning the NAT2 gene.

Supporting Information

[Figure omitted. See PDF.]

Figure S1.

Typing and phasing of mutations by means of allele-specific PCR. Panel A: PCR specific for alleles at pos. 341. Odd lanes: products specific for allele T. Even lanes: products specific for allele C. Lanes 1–2 and 7–8: results in subjects T/C; lanes 3–4: results in a subject T/T; lanes 5–6: results in a subject C/C. Panel B: Phasing of A803G with respect to T341C in a double heterozygote. PCR products as in panel A (lanes 1–2) were digested with DdeI. Lane 1: Molecular weight marker; lane 2: The product specific for 341(T) [panel A, lane 1] shows the lack of a DdeI site [803(A)], denoted by the 450 bp fragment; lane 3: The product specific for 341(C) [panel A, lane 2] shows the presence of a DdeI site [803(G)], denoted by the 423 bp fragment.

https://doi.org/10.1371/journal.pone.0003136.s001

(2.09 MB TIF)

Figure S2.

Extended Haplotype Homozygosity (EHH, y axes) vs. physical distance (x axes) for haplotypes carrying mutations which determine the slow acetylator status. The mutations considered are shown on top and are given reference position 0 (vertical bar). For each entry, two plots are shown: in the first one (top) EHH on haplotypes carrying the derived (slow-causing) allele is shown in grey and is compared to EHH on all the remaining haplotypes (in black); in the second one (bottom) the same EHH as above (grey) is compared to the EHH on the subset of fast haplotypes (i.e. carrying the ancestral state at slow-causing positions other than the one assayed) (in black). Note that grey profiles are identical within plot pairs. a) pool of NAT2 haplotypes analysed in the present study (physical distance covering the NAT2 coding region, in bp); b) Yorubans from the HapMap database ; c) Caucasians from the HapMap database; d) Japanese and Chinese from the HapMap database. In b,c,d physical distance covers chromosome 8 positions 18,250,000–18,350,000, in kb from NAT2 5′ end.

https://doi.org/10.1371/journal.pone.0003136.s002

(0.25 MB TIF)

Figure S3.

Map showing the location of 47 populations for which the frequencies of NAT2 haplotype series have been analysed as a function of subsistence style. Populations are coded as in Supplementary Table S6.

https://doi.org/10.1371/journal.pone.0003136.s003

(0.13 MB TIF)

Table S1.

https://doi.org/10.1371/journal.pone.0003136.s004

(0.05 MB DOC)

Table S2.

https://doi.org/10.1371/journal.pone.0003136.s005

(1.03 MB DOC)

Table S3.

https://doi.org/10.1371/journal.pone.0003136.s006

(0.04 MB DOC)

Table S4.

https://doi.org/10.1371/journal.pone.0003136.s007

(0.04 MB DOC)

Table S5.

https://doi.org/10.1371/journal.pone.0003136.s008

(0.03 MB DOC)

Table S6.

https://doi.org/10.1371/journal.pone.0003136.s009

(0.08 MB DOC)

Acknowledgments

We are grateful to Drs. A. Di Rienzo and J. Novembre for their advice on a first draft of this manuscript.

Author Contributions

Conceived and designed the experiments: FL LQM AK AN. Performed the experiments: FL GB. Analyzed the data: FL MB AN. Wrote the paper: FL RB EM OR GV LQM AK AN. Senior author: AK. Collected DNA samples and nutritional information: AK GV OR EM RB AN.

Citation: Luca F, Bubba G, Basile M, Brdicka R, Michalodimitrakis E, Rickards O, et al. (2008) Multiple Advantageous Amino Acid Variants in the NAT2 Gene in Human Populations. PLoS ONE3(9): e3136. https://doi.org/10.1371/journal.pone.0003136

References

1. Weber WW, Hein DW (1985) N-acetylation pharmacogenetics. Pharmacol Rev 37: 25–79.WW WeberDW Hein1985N-acetylation pharmacogenetics.Pharmacol Rev372579

2. Husain A, Zhang X, Doll MA, States JC, Barker DF, et al. (2007) Identification of N-acetyltransferase 2 (NAT2) transcription start sites and quantitation of NAT2-specific mRNA in human tissues. Drug Metab Dispos 35: 721–727.A. HusainX. ZhangMA DollJC StatesDF Barker2007Identification of N-acetyltransferase 2 (NAT2) transcription start sites and quantitation of NAT2-specific mRNA in human tissues.Drug Metab Dispos35721727

3. Kawamura A, Graham J, Mushtaq A, Tsiftsoglou SA, Vath GM, et al. (2005) Eukaryotic arylamine N-acetyltransferase. Investigation of substrate specificity by high-throughput screening. Biochem Pharmacol 69: 347–359.A. KawamuraJ. GrahamA. MushtaqSA TsiftsoglouGM Vath2005Eukaryotic arylamine N-acetyltransferase. Investigation of substrate specificity by high-throughput screening.Biochem Pharmacol69347359

4. Minchin RF (1995) Acetylation of p-aminobenzoylglutamate, a folic acid catabolite, by recombinant human arylamine N-acetyltransferase and U937 cells. Biochem J 307(Pt 1): 1–3.RF Minchin1995Acetylation of p-aminobenzoylglutamate, a folic acid catabolite, by recombinant human arylamine N-acetyltransferase and U937 cells.Biochem J307(Pt 1)13

5. Sim E, Payton M, Noble M, Minchin R (2000) An update on genetic, structural and functional studies of arylamine N-acetyltransferases in eucaryotes and procaryotes. Hum Mol Genet 9: 2435–2441.E. SimM. PaytonM. NobleR. Minchin2000An update on genetic, structural and functional studies of arylamine N-acetyltransferases in eucaryotes and procaryotes.Hum Mol Genet924352441

6. Weber WW (1987) The acetylator genes and drug response. Oxford: Oxford University Press. WW Weber1987The acetylator genes and drug responseOxfordOxford University Press

7. Hein DW (2002) Molecular genetics and function of NAT1 and NAT2: role in aromatic amine metabolism and carcinogenesis. Mutat Res 506–507: 65–77.DW Hein2002Molecular genetics and function of NAT1 and NAT2: role in aromatic amine metabolism and carcinogenesis.Mutat Res506–5076577

8. Patin E, Barreiro LB, Sabeti PC, Austerlitz F, Luca F, et al. (2006) Deciphering the ancient and complex evolutionary history of human arylamine N-acetyltransferase genes. Am J Hum Genet 78: 423–436.E. PatinLB BarreiroPC SabetiF. AusterlitzF. Luca2006Deciphering the ancient and complex evolutionary history of human arylamine N-acetyltransferase genes.Am J Hum Genet78423436

9. Wilson JF, Weale ME, Smith AC, Gratrix F, Fletcher B, et al. (2001) Population genetic structure of variable drug response. Nat Genet 29: 265–269.JF WilsonME WealeAC SmithF. GratrixB. Fletcher2001Population genetic structure of variable drug response.Nat Genet29265269

10. Fuselli S, Gilman RH, Chanock SJ, Bonatto SL, De Stefano G, et al. (2007) Analysis of nucleotide diversity of NAT2 coding region reveals homogeneity across Native American populations and high intra-population diversity. Pharmacogenomics J 7: 144–152.S. FuselliRH GilmanSJ ChanockSL BonattoG. De Stefano2007Analysis of nucleotide diversity of NAT2 coding region reveals homogeneity across Native American populations and high intra-population diversity.Pharmacogenomics J7144152

11. Magalon H, Patin E, Austerlitz F, Hegay T, Aldashev A, et al. (2008) Population genetic diversity of the NAT2 gene supports a role of acetylation in human adaptation to farming in Central Asia. Eur J Hum Genet 16: 243–251.H. MagalonE. PatinF. AusterlitzT. HegayA. Aldashev2008Population genetic diversity of the NAT2 gene supports a role of acetylation in human adaptation to farming in Central Asia.Eur J Hum Genet16243251

12. Patin E, Harmant C, Kidd KK, Kidd J, Froment A, et al. (2006) Sub-Saharan African coding sequence variation and haplotype diversity at the NAT2 gene. Hum Mutat 27: 720.E. PatinC. HarmantKK KiddJ. KiddA. Froment2006Sub-Saharan African coding sequence variation and haplotype diversity at the NAT2 gene.Hum Mutat27720

13. WHO (2002) Globalization, diets and noncommunicable diseases. Geneva, Switzerland.WHO2002Globalization, diets and noncommunicable diseasesGeneva, Switzerland

14. Hein DW (2006) N-acetyltransferase 2 genetic polymorphism: effects of carcinogen and haplotype on urinary bladder cancer risk. Oncogene 25: 1649–1658.DW Hein2006N-acetyltransferase 2 genetic polymorphism: effects of carcinogen and haplotype on urinary bladder cancer risk.Oncogene2516491658

15. Clark AG, Nielsen R, Signorovitch J, Matise TC, Glanowski S, et al. (2003) Linkage disequilibrium and inference of ancestral recombination in 538 single-nucleotide polymorphism clusters across the human genome. Am J Hum Genet 73: 285–300.AG ClarkR. NielsenJ. SignorovitchTC MatiseS. Glanowski2003Linkage disequilibrium and inference of ancestral recombination in 538 single-nucleotide polymorphism clusters across the human genome.Am J Hum Genet73285300

16. Clark AG, Hubisz MJ, Bustamante CD, Williamson SH, Nielsen R (2005) Ascertainment bias in studies of human genome-wide polymorphism. Genome Res 15: 1496–1502.AG ClarkMJ HubiszCD BustamanteSH WilliamsonR. Nielsen2005Ascertainment bias in studies of human genome-wide polymorphism.Genome Res1514961502

17. Wakeley J, Nielsen R, Liu-Cordero SN, Ardlie K (2001) The discovery of single-nucleotide polymorphisms and inferences about human demographic history. Am J Hum Genet 69: 1332–1347.J. WakeleyR. NielsenSN Liu-CorderoK. Ardlie2001The discovery of single-nucleotide polymorphisms and inferences about human demographic history.Am J Hum Genet6913321347

18. Nielsen R, Signorovitch J (2003) Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium. Theor Popul Biol 63: 245–255.R. NielsenJ. Signorovitch2003Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium.Theor Popul Biol63245255

19. Nielsen R, Hubisz MJ, Clark AG (2004) Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics 168: 2373–2382.R. NielsenMJ HubiszAG Clark2004Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data.Genetics16823732382

20. Nielsen R (2004) Population genetic analysis of ascertained SNP data. Hum Genomics 1: 218–224.R. Nielsen2004Population genetic analysis of ascertained SNP data.Hum Genomics1218224

21. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595.F. Tajima1989Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.Genetics123585595

22. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, et al. (2002) Detecting recent positive selection in the human genome from haplotype structure. Nature 419: 832–837.PC SabetiDE ReichJM HigginsHZ LevineDJ Richter2002Detecting recent positive selection in the human genome from haplotype structure.Nature419832837

23. International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.International HapMap Consortium2007A second generation human haplotype map of over 3.1 million SNPs.Nature449851861

24. Bubb KL, Bovee D, Buckley D, Haugen E, Kibukawa M, et al. (2006) Scan of human genome reveals no new loci under ancient balancing selection. Genetics 173: 2165–2177.KL BubbD. BoveeD. BuckleyE. HaugenM. Kibukawa2006Scan of human genome reveals no new loci under ancient balancing selection.Genetics17321652177

25. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479–491.L. ExcoffierPE SmouseJM Quattro1992Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data.Genetics131479491

26. Stephens JC, Schneider JA, Tanguay DA, Choi J, Acharya T, et al. (2001) Haplotype variation and linkage disequilibrium in 313 human genes. Science 293: 489–493.JC StephensJA SchneiderDA TanguayJ. ChoiT. Acharya2001Haplotype variation and linkage disequilibrium in 313 human genes.Science293489493

27. Akey JM, Eberle MA, Rieder MJ, Carlson CS, Shriver MD, et al. (2004) Population history and natural selection shape patterns of genetic variation in 132 genes. PLoS Biol 2: e286.JM AkeyMA EberleMJ RiederCS CarlsonMD Shriver2004Population history and natural selection shape patterns of genetic variation in 132 genes.PLoS Biol2e286

28. Solus JF, Arietta BJ, Harris JR, Sexton DP, Steward JQ, et al. (2004) Genetic variation in eleven phase I drug metabolism genes in an ethnically diverse population. Pharmacogenomics 5: 895–931.JF SolusBJ AriettaJR HarrisDP SextonJQ Steward2004Genetic variation in eleven phase I drug metabolism genes in an ethnically diverse population.Pharmacogenomics5895931

29. Wong GK, Yang Z, Passey DA, Kibukawa M, Paddock M, et al. (2003) A population threshold for functional polymorphisms. Genome Res 13: 1873–1879.GK WongZ. YangDA PasseyM. KibukawaM. Paddock2003A population threshold for functional polymorphisms.Genome Res1318731879

30. International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437: 1299–1320.International HapMap Consortium2005A haplotype map of the human genome.Nature43712991320

31. Currat M, Excoffier L, Maddison W, Otto SP, Ray N, et al. (2006) Comment on “Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens” and “Microcephalin, a gene regulating brain size, continues to evolve adaptively in humans”. Science 313: 172; author reply 172.M. CurratL. ExcoffierW. MaddisonSP OttoN. Ray2006Comment on “Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens” and “Microcephalin, a gene regulating brain size, continues to evolve adaptively in humans”.Science313172; author reply 172

32. Liu H, Prugnolle F, Manica A, Balloux F (2006) A geographically explicit genetic model of worldwide human-settlement history. Am J Hum Genet 79: 230–237.H. LiuF. PrugnolleA. ManicaF. Balloux2006A geographically explicit genetic model of worldwide human-settlement history.Am J Hum Genet79230237

33. Garrigan D, Hammer MF (2006) Reconstructing human origins in the genomic era. Nat Rev Genet 7: 669–680.D. GarriganMF Hammer2006Reconstructing human origins in the genomic era.Nat Rev Genet7669680

34. Harding RM, McVean G (2004) A structured ancestral population for the evolution of modern humans. Curr Opin Genet Dev 14: 667–674.RM HardingG. McVean2004A structured ancestral population for the evolution of modern humans.Curr Opin Genet Dev14667674

35. Bamshad M, Wooding SP (2003) Signatures of natural selection in the human genome. Nat Rev Genet 4: 99–111.M. BamshadSP Wooding2003Signatures of natural selection in the human genome.Nat Rev Genet499111

36. Charlesworth D (2006) Balancing selecton and its effects on sequences in nearby genome regions. PLoS Genet 2: e64.D. Charlesworth2006Balancing selecton and its effects on sequences in nearby genome regions.PLoS Genet2e64

37. Schierup MH, Mikkelsen AM, Hein J (2001) Recombination, balancing selection and phylogenies in MHC and self-incompatibility genes Genetics 159: 1833–1844.MH SchierupAM MikkelsenJ. Hein2001Recombination, balancing selection and phylogenies in MHC and self-incompatibility genes Genetics15918331844

38. Vander Molen J, Frisse LM, Fullerton SM, Qian Y, del Bosque-Plata L, et al. (2005) Population genetics of CAPN10 and GPR35: implications for the evolution of type 2 diabetes variants. Am J Hum Genet 76: 548–560.J. Vander MolenLM FrisseSM FullertonY. QianL. del Bosque-Plata2005Population genetics of CAPN10 and GPR35: implications for the evolution of type 2 diabetes variants.Am J Hum Genet76548560

39. Biswas S, Akey JM (2006) Genomic insights into positive selection. Trends Genet 22: 437–446.S. BiswasJM Akey2006Genomic insights into positive selection.Trends Genet22437446

40. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, et al. (2006) Positive natural selection in the human lineage. Science 312: 1614–1620.PC SabetiSF SchaffnerB. FryJ. LohmuellerP. Varilly2006Positive natural selection in the human lineage.Science31216141620

41. Pennings PS, Hermisson J (2006) Soft sweeps III: the signature of positive selection from recurrent mutation. PLoS Genet 2: e186.PS PenningsJ. Hermisson2006Soft sweeps III: the signature of positive selection from recurrent mutation.PLoS Genet2e186

42. Przeworski M, Coop G, Wall JD (2005) The signature of positive selection on standing genetic variation. Evolution 59: 2312–2323.M. PrzeworskiG. CoopJD Wall2005The signature of positive selection on standing genetic variation.Evolution5923122323

43. Bustamante CD, Fledel-Alon A, Williamson S, Nielsen R, Hubisz MT, et al. (2005) Natural selection on protein-coding genes in the human genome. Nature 437: 1153–1157.CD BustamanteA. Fledel-AlonS. WilliamsonR. NielsenMT Hubisz2005Natural selection on protein-coding genes in the human genome.Nature43711531157

44. Voight BF, Kudaravalli S, Wen X, Pritchard JK (2006) A map of recent positive selection in the human genome. PLoS Biol 4: e72.BF VoightS. KudaravalliX. WenJK Pritchard2006A map of recent positive selection in the human genome.PLoS Biol4e72

45. Wang ET, Kodama G, Baldi P, Moyzis RK (2006) Global landscape of recent inferred Darwinian selection for Homo sapiens. Proc Natl Acad Sci U S A 103: 135–140.ET WangG. KodamaP. BaldiRK Moyzis2006Global landscape of recent inferred Darwinian selection for Homo sapiens.Proc Natl Acad Sci U S A103135140

46. Sim E, Westwood I, Fullam E (2007) Arylamine N-acetyltransferases. Expert Opin Drug Metab Toxicol 3: 169–184.E. SimI. WestwoodE. Fullam2007Arylamine N-acetyltransferases.Expert Opin Drug Metab Toxicol3169184

47. Boukouvala S, Fakis G (2005) Arylamine N-acetyltransferases: what we learn from genes and genomes. Drug Metab Rev 37: 511–564.S. BoukouvalaG. Fakis2005Arylamine N-acetyltransferases: what we learn from genes and genomes.Drug Metab Rev37511564

48. Sim E, Pinter K, Mushtaq A, Upton A, Sandy J, et al. (2003) Arylamine N-acetyltransferases: a pharmacogenomic approach to drug metabolism and endogenous function. Biochem Soc Trans 31: 615–619.E. SimK. PinterA. MushtaqA. UptonJ. Sandy2003Arylamine N-acetyltransferases: a pharmacogenomic approach to drug metabolism and endogenous function.Biochem Soc Trans31615619

49. Jensen LE, Hoess K, Mitchell LE, Whitehead AS (2006) Loss of function polymorphisms in NAT1 protect against spina bifida. Hum Genet 120: 52–57.LE JensenK. HoessLE MitchellAS Whitehead2006Loss of function polymorphisms in NAT1 protect against spina bifida.Hum Genet1205257

50. FAO, WHO (2002) Report of a joint FAO/WHO expert consultation. Bangkok, Thailand.FAO, WHO2002Report of a joint FAO/WHO expert consultationBangkok, Thailand

51. Fernandes-Costa FJ, Marshall J, Ritchie C, van Tonder SV, Dunn DS, et al. (1984) Transition from a hunter-gatherer to a settled lifestyle in the !Kung San: effect on iron, folate, and vitamin B12 nutrition. Am J Clin Nutr 40: 1295–1303.FJ Fernandes-CostaJ. MarshallC. RitchieSV van TonderDS Dunn1984Transition from a hunter-gatherer to a settled lifestyle in the !Kung San: effect on iron, folate, and vitamin B12 nutrition.Am J Clin Nutr4012951303

52. Soloway PD (2006) Gene nutrient interactions and evolution. Nutrition Reviews 64: S52–S54.PD Soloway2006Gene nutrient interactions and evolution.Nutrition Reviews64S52S54

53. Tamura T, Picciano MF (2006) Folate and human reproduction. Am J Clin Nutr 83: 993–1016.T. TamuraMF Picciano2006Folate and human reproduction.Am J Clin Nutr839931016

54. Zang Y, Doll MA, Zhao S, States JC, Hein DW (2007) Functional characterization of single-nucleotide polymorphisms and haplotypes of human N-acetyltransferase 2. Carcinogenesis 28: 1665–1671.Y. ZangMA DollS. ZhaoJC StatesDW Hein2007Functional characterization of single-nucleotide polymorphisms and haplotypes of human N-acetyltransferase 2.Carcinogenesis2816651671

55. Perry GH, Dominy NJ, Claw KG, Lee AS, Fiegler H, et al. (2007) Diet and the evolution of human amylase gene copy number variation. Nat Genet 39: 1256–1260.GH PerryNJ DominyKG ClawAS LeeH. Fiegler2007Diet and the evolution of human amylase gene copy number variation.Nat Genet3912561260

56. Di Rienzo A, Hudson RR (2005) An evolutionary framework for common diseases: the ancestral-susceptibility model. Trends Genet 21: 596–601.A. Di RienzoRR Hudson2005An evolutionary framework for common diseases: the ancestral-susceptibility model.Trends Genet21596601

57. De Stefano GF, Martinez-Labarga C, Casalotti R, Tartaglia M, Novelletto A, et al. (2002) Analysis of three RFLPs of the COL1A2 (Type I Collagen) in the Amhara and the Oromo of Ethiopia. Ann Hum Biol 29: 432–441.GF De StefanoC. Martinez-LabargaR. CasalottiM. TartagliaA. Novelletto2002Analysis of three RFLPs of the COL1A2 (Type I Collagen) in the Amhara and the Oromo of Ethiopia.Ann Hum Biol29432441

58. Di Giacomo F, Luca F, Anagnou N, Ciavarella G, Corbo RM, et al. (2003) Clinal patterns of human Y chromosomal diversity in continental Italy and Greece are dominated by drift and founder effects. Mol Phylogenet Evol 28: 387–395.F. Di GiacomoF. LucaN. AnagnouG. CiavarellaRM Corbo2003Clinal patterns of human Y chromosomal diversity in continental Italy and Greece are dominated by drift and founder effects.Mol Phylogenet Evol28387395

59. Luca F, Di Giacomo F, Benincasa T, Popa LO, Banyko J, et al. (2007) Y-chromosomal variation in the Czech Republic. Am J Phys Anthropol 132: 132–139.F. LucaF. Di GiacomoT. BenincasaLO PopaJ. Banyko2007Y-chromosomal variation in the Czech Republic.Am J Phys Anthropol132132139

60. Scozzari R, Cruciani F, Santolamazza P, Malaspina P, Torroni A, et al. (1999) Combined use of biallelic and microsatellite Y-chromosome polymorphisms to infer affinities among African populations. Am J Hum Genet 65: 829–846.R. ScozzariF. CrucianiP. SantolamazzaP. MalaspinaA. Torroni1999Combined use of biallelic and microsatellite Y-chromosome polymorphisms to infer affinities among African populations.Am J Hum Genet65829846

61. Bromley YV, editor. (1988) Peoples of the World. Moscow: Soviet Encyclopedy Publ. YV Bromley1988Peoples of the WorldMoscowSoviet Encyclopedy Publ624

62. Deitz AC, Zheng W, Leff MA, Gross M, Wen WQ, et al. (2000) N-Acetyltransferase-2 genetic polymorphism, well-done meat intake, and breast cancer risk among postmenopausal women. Cancer Epidemiol Biomarkers Prev 9: 905–910.AC DeitzW. ZhengMA LeffM. GrossWQ Wen2000N-Acetyltransferase-2 genetic polymorphism, well-done meat intake, and breast cancer risk among postmenopausal women.Cancer Epidemiol Biomarkers Prev9905910

63. Bakayev VV, Mohammadi F, Bahadori M, Sheikholslami M, Javeri A, et al. (2004) Arylamine N-acetyltransferase 2 slow acetylator polymorphisms in unrelated Iranian individuals. Eur J Clin Pharmacol 60: 467–471.VV BakayevF. MohammadiM. BahadoriM. SheikholslamiA. Javeri2004Arylamine N-acetyltransferase 2 slow acetylator polymorphisms in unrelated Iranian individuals.Eur J Clin Pharmacol60467471

64. Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68: 978–989.M. StephensNJ SmithP. Donnelly2001A new statistical method for haplotype reconstruction from population data.Am J Hum Genet68978989

65. Cascorbi I, Drakoulis N, Brockmoller J, Maurer A, Sperling K, et al. (1995) Arylamine N-acetyltransferase (NAT2) mutations and their allelic linkage in unrelated Caucasian individuals: correlation with phenotypic activity. Am J Hum Genet 57: 581–592.I. CascorbiN. DrakoulisJ. BrockmollerA. MaurerK. Sperling1995Arylamine N-acetyltransferase (NAT2) mutations and their allelic linkage in unrelated Caucasian individuals: correlation with phenotypic activity.Am J Hum Genet57581592

66. Bandelt HJ, Forster P, Rohl A (1999) Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16: 37–48.HJ BandeltP. ForsterA. Rohl1999Median-joining networks for inferring intraspecific phylogenies.Mol Biol Evol163748

67. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R (2003) DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19: 2496–2497.J. RozasJC Sanchez-DelBarrioX. MesseguerR. Rozas2003DnaSP, DNA polymorphism analyses by the coalescent and other methods.Bioinformatics1924962497

68. Fay JC, Wu CI (2001) The neutral theory in the genomic era. Curr Opin Genet Dev 11: 642–646.JC FayCI Wu2001The neutral theory in the genomic era.Curr Opin Genet Dev11642646

69. McDonald JH, Kreitman M (1991) Adaptive protein evolution at the Adh locus in Drosophila. Nature 351: 652–654.JH McDonaldM. Kreitman1991Adaptive protein evolution at the Adh locus in Drosophila.Nature351652654

70. Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinformatics Online 1: 47–50.L. ExcoffierG. LavalS. Schneider2005Arlequin (version 3.0): an integrated software package for population genetics data analysis.Evol Bioinformatics Online14750

71. Hudson RR (2002) Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18: 337–338.RR Hudson2002Generating samples under a Wright-Fisher neutral model of genetic variation.Bioinformatics18337338

72. Adams AM, Hudson RR (2004) Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics 168: 1699–1712.AM AdamsRR Hudson2004Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms.Genetics16816991712

73. McVean GA, Myers SR, Hunt S, Deloukas P, Bentley DR, et al. (2004) The fine-scale structure of recombination rate variation in the human genome. Science 304: 581–584.GA McVeanSR MyersS. HuntP. DeloukasDR Bentley2004The fine-scale structure of recombination rate variation in the human genome.Science304581584