Genome-Wide Identification of Susceptibility Alleles for Viral Infections through a Population Genetics Approach

Summary

Viruses have exerted a constant and potent selective pressure on human genes throughout evolution. We utilized the marks left by selection on allele frequency to identify viral infection-associated allelic variants. Virus diversity (the number of different viruses in a geographic region) was used to measure virus-driven selective pressure. Results showed an excess of variants correlated with virus diversity in genes involved in immune response and in the biosynthesis of glycan structures functioning as viral receptors; a significantly higher than expected number of variants was also seen in genes encoding proteins that directly interact with viral components. Genome-wide analyses identified 441 variants significantly associated with virus-diversity; these are more frequently located within gene regions than expected, and they map to 139 human genes. Analysis of functional relationships among genes subjected to virus-driven selective pressure identified a complex network enriched in viral products-interacting proteins. The novel approach to the study of infectious disease epidemiology presented herein may represent an alternative to classic genome-wide association studies and provides a large set of candidate susceptibility variants for viral infections.

Introduction

Infectious diseases represent one of the major threats to human populations, are still the first cause of death in developing countries [1], and are therefore a powerful selective force. In particular, viruses have affected humans before they emerged as a species, as testified by the fact that roughly 8% of the human genome is represented by recognizable endogenous retroviruses [2] which represent the fossil remnants of past infections. Also, viruses have probably acted as a formidable challenge to our immune system due to their fast evolutionary rates [3]. Indeed, higher eukaryotes have evolved mechanisms to sense and oppose viral infections; the recent identification of the antiviral activity of particular proteins such as APOBEC, tetherin, and TRIM5 has shed light on some of these mechanisms. Genes involved in anti-viral response have therefore been presumably subjected to an enormous, continuous selective pressure.

Despite the relevance of viral infection for human health, only few genome-wide association studies (GWAS) have been performed in the attempt to identify variants associated with increased susceptibility to infection or faster disease progression [4]–[5]. These studies have shown the presence of a small number of variants, mostly located in the HLA region. This possibly reflects the low power of GWAS to identify variants with a small effect. An alternative approach to discover variants that modulate susceptibility to viral infection is based on the identification of SNPs subjected to virus-driven selective pressure. Indeed, even a small fitness advantage can, on an evolutionary timescale, leave a signature on the allele frequency spectrum and allow identification of candidate polymorphisms. To this aim we exploited the availability of more than 660,000 SNPs genotyped in 52 human populations distributed world-wide (HGDP-CEPH panel) [6] and of epidemiological data stored in the Gideon database.

Results

Virus diversity is a reliable estimator of virus-driven selective pressure

Previous studies [7]–[9] have suggested that the number of the different pathogen species transmitted in a given geographic location is a good estimate of pathogen-driven selection for populations living in that area. Indeed, pathogen diversity is largely dependent on climatic factors [10] and might more closely reflect historical pressures than other estimates such as the prevalence of specific infections. We therefore reasoned that virus diversity can be used as a measure of the selective pressure exerted by virus-borne diseases on human populations and, as a consequence, that SNPs showing an unusually strong correlation with virus diversity can be considered genetic modulators of infection susceptibility or progression. To explore this possibility we used a large set of SNPs that have been genotyped in the HGDP-CEPH panel, a collection of DNAs from almost 950 individuals sampled throughout the world (Table 1). Virus diversity estimates were derived from the Global Infectious Disease and Epidemiology Network database: for each country where HGDP-CEPH populations are located we counted the number of different virus species (or genera/family as described in materials and methods) that are naturally transmitted (Table 1).

One simple prediction of our hypothesis whereby virus diversity is a reliable estimator of virus-driven selective pressure is that genes known to be involved in immune response are enriched in SNPs significantly associated with virus richness. In order to verify whether this is the case we analysed the InnateDB gene list which contains 2,915 genes involved in immune response and showing the presence of at least one SNP in the HGDP-CEPH panel. Correlations with virus richness were calculated using Kendall's partial rank correlation; since allele frequency spectra in human populations are known to be affected by demographic factors in addition to selective forces [11]–[12], each SNP was assigned a percentile rank in the distribution of τ values calculated for all SNPs having a minor allele frequency (MAF) similar (in the 1% range) to that of the SNP being analysed. A SNP was considered to be significantly associated with virus diversity if it displayed a significant correlation (after Bonferroni correction with α = 0.01) and a rank higher than 0.99. As shown in Table 2, 104 SNPs in InnateDB genes showed a significant association with virus diversity. All SNPs in InnateDB genes that correlated with virus diversity are listed in Table S1. By performing 10,000 re-samplings of 2,915 randomly selected human genes (see materials and methods for details) we verified that the empirical probability of obtaining 104 significantly associated SNPs amounts to 0.010, indicating that genes in the InnateDB list display more virus-associated SNPs than expected.

It is worth mentioning that amongst these genes, UNG (MIM 191525), encoding uracil DNA glycosylase, functions downstream of APOBEC3G (MIM 607113) to mediate the degradation of nascent HIV-1 DNA [13]. SERPING1 (MIM 606860), a regulator of the complement cascade, is also involved in HIV-1 infection (MIM 609423) as its expression is dysregulated in immature dendritic cells by Tat [14]; moreover, the protein product of SERPING1 is cleaved by HCV and HIV-1 proteases [15]–[16].

Genes involved in the biosynthesis of glycan structures have also been considered as possible modulators of infection susceptibility. Indeed, since Haldane's prediction in 1949 [17] that antigens constituted of protein-carbohydrates molecules modulate the resistance/susceptibility to pathogen infection, protein glycolsylation has been shown to play a pivotal role in viral recognition of host targets [18], as well as in antigen uptake and processing and in immune modulation [19]–[20]. We therefore computed a list of genes involved in glycan biosynthesis from KEGG pathways and Gene Ontology annotations. Again these genes displayed significantly more virus-associated SNPs than expected if randomness alone were responsible (empirical p = 0.0138) (Table 2 and Table S2). Several virus-associated SNPs were located in genes coding for sialyltransferases (ST6GAL1 (MIM 109675), ST3GAL3 (MIM 606494), ST6GALNAC3 (MIM 610133), ST8SIA1 (MIM 601123), ST3GAL1 (MIM 607187) and ST8SIA6 (MIM 610139)). Notably, sialic acids represent the most prevalent terminal monosaccharides on the surface of human cells and determine the host range of different viruses including influenza A [21]–[22], polyomaviruses (i.e JCV and BKV in humans) [23], and rotaviruses (the leading cause of childhood diarrhea) [24].

Sialyltransferases also play central roles in B and T cell communication and function. In particular, the generation of influenza-specific humoral responses is impaired in mice lacking ST6GAL1 [25], while ST3GAL1 regulates apoptosis of CD8+ T cells [20]. Interestingly, ST8SIA6 is expressed in NK cells, possibly playing a role in the regulation of Siglec-7 lectin inhibitory function in these cells [26]. Four other genes (XYLT1 (MIM 608124), HS3ST3A1 (MIM 604057), UST (MIM 610752) and CHSY3 (MIM 609963)) carrying SNPs associated with virus diversity are involved in the biosynthesis of either heparan sulphate or chondroitin sulphate. The former is an ubiquitously expressed glycosaminoglycan serving as the cell entry route for herpesviruses [27], HTLV-1 [28] and papillomaviruses [29]. Chondroitin sulphate is similarly expressed on a wide array of cell types and functions as an auxiliary receptor for binding of herpes simplex virus [30] as well as a facilitator of HIV-1 entry into brain microvascular endothelial cells [31]. Finally, we identified LARGE (MIM 603590) among the genes subjected to virus-driven selective pressure (Table 2). Recent studies have demonstrated that the post-translational modification of α-dystroglycan by LARGE is critical for the binding of arenaviruses of different phylogenetic origin including Lassa fever virus and lymphocytic-choriomeningitis virus [32]–[33]. Therefore our data support the previously proposed hypothesis whereby viruses represent the selective pressure underlying the strong signal of positive selection at the LARGE locus [34].

Since genes involved in immune response and in the biosynthesis of glycan structures are likely to be subjected to selective pressures exerted by pathogens other than viruses, we verified whether a set of genes directly involved in interaction with viral proteins also displays more SNPs significantly correlated with virus diversity. To this aim we retrieved a list of 1,916 genes known to interact with at least one viral product and displaying at least one genotyped SNP in the HGDP-CEPH panel (see materials and methods). In order to perform a non-redundant analysis, genes included in the InnateDB list and involved in glycan biosynthesis were removed; the remaining 987 genes displayed 80 SNPs correlated with virus diversity, corresponding to an empirical p value of 0.017 (Table 2 and Table S3). Notably, when this same analysis was performed using the diversity of pathogens other than viruses (bacteria, protozoa and helminths), no significant excess of correlated SNPs was found (all empirical p values>0.05).

Genome-wide identification of variants subjected to virus-driven selective pressure

Given these results, we wished to identify SNPs significantly associated with virus richness on a genome-wide base. We therefore calculated Kendall's rank correlations between allele frequency and virus diversity for all the SNPs (n = 660,832) typed in the HGDP-CEPH panel. We next searched for instances which withstood Bonferroni correction (with α = 0.05) and displayed a τ percentile rank higher than the 99^th among MAF-matched SNPs. A total of 441 SNPs mapping to 139 distinct genes satisfied both requirements. Table 3 shows the 30 top SNPs (or SNP clusters) located within genic regions and associated with virus diversity, while the full list of SNPs subjected to virus-driven selective pressure is available on Table S4. It is worth noting that the SNP dataset we used contains less than 200 variants mapping to HLA genes (both class I and II), therefore covering a minor fraction of genetic variability at these loci; as a consequence HLA genes cannot be expected to be identified as targets of virus-driven selective pressure using the approach we describe herein.

We next verified whether the correlations detected between the SNPs we identified and virus diversity could be secondary to climatic variables. Hence, for all countries where HGDP-CEPH populations are located we obtained (see materials and methods) the following parameters: average annual minimum and maximum temperature, and short wave (UV) radiation flux. Results showed that none of the SNPs associated with virus diversity significantly correlated with any of these variables (Table S5).

Previous works have reported an enrichment of selection signatures within or in close proximity to human genes [12],[35]. In line with these data we verified that virus-associated SNPs are more frequently located within gene regions compared to a control set of MAF-matched variants (χ² test, p = 0.026).

Functional characterization of genes subjected to virus-driven selective pressure

We investigated the role and functional relationship among genes subjected to virus-driven selective pressure using the Ingenuity Pathway Analysis (IPA, Ingenuity Systems) and the PANTHER classification system [36]–[37]. Unsupervised IPA analysis retrieved two networks with significant scores (p = 10⁻¹⁷ and p = 10⁻¹²) which were merged into a single interaction network (Figure 1). The network contains 23 genes showing a significant correlation with virus diversity and, among these, 10 encode proteins interacting with viral products (Figure 1). Based on the number of observed human-virus interactions, this finding is unlikely to occur by chance (χ² test, p = 0.0013) as 2.88 human-virus interactions would be expected for 23 genes. Analysis of the whole network indicated that a 31 of 66 genes encode proteins interacting with viral products (Figure 1): again this number is higher than expected (expected interactions  = 8.27; χ² test, p = 2.8×10⁻¹⁰). Thus, the interaction network we have identified is enriched in genes subjected to virus-driven selective pressure and in genes coding for proteins interacting with viral products. It is worth mentioning that, in agreement with previous findings [38], many viral-interacting proteins represent hubs in the network. Conversely, most of the genes we found to be subjected to virus-driven selective pressure, irrespective of their ability to interact with viral proteins, tend to display very low connectivity (low-degree nodes). This observation might be consistent with previous indications [39]–[41] that in eukaryotes hub genes are more selectively constrained compared to low-degree nodes, these latter being more likely to evolve in response to environmental pressures.

In addition to proteins directly interacting with viral products, several network genes showing correlation with virus diversity might play central roles during viral infection. DNMT1 (MIM 126375) and MGMT (MIM 156569) are involved in DNA methylation and repair, respectively, two processes that are often dysregulated during viral infection. In particular, altered expression of DNMT1 is induced by diverse viruses including HIV-1 [42], EBV [43], BKV and adenovirsuses [44]; also, DNMT1 plays a pivotal role in the expansion of effector CD8+ T cell following viral infection [45]. A relevant role in HIV-1 infection is also played by HSPG2 (MIM 142461), the gene coding for perlecan, a cell surface heparan sulfate proteoglycan which mediates the internalization of Tat protein [46].

We next investigated the over-representation of PANTHER classification categories among genes subjected to virus-driven selective pressure. Table 4 shows the significantly over-represented PANTHER molecular functions and biological processes with the contributing genes. In line with the results we reported above, genes involved in immune response, as well as genes coding for proteins involved in cell adhesion and extracellular matrix components, resulted to be over-represented; these latter genes might mediate viral-cellular interaction and facilitate viral entry.

Discussion

The identification of non-neutrally evolving loci with a role in immunity can be regarded as a strategy complementary to classic clinical and epidemiological studies in providing insight into the mechanisms of host defense [47]. Here we propose that susceptibility genes for viral infections can be identified by searching for SNPs that display a strong correlation with the diversity of virus species/genera transmitted in different geographic areas. Similar approaches have previously been applied to study the adaptation to climate for genes involved in metabolism and sodium handling [48]–[50]. These analyses, including the one we describe herein, rely on similar assumptions and imply some caveats. First, we implicitly considered virus diversity, as we measure it nowadays, a good proxy for long-term selective pressure. This clearly represents an oversimplification, as new viral pathogens have recently emerged and the virulence of different viral species or genera might have changed over time. Still, previous studies have indicated that the geographic distribution of virus diversity is strongly influenced by climatic variables such as temperature and precipitation rates [10], suggesting that, despite significant changes in prevalence and virulence, virus diversity might have remained relatively constant across different geographic areas, possibly representing the best possible estimate of long-standing pressure. In line with these considerations, we calculated virus diversity as the number of all viral species (or genera/families) that can cause a disease in humans, irrespective of virulence or pathogenicity (Table S6).

The second issue relevant to the data we present herein is that environmental variables tend to co-vary across geographic regions: the distribution of different pathogens (e.g. parasitic worms and viruses/bacteria/protozoa) is correlated across HGDP-CEPH populations [9] and, as reported above, virus diversity is influenced by climatic factors. Therefore, our genome-wide search was preceded by analyses aimed at verifying whether virus diversity is a reliable and specific estimator of virus-driven selective pressure. In particular, we verified that genes involved in immune response and in the biosynthesis of glycans display significantly more variants associated with virus diversity than randomly selected human genes; this finding supports the idea that pathogens rather than climate or demography has driven the genetic variability at these loci. Notably, we also analysed genes that encode proteins interacting with viral components: since loci involved in immune response and in glycan biosynthesis were removed from this list, the remaining genes are expected to be specific targets of viral-driven selective pressure; consistently, we verified that a significant excess of SNPs correlating with virus diversity map to these loci. Conversely, a SNP excess was not noticed when the diversity of other human pathogens was used for the analysis, suggesting that, despite the correlation among different pathogen species across geographic locations [9], the selective pressure imposed by viruses can be distinguished from that exerted by other organisms.

As a further control for the possible confounding effects of other environmental factors, we verified that the variants we identified at the genome-wide level do not correlate with climate (temperature) and UV radiation. This analysis was motivated by the known association of virus diversity and biodiversity in general, with temperature [10],[51] and by the fact that both climate and UV exposure have long been considered among the strongest selective pressures in humans [52]. Since none of the SNPs we identified correlated with either short wave radiation flux or temperature, we consider that their geographic distribution is likely to have been shaped by virus-driven selective pressure. In this respect it is worth mentioning that UV irradiation has been shown to be immunosuppressive in mice (reviewed in [53]–[54]), but the effect of sun exposure on immune functions in humans is still poorly understood. Yet, herpes viruses (both simplex and zoster) and some papillomavirus types have been shown to be reactivated by UV exposure, suggesting that the link between short wave radiation flux and virus-driven selective pressure might be more complex than simply predicted on the basis of geographic variation.

Our genome wide search for genes subjected to virus-driven selection allowed the identification of a gene interaction network that is enriched in both genes associated with virus diversity and in genes encoding proteins that interact with viral products. Many of the genes included in the identified network are of great interest as they are known to be involved in the activation of mechanisms that have direct or indirect protective effects against viruses. Thus, beside the well known activities of IL1A (MIM 147760) and B (MIM 147720), IL4 (MIM 147780), TGFB1 (MIM 190180), IL16 (MIM 603035), IFNG (MIM 147570) and TNF (MIM 191160), OAS2 (MIM 603350) encodes a protein that activates latent RNases, resulting in the degradation of viral RNA and in the inhibition of viral replication [55]. CCL17 (MIM 601520) induces T lymphocytes chemotaxis, thus potentiating the immune responses, and PPP3CA (MIM 114105), also known as calcineurin, activates NFATc [56], a key factor in the up-regulation of IL2 (MIM 147680) [57], the main cytokine responsible for T lymphocytes growth and differentiation. Finally, ULBP2 (MIM 605698) encodes an MHC1-related protein that binds to NKG2D (MIM 602893) [58], an activating receptor expressed on CD8 T cells as well as on NK cells, NKT cells and γδ T cells. In the light of the viral pathogenesis of a growing number of neoplasia, it is very interesting that other members of the network play a well described role in the inhibition of tumoral growth. In particular, E2F1 (MIM 189971) is known to have a pivotal role in the control of cell cycle and in the activation of tumour suppressor proteins and, together with TP53I3, TADA3L, and TP53BP2 mediates p53-dependent and independent apoptosis [59]–[60]. CCND3 (MIM 123834) is involved in cell cycle progression through the G2 phase, whereas RAD23A (MIM 600061) up-regulates the nucleotide excision activity of 3-methyladenine-DNA glycosylase [61], therefore playing a role in DNA damage recognition in base excision repair. Finally, NR4A2 (MIM 601828) encodes a nuclear orphan receptor expressed in T cells and involved in apoptosis [62]. NR4A2 is also known to play a central role in eliciting the production of inflammatory cytokines in multiple sclerosis (MS (MIM 126200)) [63]. Notably, variants in PPP3CA (Figure 1) have recently been reported to correlate with MS severity as well [64]. We therefore investigated whether other genes carrying SNPs which correlate with virus diversity have been identified in GWAS for MS susceptibility or severity. Three additional genes, JMJD2C (MIM 605469), C20orf133 (also known as MACROD2, (MIM 611567)) and CSMD1 (MIM 608397) have been associated with MS [64] and display SNPs significantly correlated with virus diversity (Table S1). While the function of C20orf133 is unknown, JMJD2C encodes a histone demethylase expressed at very high levels in B cells and cytotoxic lymphocytes (see materials and methods), a pattern consistent with its being subjected to virus-driven selective pressure. Finally, CSMD1, in analogy to the aforementioned SERPING1, acts as a regulator of the complement system [65]; notably, complement activation plays a central role in both response to viruses and inflammatory reactions, particularly in the central nervous system [66].

Analysis of the 30 stronger associations (Table 3) indicated that several genes are part of the network described above or have been involved in immune response (see InnateDB gene list, Table 2). Conversely, others encode relatively unknown products (e.g. KIAA1529 (MIM 611258), LHFPL3 (MIM 609719), LOC51149, RNF217, TMEM132B, LEPREL1 (MIM 610341), ANKFN1, MYO5C (MIM 610022), ANXA4 (MIM 106491) and SCRN3). Among these genes, MYO5C, ANXA4 and SCRN3 are involved in membrane trafficking events along exocytotic and endocytotic pathways, suggesting that they might play a role in either viral cell entry [67] or lytic granule exocytosis; this might be the case for ANXA4 which is expressed at high levels in NK cells (see materials and methods). Most interestingly, EYA4 (MIM 603550) (Table 3) has recently been described as a phosphatase involved in triggering innate immune responses against viruses [68]. Finally, both PDE2A (MIM 602658) and SCNN1A (MIM 600228) might play a role in maintaining lung epithelial barrier homoeostasis during viral infection. Indeed, both genes can be induced by TNF-alpha in lung epithelial cells [69]–[70] and can influence lung fluid reabsorption and, therefore, edema formation. In line with these observations, expression of the amiloride-sensitive epithelial Na+ channel (SCNN1A codes for the α subunit) is affected by infection with influenza virus, severe acute respiratory syndrome coronavirus and respiratory syncitial virus.

In humans, resistance to infectious diseases is thought to be under complex, multigenic control with single loci playing a small protective role [47]. This concept also holds for viral infection as demonstrated by the role of genetic variants in modulating the susceptibility to HIV infection or disease progression (reviewed in [71]). Classic GWAS offer a powerful resource to identify susceptibility loci for infectious diseases; yet GWAS typically have limited power to detect variants with a low frequency or a small effect. Indeed, recent GWAS for SNPs determining the host control of HIV-1 [4]–[5] failed to identify most known loci with a role in AIDS progression. The alternative approach we have proposed here is based on the identification of variants subjected to virus-driven selective pressure. Similarly to the GWAS results mentioned above we did not identify well known antiviral-response genes. Still, we noticed that variants in TRIM5 (MIM 608487) (rs2291845, τ = 0.44, p = 1.86×10⁻⁵, rank = 0.97) and IFIH1 (MIM 606951) (also known as MDA5, rs10439256, τ = 0.51, p = 5.4×10⁻⁷, rank = 0.99) showed significant associations with virus-diversity, although they did not withstood genome-wide analysis. Also, it is worth mentioning that variants with a well established role in resistance to viral infections may be neutrally evolving; this is the case for the Δ32 allele of CCR5 (MIM 601373) for example, which confers protection against HIV-1 infection and possibly against other pathogens, but displays no selection signature [72]. This is possibly due to how long and how strong the selective pressure has been exerted. Conversely, variants subjected to selective pressure must have (or have had along human history) some selective advantage, indicating that the SNPs we have identified can be regarded as candidate modulators of infection susceptibility or disease progression.

Materials and Methods

Environmental variables

Virus absence/presence matrices for the 21 countries where HGDP-CEPH populations are located were derived from the Global Infectious Disease and Epidemiology Network database (Gideon, http://www.gideononline.com), a global infectious disease knowledge tool. Information in Gideon is weekly updated and derives from World Health Organization reports, National Health Ministries, PubMed searches and epidemiology meetings. The Gideon Epidemiology module follows the status of known infectious diseases globally, as well as in individual countries, with specific notes indicating the disease's history, incidence and distribution per country. We manually curated virus absence/presence matrices by extracting information from single Gideon entries. These may refer to either species, genera or families (in case data are not available for different species of a same genus/family). Following previous suggestions [7]–[9], we recorded only viruses that are transmitted in the 21 countries, meaning that cases of transmission due to tourism and immigration were not taken into account; also, species that have recently been eradicated as a result, for example, of vaccination campaigns, were recorded as present in the matrix. A total of 81 virus species/genera/families were retrieved (Table S6). The same approach was applied to calculate the diversity of other pathogens, namely bacteria, protozoa and helminths [9]. The annual minimum and maximum temperature were retrieved from the NCEP/NCAR database (http://www.ngdc.noaa.gov/ecosys/cdroms/ged_iia/datasets/a04/, Legates and Willmott Average, re-gridded dataset) using the geographic coordinates reported by HGDP-CEPH website for each population (http://www.cephb.fr/en/hgdp/table.php). Similarly, net short wave radiation flux data were obtained from NCEP/NCAR (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html, Reanalysis 1: Surface Flux); these data were read using Grid Analysis and Display System (GrADS, http://www.iges.org/grads/). Daily values for four years (1948–1951) were averaged to obtain an annual mean.

Since virus diversity, due to data organization in Gideon, can only be calculated per country (rather than per population), the same procedure was applied to climatic variables. Therefore the values of annual temperature and radiation flux were averaged for populations located in the same country. This assures that a similar number of ties is maintained in all correlation analyses.

Data retrieval and statistical analysis

Data concerning the HGDP-CEPH panel derive from a previous work [6]. Atypical or duplicated samples and pairs of close relatives were removed [73].

A SNP was ascribed to a specific gene if it was located within the transcribed region or no farther than 500 bp upstream the transcription start site. MAF for any single SNP was calculated as the average over all populations. The list of immune response genes was derived from the InnateDB website (http://www.innatedb.com/) and it contains a non-redundant list of 5,070 immune genes derived from ImmPort, IRIS, Septic Shock Group, MAPK/NFKB Network and Immunome Database; it only includes genes derived from curated immune gene lists.

Genes involved in glycan biosynthesis were obtained by merging genes from two KEGG pathways (“Glycan structures - biosynthesis 1” and “Glycan structures - biosynthesis 2”). Additional genes were identified by searching Gene Ontology categories for genes that act as glycosyltransferases (GO:0016757) and are located in either the Golgi or the endoplasmic reticulum (GO:0005783, GO:0005793 and GO:0005794). The list of human genes coding for proteins interacting with viral products was derived from three sources: a previously published study [38], the VirHostNet website [74] (http://pbildb1.univ-lyon1.fr/virhostnet/) and the HIV-1 Human Protein Interaction Database [75] (http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/).

Expression data were obtained from SymAtlas (http://symatlas.gnf.org/). The location of genomic elements that are highly conserved among vertebrates was derived from UCSC annotation tables (http://genome.ucsc.edu/; “PhastCons Conserved Elements, 44-way Vertebrate Multiz Alignment” track).

All correlations were calculated by Kendall's rank correlation coefficient (τ), a non-parametric statistic used to measure the degree of correspondence between two rankings. The reason for using this test is that even in the presence of ties, the sampling distribution of τ satisfactorily converges to a normal distribution for values of n larger than 10 [76].

In order to estimate the probability of obtaining n SNPs located within m genes and significantly associated with virus diversity, we applied a re-sampling approach: samples of m genes were randomly extracted from a list of all genes covered by at least one SNP in the HGDP-CEPH panel (number of genes  = 15,280) and for each sample the number of SNPs significantly associated with virus diversity was counted. The empirical probability of obtaining n SNPs was then calculated from the distribution of counts deriving from 10,000 random samples. A SNP was ascribed to a gene if it was located within the transcribed region or in the 500 upstream nucleotides.

Analysis of PANTHER over-represented functional categories and pathways was performed using the “Compare Classifications of Lists” tool available at the PANTHER classification system website [77] (http://www.pantherdb.org/). Briefly, gene lists are compared to the reference list using the binomial test for each molecular function, biological process, or pathway term in PANTHER.

All calculation were performed in the R environment [78] (http://www.r-project.org/).

Network construction

Biological network analysis was performed with Ingenuity Pathways Analysis (IPA) software using an unsupervised analysis (www.ingenuity.com). IPA builds networks by querying the Ingenuity Pathways Knowledge Base for interactions between the identified genes and all other gene objects stored in the knowledge base; it then generates networks with a maximum network size of 35 genes/proteins. We used all genes showing at least one significantly associated SNP as the input set; in this case a SNP was ascribed to a gene if it was located within the transcribed region or in the 25 kb upstream. All network edges are supported by at least one published reference or from canonical information stored in the Ingenuity Pathways Knowledge Base. To determine the probability of the analysed genes to be found together in a network from Ingenuity Pathways Knowledge Base due to random chance alone, IPA applies a Fisher's exact test. The network score represents the -log (p value).

Supporting Information

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