Jeremy ; Manry

Keywords: IFNG, IFNGR1, IFNGR2, natural selection, polymorphism, population genetics

Comments to the Author Manry et al examine genetics of the IFN-g pathway at a population genetics level. This study presents several interesting observations about selective pressures on the IFNg pathway. The statistical methods are thorough and the resequenced dataset is valuable. Some weaknesses include an unusual selection of samples that may have a sample size and composition that is not ideally matched for the analytic goals of the project. In addition, publicly available Phase III HapMap data is not utilized effectively to enrich the data set and analysis.

Major Points 1. Populations and sample size: 186 individuals were resequenced for 3 genes. The 186 are selected from at least 11 populations. How were these numbers chosen and why were these populations chosen? The rationale is important and not articulated. It is important because the conclusions regarding purifying selection may have more or less generalizability based on the input populations. To make arguments about selection, it is important to have larger numbers of samples from distinct populations where evidence of selection can be observed. To characterize global SNP diversity and genetic effects of population migration, smaller numbers of samples from multiple populations is beneficial. Given that one of the primary goals of this project is to examine natural selection, the sample size for each population is very small (6 Orcadians, 4 Cambodians, 10Japanese in particular). In particular, some of the selected populations are almost certainly composed of highly heterogeneous subpopulations. For example, the 33 Chinese minorities. Are these from geographically proximal or distant locations?

We thank the reviewer for this comment. With respect to the study design and sample choice, this was based on the double and complementary goals of our study. Indeed, as the reviewer suggests, one aim of our study was the discovery of new polymorphisms, in the line of one of the Human Mutation interests. To this end, we included individuals from many distinct ethnic origins. The second aim was to identify signatures of natural selection, where many individuals per continental region are needed. Our population choice reflects the need to balance between the two goals of the study. We think that this choice is the best compromise to allow us to address these two questions: several sub-populations to detect new polymorphisms, 186 individuals taken as a whole to detect signatures of natural selection species wide, and 62 individuals per continental group to detect more subtle signatures of local selection.

The reviewer is also right in wondering the extent to which this population choice may affect our results concerning natural selection. In particular, grouping and analysing together populations that present substantial genetic differentiation could generate biases in neutrality statistics. We have now formally tested this possibility by quantifying the levels of population differentiation among populations we grouped in our analyses, namely those living in the same continental region. Specifically, we have performed an AMOVA to estimate the fraction of the genetic variance of our dataset explained by (i) differences between individuals within a given population, (ii) differences among populations within the same continental region, and (iii) differences among the three continental groups (i.e. each group representing the merge of

the different populations of a given continent). Our analyses show that the fraction of the genetic variance explained equals to 89.27%, 0.45% and 10.28%, respectively. In this view, we can say that the genetic differentiation among populations from the same continental region is negligible (mean=0.45%; African=0.73%, European=0.08%, and Asian=0.55%) and non-significant in our dataset. In addition, and consistent with our data, genome-wide genotyping datasets performed on the same individuals and populations here studied have recently shown that the levels of population structure within continental regions is limited (Li et al. 2008, Science). This is also true for other genome-wide datasets on similar populations, e.g. the HapMap samples of Han Chinese and Japanese have been merged in all analyses due to their high genetic resemblance (Frazer et al. 2007, Nature). Altogether, our analyses, fuelled by the results of recent genome-wide datasets, indicate that the genetic differentiation observed among subpopulations from the same continent is weak enough not to influence any of our conclusions regarding natural selection acting in each continental population group.

With respect to the detection of purifying selection, the test used (MKPRF) is based on the whole sample of 186 individuals merged together, because our aim was to detect how intense have been the selective constraints (i.e. strong purifying selection against amino-acid changes, estimated using ω) at the species-wide level (in all humans). Apart from that, this test is insensitive to the number of populations sampled, because ω relies on the comparison of amino-acid altering and silent sites, where silent sites represent an internal control of what is expected in the absence of selection. Consequently, the sample design, whatever it is, will equally influence both types of segregating sites and will not influence the parameter estimation. In addition, the intensity of purifying selection on IFNG estimated from our dataset (ω = 0.0189) is extremely similar to that obtained using another population panel (ω = 0.0184 from Bustamante et al. 2005, Nature), indicating that the detection of strong purifying selection is not sensitive to the population considered.

With respect to the detection of positive selection, the reviewer is right in that the potential presence of population substructure in some of our continental populations may influence inferences concerning the effects of local positive selection. Indeed, in contrast with tests that considered the human species as a whole (see MKPRF above), some of the tests aiming to detect local positive selection are influenced by the structure of the studied populations, even if low. For example, tests based on the allele frequency spectrum (Tajima's D, Fu & Li's D* and F*) are sensitive to the amount of singletons, a feature that can reflect both positive selection and population substructure (Ptak and Przeworski, 2002, Trends Genet). However, we never made any claim of local selection based only on tests that are sensitive to population substructure. For example, as the reviewer points out, the presence of Chinese minorities in the East-Asian sample could be a problem in this respect. However, our results for the IFNGR2 +23133A allele, which is the only case for which we claim selection in Asia, kept being significant when removing Chinese minorities for the DIND test and the F ST statistics (see analyses here below). For this analysis, we used the 2 major Asian populations only: Han Chinese and Japanese (similarly to what was previously done to detect Asian specific signatures of recent positive selection using the HapMap dataset (Voight et al., 2006, PLoS Biol).

DIND test excluding the Asian minorities:

We have now clarified our choice regarding the sampling design in Material and Methods (page 5, and a new Supp. Table S1 to describe our population panel), and we have cautiously discussed the possibility of population structure in the discussion (page 18).

2. HapMap data: Phase 3 of HapMap has full genome sequencing on 11populations and hundreds of individuals. Phase 3 the HapMap dataset is more extensive than the current paper. This paper appears to utilize some data from Phase II of Hapmap" but even this effort does not fully utilize the data. This is a lost opportunity to not use the Phase III Hapmap data and to fully analyze it in conjunction with the authors primary data.

We entirely agree with the reviewer in that the use of genome-wide datasets (e.g. HapMap, 1000 genomes, Celera, etc) is very useful for comparative purposes. Specifically, the reviewer points out that HapMap Phase III has sequenced 10 regions of 100kb on 11 populations and about 1000 individuals. However, there are a number of reasons that prevented us to make full use of the HapMap Phase III dataset: (i) the 10 resequenced regions do not include our 3 genes; (ii) because the remaining data (and this is valid also for HapMap Phase II) is based on genotyping only, we cannot perform neutrality tests such as Tajima's D, Fu & Li's D* and F* and Fay & Wu's H, which need full resequencing data; and (iii) HapMap data is in turn useful for tests based on long-range haplotype homozygosity, in which case we had to use the HapMap Phase II dataset because the SNP density is greater (~3.1 million genotyped SNPs) than that of HapMap Phase III (~1.4 million genotyped SNPs). In the context of our paper, we now used the HapMap Phase III data for allele frequency comparisons (see point 3), obviously for those SNPs that have been genotyped in this dataset.

To circumvent the limitations associated with the nature of the HapMap data (i.e. SNP genotyping which is subject to ascertainment biases), one has to use datasets that are based on full resequencing (e.g. 1000 genomes, Celera dataset, etc). The 1000 genomes project is in a pilot phase that includes 3 distinct approaches (Durbin et al, 2010, Nature). The first is a trio project where 2 trios have been whole-genome sequenced at high coverage, a sample size that is too low to provide with reliable comparisons in the context of our study. The second is an exon-targeted resequencing project where 900 genes have been sequenced at high coverage in 697 individuals. In this case, the number of exploitable genes is much lower than that of other public datasets (see Celera dataset discussed below), especially when it comes to make comparisons among different functional classes as the reviewer suggests. The third project is a low-coverage whole-genome sequencing in 179 individuals. Although the use of the third project of 1000 genomes project is seducing, there are a number of reasons for which it is hardly comparable with our data: (i) the sequence coverage varies among populations, which complicates comparisons of neutrality statistics among populations (see Suppl Fig 2 in Durbin et al., 2010 Nature), (ii) the variation in coverage along the genome strongly limits comparisons between genes within the same population, (iii) the low coverage of most of this dataset (in average 3.6x) limits the detection of low frequency variants, which in turn, represent the substrate to detect and estimate the intensity of purifying selection (i.e. one aim of our study). Indeed, it has been shown that, because non-synonymous variants are generally found at low frequencies (<5%), this dataset has reduced power to discover variants in this range, and therefore alter interpretation as to selection pressures (Durbin et al, 2010, Nature). In this view, we decided to use the Celera dataset, which contains 11,624 genes that have been fully resequenced (Bustamante et al., 2005, Nature), for the following reasons: (i) it is, by the time being, the resequencing project providing the largest number of resequenced genes (11,624); (ii) genes have been resequenced using standard PCR-based techniques, thereby excluding any of the limitations introduced by next-generation sequencing (e.g. coverage variation), and (iii) this dataset has been already specifically used for the genomewide detection of the intensity of purifying selection (Bustamante et al., 2005, Nature), constituting therefore a perfect comparative dataset in the context of our study.

In the revised version of the manuscript, following the reviewers' and editorial advices, we now compare our data in the context of the genome-wide Celera dataset (see Material and Methods page 7, Results pages 12-13 and Discussion page 19), where we integrate our results in the more general context provided by these 11,624 genes. In addition, we have now added in Supp. Table S3 the SNPs that are also present in HapMap Phase III. We have also complemented this comparison with a new Table (see Supp. Table S4). S2: Pertinent to point #2, the authors report 53.5% novel mutations in their SNP discovery effort when compared to HapMap. Is this in comparison to Hapmap Phase II with 4 populations? This comparison should be done with Phase III. And, the claim for novelty should be put in a frequency context. Are the novel SNPs singletons in isolated populations? A table summarizing the genotype frequencies(rather than allele frequencies) listed by population is needed to put the data in a frequency and population context.

SNP Discovery & Table

In the same line of the previous response, there are less SNPs in Phase III than in Phase II. The 53.5% of novel mutations corresponds to what is referenced today in dbSNP (every SNP in HapMap is included in dbSNP). Since the time of submission of our paper, some of the novel SNPs have been now referenced in the dbSNP database, so we have now 47.2% of novel mutations in our paper. These have been submitted to the dbSNP database. Concerning the novel SNPs that are found as singletons, there are neither particularly restricted nor more present in particular/isolated populations.

As suggested by the reviewer, we have now added a table (Supp. Table S4) summarizing the genotype frequencies of each SNP in our 3 continental populations and added the genotype frequencies given by HapMap Phase II and III to put our results in a frequency and population context. 2): the authors use the Polyphen algorithm to predict fitness effects of NS SNPs. Bioinformatic predictions of fitness are highly inaccurate and this data analysis is unlikely to be very meaningful. Very subtle amino acid changes can have dramatic effects on function. Large amino acid changes on the protein surface in unconstrained areas can have no effect on function.

Most bioinformatic predictions of fitness are based on 3D protein structure, for the few genes whose protein structure has been characterized, and on the conservation of polymorphic sites across numerous species and gene paralogs. These methods have been shown to present a high false negative rate, i.e. they are highly conservative. Consequently, those mutations that are predicted to be probably damaging have a high probability to be true positives, while many mutations predicted as benign have good probability to be damaging. Importantly, as said by the reviewer, these algorithms have low power to predict the exact impact of a mutation on protein activity or stability, but rather indicate the relevance of the site of interest for survival, because they are based conservation through evolution. We used the PolyPhen algorithm to give some clues about the putative functional role of each non-synonymous mutation. While in the first version of the manuscript we used the PolyPhen v1, we have now used the recently released updated version of the algorithm (PolyPhen v2) (Adzhubei et al., 2010, Nature Methods). PolyPhen-2 achieved true positive prediction rates of 92% on HumDiv dataset. This updated version has been shown to provide a better confidence than PolyPhen v1 (82%) and than any other predictive tool (Adzhubei et al., 2010, Nature Methods). Indeed, in agreement with the reviewer's comment, some polymorphisms that were predicted to be benign by Polyphen v1 now become possibly or probably damaging with Polyphen v2. However, in our paper, the most important point supporting the somehow deleterious effects of non-synonymous mutations does not come from the PolyPhen predictions but instead from their observed frequencies in natura, which are all at very low frequency except one in IFNGR2 (interestingly predicted as being benign). More generally, this result is also observed at the genome-wide level, since mutations predicted to be damaging by Polyphen are generally found at lower population frequencies than mutations predicted as being benign (Ng et al., 2009 Nature).

We have now updated the PolyPhen predictions using PolyPhen v2 and clarified the actual meaning of these predictions, which are expected to be more accurate (Material and Methods page 8, and Results pages 11-12). Because PolyPhen is widely used by the scientific community, we still believe that our results may provide some comparative data to scientists interested in predictive methods. Figs 2,3). This data looks solid and interesting and includes reference to a CA microsatellite and SNP 874 which have been partially functionally analyzed previously. Although the functional effect of these polymorphisms on IFNg production remains incompletely characterized, the connection of the data in this manuscript to this locus is suggestive of a biologically meaningful genetic observation. One major question is whether the magnitude of effect of the purifying selection (omega value and DIND test) are relatively high compared to other genes. Although there is an interesting difference compared to IFNGR1 and IFNGR2, how does this compare to other immune genes? Or other gene classes? Although this could partially be treated in the Discussion, it could also be addressed with analysis of HapMap data to emphasize the relative importance of the IFNg finding. We thank the reviewer for this useful comment. To put our results on a broader genome-wide context of purifying selection, HapMap data is not suitable because it is based on genotyping data and we need full resequencing data. In this case, 1,000 genomes data could be useful but the low coverage of most of the pilot data available does not allow proper genotype calls (see detailed explanation in point 2). To circumvent this limitation, we have now used the genomewide sequence dataset of 11,624 genes provided by Celera (Bustamante et al., 2005, Nature) (see again point 2 for detailed explanation). This dataset, which has been already specifically used for the genome-wide detection of purifying selection, provides reliable data on both divergence and polymorphism of silent and non-synonymous sites. Among the 7,557 genes presenting at least one nonsynonymous variant, we found that only 7.7% exhibit an ω value (indicating purifying selection) lower than that observed for our IFNG. When restricting the analyses to genes classified as being involved in immune system process, only 10.3% of them presented ω values lower than that observed for IFNG.

Purifying selection of IFNg: An important finding of the paper is that IFNg has evidence of purifying selection compared to IFNGR1 and IFNGR2 (

We have now added in the revised version of our paper this detailed analysis (see Material and Methods pages 7, Results pages 12-13 and Discussion page 19), where we integrate our results in the more general context provided by these 11,624 genes. of nucleotide diversity at linked sites than expected(excluding singletons and doubletons). In addition, long-range haplotypes were sought. For amino-acid-altering mutations, predicted fitness effects were also examined, based on predicted protein structure or sequence conservation information. The bottom line findings from these results and analyses, were that the IFNG gene shows evidence of strong purifying selection against non-synonymous variants, consistent with intolerance to disruption in the function of this gene product, whereas the other two genes in the pathway examined, seemed to have evolved under more relaxed selective constraints. The evidence for possible population-specific positive selection is presented, but somewhat weaker. Overall, this is a thorough, thoughtful, elegant, and significant scientific contribution that is interesting and important from several points of view: -Biologically interesting, in terms of the genes examined and the innate immune pathway.

-A tour de force and important template for using re-sequencing and combining multiple tests and approaches to unravel the relative influence of selection and demographic history in shaping human genomic diversity at loci of interest.

We thank the reviewer for these general comments and suggestions. -While it is laudable that the authors have based their analyses and inferences on their own re-sequencing data, it is not clear whether they are taking advantage of additional data now available in the public domain from the 1000 Genomes Project, which could possibly strengthen and sharpen their conclusions.

We entirely agree with the reviewer in that the use of genomewide resequencing datasets (e.g. 1000 genomes, Celera, etc) is very useful for comparative purposes, especially when it comes to detect natural selection. The 1000 genomes project is in a pilot phase that includes 3 distinct approaches (Durbin et al, 2010, Nature). The first is a trio project where 2 trios have been whole-genome sequenced at high coverage, a sample size that is too low to provide with reliable comparisons in the context of our study. The second is an exon-targeted resequencing project where 900 genes have been sequenced at high coverage in 697 individuals. In this case, the number of exploitable genes is much lower than that of other public datasets (see Celera dataset discussed below), especially when it comes to make comparisons among different functional classes as the reviewer suggests. The third project is a low-coverage whole-genome sequencing in 179 individuals. Although the use of the third project of the 1000 genomes project is seducing, there are a number of reasons for which its use is not appropriate in the context of our study: (i) the sequence coverage varies among populations (see Suppl Fig 2 in Durbin et al., 2010 Nature), which complicates comparisons of neutrality statistics among populations, (ii) the variation in coverage along the genome strongly limits comparisons between genes within the same population, (iii) the low coverage of most of this dataset (in average 3.6x) limits the detection of low frequency variants, which in turn, represent the substrate to detect and estimate the intensity of purifying selection (i.e. one aim of our study). Indeed, it has been shown that, because non-synonymous variants are generally found at low frequencies (<5%), this dataset has reduced power to discover variants in this range, and therefore alter interpretation as to selection pressures (Durbin et al, 2010, Nature). In this view, we decided to use the Celera dataset, which contains 11,624 genes that have been fully resequenced (Bustamante et al., 2005, Nature), for the following reasons: (i) it is, by the time being, the resequencing project providing the largest number of resequenced genes (11,624); (ii) genes have been resequenced using standard PCR-based techniques, thereby excluding any of the limitations introduced by next-generation sequencing (coverage variation), and (iii) this dataset has been already specifically used for the genomewide detection of the intensity of purifying selection (Bustamante et al., 2005, Nature), constituting therefore a perfect comparative dataset in the context of our study.

In our revised version of the manuscript, we now use the Celera genome-wide dataset for comparative analyses (see Material and Methods page 7, Results pages 12-13 and Discussion page 19). For frequency comparisons, in turn, we have now used the HapMap Phase III data, obviously for those SNPs that have been genotyped by HapMap III (see Supp. Table S3). We have also complemented this comparison with a new Table (see Supp. Table S4).

-The authors could consider using ASD (allele sharing distance) for population demographic history, as it has a number of advantages related to potential biasing inherent in F ST . We agree with the reviewer than methods such as ASD can be helpful to detect population relationships and admixture. However, the reason by which we did not use these methods is that the aim of this paper is not to provide data on population demographic scenarios. In addition, the analyses we performed using F ST were not intended to enlighten population demographic history but instead provide a background empirical distribution to better understand the effects of natural selection. Indeed, the only reason by which we need a demographic model is to correct the mimicking effects that demography and selection have on the patterns of genetic diversity. To this end, we used the demographic scenario given by (Voight et al. 2005, PNAS) determined using non coding regions, which are expected to be more appropriated for demographic studies. Consequently, to detect robust signatures of natural selection in our genes, we performed neutral simulations incorporating this demographic model. In this respect, the Voight's model does not use F ST to infer any population demographic parameter. Furthermore, to avoid biases associated with the use of a single demographic model (with its own methodological strengths and weaknesses), we have now used in parallel a second demographic model that also used data from independent noncoding regions (Laval et al., 2010, PLoS One). Interestingly, all tests for selection that were significant under the Voight's model remain significant under the Laval's model. The only discrepancy was the Fay and Wu's H statistics for IFNG in the African population, which was found to be sensitive to intercontinental migration rates. This additional information has now been added, and accordingly discussed, in the Material and Methods section (page 7), Results (pages 14-15) and Discussion (page 20). We thank the reviewer for this comment, which has allowed us to improve our understanding and interpretations of our results.

-The authors describe an interesting result with respect to SNP density. A higher SNP density was found in African, than in the two Eurasian population groups which were re-sequenced. Once again, an examination of the 1000 Genomes data would be helpful here, although this would probably simply confirm the finding. However, it might allow a more quantitative analysis and inference, to make this finding more significant and far-reaching, and enable discussion and interpretation that is more confident. For example, in the Results, the authors indicated that even if most of these populationspecific SNPs were found as singletons, a number of them display minor allele frequencies exceeding 5%, as indicated in the Supplementary Table . This specific finding could be solidified and consolidated with additional data from the 1000 Genomes Project.

We agree with the reviewer in that obtaining a higher SNP density in Africa compared to Europe and Asia is expected and it is indeed confirmed by several studies (for an exhaustive review, see Campbell & Tishkoff, 2010, Curr Biol). Also, we agree with the reviewer in that comparing our data with the final phase of the 1000 Genomes project could allow a more quantitative view of our results, in a genome-wide context. However, as described in our response to the first point raised by the reviewer, the 1000 Genomes project in its pilot phase does not allow such a quantitative analysis, because the sequencing coverage of African and non-African populations is different. Specifically, the power of SNP discovery is increased in European-Americans (CEU) with respect to Yoruba (YRI) and East-Asians (CHB+JPT).

We are now clearly mentioning this in the main text (Results page 11).

-The examination of length variation at the +875 (CA)n microsatellite within IFNG, is perhaps the least convincing and unimportant part of this manuscript. One can consider omitting it without much loss of the otherwise elegant and strong manuscript. In fact, I think that this is a weakness, especially given the problematic nature of understanding the evolution of microsatellite diversity.

We entirely agree with the reviewer that the section related to the microsatellite is certainly the less strong and the manuscript could perfectly "survive" without it. However, there is a wide literature concerning the IFNG microsatellite, and many scientists are interested in this topic beyond the aspect of natural selection. Therefore, we prefer to keep this extra observation, more as descriptive data for those interested in this, than as an evidence of natural selection. We think that this result can be interesting for immunologists interested in the mechanisms of regulation of the production of IFN-γ.

-A short description of the structure of the gene in the Introduction would help orient the reader (here I am referring to chromosomal location, size, exons). This is particularly true for the non-initiated reader.

We added a Supp. Figure S1 to illustrate chromosomal location, size and exons. We also describe the 3 genes and the proteins they encode in the revised version of the Introduction (see page 4).

-The authors should indicate the total size that was sequenced.

The size of each sequenced region is now given in Supp. Table S2.

-The GenBank number for each reference to sequence that was used should be provided.

The GenBank number is now added in Supp. Table S2.

-A simple linkage disequilibrium (LD) Figure for each gene within each population would be helpful to demonstrate high degrees of LD among SNPs. This is of course very simple to create using Haploview.

We have now added a new Supplementary Figure S3 to illustrate the LD between each SNP with Minor Allele Frequency >1% for each gene within each population (see also Material and Methods, page 6) and Results (page 15).

-The ancestral state of SNPs would be of interest in some cases for which the haplotype tree is not rooted.

We added the ancestral and derived alleles of each SNP in the Supp. Figure S5.

-One would assume that novel SNPs found in this study have been submitted to the dbSNP database.

We have now submitted the novel SNPs to the dbSNP database, and are waiting for the corresponding #rs numbers.

EDITORIAL BOARD'S COMMENTS

This paper has several interesting observations stemming from a solid statistical analysis of resequencing data applied to sequence diversity results at specific genomic loci to uncover and distinguish the effects of natural selection and demographic history in shaping human genomic diversity at loci of interest. As such the paper is of interest yet it would benefit from proper consideration and handling of the reviewers' comments. In agreement with the latter we strongly suggest that the authors do a more thorough analysis of publicly available datasets and avoid possible biases from an unusual selection of samples for their dataset. In addition, the authors do not fully articulate the significance of their findings. In the HapMap age (not to mention the 1000G project), population geneticists can finally make more genome-wide statements regarding selective pressure on a candidate region. Ultimately we would like to know whether IFNgamma shows evidence of a different level of purifying selection than other immune genesâ€" the authors only partially get to that answer and they fill that out much more thoroughly with publicly available databases and a modest amount of work. Such additional substance would raise the impact of this contribution.

In revision, since the format is being changed, please carefully indicate in your response exactly what was added/removed in the revision process. First of all, we thank the two reviewers as well as the editorial board for both the comments and suggestions as well as for the "upgrade" to research Article. In the revised version of the manuscript, we have followed all reviewers' and editorial's suggestions, which in our opinion have greatly contributed to improve the clarity and the quality of the manuscript. More generally, the revised version of the manuscript has been considerably changed and new figures and tables are now provided. The corresponding changes are now highlighted in red in the revised version of the manuscript.

In brief,

1.

Population choice of samples: we now explain carefully the criteria and rationale of our population choice and study design, and showed that the presence of minorities in our sample collection does not influence our claims on selection (see detailed response to point 1 of Reviewer 1, and pages 5 and 18 of the revised manuscript, and the new Supp. Table S1).

2.

Use of public genome-wide datasets: We entirely agree with both the reviewers and the editorial board in that the use of genomewide datasets (e.g. HapMap, 1000 genomes, Celera, etc) is very useful for comparative purposes. In the revised version of the manuscript, we are now comparing our data with genome-wide datasets, obviously when the data are comparable. populations, (ii) the variation in coverage along the genome strongly limits comparisons between genes within the same population, (iii) the low coverage of most of this dataset (in average 3.6x) limits the detection of low frequency variants, which in turn, represent the substrate to detect and estimate the intensity of purifying selection (our study). Indeed, it has been shown that, because non-synonymous variants are generally found at low frequencies (<5%), this dataset has reduced power to discover variants in this range, and therefore alter interpretation as to selection pressures (Durbin et al, 2010, Nature). In this view, we decided to use the Celera dataset, which contains 11,624 genes that have been fully resequenced (Bustamante et al., 2005, Nature), for the following reasons: (i) it is, by the time being, the resequencing project providing the largest number of resequenced genes (11,624); (ii) genes have been resequenced using standard PCR-based techniques, thereby excluding any of the limitations introduced by next-generation sequencing (coverage variation), and (iii) this dataset has been already specifically used for the genomewide detection of the intensity of purifying selection (Bustamante et al., 2005, Nature), constituting therefore a perfect comparative dataset in the context of our study.

We now discuss the results obtained for the IFNG in a much wider genome-wide context. For all these changes, see Material and Methods pages 7, Results pages 12-13 and Discussion page 19, as well as Supp. Table S3 and Supp. Table S4.

Microsatellite:

The reviewer 2 suggests omitting the section related to the IFNG microsatellite, and we agree that the manuscript could perfectly "survive" without it. However, there is a wide literature concerning the IFNG microsatellite, and many scientists are interested in this topic beyond the aspect of natural selection. Therefore, we prefer to keep this extra observation, more as descriptive data for those interested in this, than as an evidence of natural selection. We think that this result can be interesting for immunologists interested in the mechanisms of regulation of the production of IFN-γ. Moreover, note that reviewer 1 comments the interest of using the microsatellite to provide some functional/biological meaning of our results on natural selection.

4.

Other points: in addition to your requests, and those of the reviewers, new analyses have been done including: o We have used the most updated, and recently released, version of the Polyphen algorithm, which has been shown to have a highly accurate predictive power (PolyPhen v2, Adzhubei et al., 2010, Nature Methods) (see Material and Methods page 8, and Results pages 11-12) o We now correct our selection tests for two demographic models (instead of only one), see Material and Methods section (page 7), Results (pages 14-15) and Discussion (pages 20) o We have also taken advantage of genome-wide data from HapMap to perform another test of selection (XP-EHH, which is based on the degree of long range haplotype homozygosity). We particularly thank the reviewers and the editorial board to "push" us to go further in the genome-wide analyses since we have obtained an interesting result regarding the IFNGR1 gene in Africa (pages 8, 16, 22-23). o Because one of the reviewers asks for a table reporting the genotype information per individual, we have had to create an excel file (it is impossible to do as a .doc file) which is being sent as a separate email attachment to the editorial office.

We hope that we have now satisfied both the reviewers' and the editorial board's comments and that the manuscript is suitable for publication in Human Mutation. We added the OMIM number of the 3 genes at first mention in the introduction (see page 4).

2) The mutation nomenclature must follow the format indicated in the Author Instructions (see the website http://www.hgvs.org/mutnomen/ and also the nomenclature checklist at http://www.hgvs.org/mutnomen/checklist.html ). Things to watch for: a-If there is a dbSNP accession number, then descriptions like rs123456:A>G are acceptable.

The mutation nomenclature follows the format indicated in the Author Instructions, and also the nomenclature checklist.

b-Mention the GenBank reference sequence and version number for the genes studied (i.e., include the decimal point following the accession number in the sequence record) in the Materials and Methods and as a footnote to the relevant tables.

We mentioned the GenBank reference sequence and version number for the 3 genes studied in the Material and Methods (Page 6) and in Supp. Table S2.

c-Clearly indicate in the Methods text and tables that the DNA mutation numbering system you follows the journal's approved nomenclature. For example: "Nucleotide numbering reflects cDNA numbering with +1 corresponding to the A of the ATG translation initiation codon in the reference sequence, according to journal guidelines (www.hgvs.org/mutnomen). The initiation codon is codon 1."

We better described the journal's approved nomenclature (page 5). We checked that the references follow the journal's standard format and are cited properly. 1 and2 currently require color in the print version in order to be fully understood because the color "red" is mentioned and must be identified. In Figure 1, it could be re-drawn to use black or grey, thus eliminating need for color. Figure 2 may still require color in order to be understandable unless different levels of gray/white are used.

4) Figures

Figures 1 and 2 are now in black and white.

As noted in our Author Instructions, there are no page charges for publication in Human Mutation but there are costs associated with publication of color images in print: $500 USD per printed color page. Alternatively, there is an option of publishing the color images in black-and-white in the print article and in color online, at no cost to you -but no information must be lost in a conversion to b/w from color for the print version. Please confirm your preference in reply.

We prefer Figure 3 to appear in colors in both the online and the printed versions. Jérémy Manry 1,2 , Guillaume Laval 1,2 , Etienne Patin 1,2 , Simona Fornarino , 2006;Nielsen, et al., 2007;Quintana-Murci, et al., 2007;Sabeti, et al., 2006].

5) Human

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The aims of this study were to (i) identify the whole spectrum of population genetic variation, 15 based on a full resequencing scheme, in the three core genes involved in the IFN-γ pathway 16 (IFNG, IFNGR1 and IFNGR2), and (ii) investigate, using an evolutionary genetics approach, For each gene, the totality of the exonic region and at least an equivalent amount of non-exonic 13 regions were sequenced, including intronic, 5' and 3' regions (Supp. Table S2). Nucleotide To identify recent events of positive selection acting on the three genes involved in the IFN-γ 1 pathway, we used the Derived Intra-allelic Nucleotide Diversity (DIND) test that makes 2 maximum use of resequencing data [Barreiro et al., 2009]. The rationale of this test is that a 3 derived allele under positive selection that is at high population frequencies should present lower 4 levels of nucleotide diversity at linked sites than expected. We applied the DIND test to our entire 5 dataset by plotting for all SNPs identified in the 3 genes, the ratio between the ancestral and the 6 derived internal nucleotide diversity (iπ A /iπ D ) against the frequencies of the derived alleles An alternative approach to detect population-specific events of positive selection is to calculate 8 genetic distances among populations, using the F ST statistic [Excoffier et al., 1992;Weir, 1984]. 17 functional data on one of these SNPs to better understand the nature of the selective event.

1 Indeed, the +874T allele has already been documented to create an NF-κB binding site, and to 2 lead to higher production of IFN-γ [Pravica et al., 1999;Pravica et al., 2000] 2B). We failed in detecting departures from neutrality based on classical 1 neutrality tests, most likely because of their inadequate power under a scenario of positive 2 selection acting on standing variation [Pritchard et al., 2010;Przeworski et al., 2005]. Indeed, 3 both SNPs +874T and +5173G were already present before the out-of-Africa exodus, as attested 4 by their appreciable frequencies among African populations, a situation that explains the low F ST 5 values of these two SNPs between Europeans and the other populations. However, the fact that 6 the SNP +874A>T is well known to have functional consequences further reinforces our 7 population genetics prediction. Indeed, the +874T allele has been shown to provide a binding site 8 for the transcription factor NF-κB, and to be associated with both higher production of IFN-γ 9 [Pravica et al., 2000] and higher resistance against intracellular pathogens such as 10

Mycobacterium tuberculosis [Ding et al., 2008;Etokebe et al., 2006;Lopez-Maderuelo et al., 11 2003;Rossouw et al., 2003;Sallakci et al., 2007;Tso et al., 2005] Voight, et al., 2005]; † †/ † P-values ≤ 0.01 and ≤ 0.05, respectively, according to the model of [Laval, et al., 2010]. Pvalues were obtained from coalescent simulations, according to the models proposed by [Voight, et al., 2005], which considers each continental population separately, and [Laval, et al., 2010], which considers inter-continental population migration. A full description of all SNPs identified in this study (coding and non-coding) is available in Supp. Table S3. The position of each SNP was determined using the reference sequence listed in Supp. Table S2. The first amino-acid corresponds to the ancestral state, as defined considering the sequences of human, chimpanzee, gorilla, orangutan and rhesus. Chromosome location of each SNP is given according to the hg19 (GRCh37) human assembly. Protein domains are given by the UniProt Database. For the IFNGR2 SNPs c.708A>T and c.889G>A, the domain is predicted by the UniProt Database as a potential domain. For the Polyphen analyses, "pr" stands for probably and "po" for possibly. For the Panther analyses, the P-deleterious values are shown. The frequencies, given in %, of each SNP in the different continental populations refer to the Derived Allele Frequency. The position of each SNP was determined using the reference sequence listed in Supp. Table S2. The first amino-acid corresponds to the ancestral state, as defined considering the sequences of human, chimpanzee, gorilla, orangutan and rhesus. Chromosome location of each SNP is given according to the hg19 (GRCh37) human assembly. Protein domains are given by the UniProt Database. For the IFNGR2 SNPs c.

F o r P e e r R e v i e w

  the Journal Human Mutation Title: Evolutionary genetics evidence of an essential, non-redundant role of the IFN -γ pathway in protective immunity Manuscript number: humu-2010-0462Authors: Manry J et al. In this manuscript the authors use a number of classical and well-established, as well as more recent powerful tests applied to DNA sequence diversity results at genomic loci encoding three specific genes in the IFN-g pathway to uncover and distinguish the effects of natural selection. The choice of the genomic loci is well-based, because of the role of the gene products in innate immunity and adaptive cell-mediated immunity against intracellular pathogens. These have been major pathogens affecting human populations, and susceptibility or protection almost certainly has had an overarching effect on reproductive success throughout human evolutionary history. Three genes in the pathway (IFNG, IFNGR1, andIFNGR2) were re-sequenced in 186 individuals from a number of populations. After confirming Hardy-Weinberg equilibrium for detected SNPs, and after haplotype reconstruction â€" a number of tests of neutrality and evolutionary selection were carried out, and the results analyzed and compared. Some of these tests are classical, but cannot readily distinguish effects of selection from those of population demographic events, whereas others which are based on re-sequencing and other approaches are more useful in making this distinction. More particularly, in order to correct for the mimicking effects of demography, the authors incorporated demographic models based on re-sequencing data of non-coding regions in different samples from comparable populations reported in the literature. Tests included neutrality statistics, such as Tajima's D, or Fu and Li's D and F, as well as Fay and Wu's H. In addition, tests considering both inter-species, and within-species diversity were conducted using the McDonald-Kreitman Poisson Random Field (MKPRF) test. Perhaps the most powerful and enlightening analysis in this study, looks for evidence of recent positive selection using DIND (Derived Intra-allelic Nucleotide Diversity), based on the levels of nucleotide diversity associated with haplotypes in the ancestral, versus the derived alleles. This test is based on the presumption that the derived allele under positive selection which reaches high population frequencies should present lower levels

  the manuscript could be somewhat improved with attention to the following points:

  gene symbols must be in all caps-italics.--Please ensure that you use HUGO HGNC-approved gene symbols. Common gene symbol aliases may also be used at first mention. OMIM entries do not always feature the approved symbol prominently. Verify gene symbols at http://www.genenames.org/

  We checked sequence variants descriptions using the Mutalyser program.e-Please verify that the mutations reported (especially novel ones) have been or will be submitted to an existing locus-specific database for the genes involved. Visit the HGVS-LSDB list to search for databases: http://www.hgvs.org/dblist/glsdb.html Novel SNPs have been now submitted to the dbSNP database, and we are waiting for the corresponding #rs numbers. for main article must be submitted as separate files with high resolution (at least 200 dpi) as *.tif or *.eps format only. --For color figures in print: submit two files for each color figure: one in CMYK color space and one in RGB color space (with the true color you wish to have published). If you cannot provide CMYK color e-files for the print version, please let me know. (see below regarding color costs) --For online-only color figures, please only submit one RGB color space file. Only Figure 3 is submitted with colours, in two separate files (CMYK and RGB). d-Tables must be submitted individually as separate *.doc files (with their titles and legends included). Please use the MS Word table format if possible. Excel (*.xls) files should not be submitted. --Do not use custom paper sizes -only use Letter or A4.

  Mutation can accommodate researchers funded by agencies requiring open access publication. More information on Wiley-Blackwell's policy is available at: http://olabout.wiley.com/WileyCDA/Section/id-406241.html Human Mutation abides by the NIH Mandate. If your work was funded by the NIH, be sure to include a grant number in an "Acknowledgments" section of the manuscript right before the References. Visit this site for more information: http://www.wiley.com/go/nihmandate 6) IMPORTANT INFORMATION REGARDING PREPRINTS a-Human Mutation is now publishing online preprints of accepted manuscripts prior to typesetting and page proof corrections. It is therefore crucial that you revise your manuscript carefully so that errors (typographical and grammatical) are corrected BEFORE the final accepted manuscript is posted online. The accepted preprint version will remain online until the corrected proofs are received and the typeset manuscript is finalized. At that time, the preprint version will be replaced with the final, typeset version online, in Early View. b-It is essential that you submit a copyright transfer agreement (CTA) upon submission of your revised manuscript. This will avoid delay in publication of your article upon acceptance. If possible, the CTA must be signed by the corresponding author and should be signed by all contributing authors if practical. All authors must be made aware of the CTA and the rights it conveys to them. The CTA can be found here: www.wiley.com/go/ctaaus

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  lymphocyte activation. IFNs are today classified into three types, on the basis of gene sequence 5 similarity, chromosomal location, and receptor specificity (see[Pestka et al., 2004] for an 6 extensive review). The first IFNs to be identified were classified as type-I IFNs (17 molecules of 7 IFN-α/β and related molecules) and signal through a ubiquitously expressed receptor composed 8 of two chains: IFN-αR1 and IFN-αR2. The last IFNs to be described are known as type-III IFNs, 9 and the three molecules that have been identified so far (IL28A, IL28B and IL29) activate the 10 same main signaling pathway as type-I IFNs but have evolved a completely different receptor 11 structure. Type-III IFNs act through a receptor composed of two chains, a type-III IFN-specific 12 IL28RA selectively expressed in certain cell types and the ubiquitously expressed IL10RB. 13 Finally, only one type-II IFN has been identified, IFN-γ, which presents a distinct sequence, role 14 and functions with respect to type-I and type-III IFNs [Schroder et al., 2004]. 15 The type-II IFN-γ binds to its own receptor made of the 2 transmembrane proteins, IFN-16 γR1 and IFN-γR2, to both induce antimicrobial and antitumor mechanisms and to up-regulate 17 antigen processing and presentation pathways. More precisely, IFN-γ, which is produced mostly 18 by natural killer (NK) and T lymphocytes, orchestrates leukocyte attraction and directs growth, 19 maturation, and differentiation of many cell types, in addition to enhancing NK cell activity and 20 regulating B-cell functions such as immunoglobulin (Ig) production and class switching 21 [Schroder et al., 2004]. Consequently, IFN-γ plays a central role in innate immunity and in 22 adaptive cell-mediated immunity against intracellular pathogens and is the major macrophage-

  17 whether and how natural selection has targeted the type-II IFN system. Identifying the intensity 18 and type of natural selection exerted upon these 3 genes should help to better understand the 19 mode in which the different components of the type-II IFN system have contributed to host 20 defense. Likewise, the evolutionary dissection of IFNG, IFNGR1 and IFNGR2 should shed light 21 on how genetic variation at these genes may be involved in the current susceptibility to, and 22 pathogenesis of, infectious diseases.

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  14numbering reflects cDNA numbering with +1 corresponding to the A of the ATG translation 15 initiation codon in the reference sequence, according to journal guidelines 16 (www.hgvs.org/mutnomen). The initiation codon is codon 1. In addition to the annotation of 17 coding DNA described above[den Dunnen and Antonarakis, 2000], we used another SNP 18 annotation of genomic DNA (referred to as "ATG position") starting at the first nucleotide of the ATG-translation initiation codon (+1) and including in the counting both coding and noncoding 20 nucleotides. All sequences were obtained using the Big Dye terminator kit and the 3730 XL 21 automated sequencer from Applied Biosystems. Sequence files and chromatograms were 22 inspected using the GENALYS software[Takahashi et al., 2003]. All singletons or ambiguous 23 polymorphisms were systematically reamplified and resequenced. We were unable to resequence 24 of IFNGR2 because of technical reasons, most likely resulting from the very high 1 GC content of the region (73%). We used the NG_015840.1, NG_007394.1 and NG_007570.1 as 2 reference sequences for IFNG, IFNGR1 and IFNGR2, respectively. 3 IFNG +875 CA microsatellite genotyping 4We performed a standard PCR protocol to amplify the fragment using the True Allele PCR 5 Premix by Applied Biosystems with 20ng of genomic DNA, and using primer sequences 6 previously reported[Ding et al., 2008]. The mixture was then subjected to the PCR reaction for 7 15 min at 95°C, followed by 35 cycles of denaturation for 30 sec at 95°C, annealing for 1 min at 8 56°C, and extension for 1 min at 72°C, followed by a final extension of 10 min at 72°C. The 9 fluorescent dye-labelled PCR products were electrophoresed on an Applied Biosystems 3130XL 10 Genetic Analyser. The results were analyzed by Genemapper Analysis software 3.2. 11 Statistical Analyses 12We checked the Hardy-Weinberg equilibrium for each SNP using Arlequin Software v3 13[Excoffier et al., 2005]. We used the Haploview software[Barrett et al., 2005] to illustrate the 14 levels of linkage disequilibrium (LD) between each SNP for each gene. Haplotype reconstruction 15 was performed by means of the Bayesian statistical method implemented inPhase (v.2.1.1) 16[Stephens and Donnelly, 2003]. We applied the algorithm five times, using different randomly 17 generated seeds, and consistent results were obtained across runs. The entire dataset was used to 18 perform a number of sequence-based neutrality-statistics, including Tajima's D, Fu & Li's D*, 19 Fu & Li's F*, Fay & Wu's H, using DnaSP v5.1 [Rozas et al., 2003]. P-values for the various 20 neutrality tests were estimated from 10 4 coalescent simulations, performed using SIMCOAL 2.0 21 [Laval and Excoffier, 2004], under a finite-site neutral model and considering the recombination 22 rate of the tested region reported in HapMap Phase II [Frazer et al., 2007; International-HapMap-23 Consortium, 2005]. Each of the 10 4 coalescent simulations was conditional on the observed 24 the number of segregating sites observed in each gene. To correct for the 1 mimicking effects of demography on the patterns of diversity, we considered two previously 2 validated demographic models based on resequencing data of noncoding regions in a set of 3 populations similar to ours (i.e., African, European and Asian) [Laval et al., 2010; Voight et al., 4 2005]. The main difference between these two demographic models is that the model of [Laval et 5 al., 2010] considers inter-continental population migration. 6 To detect the effects of natural selection considering both inter-species divergence and 7 within-species polymorphism, we used the McDonald-Kreitman Poisson Random Field 8 (MKPRF) test [Bustamante et al., 2005; Sawyer and Hartl, 1992]. We compared these MKPRF 9 results with a genome-wide dataset where 20 European-Americans and 19 African-Americans 10 have been resequenced at 11,624 genes by exon-specific PCR amplification [Bustamante et al., 11 2005]. We used information on the number of divergent silent sites (d S ), and polymorphic silent 12 sites (p S ), divergent nonsynonymous sites (d N ) and polymorphic nonsynonymous sites (p N ) for 13 each gene. Divergent sites refer to positions that are different between the human and chimpanzee 14 lineages, whereas polymorphic sites refer to the situation in which the two alleles are segregating 15in humans. In addition, we used the Gene Ontology database (http://www.geneontology.org/) to 16 extract genes involved in the biological process referred as to "immune system process" events of positive selection, we used the Derived Intra-allelic Nucleotide 19 Diversity (DIND) test based on the ratio iπ A /iπ D , where iπ A and iπ D are the levels of nucleotide 20 diversity associated with the haplotypes carrying the ancestral and the derived allele, respectively 21 [Barreiro et al., 2009]. The rationale of this test is that a derived allele under positive selection 22 that is at high population frequencies should present lower levels of nucleotide diversity at linked 23 sites than expected. Singletons and doubletons are excluded from this analysis. We also used tests 24 levels of haplotype homozygosity, such as the Long Range Haplotype test [Sabeti et 1 al., 2002] and the Cross Population Extended Haplotype Homozygosity (XP-EHH) test [Sabeti et 2 al., 2007]. In addition, we assessed the levels of population differentiation for the entire SNP 3 panel, using the F ST statistics derived from the analysis of variance (ANOVA) [Excoffier et al., 4 1992]. To identify SNPs presenting extreme levels of population differentiation, we compared the 5 observed F ST values at the level of individual SNPs in IFNG, IFNGR1 and IFNGR2 against the 6 ST distribution of 659,000 SNPs genotyped on the same subset of individuals of the HGDP-7 CEPH, except for 5 individuals who were not genotyped [Li et al., 2008]. F ST comparisons were 8 conditioned to SNPs presenting similar allele frequencies (i.e., similar expected heterozygosity). 9 Empirical P-values for each SNP at the 3 genes were estimated as previously described [Barreiro 10 et al., 2009].11The fitness status of all amino-acid-altering mutations (i.e., benign, possibly damaging 12 and probably damaging) was predicted using the Polyphen algorithm v2 HumDiv[Adzhubei et 13 al., 2010]. This method, which considers protein structure and/or sequence conservation 14 information for each gene, has been shown to be the best predictor of the fitness effects of amino-15 acid substitutions[Williamson et al., 2005]. To independently assess the functional impact of 16 these mutations, we replicated the analyses using the Panther algorithm[Thomas et al., 2003] 17 To compute the IFNG gene tree, we used the GENETREE software [Griffiths and Tavare, 18 1994], under a standard coalescent model. Since this model assumes no recombination, we 19 excluded 3 recombinant haplotypes to perform the analysis. We used the θ W obtained for the 20 entire IFNG sequenced region and the mutation rate per gene per generation (µ = 1.98x10 -8 ) was 21 deduced from Dxy (0.0095), the average number of nucleotide substitutions per site between 22 human and chimpanzee calculated by DnaSP v5.1 [Rozas et al., 2003], with consideration that 23 the two species diverged 240,000 generations ago. Time, scaled in 2N e units, with Ne the 24 , was converted into years by the use of a 25-year generation time and an 1 N e value obtained as θ W divided by 4µ. levels of full sequence-based diversity in the human IFNG and the 2 genes 3 encoding its receptor (IFNGR1 and IFNGR2), we comprehensively resequenced the 3 genes in a 4 panel of 186 healthy individuals originating from 11 different populations from sub-Saharan 5 Africa, from Europe and from East-Asia. Each individual was sequenced for a total of 14.8 kb, 6 32.5% of which corresponded to exonic regions, the rest comprising intronic and promoter 7 regions (Supp.

7(Page

  FigureS2 B). Indeed, although the frequency of these two SNPs -which are found to be in

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  Indeed, local positive selection is known to increase the levels of population differentiation with 10 respect to neutrally-evolving loci[Barreiro et al., 2008; Kreitman, 2000; Nielsen et al., 2007; 11Sabeti et al., 2006; Voight et al., 2005]. We thus estimated the F ST values for the 117 SNPs 12 identified in our study. To obtain a background expectation of genome-wide F ST , we used the 13 genome-wide data of the HGDP-CEPH (~659,000 SNPs from [Li et al., 2008]) from the same set 14 of individuals we sequenced in this study. When plotting the F ST values as a function of expected 15 heterozygosity for our 117 SNPs together with the background genome-wide expectations, 2 16 SNPs in IFNGR2 (SNP +23133 [c.413-209G>A] and SNP +23501 [c.561+11G>C]) displayed 17 extreme levels of population differentiation, falling out of the 95 th percentile of the global F ST 18 distribution (Figure 3). Indeed, SNP+23133A reaches 37% in East-Asia while is absent in Africa, 19 and SNP+23501C reaches 32% in Europe while is virtually absent in East-Asia. on the DIND results, we suspected that positive selection has targeted the IFNG 23 SNPs +874T (c.115-483A>T) and +5173G (c.*910A>G) in Europe, we took profit of existing 24

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 1 11et al., 2008] for an extensive review). The level of cellular responsiveness to IFN-γ seems to 12 strongly correlate with the clinical severity of mycobacterial disease; e.g., patients with complete 13 IFN-γR1 or IFN-γR2 deficiency display mycobacterial diseases early in life and have a poor 14 prognosis. Altogether, population and clinical data clearly show that no variation having a 15 significant impact on protein function is tolerated at IFNGR1 and IFNGR2, highlighting more 16 generally that the entire IFN-γ pathway is essential in host survival. 17 Then, we have also observed population-specific signatures of positive selection in both 18 genes encoding the IFN-γ receptor. IFNGR1 displays signatures of positive selection in Africa as 19 attested by the results of XP-EHH[Sabeti et al., 2007] (Supp. FigureS4). The observation that 20 the SNP +130G>A (c.85+45G>A) presents the highest levels of population differentiation 21 between African and non-African populations (e.g., F ST =0.45, African versus Asian) suggests 22 that this SNP, or another in LD, could be the target of positive selection. If the SNP +130G>A 23 (c.85+45G>A) was the genuine selected allele, it is interesting to note that natural selection 24 the frequency of the ancestral allele [Di Rienzo, 2006] up to 95.2% in African populations. We have also observed a population-specific signature of 2 positive selection in the gene encoding the second subunit of the IFN-γ receptor. Indeed, we 3 identified a strong signature of positive selection in Asian populations. This signature is most 4 likely explained by the intronic SNP +23133G>A (c.413-209G>A), as attested by the significant 5 values obtained for both the DIND test as well as the levels of population differentiation (Figs. 2 6 and 3). The derived allele at the SNP +23133 is absent in Africa but reaches 37% in Asia, 7 therefore indicating that this mutation likely appeared after the Out-of-Africa exodus. The 8 functional characterization of these two variants is now needed. 9 Taken together, the integration of our population genetics data into a clinical framework 10 clearly demonstrates that the IFN-γ pathway is essential and non-redundant in host defense, most 11 likely in protective immunity against mycobacteria. Future population genetics data will shed 12 light on how redundant or essential in host defense are the other multiple members of the human 13 IFN family, including type-I and type-III IFNs. Sabeti PC, Adebamowo CA, Foster MW, Gordon DR, Licinio J, Manca MC, Marshall 12 PA, Matsuda I, Ngare D, Wang VO, Reddy D, Rotimi CN, Royal CD, Sharp RR, Zeng C, 13 Brooks LD, McEwen JE. 2010. Integrating common and rare genetic variation in diverse 14 human populations. 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Worldwide human relationships 20 inferred from genome-wide patterns of variation. Science 319(5866):1100-1104. 21 Lopez-Maderuelo D, Arnalich F, Serantes R, Gonzalez A, Codoceo R, Madero R, Vazquez JJ, 22 Montiel C. 2003. Interferon-gamma and interleukin-10 gene polymorphisms in 23 pulmonary tuberculosis. Am J Respir Crit Care Med 167(7):970-975. Asderakis A, Perrey C, Hajeer A, Sinnott PJ, Hutchinson IV. 1999. In vitro production 7 of IFN-gamma correlates with CA repeat polymorphism in the human IFN-gamma gene. 8 Eur J Immunogenet 26(1):1-3. 9 Pravica V, Perrey C, Stevens A, Lee JH, Hutchinson IV. 2000. A single nucleotide 10 polymorphism in the first intron of the human IFN-gamma gene: absolute correlation with 11 a polymorphic CA microsatellite marker of high IFN-gamma production. Pickrell JK, Coop G. 2010. The genetics of human adaptation: hard sweeps, soft 14 sweeps, and polygenic adaptation. Curr Biol 20(4):R208-215. 15 Przeworski M, Coop G, Wall JD. 2005. The signature of positive selection on standing genetic 16 variation. Evolution 59(11):2312-2323. 17 Rossouw M, Nel HJ, Cooke GS, van Helden PD, Hoal EG. 2003. Association between 18 tuberculosis and a polymorphic NFkappaB binding site in the interferon gamma gene. Sanchez-DelBarrio JC, Messeguer X, Rozas R. 2003. DnaSP, DNA polymorphism 21 analyses by the coalescent and other methods. Bioinformatics 19(18):2496-2497. 22 Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF, Gabriel SB, Platko JV, 23 Patterson NJ, McDonald GJ, Ackerman HC, Campbell SJ, Altshuler D, Cooper R, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll 6 SA, Gaudet R, Schaffner SF, Lander ES, Frazer KA, Ballinger DG, Cox DR, Hinds DA, 7 Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, 8 Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu 9 S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, 10 Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, 11 Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, 12 Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, 13 Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MM, Tsui SK, Xue H, Wong 14 JT, Galver LM, Fan JB, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, 15 Chagnon F, Ferretti V, Leboeuf M, Olivier JF, Phillips MS, Roumy S, Sallee C, Verner 16 A, Hudson TJ, Kwok PY, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller 17 P, Xiao M, Tsui LC, Mak W, Song YQ, Tam PK, Nakamura Y, Kawaguchi T, Kitamoto 18 T, Morizono T, Nagashima A, Ohnishi Y, Sekine A, Tanaka T, Tsunoda T, Deloukas P, 19 Bird CP, Delgado M, Dermitzakis ET, Gwilliam R, Hunt S, Morrison J, Powell D, 20 Stranger BE, Whittaker P, Bentley DR, Daly MJ, de Bakker PI, Barrett J, Chretien YR, 21 Maller J, McCarroll S, Patterson N, Pe'er I, Price A, Purcell S, Richter DJ, Sabeti P, 22 Saxena R, Schaffner SF, Sham PC, Varilly P, Altshuler D, Stein LD, Krishnan L, Smith 23 AV, Tello-Ruiz MK, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Lin

Figure 1 .

 1 FIGURE LEGENDS

Figure 2 .

 2 Figure 2. Signature of positive selection at IFNG, IFNGR1, and IFNGR2. DIND test in

Figure 3 .

 3 Figure 3. Levels of population differentiation, measured by F ST . Population pair-wise

  Estimation of the intensity of natural selection acting on IFNG, IFNGR1 and IFNGR2. 140x70mm (300 x 300 DPI) Signature of positive selection at IFNG, IFNGR1, and IFNGR2. 160x160mm (300 x 300 DPI) Black boxes correspond to coding exons, empty boxes to non-coding exons. Introns are represented by a line. The violet arrows indicate the orientation of the genes. S2. Signatures of positive selection, based on the DIND test, at IFNG, IFNGR1, and IFNGR2 when considering the demographic model of [Laval et al., 2010] DIND test in Africans (A,D,G), Europeans (B,E,H) and East-Asians (C,F,I). We plotted the iπ A /iπ D values against the Derived Allele Frequencies (DAFs). P-values were obtained by comparing the iπ A /iπ D values for the 3 genes against the expected iπ A /iπ D values obtained from 10 4 simulations considering a previously validated demographic model [Laval et al., 2010]. The higher dashed line of each graph corresponds to the 99 th percentile, and the lower to the 95 th percentile. African (A, D, G), European (B, E, H), and East-Asian (C, F, I) populations. LD was estimated for SNPs with MAF>0.01. In each square, r² values are presented. Red squares without any value correspond to r²=1. of positive selection acting on IFNGR1 in Africa, using the XP-EHH test.The region is centered on IFNGR1. This graph was obtained using the HGDP selection browser (http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP/).

  S5. IFNG gene treeTime is scaled in millions of years. Mutations are named for their physical positions along the IFNG genic region, using the "ATG position" annotation (see Materials and Methods for details).Absolute frequencies, in numbers of chromosomes observed, of each haplotype lineage in Africa, Europe and East-Asia are reported. For each SNP, the first allele corresponds to the most parsimonious ancestral allele.

  708A>T and c.889G>A, the domain is predicted by the UniProt Database as a potential domain. For the Panther analyses, the P-deleterious values are shown. The "x" means that the SNP is genotyped in HapMap Phase III and/or Phase II. The frequencies, given in %, of each SNP in the different merged continental populations refer to the Derived Allele Frequency in Africans (AF), Europeans (EU) and Asians (AS) of our study. Singletons observed are reported per individual population in our population sample; Ady: Adygei. Ban: Bantu. Dau: Daur. Cam: Cambodian. Fre: French. Hez: Hezhen. Jap: Japanese. Man: Mandenka. Nax: Naxi. Orc: Orcadian. Rus: Russian. Sar: Sardinian. Yor: Yoruba.

Table 1

 1

and Table

2

are provided as separate *.doc files, with their titles and legends.

e-

Any Supporting Tables or Figures should be named and cited from the text as follows: 'Supp. Table S1' and 'Supp. Figure S1' (see below). --If possible, do not use custom paper sizes -Letter or A4 are preferred. Done f-Supporting Figures and Tables should be prepared in a single MS Word *.doc file labeled 'Supp_Mat', with Figures preceding Tables. Each table/figure should be accompanied with its legend.

	3)
	F o r
	P
	e
	e r
	R
	e
	v i e
	w

All Supporting Tables and Figures have been merged into a single *.doc file. However, the Supp. Table

S4

, which contains an excessive number of columns to be included in a standard format, is provided as a *.xls file. In addition, this file in .xls format will be highly useful for the readers (possibility of filtering, data mining, etc)

Please check to see that the references follow the journal's standard format and are cited properly. See our online Author Instructions http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098- 1004/homepage/ForAuthors.html

  TableS2). Our population-based resequencing effort allowed us to identify 127

	14
	nucleotide diversity per site (π): 4 x10 -4 , 4.7 x10 -4 and 7.2 x10 -4 for IFNG, IFNGR1 and IFNGR2,

8

mutations, including 117 single nucleotide polymorphisms (SNPs), 2 insertions, 5 deletions and 3 9 duplications (Supp. Table

S3

). Out of the 127 polymorphisms here reported (excluding the IFNG 10 +875 (CA) n microsatellite), 60 (47.2%) were novel and not previously reported in the dbSNP 11 database, and 105 (82.7%) have not been genotyped by the HapMap Consortium [Altshuler et al., 12 2010; Frazer et al., 2007] (Supp. Table

S3

).

13

The three genes, particularly IFNG and IFNGR1, displayed generally low levels of 15 respectively. To compare these levels of nucleotide diversity with background genic expectations, 16 we used the SeattleSNPs database, which reports the sequence diversity of 327 genes involved in 17 inflammatory responses in similar human populations. IFNG and IFNGR1 were found to fall in 18 the 15 th percentile of genes presenting the lowest nucleotide diversity, whereas IFNGR2 was 19 included in the 46 th (Table

1

of Positive Selection 23 Page 30 of 73 John Wiley & Sons, Inc.

  ). Population-wise, IFNG and IFNGR2, genetic diversity was higher 20 in Europeans relative to both Africans and East-Asians (Table1). At the haplotype level, we 21 observed the expected picture of Africans displaying higher levels of haplotype diversity than With respect to SNP density, we observed more SNPs in Africa than in the two Eurasian 1 population groups, an observation that is compatible with public datasets based on genome-wide 2 genotyping data, such as the HapMap Phase II and III datasets[Altshuler et al., 2010; Frazer et , d N , p S and p N values are provided [Bustamante et al., 2005]. This study proposed 2 that the set of genes that are informative to detect the effects of selective constraints against

	Human Mutation P o F r e e r R e v i e w al., 2007], or whole genome sequencing data, such as the 1000 Genomes Project [Durbin et al., 2010]. We found 75, 39 and 45 SNPs in Africa, Europe and Asia, including 53, 16 and 24 population-specific SNPs, respectively. Interestingly, even if most of these population-specific SNPs are found as singletons (28, 12 and 15 in Africa, Europe and Asia, respectively), a number three continental population groups, presenting a derived allele frequency ranging from 62% in Asia to 88% in Europe. It is worth noting that no nonsynonymous mutations were observed at IFNG. To give some clues on the potential functional effect of the non-synonymous mutations we found, we predicted the functional effects of both nonsynonymous mutations fixed between the human and the chimpanzee lineages and those that are polymorphic within humans, using the Human Mutation F o r P e e r R e v i e w predictor of the fitness effects of nonsynonymous variants [Adzhubei et al., 2010; Williamson et al., 2005]. Concerning nonsynonymous mutations that are fixed between the two species, we found one in IFN-γ at position p.Val147Ala, two in IFN-γR1 at positions p.Leu198Ile and p.Ala366Val, and three in IFN-γR2 at positions p.Thr76Met, p.Ser205Phe and p.Ala207Val. Five of these fixed, divergent nonsynonymous substitutions were predicted to be benign, and one was predicted to be probably damaging in IFN-γR2 (p.Ser207Phe). Of the 16 nonsynonymous mutations that are polymorphic in humans (Table 2), 9 were predicted to be benign. The which measures the selective pressure acting on amino-acid substitutions. Under neutrality, ω is not significantly different from 1. Lower values are consistent with selection against nonsynonymous variants (strong purifying selection), whereas higher values reflect selection Human Mutation F o r P e e r R e v i e w (Figure 1A). We compared our data with a genome-wide resequencing dataset of 11,624 genes, nonsynonymous variation are those displaying at least two variable non-synonymous sites (d N +p N ≥2). However, because the IFNG gene does not fall into this category (i.e., d N +p N =1), we relaxed this criterion by comparing the results of the ω parameter for IFNG with all genes displaying a d N +p N ≥1. Among the 7,557 genes falling into this category, we found that only 7.7% of them IFNGR1 presented a γ value significantly lower than 0 (γ=-1.3) (Figure 1B). This, together with the fact that all nonsynonymous SNPs are found as singletons or doubletons, suggests that weak negative selection maintains mutations causing amino-acid changes in IFNGR1 at low population frequencies because of their likely weakly deleterious effects on an individual's fitness. Page 29 of 73 3 4 5 6 17 18 19 20 21 1 2 3 4 5 6 19 20 21 3 4 5 6 18 19 20 21 22 23 Human Mutation F o r P e e r R e v i e w Intra-Species Sequence-Based Neutrality Tests 2 and Wu's H) allowed us to evaluate whether the frequency spectrum of the 3 genes deviates from 3 expectations under neutrality and to detect therefore selection within human populations [Nielsen 4 et al., 2007; Sabeti et al., 2006; Wall, 1999]. Because these tests are known to be sensitive to the 5 mimicking effects that demography and selection have on the patterns of genetic diversity, we 6 considered a demographic model previously validated using a set of 50 unlinked noncoding 19 intercontinental migration [Laval et al., 2010], the significance of the Fay and Wu's H for IFNG 20 disappeared. 21 22 F o r P e e r Detecting Recent Events Human Mutation R e v i e w
	24
	11 12 13 John Wiley & Sons, Inc. 14 15

22

non-African samples for the 3 genes (Table

1

). 23 Page 26 of 73 John Wiley & Sons, Inc.

7

of them display minor allele frequencies ≥ 5% (Supp. Table

S3

). For those SNPs identified in our 8 study that have been genotyped by the HapMap Phase II and/or III, we compared their genotype 9 frequencies in our population panel with those of multiple populations worldwide [Altshuler et 10 al., 2010] (Supp. Table

S4

). In virtually all cases, population frequencies were highly comparable 11 at the level of the different continental populations. 12 13 Putative Functional Consequences of Nonsynonymous Variants 14 Among the 26 exonic mutations identified, 16 SNPs corresponded to nonsynonymous mutations: 15 10 in IFNGR1 and 6 in IFNGR2 (Table

2

). All these nonsynonymous mutations were found at 16 frequencies lower than 5% but one, in IFNGR2, that displayed high population frequencies in the 22 Polyphen v2 HumDiv algorithm

[Adzhubei et al., 2010]

. This method, which considers protein 23 structure and/or sequence conservation for each gene, has been shown to be the best available 24 Page 27 of 73 John Wiley & Sons, Inc.

7

remaining non-synonymous polymorphisms were predicted to be possibly or probably damaging.

8

It is worth noting that the sole nonsynonymous polymorphism with a frequency higher than 5% is 9 predicted to be benign. 10 11 Measuring the Intensity of Natural Selection in the Human Lineage 12 To assess whether and how natural selection has operated on the human IFNG, IFNGR1 and 13 IFNGR2, we first estimated the direction and strength of selection acting in the human lineage as 14 a whole. To this end, we measured d S and d N , i.e. the number of silent and nonsynonymous fixed 15 differences between species (humans versus chimpanzee) as well as p S and p N , i.e. the number of 16 silent and nonsynonymous polymorphic sites observed within species (within humans). We used 17 the McDonald-Kreitman Poisson Random Field (MKPRF) test [Bustamante et al., 2005; 18 Bustamante et al., 2002; Sawyer and Hartl, 1992] in order to estimate ω (i.e., ω α log[θ R /θ S ]), 22 favouring amino-acid changes (positive selection). IFNG presented a ω value significantly lower 23 than 1, indicating that this gene has evolved under the effects of strong purifying selection 24 Page 28 of 73 John Wiley & Sons, Inc.

1 for which the d S 7 exhibit a ω value lower than that observed for IFNG. When restricting the analyses to those genes 8 classified as being involved in "immune system process", only 10.3% of them presented ω values 9 lower than that displayed by IFNG. 10 We next used the population selection parameter γ [Bustamante et al., 2005; Bustamante 11 et al.

, 2002; Gilad et al., 2003]

to identify whether IFNG, IFNGR1 and/or IFNGR2 were subject 12 to selection operating on nonsynonymous mutations that are polymorphic in humans (i.e., 13 segregating non lethal mutations). The parameter γ is negative if a gene displays an excess of 14 amino-acid polymorphisms within humans with respect to amino-acid divergence between 15 species (weak negative and/or balancing selection). Conversely, positive γ values reflect an 16 excess of amino-acid divergence with respect to amino-acid polymorphism (positive selection in 17 the human lineage)

[Bustamante et al., 2005; Bustamante et al., 2002; Gilad et al., 2003

]. 1 Intra-species sequence-based neutrality tests (i.e., Tajima's D, Fu and Li's D* and F* and Fay 7 regions sequenced in a set of populations similar to ours (i.e., African, European and Asian) 8 [Voight et al., 2005]. This model considers a bottleneck in non-African populations starting 9 40,000 years ago in an ancestral population of 9,450 individuals, and an exponential expansion in 10 African populations. In addition, we used an independent demographic model based on the 11 patterns of sequence diversity at 20 unlinked, non-coding regions in a set of populations from 12 Africa, Europe and Asia [Laval et al., 2010]. In contrast with the model of [Voight et al., 2005], 13 the latter does consider the occurrence of inter-continental population migration [Laval et al., 14 2010]. Most tests indicated that the frequency spectrum of the three genes does not significantly 15 deviate from neutral expectations (Table

1

). The exceptions were IFNG, for which we detected a 16 significant excess of high-frequency derived variants in Africa as attested by the significantly 17 negative values of Fay and Wu's H, and IFNGR2, for which we detected an excess of singletons 18 in Asia as attested by both Fu and Li's D* and F* tests (Table

1

). Using the model that considers

  The strong selective constraint acting on IFNG is supported by 2 our inter-species analyses that clearly indicated that IFNG has been subject to intense purifying

		Human Mutation Human Mutation Human Mutation Human Mutation
	1 1	DISCUSSION The first interesting observation that can be made from this study is the absence of non-the [Voight et al., 2005] model. This pattern was accounted for by the presence of 3 African (c.*910A>G) (Figure
	2 2	In this study, we sought to describe the levels of naturally-occurring variation in the three specific chromosomes each carrying 3 SNPs at the ancestral state, i.e. +3610A>G, +4802G>A, and
	3 3 3 3	. Moreover, it has genes controlling the IFN-γ pathway (IFNG, IFNGR1, and IFNGR2) and to assess whether and +5164C>T (c.[367-519A>G; *539G>A; *901C>T]), while the remaining chromosomes, in both
	4 4 4 4	been shown that the +874T allele is associated with the 12 CA repeat-allele at the IFNG how natural selection has acted on them. The first objective, the discovery of new selection. In addition, when considering a genome-wide resequencing dataset of genes showing Africans and Eurasians, harbor the derived state at these three positions (Supp. Figure S5). Two
	5 5 5 5	+875(CA) n (c.115-482CA11_18) microsatellite. We thus decided to assess the patterns of polymorphisms, is optimal when sampled individuals come from many different ethnic origins, similar features than IFNG [Bustamante et al., 2005], this gene falls into the ~10% of genes plausible explanations can be put forward to explain this pattern. First, it may testify the
	6 6 6 6	microsatellite diversity associated with the +874A/T alleles. Among the 372 chromosomes we while the second objective, the identification of signatures of natural selection, requires many involved in immune system processes that display the strongest selective constraints on amino-occurrence of an almost-complete selective sweep worldwide, attesting for a selective advantage
	7 7 7 7	genotyped at this microsatellite, we found 8 different alleles ranging from 11 to 18 CA repeats. In individuals per continental region. Our sample was designed to achieve both objectives, by acid variation. Interestingly, the intensity of purifying selection on IFNG estimated from our associated with the derived states at the 3 SNPs. Although none of these SNPs corresponds to
	8	contrast with previous observations proposing an absolute correlation between the 12 CA repeat-F o r including 186 individuals originating from multiple ethnic groups living in sub-Saharan Africa, F o r dataset (ω = 0.0189) is extremely similar to that obtained using another population panel (ω = F o mutations changing the amino-acid sequence, they could themselves have functional F o 0.0184 from [Bustamante et al., 2005]), indicating that the detection of strong purifying selection r r
		P P P P
		e e r e e r e e r e e r
	14 15 16 17 18 17 18 17 18	R e v i e w among populations of a given continent, and among continental groups, and obtained values of 89.27%, 0.45% and 10.28%, respectively. The negligible, non-significant differentiation observed among populations from the same continent in our dataset (0.45%) is consistent with a genome-wide study conducted in the same individuals and populations, where population R e v i e w infections, but has a smaller impact on viral clearance [Dorman et al., 2004; Filipe-Santos et al., R e v i e w obtained a significant Fay and Wu's H in Africa (P≤0.05), indicating that non-negligible R e v i e w
	19 19 19	structure within continental regions was found to be limited [Li et al., 2008]. In addition, this is 2006; Zhang et al., 2008], it is likely that IFN-γ is essential and non-redundant for protective intercontinental migration could explain the patterns observed. Even if the ancestral alleles of
	20 20 20	true for other genome-wide datasets from similar populations, e.g. the HapMap samples of Han immunity against mycobacterial diseases. these 3 SNPs are absent from European and Asian individuals of our panel, the intercontinental
	21 21 21	Chinese and Japanese have been merged in all analyses due to their high genetic resemblance Leaving aside the strong selective constraint maintaining intact the IFN-γ protein migration scenario is likely, since 2 of these 3 alleles do segregate in some European and/or
		[Frazer et al., 2007]. Altogether, our analyses, fuelled by the results of genome-wide datasets, sequence at the species-wide level, our analyses unmasked more subtle evolutionary events Asian individuals from the HapMap Phase III data (Supp. Table S4).
		18 19 20 21

8

allele at the microsatellite and the T allele at SNP +874

[Ding et al., 2008; Pravica et al., 2000

], 9 we found individuals homozygous for the +874T allele, who were heterozygous for the 10 microsatellite. Indeed, at the haplotype level, we found 12 chromosomes, among the 112 carrying 11 the +874T allele, that were non-12 CA repeat (i.e. 13 or 15 CA repeats), and 13 chromosomes, 12 among the 260 carrying the +874A allele, that were 12 CA repeat (Supp. Table

S5

). It would be 13 now interesting to distinguish which of these two mutational events, i.e. the +874T allele or the 14 12 CA repeat, account for by the previously observed higher production of IFN-γ. More 15 importantly, we observed that the microsatellite diversity associated with the +874T allele was 16 much lower than that associated with the +874A allele (expected global heterozygosity 0.19 vs. 17 0.64, respectively). This observation further supported the notion that positive selection has 18 targeted the IFNG SNP +874T. 19 Page 33 of 73 John Wiley & Sons, Inc.

8

Europe and Asia. However, a possible limitation of this population scheme is the potential 9 presence of genetic structure among populations from the same continent, which are merged in 10 our analyses, a situation that can lead to significant deviations of the allele frequency spectrum 11

[Przeworski et al., 2005]

and therefore create spurious signals of selection. To test this 12 possibility, we performed an AMOVA

[Excoffier et al., 1992]

to estimate the fraction of the 13 genetic variance of our dataset that is explained by genetic differences within a given population, 22 indicate that the genetic differentiation observed among subpopulations from the same continent 23 is weak enough not to influence any of our conclusions regarding the action of natural selection. 24 Page 34 of 73 John Wiley & Sons, Inc. 1 synonymous mutations in IFNG. 9 is not sensitive to the population considered. The fact that nonsynonymous mutations are not 10 tolerated suggests that amino-acid replacements at IFNG may have fatal consequences and be 11 quickly removed from the population. Such extreme protein conservation makes this gene an 12 excellent candidate to be involved in severe, rather lethal, diseases. A similar situation has been 13 observed for toll-like receptor 3 (TLR3); this gene is under purifying selection [Barreiro et al., 14 2009] and TLR3 defects confer predisposition to childhood herpes simplex encephalitis [Zhang et 15 al., 2007]. Altogether, our data indicate that IFN-γ plays an essential, non-redundant role in host 16 survival. Because it has been proposed that IFN-γ plays a key role against mycobacterial 22 acting at the population-specific level. For example, we identified an excess of high-frequency 23 derived alleles in IFNG in Africa, as attested by the significance of the Fay and Wu's H test using 24 Page 35 of 73 John Wiley & Sons, Inc.

8

consequences or be associated with mutations located further away in regulatory regions. 9 Alternatively, this pattern could also result from past population demographic events, such as 10 population structure within Africa

[Wall and Hammer, 2006]

and intercontinental migration 11

[Zeng et al., 2006]

. Indeed, when using the model of

[Laval et al., 2010]

, which assumes 12 intercontinental gene flow, the Fay and Wu's H at IFNG lost its significance in Africa (Table

1

).

13 Because the differences in significance at IFNG could result from other parameter estimates that 14 differ between the two demographic models (e.g. intensity of the out-of-Africa bottleneck), we 15 estimated the P-value of Fay & Wu's H at IFNG using the model of [Laval et al., 2010] but 16 assuming no intercontinental migration. In this case, similarly to [Voight et al., 2005], we 22 Our DIND analyses identified a population-specific signature of positive selection 23 targeting IFNG among Europeans, specifically at the SNPs +874T (c.115-483A>T) and +5173G 24 Page 36 of 73 John Wiley & Sons, Inc.

  . Further support to the action

		Human Mutation
	1	the notion that these two genes are not entirely free to accumulate functional variation. IFNGR1
	2	appears to evolve under the action of weak negative selection, indicating that nonsynonymous
	3	mutations, although tolerated, are kept at low population frequencies because they may have
	4	weakly deleterious effects. In turn, amino-acid variation at IFNGR2 seems to be somehow
	5	constrained, as attested by the value of ω that is lower than 1. In addition, the only
	6	nonsynonymous mutation observed at IFNGR2 that is found at a high population frequency is
		predicted to be benign (i.e., mutation likely not to impact protein function or weakly deleterious),
		F o
		r
		P
	12 13	of positive selection targeting the +874T allele comes from our survey of associated e e r
	14 15 16 17 18	microsatellite variation. Indeed, despite the chromosomes harboring the +874T allele are very frequent in Europe (54%), they present a lower microsatellite diversity than those harboring the +874A allele (expected heterozygosities in Europe of 0.17 vs. 0.26, respectively). Our results are collectively consistent with a selective advantage for a higher production of IFN-γ in Europeans, suggesting the existence of different, or stronger, selective pressures in Europe associated with R e v i e w
	19	IFN-γ production. Taken together, although a strong selective constraint prevents qualitative
	20	changes of the IFN-γ protein, there is a fine-tuned regulation of IFN-γ expression that seems to
		evolve adaptively.
		22

21

In contrast to IFNG, where nonsynonymous mutations are not tolerated, our results 22 unmasked more relaxed selective constraints at IFNGR1 and IFNGR2, where we observed 10 and 23 6 nonsynonymous mutations, respectively (Table

2

). However, several lines of evidence support 24 Page 37 of 73 John Wiley & Sons, Inc. 7 an observation that indicates that mutations leading to major changes in protein function are not 8 allowed to increase in frequency in the population. From the clinical genetics angle, several other 9 mutations in IFNGR1 and IFNGR2 have been shown to be associated with impaired cellular 10 responses to IFN-γ and to result in Mendelian susceptibility to mycobacterial disease (see [Zhang

Page 47 of 73 John Wiley & Sons, Inc.

	Page 49 of 73	Human Mutation Human Mutation Human Mutation	Page 48 of 73
	1 1	Stephens M, Donnelly P. 2003. A comparison of bayesian methods for haplotype reconstruction Zeng K, Fu YX, Shi S, Wu CI. 2006. Statistical tests for detecting positive selection by utilizing
	2 2	from population genotype data. Am J Hum Genet 73(5):1162-1169. high-frequency variants. Genetics 174(3):1431-1439.
	3 3	Takahashi M, Matsuda F, Margetic N, Lathrop M. 2003. Automated identification of single Zhang SY, Boisson-Dupuis S, Chapgier A, Yang K, Bustamante J, Puel A, Picard C, Abel L,
	4 4	nucleotide polymorphisms from sequencing data. J Bioinform Comput Biol 1(2):253-265. Jouanguy E, Casanova JL. 2008. Inborn errors of interferon (IFN)-mediated immunity in
	5 5	Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan humans: insights into the respective roles of IFN-alpha/beta, IFN-gamma, and IFN-
	6 6	A, Narechania A. 2003. PANTHER: a library of protein families and subfamilies indexed lambda in host defense. Immunol Rev 226:29-40.
	7 7	by function. Genome Res 13(9):2129-2141. Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, Segal D, Sancho-Shimizu V,
	8 9 8 9	F o r F o r Todd PA, Goa KL. 1992. Interferon gamma-1b. A review of its pharmacology and therapeutic potential in chronic granulomatous disease. Drugs 43(1):111-122. F Lorenzo L, Puel A, Picard C, Chapgier A, Plancoulaine S, Titeux M, Cognet C, von o r Bernuth H, Ku CL, Casrouge A, Zhang XX, Barreiro L, Leonard J, Hamilton C, Lebon P,
	10 11 12 13 10 11	P e e r P e e r Tso HW, Ip WK, Chong WP, Tam CM, Chiang AK, Lau YL. 2005. Association of interferon gamma and interleukin 10 genes with tuberculosis in Hong Kong Chinese. Genes Immun 6(4):358-363. Voight BF, Adams AM, Frisse LA, Qian Y, Hudson RR, Di Rienzo A. 2005. Interrogating Heron B, Vallee L, Quintana-Murci L, Hovnanian A, Rozenberg F, Vivier E, Geissmann P F, Tardieu M, Abel L, Casanova JL. 2007. TLR3 deficiency in patients with herpes e r e simplex encephalitis. Science 317(5844):1522-1527.
	16 17 18 14 15 16 17 18	R e v i e w characterization of positive selection in human populations. Nature 449(7164):913-918. Sallakci N, Coskun M, Berber Z, Gurkan F, Kocamaz H, Uysal G, Bhuju S, Yavuzer U, Singh M, Yegin O. 2007. Interferon-gamma gene+874T-A polymorphism is associated with R e v i e w multiple aspects of variation in a full resequencing data set to infer human population size changes. Proc Natl Acad Sci U S A 102(51):18508-18513. Wall JD. 1999. Recombination and the power of statistical tests of neutrality. Genetical Research 74(1):65-79. Wall JD, Hammer MF. 2006. Archaic admixture in the human genome. Curr Opin Genet Dev R e v i e w
	19 19	tuberculosis and gamma interferon response. Tuberculosis (Edinb) 87(3):225-230. 16(6):606-610.
	20 20	Sawyer SA, Hartl DL. 1992. Population genetics of polymorphism and divergence. Genetics Weir BS. 1984. Estimating F-statistics for the analysis of population structure. Evolution
	21 21	132(4):1161-1176. 38(6):1358-1370.
	22 22	Schroder K, Hertzog PJ, Ravasi T, Hume DA. 2004. Interferon-gamma: an overview of signals, Williamson SH, Hernandez R, Fledel-Alon A, Zhu L, Nielsen R, Bustamante CD. 2005.
	23 23	mechanisms and functions. J Leukoc Biol 75(2):163-189. Simultaneous inference of selection and population growth from patterns of variation in
	24	the human genome. Proc Natl Acad Sci U S A 102(22):7882-7887.
			John Wiley & Sons, Inc. 33 John Wiley & Sons, Inc. 34

Table 1 . Mean diversity indices and neutrality tests across IFNG, IFNGR1 and IFNGR2 genomic regions

 1

			IFNG			IFNGR1			IFNGR2
		Africa	Europe	Asia	Global	Africa	Europe	Asia	Global	Africa	Europe	Asia	Global
		(N=124)	(N=124)	(N=124)	(N=372)	(N=124)	(N=124)	(N=124)	(N=372)	(N=124)	(N=124)	(N=124)	(N=372)
	H	17	8	7	26	25	12	16	43	25	17	18	49
	Hd	0.71	0.64	0.64	0.72	0.82	0.69	0.81	0.81	0.90	0.79	0.74	0.87
	Syn	0	0	0	0	1	1	2	2	4	0	3	7
	Non-syn	0	0	0	0	8	1	3	10	5	3	1	6
	S	21	9	6	28	25	11	17	42	29	16	19	47
	Singletons	7	3	1	11	10	5	5	17	14	6	12	28
	INDELS	2	1	1	2	3	1	1	5	4	1	1	4
	π (10 -4 )	3.7	4.2	3.2	4	4.3	4.1	5	4.7	6.9	7.1	5.5	7.2
	θ W (10 -4 )	8.5	3.6	2.4	9.4	8.5	3.7	5.8	11.8	11.3	6.2	7.4	15.1
	TD	-1.60	0.34	0.70		-1.44	0.27	-0.37		-1.13	0.39	-0.73
	D	-1.39	-1.02	0.12		-2.13	-2.02	-0.95		-3.09	-1.63	-4.08**/ † †
	F	-1.76	-0.65	0.37		-2.23	-1.43	-0.88		-2.77	-1.05	-3.36**/ † †
	H	-5.76*	-0.42	-0.10		-0.82	0.71	1.26		-1.27	-1.18	-2.33
	N, number of chromosomes sequenced in the corresponding population; H, number of haplotypes; Hd, haplotype diversity; Syn, number of
	synonymous mutations; Non-syn, number of nonsynonymous mutations; S, number of segregating sites; INDELS, number of INDELS including
	the IFNG +875(CA)

n microsatellite; π, nucleotide diversity per site from average pairwise differences; θ W , nucleotide diversity per site from number of segregating sites; TD, Tajima's D; D, Fu & Li's D*; F, Fu & Li's F*; H, Fay & Wu's H. **/*P-values ≤ 0.01 and ≤ 0.05, respectively, according to the model of [

Table 2 .

 2 Nonsynonymous changes in the IFNGR1 and IFNGR2 genes identified in this study.

					Chromosomal
	Gene	c.SNP	ATG position	Aminoacid change	location	Gene Location	Protein domain	Polyphen	Panther	dbSNP	Africa	Europe	Asia
	IFNGR1	c.5C>T	+5	p.Ala2Val	chr6:137,540,460	exon 1	signal	po damaging	NA				0.8
	IFNGR1	c.40G>A	+40	p.Val14Met	chr6:137,540,425	exon 1	signal	po damaging	0.55613	rs11575936			1.6
	IFNGR1	c.181G>A	+12346	p.Val61Ile	chr6:137,528,119	exon 2	extracellular	pr damaging	0.81834	rs17175322	0.8
	IFNGR1	c.538G>A	+14988	p.Gly180Arg	chr6:137,525,477	exon 4	extracellular	benign	0.67155		1.6
	IFNGR1	c.864C>G	+20691	p.Ile288Met	chr6:137,519,774	exon 7	cytoplasmic	benign	0.77695		1.6
	IFNGR1	c.1004A>C	+20831	p.His335Pro	chr6:137,519,634	exon 7	cytoplasmic	benign	0.69608	rs17175350	1.6
	IFNGR1	c.1027G>A	+20854	p.Val343Met	chr6:137,519,611	exon 7	cytoplasmic	benign	0.4295		0.8
	IFNGR1	c.1034A>G	+20861	p.His345Arg	chr6:137,519,604	exon 7	cytoplasmic	pr damaging	0.48851		0.8
	IFNGR1	c.1268G>A	+21095	p.Ser423Asn	chr6:137,519,370	exon 7	cytoplasmic	benign	0.85071		0.8	0.8
	IFNGR1	c.1400T>C	+21227	p.Leu467Pro	chr6:137,519,238	exon 7	cytoplasmic	benign	NA	rs1887415	0.8		4.8
	IFNGR2	c.173C>G	+11445	p.Thr58Arg	chr21:34,787,294	exon 3	extracellular	po damaging	0.49217	rs4986958	12.9	0.8
	IFNGR2	c.191G>A	+11463	p.Arg64Gln	chr21:34,787,312	exon 3	extracellular	benign	0.68807	rs9808753	79.0	87.9	62.1
	IFNGR2	c.466A>C	+23395	p.Ile156Leu	chr21:34,799,244	exon 5	extracellular	benign	0.57445			1.6
	IFNGR2	c.544A>G	+23473	p.Lys182Glu	chr21:34,799,322	exon 5	extracellular	benign	0.5363	rs17878711	4.0
	IFNGR2	c.708A>T	+28781	p.Glu236Asp	chr21:34,804,630	exon 6	transmembrane	po damaging	0.69961		0.8
	IFNGR2	c.889G>A	+33295	p.Asp297Asn	chr21:34,809,144	exon 8	cytoplasmic	pr damaging	0.76231		0.8

Table S2 . Details on resequenced regions and fragments for the IFNG, IFNGR1 and IFNGR2 genes.

 S2 Chromosome location is given according to the hg19 (GRCh37) human assembly coordinates. Positions are relative to the start coding site of the corresponding gene. Lengths are given in base pairs.

	Page 65 of 73		Human Mutation
			Sequenced
	Gene	Chromosomal location	fragments	Sequenced lenght	Exonic	Non-exonic
	IFNG NM_000619.2 NG_015840.1 IFNGR1 NM_000416.2 NG_007394.1 IFNGR2 NM_005534.3 NG_007570.1	-596 : 900 988 : 2,305 3,586 : 5,347 -261 : 333 12,159 : 13,533 14,726 : 16,051 17,435 : 17,637 17,898 : 18,639 20,618 : 21,856 7,146 : 7,709 10,950 : 11,667 17,221 : 18,260 22,990 : 23,613 28,502 : 29,361 33,038 : 33,192 33,275 : 34,090 F o r P e e r R e v i e w chr12:68,548,550-68,553,521 4,576 chr6:137,518,621-137,540,567 5,479 chr21:34,775,202-34,809,827 4,777	1,210 2,059 1,555	3,366 3,420 3,222
			John Wiley & Sons, Inc.

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Table S3. Full list of polymorphisms found at IFNG, IFNGR1 and IFNGR2 genes.

	Page 67 of 73 Page 69 of 73					Human Mutation Human Mutation Human Mutation Human Mutation Human Mutation								Page 66 of 73 Page 68 of 73 Page 70 of 73
	IFNG IFNGR1 IFNGR1 IFNGR2 IFNGR2	chr12:68548594-68548594 chr6:137527157-137527157 chr6:137518718-137518718 chr21:34799132-34799132 chr21:34809686-34809686	c.*539G>A c.373+116T>C c.*450C>T c.413-59A>G c.*417G>A	+4802 +13308 +21747 +23283 +33837	3'UTR intron 3 3' UTR intron 3 3' UTR					rs2069723 rs12655	x	x	97.6 0.8 3.2	100 0.8 0.8	100	Ban Orc Sar
	IFNG IFNGR1 IFNGR1 IFNGR2 IFNGR2	chr12:68548515-68548515 chr6:137527020-137527020 chr6:137518669-137518669 chr21:34799138-34799143 chr21:34809693-34809693	c.*618C>T c.373+253G>A c.*499delT c.413-53_413-48delTCTATA c.*424T>C	+4881 +13445 +21796 +23290 +33844	3' intron 3 3' UTR intron 3 3' UTR					rs2069724 rs115970011 rs17181758 rs1059293	x x	x	2.4 1.6 5.6 12.8 12.9 55.6 15.3
	IFNG IFNGR1 IFNGR2 IFNGR2	chr12:68548386-68548386 chr6:137526967-137526968 chr21:34783257-34783257 chr21:34799166-34799166	c.*747G>A c.373+297dupA c.74-3938C>T c.413-25T>C	+5010 +13498 +7408 +23317	3' intron 4 intron 1 intron 3					rs2069725	x		1.6 0.8		0.8 0.8	Ban Mon Nax
	Gene IFNG IFNGR1 IFNGR2 IFNGR2	Chromosomal position chr12:68548356-68548356 chr6:137525477-137525477 chr21:34783522-34783522 chr21:34799244-34799244	c.SNP c.*777C>T c.538G>A c.74-3673C>T c.466A>C	ATG position Location +5040 3' +14988 exon 4 +7673 intron 1 +23395 exon 4	Protein domain extracellular extracellular	Amino-acid change p.Gly180Arg p.Ile156Leu	Polyphen benign benign	Panther 0.67155 0.57445	dbSNP rs115027181	Hap MapII	Hap MapIII AF 0.8 1.6 0.8	EU 2.4 1.6	AS	Singleton Yor
	IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNG IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR1 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2 IFNGR2	chr12:68553766-68553766 chr12:68553703-68553703 chr12:68552522-68552522 chr12:68552208-68552208 chr12:68552195-68552195 chr12:68551931-68551931 chr12:68551554-68551554 chr12:68551409-68551409 chr12:68551343-68551343 chr12:68551333-68551333 chr12:68551196-68551196 chr12:68549786-68549786 chr12:68549767-68549767 chr12:68549710-68549710 chr12:68549686-68549686 chr12:68549549-68549549 chr12:68549494-68549494 chr12:68549377-68549377 chr12:68549033-68549036 chr12:68548953-68548953 chr12:68548770-68548770 chr12:68548680-68548680 chr12:68548232-68548232 chr12:68548223-68548223 chr12:68548066-68548066 chr6:137540719-137540719 chr6:137540645-137540645 chr6:137540633-137540633 chr6:137540536-137540536 chr6:137540520-137540520 chr6:137540460-137540460 chr6:137540425-137540425 chr6:137540417-137540417 chr6:137540370-137540370 chr6:137540362-137540362 chr6:137540335-137540335 chr6:137540133-137540133 chr6:137528290-137528290 chr6:137528259-137528259 chr6:137528119-137528119 chr6:137528085-137528085 chr6:137528082-137528082 chr6:137527627-137527628 chr6:137527578-137527578 chr6:137524953-137524953 chr6:137524926-137524926 chr6:137524553-137524553 chr6:137524475-137524475 chr6:137522951-137522951 chr6:137522488-137522488 chr6:137522217-137522217 chr6:137521976-137521976 chr6:137519780-137519780 chr6:137519774-137519774 chr6:137519634-137519634 chr6:137519611-137519611 chr6:137519604-137519604 chr6:137519588-137519588 chr6:137519408-137519434 chr6:137519370-137519370 chr6:137519238-137519238 chr6:137519097-137519097 chr6:137519028-137519028 chr6:137518962-137518962 chr6:137518951-137518951 chr6:137518855-137518855 chr21:34786861-34786861 chr21:34787155-34787155 chr21:34787294-34787294 chr21:34787312-34787312 chr21:34787401-34787401 chr21:34793151-34793151 chr21:34793241-34793241 chr21:34793380-34793381 chr21:34793564-34793564 chr21:34793588-34793588 chr21:34793664-34793664 chr21:34793678-34793678 chr21:34793706-34793706 chr21:34793707-34793707 chr21:34793917-34793917 chr21:34798839-34798839 chr21:34798881-34798881 chr21:34798982-34798982 chr21:34798994-34798994 chr21:34799023-34799023 chr21:34799099-34799099 chr21:34799111-34799112 34809621 chr21:34809621-34809271 chr21:34809271-34809263 chr21:34809263-34809239 chr21:34809239-34809200 chr21:34809200-chr21:34809144-34809144 chr21:34799288-34799288 chr21:34799306-34799306 chr21:34799322-34799322 chr21:34799350-34799350 chr21:34804391-34804391 chr21:34804630-34804630 chr21:34804650-34804650 chr21:34804732-34804732 chr21:34804816-34804816 chr21:34804930-34804930 chr21:34804966-34804966 chr21:34804979-34804979 chr21:34805079-34805079 chr21:34805197-34805197 34809022 chr21:34809000-	c.-371T>A c.-308G>T c.115-483A>T c.115-169C>T c.115-156A>G c.183+40T>C c.366+139T>C c.366+284G>A c.366+350A>T c.366+360G>C c.366+497T>C c.367-519A>G c.367-500C>T c.367-443G>A c.367-419T>C c.367-282T>A c.367-227G>C c.367-110G>T c.*97_*100delTCAA c.*180C>T c.*363G>A c.*453G>C c.*901C>T c.*910A>G c.*1067A>T c.-255C>T c.-181T>G c.-169C>A c.-72C>T c.-56C>T c.5C>T c.40G>A c.48G>A c.85+10C>T c.85+18A>C c.85+45G>A c.85+247T>G c.86-76C>T c.86-21T>G c.181G>A c.200+15T>G c.200+18A>G c.201-183_201-182delAG F o r P e e r R e v i e w -371 5' -308 5' +874 intron +1188 intron +1201 intron +1465 intron +1842 intron +1987 intron +2053 intron +2063 intron +2200 intron +3610 intron +3629 intron +3686 intron +3710 intron +3847 intron +3902 intron +4019 intron +4360 3'UTR +4443 3'UTR +4626 3'UTR +4716 3'UTR +5164 3' +5173 3' +5330 3' -255 5' -181 5' -169 5' -72 5' -56 5' +5 exon 1 signal p.Ala2Val possibly damaging NA +40 exon 1 signal p.Val14Met possibly damaging 0.55613 +48 exon 1 +95 intron 1 +103 intron 1 +130 intron 1 +332 intron 1 +12175 intron 1 +12206 intron 1 +12346 exon 2 extracellular p.Val61Ile probably damaging 0.81834 +12380 intron 2 +12383 intron 2 +12837 intron 2 c.201-133G>A +12887 intron 2 F o r P e e r R e v i e w c.547-131C>T +15512 intron 4 c.547-104A>G +15539 intron 4 c.733+83C>T +15912 intron 5 c.733+161G>T +15990 intron 5 c.734-806C>T +17514 intron 5 c.734-343C>A +17977 intron 5 c.734-72T>G +18248 intron 5 c.861+42T>G +18489 intron 6 c.862-4A>G +20685 intron 6 c.864C>G +20691 exon 7 cytoplasmic p.Ile288Met benign 0.77695 c.1004A>C +20831 exon 7 cytoplasmic p.His335Pro benign 0.69608 c.1027G>A +20854 exon 7 cytoplasmic p.Val343Met benign 0.4295 c.1034A>G +20861 exon 7 cytoplasmic p.His345Arg probably damaging 0.48851 c.1050T>G +20877 exon 7 c.1204_1230dupTGTTCTGAGA GTGATCACTCCAGAAAT +21058 exon 7 p.Cys402_Asn410 dup c.1268G>A +21095 exon 7 cytoplasmic p.Ser423Asn benign 0.85071 c.1400T>C +21227 exon 7 cytoplasmic p.Leu467Pro benign NA c.*71G>T +21368 3' UTR c.*140G>A +21437 3' UTR c.*206A>G +21503 3' UTR c.*217T>A +21514 3' UTR c.*313delC +21610 3' UTR F o r P e e r R e v i e w c.74-334G>A +11012 Intron 1 c.74-40C>T +11306 intron 1 c.173C>G +11445 exon 2 extracellular p.Thr58Arg possibly damaging 0.49217 c.191G>A +11463 exon 2 extracellular p.Arg64Gln benign 0.68807 c.206+74A>T +11552 intron 2 c.207-636C>T +17302 intron 2 c.207-546C>T +17392 intron 2 c.207-407_207-406insATT +17532 intron 2 c.207-223A>G +17715 intron 2 c.207-199A>G +17739 intron 2 c.207-123G>T +17815 intron 2 c.207-109A>G +17829 intron 2 c.207-81T>C +17857 intron 2 c.207-80G>A +17858 intron 2 c.337C>T +18068 exon 3 c.413-352C>G +22990 intron 3 c.413-310A>C +23032 intron 3 c.413-209G>A +23133 intron 3 c.413-197T>C +23145 intron 3 c.413-168T>C +23174 intron 3 c.413-92T>C +23250 intron 3 c.413-80_413-79dupTA +23263 intron 3 c.*352C>T +33772 3' UTR c.*2C>T +33422 3' UTR c.1008G>A +33414 exon 7 c.984G>A +33390 exon 7 c.945C>T +33351 exon 7 c.889G>A +33295 exon 7 cytoplasmic p p.Asp297Asn damaging 0.76231 probably F o r P e e r R e v i e w c.510G>A +23439 exon 4 c.528T>C +23457 exon 4 c.544A>G +23473 exon 4 extracellular p.Lys182Glu benign 0.5363 c.561+11G>C +23501 intron 4 c.562-93C>T +28542 intron 4 c.708A>T +28781 exon 5 transmembra ne p p.Glu236Asp possibly damaging 0.69961 c.721+7T>C +28801 intron 5 c.721+89T>C +28883 intron 5 c.721+173A>C +28967 intron 5 c.722-91A>T +29081 intron 5 c.722-55T>C +29117 intron 5 c.722-42C>T +29130 intron 5 c.780G>T +29230 exon 6 c.879+19C>T +29348 intron 6 AGACTCCA 113dupGCCTAGGCAAGAGTA +33174 intron 6 c.880-135_880-F o r P e e r R e v i e w	rs3814242 rs2069709 rs2430561 rs117641733 rs74099944 rs1861494 rs1861493 rs2069719 rs116174811 rs2069720 rs55662249 rs2069722 rs55991209 rs7957366 rs2069726 rs2069727 rs2069736 rs7753590 rs17175078 rs17181457 rs2234711 rs11575936 rs11575931 rs7749390 rs11754268 rs76168031 rs41477052 rs17175322 rs17175329 rs41505745 rs3839520 rs76198934 rs74822325 rs17181751 rs3799488 rs17175350 rs11914 rs1887415 rs55665036 rs1887416 rs1887417 rs75444035 rs114703465 rs4986958 rs9808753 rs78607908 rs17885013 rs13051491 rs2834214 rs118015414 rs78407108 rs112107702 rs41356148 rs1802585 rs17878711 rs11910627 rs41351148 rs1532 rs17883129	x x x x x x x x x x x x x x x x x x	x x x x x x x x x	1.6 18.5 54.0 17.7 5.6 0.8 0.8 2.4 0.8 87.1 73.4 71.8 0.8 0.8 8.9 26.6 29.0 97.6 100 100 0.8 4.0 0.8 0.8 0.8 1.6 1.6 5.6 1.6 0.8 97.6 100 100 17.7 54.0 18.5 1.6 4.8 11.3 0.8 11.3 51.6 55.6 40.3 0.8 1.6 4.8 51.6 55.6 40.3 0.8 4.8 27.4 54.0 11.3 0.8 1.6 0.8 0.8 3.2 0.8 4.0 2.4 0.8 1.6 0.8 13.7 0.8 0.8 0.8 16.1 29.0 1.6 1.6 0.8 0.8 5.6 18.5 12.1 0.8 0.8 0.8 0.8 4.8 0.8 0.8 4.8 4.8 0.8 5.6 0.8 12.9 0.8 79.0 87.9 62.1 3.2 0.8 2.4 0.8 0.8 26.6 58.1 17.7 0.8 0.8 73.4 41.9 82.3 0.8 0.8 0.8 0.8 0.8 0.8 34.7 10.5 18.5 0.8 5.6 0.8 0.8 12.1 37.1 0.8 0.8 6.5 0.8 0.8 4.0 12.1 32.3 0.8 0.8 0.8 5.6 0.8 0.8 0.8 86.3 67.7 99.2 0.8 0.8 41.9 30.6 45.2 0.8	Fre Fre Ban Ban Ban Man Oro Sar Man Ban Ady Jap Orc Yor Yor Yor Jap Man Dai Cam Mon Tu Ban Man Man Fre Yor Orc Man Man Yor Cam Yor Han Fre Ban Rus Man Man Ban Dau Yor Dai Ban Yor Ban She Hez Ban Jap Ban Rus Fre Han Ban
					John Wiley & Sons, Inc. 10 John Wiley & Sons, Inc. 11 John Wiley & Sons, Inc. 12 John Wiley & Sons, Inc. 13 John Wiley & Sons, Inc. 14

Table S4 : Genotype frequencies of each SNP in our population panel (HGDP-CEPH sub-panel) as well as in the HapMap Phase II and III populations

 S4 . This table can be found as Supp. TableS4.xls file.

	Human Mutation	Page 72 of 73
	F o r P e e r R e v i e w
	John Wiley & Sons, Inc.

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Supp.

Table S5. Absolute frequencies of the CA-length polymorphism at IFNG +875(CA) n associated with the IFNG +874A>T alleles.

  Af Eu As Af Eu As Af Eu As Af Eu As Af Eu As Af Eu As Af Eu As Af Eu As

					IFNG+875(CA) n rs3138557
		11	12	13	14	15	16	17	18
	IFNG+874A	12	11	2 46 49 47 4 3 1 11 4 47 10 1 2 7	1	2
	IFNG+874T		18 61 21 5 6		1

Af: African populations, Eu: European populations, As: East-Asian populations

Page 73 of 73 John Wiley & Sons, Inc. Human Mutation

John Wiley & Sons, Inc.Human Mutation

ACKNOWLEDGEMENTS 1

We thank Jean-Laurent Casanova, Eileen Hoal, Roberto Toro and Sandra Pellegrini for helpful 2 suggestions and for critical reading of the manuscript. This work has been supported by the

REFERENCES 1

Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, 2 Sunyaev SR. 2010. A method and server for predicting damaging missense mutations.