between promoter methylation and selective pressure in Homo Sapiens are still lacking. Several tools are needed to study the relation between CGIs methylation and selective pressure in a genomic perspective. First, we need tools that recognize genomic signals of selective pressure. Many methods have been developed to exploit signatures left by natural selection, each signature providing distinct information about selective events [17]. Since one of the main effects of selection is to modify the levels of variability within and between species, these methods could be roughly classified into two groups. To the first group belong the methods that use a population genetic approach, while to the second group belong methods that use a comparative approach. While population genetic approaches aim to detect recent selection events occurring in a population, comparative approaches, involving data from multiple different species, are suitable for detecting more ancient selections [17]. By these methods, hundreds of such regions putatively under selective pressure have been identified. They are typically as large as few hundreds of kilobases to megabases, and may contain many genes. The second requirement to study the relation between CGIs methylation and evolution is the availability of methylation data at genomic scale. Recent advances in high-throughput sequencing technologies are enabling epigenetics to progress rapidly into an ‘omic’ science [18]. In particular, the Encyclopedia of DNA Elements (ENCODE) Consortium [19,20] is providing masses of methylation data that may be accessed and used by the entire scientific community. The analysis of these relevant datasets by computational methods could complement experimental ap- proaches to further our understanding of DNA methylation [21] [22]. In this study, we explored the relationship between CGIs methylation and signatures of selective pressure in Homo Sapiens, using a computational methodology. We compared the CGIs methylation level in genomic regions under selective pressure with CGIs localized in the remaining genome. We evaluated DNA methylation levels both by direct analysis of CpG methylation in cell lines and by an indirect approach that uses the analysis of genetic variation inside CGIs. To define genomic regions under selective pressure, we used three different methods oriented to provide information about selective events happened in different periods of human evolution. Independently of the methods used both to evaluate CGIs methylation and to estimate selective pressure, we found evidences of undermethylation of CGIs in human genomic regions that undergone selection. Results DNA methylation in cell lines and signatures of selective pressure Based on datasets available in public repository we estimated the CGIs methylation in 25 cell lines. Genomic coordinates of 28,691 CGIs were obtained from UCSC Genome Browser ‘‘CpG Islands’’ track. As known, USCS CGIs file contains also data related to sequence for alternative haplotypes (present mainly in chr6, for the inclusion of alternative versions of the MHC region). Of course, in our analysis we filtered the file excluding these duplicated data. Excluding CGIs corresponding to sequences for alternative haplotypes, we obtained 27.718 unique CGIs. Cell line methylation data were obtained by downloading them from UCSC Genome Browser ‘‘HAIB Methyl RRBS’’ track. This track reports the percentage of DNA molecules that show cytosine methylation at specific CpG dinucleotides in several cell lines. The 25 cell lines that we used CpG Islands Methylation and Selective Pressure could be roughly divided in three groups: cancer transformed cells (n = 6), EBV transformed cells (n = 2) and normal untransformed cells (n = 17). The complete list of the cell used, with their characteristics are shown in Table S1. We extracted only the methylation values of those CpGs that were localized inside CGIs (order 105 per cell line). To estimate the methylation of each CpG island we calculated the mean of all CpGs methylation values into a CpG island. We were able to estimate the methylation status of about 104 CGIs for each cell line. Table S2 lists, for each cell type, the description of the CpGs analyzed. As expected, the CGIs mean methylation values were higher in Cancer Transformed (mean = 26.91, SE = 2.84) and lower in Normal Untransformed cells (mean = 14.34, SE = 0.57), EBV transformed cell showing inter- mediate levels (mean = 18.93, SE = 1.46) (Figure S1). To explore the possible relationship between CGIs methylation and selective pressure we compared the methylation of the CGIs inside genomic regions showing signature of selective pressure with the methylation of the CGIs in the remaining genomic regions. To obtain genomic regions with signatures of selective pressure, we used three different approaches. As first approach, we used the per-continent Integrated Haplotype Score (iHS) [23]. This score belongs to the Extended Haplotype Homozygosity (EHH) statistic ‘‘family’’, proposed by Sabeti et al. [24]. The iHS measures the decay of identity, as a function of distance, of haplotypes that carry a specified ‘‘core’’ allele at one end and it is considered a measure of recent positive selection. The normalized iHS scores (see materials and methods) were obtained from UCSC Genome Browser ‘‘HGDP iHS’’ track. To define genomic regions putatively under selective pressure by this method, we scanned normalized iHS scores across the whole genome and selected the genomic intervals where iHS score values $2. Once detected such compact regions, we extended their boundaries to the nearest loci where iHS was exactly vanishing. According to these criteria, 586 regions were identifies. We denoted these regions as ‘‘High iHS regions’’ (HIR). Table S3 reports the HIRs that we identified and their boundaries. Next we identified CGIs localized within HIRs. We found that 2,545 CGIs were localized inside HIRs whereas the remaining 26,146 were placed outside. We compared the methylation of CGIs inside HIRs with the methylation of CGIs localized outside these regions. Figure 1 shows the results obtained. In all cell lines analyzed, the CGIs inside HIR regions were less methylated than the CGIs in the remaining part of the genome. The differences were highly statistical significant (Bootstrap p-value#1024) in all cell lines analyzed. Table S4 reports in detail the results of this analysis. The Bootstrap procedure adopted to evaluate the difference between means of distributions is described in Materials and Methods. An additional method able to detect regions putatively under selective pressure is represented by the Selective Sweep Scan (S) score, which is based on the comparison of Homo Sapiens DNA with Neanderthal DNA [25]. This score, when positive, indicates more derived alleles in Neanderthal than expected, given the frequency of derived alleles in human. On contrary, a negative score indicates fewer derived alleles in Neanderthal, and may suggest an episode of positive selection in early humans, after divergence with Neanderthal and before human populations divergence. We used the 212 regions with S scores in the lowest 5% of the distribution (5% Lowest S Regions, 5LSR) contained in the UCSC Genome Browser (see materials and methods). Table S5 reports the regions used with their relative boundaries. We found that 348 CGIs were localized inside 5LSRs and the remaining 28,343 outside them. Figure 2 shows the results PLoS ONE | www.plosone.org 2 August 2011 | Volume 6 | Issue 8 | e23156