{"paper_id":"7900b3d0-eccf-4d70-a3f3-d5af0873ae6e","body_text":"With the advent of genome-wide association studies (GWASs), a large number of single nucleotide polymorphisms (SNPs) associated with human complex diseases have been discovered [ 1 ]. The findings from GWASs have provided previously unsuspected biological pathways relevant to the diseases [ 2 ] such as complement system in age-related macular degeneration [ 3 ] and autophagy in Crohn’s disease [ 4 ]. Furthermore, the annotation of the disease-associated SNPs to plausible susceptibility genes and pathways can be translated into drug discovery and repositioning [ 5 ]. However, functional characterization of the identified SNPs remains a formidable challenge [ 6 ].\nMost of the identified SNPs are located on intergenic regions and introns rather than coding regions [ 7 ], suggesting that these non-coding SNPs are associated with the disease risk through regulation of expression levels of nearby genes. This argument has been supported by the fact that the disease-associated SNPs were overrepresented in expression quantitative trait loci (eQTL) of genes [ 8 – 10 ]. The ENCODE and Roadmap Epigenomics projects have explored functional elements across the human genome in a wide variety of cell types [ 11 ,  12 ]. The integrative approaches for linking the regulatory elements with the SNPs identified by GWASs highlighted the importance of DNase I hypersensitive sites (DHSs) in the genotype-phenotype correlations [ 11 ,  13 ], which signify accessible open chromatin regions. A large proportion of the SNPs identified by GWASs or tagged by them were located on DHSs in cell types relevant to the corresponding diseases and overlapped transcription factor (TF) recognition sequences [ 14 ]. These systematic analyses present a plausible model in which the disease-associated SNPs alter activities of  cis -regulatory elements including promoters, enhancers, insulators, and silencers. There is a growing body of evidence supporting this view from functional genomic studies in coronary artery disease (CAD) [ 15 ], cholesterol levels [ 16 ], obesity [ 17 ], type 2 diabetes [ 18 ], cancers [ 19 – 21 ], erythroid traits [ 22 ], and pigmentation traits [ 23 – 25 ]. It has been also reported that mutations on  cis -regulatory elements cause severe developmental disorders showing a Mendelian pattern of inheritance [ 26 ].\nCurrent GWAS platforms contain now up to 1M SNPs, which are designed to efficiently surrogate known common variants covering the human genome. Therefore, there is a possibility that the SNPs identified by GWASs are merely surrogate markers for causal variants. Although a comprehensive list of SNPs in the associated loci has been genotyped, the association signals for many correlated SNPs within a linkage disequilibrium (LD) block are statistically indistinguishable. Under these circumstances, it can be a straightforward way to prioritize candidate causal SNPs by searching for SNPs that are in strong LD with the SNPs identified by the GWASs and located on DHSs in cell types relevant to the disease for subsequent functional genomic approaches.\nEndometriosis is a common gynecological disorder that is characterized by the presence of uterine endometrial tissue outside the normal location. The prevalence in women of reproductive age is estimated to be 6–10% [ 27 ]. Endometriosis is a risk factor for several subtypes of ovarian cancer [ 28 ]. GWASs of endometriosis in Japanese and European populations have identified several susceptibility loci [ 29 – 33 ]. Uno and colleagues reported that rs10965235, locating in an intron of  ANRIL  ( a ntisense  n on-coding  R NA in the  I NK4  l ocus or  CDKN2B-AS1 ) on chromosome 9p21, was significantly associated with endometriosis in Japanese population [ 29 ]. rs10965235 has been reported to be most strongly associated with the risk of endometriosis in Japanese population (per allele odds ratio of 1.44) but rare or absent in European descent populations [ 29 ]. This association was replicated in an independent GWAS in Japanese [ 30 ]. Meta-analysis of European and Japanese GWAS data sets identified rs1537377 at 49kb downstream of  ANRIL  [ 32 ]. rs1537377 is common both in European descent and Japanese populations and associated with modest increase of the risk for endometriosis (per allele odds ratio of 1.15) [ 32 ]. These two SNPs on 9p21 were shown to be independent association signals [ 32 ], but their functional roles have not been characterized.\nHere we investigated regulatory mechanism of the endometriosis risk locus on 9p21. Coupled with target re-sequencing of 9p21 region and DNase-seq data from the ENCODE project, we prioritized candidate causal variants that were in perfect LD with the SNP identified by the original GWAS and located on DHSs. Subsequent functional genomic approaches revealed that the SNP site functioned as a  cis -regulatory element of  ANRIL  through allele-specific long-range chromatin interaction driven by preferential bindings of TCF7L2 and EP300. Furthermore, we demonstrated that expressions of  ANRIL  and  CDKN2A/2B  were closely associated via Wnt signaling pathway. These results suggest that the 9p21 risk locus is involved in the development of endometriosis by modulating the expression level of  ANRIL  and  CDKN2A/2B .\n\nIn order to assemble a comprehensive set of genetic variants on 9p21 region, we re-sequenced 1.29 Mb interval encompassing two endometriosis-associated SNPs (rs10965235 and rs1537377) in 48 Japanese individuals with the average depth of 186.6 ( S1 – S3  Figs). We detected 4,215 single nucleotide variants (SNVs) and 664 insertions and deletions (indels) with a high degree of confidence ( S4 – S6  Figs). We compiled a list of variants that were in strong LD with rs10965235 or rs1537377 ( Fig 1A  and  S1 Table ). There were 24 SNPs and two indels exhibiting strong LD with rs10965235 ( r 2  > 0.8). Among them, 16 SNPs and two indels were in perfect LD ( r 2  = 1.0), and therefore the association cannot be distinguished. For rs1537377, seven SNPs and one indel were detected. The intervals containing the variants exhibiting strong LD with rs10965235 and rs1537377 are located in the 3’ region of  ANRIL  ( Fig 1B ). We confirmed that rs10965235 and rs1537377 were in weak LD each other ( r 2  = 0.02), and in weak and moderate LD with the 9p21 SNPs associated with other diseases ( S7 Fig ), indicating that these two associations were independent endometriosis-specific signals.\nA) Distribution of variants exhibiting strong LD ( r 2  > 0.8) with two endometriosis-associated SNPs (rs10965235 and rs1537377). SNPs and indels are coded by diamonds and squares, respectively. The positions of rs10965235 and rs1537377 are shown as vertical lines. The intervals encompassing all the variants showing strong LD with rs10965235 and rs1537377 are highlighted by light green and pink shades, respectively. B) DHSs in endometrial carcinoma cell lines across the two intervals. The transcript structures of  ANRIL  and the sites of the variants that are in strong LD are depicted. The densities of aligned reads from DNase-seq estimated by F-Seq are plotted. DHSs in which the densities of aligned reads significantly surpassing the threshold are represented by dark blue. Locations in which aligned reads are depleted are depicted by light blue. The positions of the SNPs identified by GWASs (rs10965235 and rs1537377) and candidate causal SNPs (rs17834457 and rs17761446) are highlighted by blue and red arrows, respectively. C) DNase-seq signals at the variant sites showing strong LD. Signals are represented as relative values to the threshold determined by F-Seq. If the relative signal surpasses 1.0, the corresponding variant site coincides with significant DHS.\nWe explored DHSs to prioritize candidate causal variants. Among the ENCODE cell lines with DNase-seq data, we focused on endometrial carcinoma cell lines (Ishikawa and ECC1) as a cell type potentially relevant to endometriosis because endometrial carcinoma arose from the endometrium. Additionally, we examined six cell lines, consisting of lymphoblastoid (GM12878), chronic myeloid leukemia (K562), H1 embryonic stem cell (H1-hESC), hepatoblastoma (HepG2), cervical cancer (HeLa-S3), and umbilical vein epithelial cell (HUVEC) with high priority in the ENCODE project (Tiers 1 and 2). We detected a distinct set of DHSs across 101 kb interval containing all the variants showing strong LD with rs10965235 and rs1537377 (chr9: 22072730–22173676) in these cell lines (Figs  1B  and  S8 ). The two SNPs identified by the original GWAS did not lie on DHSs in the endometrial carcinoma cell lines and other cell lines analyzed ( Fig 1B and 1C ); therefore, we excluded these two variants from candidate. We identified a site harboring two SNPs (rs17761446 and rs17834457) where significant DNase-seq signals were consistently detected in the endometrial carcinoma cell lines ( Fig 1B and 1C ). rs17761446 and rs17834457 are closely located (76 bp apart) and in perfect LD with rs10965235 ( r 2  = 1.0). The DHS harboring rs17761446 and rs17834457 seems to be cell-type specific open chromatin site (Ishikawa, ECC1, and HeLa-S3) rather than ubiquitous one ( Fig 1C ). We could not identify any SNPs that were in strong LD with rs1537377 and coincided on DHSs. Therefore, we focused on the DHS containing rs17761446 and rs17834457 for subsequent functional genomic studies.\nThe DHS harboring rs17761446 and rs17834457 located on an intron of long isoforms of  ANRIL  ( Fig 1B ), approximately 123, 143, and 109 kb apart from the transcription start site of  ANRIL ,  CDKN2A , and  CDKN2B , respectively. Therefore, we hypothesized that the DHS was a distal regulatory element contacting with the promoter of  ANRIL , or  CDKN2A/2B  through chromatin looping interaction. We investigated long-range interactions using chromosome conformation capture (3C) assay [ 34 ]. As a first step, we examined chromatin loops formed between the restriction fragment containing the candidate causal SNPs (rs17761446 and rs17834457) and the consecutive restriction fragments around  ANRIL ,  CDKN2A , and  CDKN2B  ( S2 Table ). The consistent PCR amplifications of the interacting fragments between the candidate causal SNPs and the promoter of  ANRIL  were observed in replicated experiments from HEC251 and HEC265 ( S9A Fig ). Additionally, Sanger sequencing for the PCR product confirmed that the sequence for the ligation junction between fragments between the candidate causal SNPs and the promoter of  ANRIL  was successfully identified, supporting the presence of the chromatin interaction ( S9B Fig ). Therefore, we focused attention on the chromatin interaction between the SNP site and the promoter of  ANRIL  ( Fig 2A ).\nA) The interacting fragments between the candidate causal SNPs (rs17834457 and rs17761446) and the promoter of  ANRIL  are color-coded by blue and red, respectively.  Hin dIII fragments are depicted by gray rectangles. The alternative transcript structures  ANRIL ,  CDKN2A , and  CDKN2B  are represented. B) Plots of coverage depth for AS3C-seq libraries from two endometrial carcinoma cell lines (HEC251 and HEC265). The fragments between the candidate causal SNPs (rs17834457 and rs17761446) and the promoter of  ANRIL  are color-coded by blue and red, respectively. The ligation junction (i.e., restriction enzyme recognition sequence of  Hin dIII, AAGCTT) is shown by yellow. For heterozygous SNP sites, the proportions of allele-specific paired-end read tag counts supporting the alleles on the risk and protective haplotypes are shown by light blue and dark blue, respectively. It can be seen that a region within the fragment containing  ANRIL  promoter shows reduced depth of coverage. This seems to be caused by sequence or alignment errors because the region corresponds to a homopolymer stretch with 15 consecutive adenines (see also  S12 Fig ), indicating that this does not largely affect our results. C) Proportions of allele-specific paired-end read tag counts supporting the risk and protective haplotypes tagged by T and G alleles of rs17761446, respectively. Results for the two cell lines and for pooled data are shown. D) Simulation study for evaluating mapping bias. Simulation scenarios considering different sequence lengths (50bp, 75bp, and 100bp) and different error rates for each sequenced base (0.01, 0.005, and 0.001). Box plots are based on 10,000 replicates. rs17761446 and rs17834457 are apart from 76bp and in perfect linkage disequilibrium ( r 2  = 1); therefore, we consider the haplotype structure of these two variants in the simulation scenario in which sequence length is 100bp. E) Sensitivity analysis for evaluating mapping bias. In sensitivity analyses, we aligned reads from AS3C-seq to two types of sequences that were different in the base at the SNP site: the “original” and “changed” sequences have “reference” and “alternative” alleles, respectively. Note that the significant result from AS3C-seq exhibited increased proportion of alternative allele.\nWe developed a novel 3C-based approach to detect allele-specific chromatin interactions (AS3C-seq) ( Methods  and  S10  and  S11  Figs). Briefly, the AS3C-seq quantifies a difference in chromatin interaction frequencies between two alleles of a SNP in the sequence reads of the fragmented 3C PCR product by using cell lines that are heterozygous for the SNP. In this study, we used HEC251 and HEC265 that were heterozygous for the candidate SNPs (rs17761446 and rs17834457). The substantial numbers of reads (>1,000) were aligned to the ligation junction, supporting the presence of the chromatin interaction between  ANRIL  promoter and the candidate causal SNPs (Figs  2B  and  S11  and  S12 ).\nrs17834457 and rs17761446 are in perfect LD and therefore constitute two haplotypes; risk and protective haplotypes. The proportions of the read tags supporting the protective haplotype over the total mapped tag counts were 59.4% and 75.1% in HEC251 and HEC265, respectively ( Fig 2B ). These were significantly deviated from the expected proportion of 50% ( P  < 10 −16 ; binomial test). Additionally, HEC265 was heterozygote for a SNP (rs3731197) within the fragment containing the promoter of  ANRIL . The re-sequencing of the 1.29 Mb interval and haplotype phasing showed that the C allele of rs3731197 resided on the protective haplotype in HEC265. The AS3C-seq result showed the proportion of the C allele of rs3731197 was similar to those at the two candidate causal SNPs (71.7%;  P  < 10 −16 ) ( Fig 2B ). The consistent results in both of the interacting fragments suggest the quantification of allele-specific read counts in the AS3C-seq is reliable. In the pooled data set from HEC251 and HEC265, the proportion of allele-specific read counts supporting the protective haplotype was 66.5% ( P  < 10 −16 ; likelihood ratio test), indicating that the protective haplotype tagged by the G allele of rs17761446 forms two times greater chromatin interactions with the promoter of  ANRIL  compared to the risk haplotype ( Fig 2C ).\nOne reservation on allele specific functional genomic studies based on next generation sequencing technologies is mapping bias [ 35 ]. In order to evaluate mapping bias on our AS3C-seq analysis, we performed simulation and sensitivity analyses ( Methods  and  S10C Fig ). According to the results of the simulation and sensitivity analyses, we confirmed that mapping bias does not affect our result ( Fig 2D and 2E ;  Methods ).\nWe examined whether rs17761446 and rs17834457 overlapped with consensus TF binding site within the DHS by bioinformatics analysis ( S13  and  S14  Figs and  Methods ). As a result, we found that rs17761446 lay adjacent to the core motif of HMG (high mobility group) class of TFs including TCF/LEF and SOX families (Figs  3A  and  S15 ). According to the position weight matrices (PWMs) in the HOCOMOCO database [ 36 ], rs17761446 lies within the binding motifs of TCF7 and TCF7L2, which favors the G allele over the T allele ( Fig 3A ). Additionally, the G allele matches with the consensus binding motif of drosophila TCF family member (AA G ATCAAAGG) [ 37 ].\nA) Alignments of consensus motifs of TCF/LEF family to sequence surrounding rs17761446. Core motif of HMG class of TFs are highlighted by red. The position of rs17761446 is highlighted by blue. B) ChIP-seq signals for TCF7L2, EP300, H3K27ac, and TBP in HeLa-S3 cells from ENCODE project. Plots are created for 10 kb around rs17761446. Significant peak regions are highlighted by light green. C) ChIP assays for TCF7L2, EP300, H3K27ac, and RNA polymerase II at rs17761446 site in HEC251 cells.  MYC  promoter and α satellite repeat element were used as positive and negative control regions, respectively. The enrichment of the normalized binding affinity for the candidate SNP site or positive control region was normalized by dividing by that for negative control region. D) Binding strength of the G allele relative to the T allele of rs17761446 in HEC251 cells. Data represents the mean and standard deviation of triplicated ChIP assays. Pol2, RNA polymerase II; TCF7L2 (CS) and TCF7L2 (OG), TCF7L2 antibodies by Cell Signaling Technology and OriGene Technologies, respectively.\nChromatin immunoprecipitation (ChIP)-seq data from the ENCODE project has demonstrated the binding of TCF7L2 to rs17761446 site in HeLa-S3, which harbors a DHS in this SNP site (Figs  1C  and  3B ). Additionally, the bindings of EP300 (a coactivator for LEF/TCF family of transcription factors) and H3K27ac (a histone modification characteristic of active enhancer) in HeLa-S3 cells indicate that the SNP site facilitates a distal enhancer activity ( Fig 3B ). Additionally, colocalization of TATA-binding protein (TBP) with the enhancer marks at rs17761446 seems to reinforce the presence of the enhancer-promoter interaction ( Fig 3B ). On the other hand, we could not find consistent results between predicted binding motifs overlapping with rs17834457 and evidence of bindings from the ENCODE ChIP-seq analyses. Accordingly, we focused attention on rs17761446, and then demonstrated the bindings of TCF7L2, EP300, H3K27ac, and RNA polymerase II to the SNP site in HEC251 cells by ChIP assays ( Fig 3C ).\nWe quantified allele-specific factor bindings at rs17761446 using TaqMan-based allelic discrimination assay. The allelic ratios in immunoprecipitated and input chromatins were determined by fitting their log2 transformed VIC/FAM ratios to the standard curve constructed from DNA mixtures with known allelic ratios ( S16  and  S17  Figs). As a result, we detected significant allelic imbalances in the occupancies of TCF7L2 in two distinct antibodies (Cell Signaling,  P  = 0.019; OriGene,  P  = 0.012; combined  P  = 5.7×10 −5 ) and EP300 ( P  = 7.0×10 −3 ) ( S17 Fig ). These factors selectively bound to the G allele over the T allele of rs17761446, in which the occupancies of the G allele reached ~70% (TCF7L2 Cell Signaling, 68.3%; OriGene, 66.0%; and EP300, 65.7%) ( S18 Fig ). This indicates that the G allele exerts 2.06- and 1.95-fold greater binding affinities to TCF7L2 and EP300, respectively ( Fig 3D ). RNA polymerase II strongly bound to the G allele by a factor of 1.55 ( Fig 3D ), although the allelic imbalance did not reach to significance level ( P  = 0.14). On the other hand, H3K27ac did not exhibit allele-specific bindings ( P  = 0.50).\nWe evaluated whether rs17761446 worked as a  cis -regulatory element of  ANRIL ,  CDKN2A , and  CDKN2B  with the allele specific expression (ASE) analysis using eutopic endometrial tissues and endometrial carcinoma cell lines. We determined the haplotype phases with re-sequencing data for 9p21 region, and then screened the samples that were heterozygous both for rs17761446 and SNPs within transcribed regions of 9p21 genes with the same haplotypes ( Fig 4A  and  Methods ). The allele-specific paired-end read tag counts were measured by the deep sequencing of the reverse transcription PCR products. Genomic DNAs from the corresponding samples were also deeply sequenced as control. The average of the paired-end read tag counts was greater than 20,000. While the average proportion of the G allele of rs10965215, which was located on an exon of  ANRIL  and resided on the protective haplotype tagged by the G allele of rs17761446, was 49.4% over the genomic DNA samples, the paired-end read tags supporting the protective haplotype were over-represented in the RNA samples (on average 64.0%). The difference between the genomic DNA and RNA samples was statistically significant ( P  = 0.026) ( Fig 4B ). This result indicates that rs17761446 was a  cis -regulatory element of  ANRIL  and the protective allele was associated with 1.78-fold higher expression of  ANRIL  than the risk allele. On the other hand, the two alleles were not differentially transcribed in  CDKN2A  ( P  = 0.20) and  CDKN2B  ( P  = 0.61) ( Fig 4B ). These results seem to be consistent with the result that the G allele of rs17761446 showed stronger chromatin interaction with the promoter of  ANRIL  rather than  CDKN2A/2B . We confirmed that the result of our ASE analysis was robust against mapping bias by using simulation and sensitivity analyses ( Fig 4C and 4D ;  Methods ).\nA) Haplotypes constructed with candidate causal SNP rs17761446 and SNPs within transcribed region of 9p21 genes ( ANRIL , rs10965215;  CDKN2A , rs3814960; and  CDKN2B , rs3217992). B) Proportions of allele-specific paired-end tag counts supporting the protective haplotype in genome DNA and RNA samples from eutopic endometrial tissues (circles) and endometrial carcinoma cell lines (triangles). C) Simulation study for evaluating mapping bias. Simulation scenarios considering different sequence lengths (50bp, 75bp, and 100bp) and different error rates for each sequenced base (0.01, 0.005, and 0.001). Box plots are based on 10,000 replicates. D) Sensitivity analysis for evaluating mapping bias. In sensitivity analyses, we aligned reads from ASE to two types of sequences that were different in the base at the SNP site: the “original” and “changed” sequences have “reference” and “alternative” alleles, respectively. For each bar plot, mean and standard deviation are shown. Note that the significant result from  ANRIL  exhibited increased proportion of alternative allele.\nAccording to the results showing that the  cis -regulatory element at rs17761446 regulated  ANRIL  expression and a key TF for canonical Wnt signaling, TCF7L2 [ 38 ], differentially bound to the SNP alleles, we hypothesized that the induction of Wnt signaling changed expression level of  ANRIL . Wnt signaling was induced by CHIR99021 (CHIR) in HEC251 cell ( S19 Fig ). CHIR is a small molecule inhibitor of GSK3, and therefore can activate Wnt pathway through decreased phosphorylation and subsequent stabilization of β-catenin [ 38 ,  39 ]. We examined the effects of CHIR treatment in HEC251 cells on the expression of long and short isoforms of  ANRIL  ( S20A Fig ). Compared to cells treated with vehicle only (DMSO), the CHIR treatment significantly increased the expression levels of long and short isoforms of  ANRIL  by 33.2% ( P  = 5.7×10 −3 ) and 53.9% ( P  = 1.6×10 −3 ), respectively ( S20 Fig ). The effect was greater on the short isoform of  ANRIL . We confirmed the increase in overall  ANRIL  transcripts using the primer pair amplifying both long and short isoforms (29.4%,  P  = 9.4×10 −4 ) ( S20B Fig ).  ANRIL  is thought to regulate the INK4/ARF tumor suppressor locus [ 40 ,  41 ]; therefore, we examined expressions of two  CDKN2A  transcripts ( p16 INK4A  and  p14 ARF ) and one  CDKN2B  transcript ( p15 INK4B ). The expression levels of  p16 INK4A  and  p15 INK4B  significantly increased by 38.7% ( P  = 5.5×10 −6 ) and 35.7% ( P  = 5.0×10 −4 ), respectively, with the CHIR treatment ( S20B Fig ). On the other hand,  p14 ARF  did not change ( P  = 0.24). This result implies that the induction of Wnt signaling activates  ANRIL  expression, which in turn leads to increased expressions of G1 cell-cycle inhibitors,  p16 INK4A  and  p15 INK4B . In order to present a more direct evidence linking the candidate SNP and the change in  ANRIL  expression by inducing Wnt signaling via CHIR treatment, we evaluated whether degree of allelic imbalance of  ANRIL  expression increased after inducing Wnt signaling via CHIR treatment. For this purpose, we performed ASE analysis of  ANRIL  by using CHIR- and DMSO-treated endometrial carcinoma cell line (HEC251). Although we replicated ASE of  ANRIL  where protective haplotype was associated with increased expression (CHIR,  P  = 2.8×10 −4 ; DMSO,  P  = 7.8×10 −4 ; one sample  t -test), there was no difference in ASE between two treatments ( P  = 0.83; Welch’s  t -test) ( S21 Fig ).\n\nThe major challenge in the post-GWAS stage is to elucidate the functional aspect of genotype-phenotype correlations, i.e., molecular mechanisms and biological pathways, affected by the identified genetic variants. The post-GWAS analysis is essential for the translation of genetic knowledge to clinical application. Functional studies focusing only on the SNPs identified by GWASs may not be promising because the identified SNPs are most likely surrogate markers for causal variants. The information about regulatory elements distributed across the risk locus provided by the ENCODE project can be a powerful tool to prioritize putative causal variants. Additionally, the regulatory mechanism modulated by candidate causal variant must be experimentally unraveled. In this study, we addressed these two steps to elucidate transcriptional regulation of the endometriosis-associated variants on chromosome 9p21.\nWe demonstrated that integrative analysis of the LD profile created by re-sequencing of the risk locus with information about regulatory elements from DNase-seq data provided by the ENCODE consortium was effective to prioritize putative causal variants. As a consequence, we found that the SNP identified by the original GWAS did not coincide with DHSs in the endometrial carcinoma cell lines and other representative cell types. On the other hand, we identified two SNPs (rs17761446 and rs17834457) showing strong DNase-seq signals in the endometrial carcinoma and HeLa cells ( Fig 1 ).\nWe leveraged “allele-specific” functional genomic approaches to full advantage for the identification of allelic imbalances in distinct stages of transcriptional regulation including binding affinities of TF and its coactivator, chromatin interactions, and gene expressions. The advantage of allele-specific functional genomic approaches is that the use of tissues or cell lines that were heterozygote for a SNP of interest allows to directly compare functional difference between the two alleles of the SNP under the same cellular environment [ 42 ]. To the best of our knowledge, only a few studies addressed allelic differences in the frequencies of chromatin interactions between two alleles of causal SNPs [ 21 – 23 ]. In this study, we developed a method to detect allele-specific chromatin interactions (AS3C-seq) ( S10 Fig ). The AS3C-seq utilizes the next-generation sequencing technology to quantify the difference in chromatin interaction frequencies between the two alleles of a SNP of interest. By using AS3C-seq, we detected an allelic imbalance in the frequencies of chromatin interactions between candidate causal SNP and the promoter of  ANRIL . The AS3C-seq showed that a DHS in an intron of  ANRIL  containing two candidate causal SNPs formed chromatin looping with the promoter of  ANRIL  and the interaction frequencies differed according to the SNP alleles ( Fig 2 ). One of these two SNPs (rs17761446) overlapping with binding motifs of HMG class of TFs including TCF/LEF and SOX families was shown to alter the binding affinities to TCF7L2 and EP300 ( Fig 3 ). The differential bindings of TCF7L2 and EP300 between the two alleles of rs17761446 seem to result in the observed allelic imbalance in chromatin interactions between the SNP site and the promoter of  ANRIL , because the DNA bending properties of TCF/LEF lead to the formation of DNA loops favoring the concentration of distant transcriptional complexes [ 43 ] and their coactivator EP300 facilitates as a bridge between DNA-binding TFs and basal transcription machinery such as TFIIB and TBP [ 44 ]. The ASE analysis showed the allelic imbalance in the transcription of  ANRIL  between the SNP alleles by using eutopic endometrial tissues and endometrial carcinoma cell lines, suggesting that rs17761446 work as a  cis -acting eQTL of  ANRIL  ( Fig 4 ). The results of this study sheds light on regulatory mechanisms underlying 9p21 endometriosis risk locus, in which preferential bindings of TCF7L2 and its coactivator EP300 to the protective G allele of rs17761446 lead to stronger chromatin interaction with the promoter of  ANRIL , which in turn activate transcription of the non-coding RNA.\nAlthough we initially focused on binding of TCF7L2 according to consistent results between the motif search and the ENCODE ChIP-seq analyses, SOX family members can be a good candidate for a regulator of the enhancer ability at the 9p21 endometriosis risk locus. Indeed, we found that SOX4 strongly bound to the candidate SNP site ( S22 Fig ). Co-localization of SOX4 with TCF7L2 at the SNP site seems to be intriguing, because SOX4 has been reported to regulate Wnt signaling by direct binding to TCF family members and β-catenin [ 45 ]. According to the result of motif search, other SOX family members (SOX5, SOX9, SOX13, SOX15, and SRY) were also predicted to bind to the SNP site ( S13  and  S15  Figs). It has been reported that some SOX factors work as agonist and promote Wnt signaling, others act as antagonist [ 46 ]. Therefore, further analyses for evaluating bindings of a set of SOX family members at the SNP site together with allele-specific binding analyses may be useful to uncover regulatory mechanism of the 9p21 endometriosis risk locus.\nMotivated by the fact that TCF7L2 is a key TF in canonical Wnt pathway [ 46 ], we demonstrated that expression level of  ANRIL  was increased by inducing Wnt signal by CHIR treatment. At the same time, we found increased expressions of  CDKN2A  ( p16 INK4A ) and  CDKN2B  ( p15 INK4B ) ( S20B Fig ). As shown in  Fig 4 , rs17761446 did not exert  cis -regulatory effects on  CDKN2A  and  CDKN2B . Therefore, it is speculated that  ANRIL  modulates expressions of  CDKN2A  and  CDKN2B  through  trans -regulation, though we could not rule out the possibility that other regulatory mechanisms could account for this observation. These findings imply that lower expression level of  ANRIL  in the risk allele is associated with reduced expressions of G1 cell-cycle inhibitors,  p16 INK4A  and  p15 INK4B , which may contribute to a proliferative phenotype of ectopic endometrial cells and promote the development of endometriosis. To uncover modular regulatory mechanisms of the candidate SNP, Wnt signaling, and  ANRIL  expression, which leads to the development of endometriosis, further functional studies are required. A straightforward approach is to evaluate effect of disrupting the binding motif of HMG class of TFs at the SNP site on  ANRIL  and genome-wide expression profiles and cellular phenotypes, because it has been reported that  ANRIL  regulates gene expression network in a  trans -acting manner [ 47 ,  48 ].\nGenetic variants on 9p21 have been reported to be associated with numerous diseases such as CAD [ 49 ], intracranial aneurysm [ 50 ], type 2 diabetes [ 51 ], glaucoma [ 52 ], and several types of cancers [e.g.,  53 ]. Harismendy et al. demonstrated that a 9p21 CAD associated SNP rs10757278 disrupted a binding site for STAT1, and this enhancer site modulated  ANRIL  expression via IFN-γ stimulation [ 15 ]. Interestingly, the binding of STAT1 at the enhancer site exerted cell-type specific regulations of  ANRIL  (repression in lymphoblastoid cells lines; activation in HUVEC) [ 15 ].  ANRIL  is transcribed into several different splicing variants. The risk haplotype for CAD have been shown to be consistently associated with decreased levels of isoforms containing the 5’ exons [ 54 ], which is similar to our ASE result. The elucidation of tissue-specific regulatory mechanisms of  ANRIL  isoforms may clarify the pleiotropic effects of 9p21 region.\nLimitations of our study should be noted. The methods of prioritizing two SNPs (rs17761446 and rs17834457) for subsequent functional studies were based on several criteria: degree of LD with the original GWAS SNPs; DNase-seq data from ENCODE cell types; and detection of significant DHSs. Although the inclusion criteria were set in order to elucidate more generalized functional aspect, we can not rule out a possibility that the variants that do not fulfill the criteria can be responsible for the 9p21 endometriosis risk locus. We prioritized rs17761446 over rs17834457 based on the motif search and the ENCODE ChIP-seq analyses. As both of these two analyses are incomplete, there is a possibility that uncharted TFs bind to rs17834457. It is also plausible that both rs17761446 and rs17834457 facilitate as a  cis -regulatory haplotype in a coordinated fashion. In order to evaluate whether the haplotype constructed by these variants or single variant is truly functional, it is useful to create mutant cell lines that harboring these variants separately or some combinations by using genome editing technique such as CRISPR/Cas9 system [ 55 ,  56 ].\nAmong the SNPs identified by GWASs of endometriosis, most replicated SNP is located on chromosome 1p36 in a region close to  WNT4  [ 31 – 33 ]. Together with our results, these findings raise the possibility that Wnt signaling pathway is involved in the pathogenesis of endometriosis. The regulatory mechanisms of these susceptibility loci might converge to shared molecular network underlying the pathogenesis of endometriosis, which will provide clues for potential targets exploited for new drug development.\n\nStudy subjects were Japanese and recruited at Niigata University and Nippon Medical School. All the participants provided written informed consent. The Ethics Committee of National Institute of Genetics (nig1128, 2012. 10. 9), Niigata University (488, 2014. 1. 27), and Nippon Medical School (24-12-273, 2014. 3. 15) approved the study protocols.\nWe selected 1.29 Mb interval of chromosome 9 (chr9:21299764–22590271, hg19) as a target region for next-generation sequencing ( S1 Fig ). The method for selecting target region is described in  S1 Fig . We used NimbleGen SeqCap EZ choice system as a target enrichment method (Roche Diagnostics). A DNA probe set complementary to the target region was designed by NimbleDesign ( https://design.nimblegen.com ).\nGenomic DNA was prepared from 48 Japanese patients with endometriosis using QIAamp DNA Blood Maxi Kit (QIAGEN) according to the manufacturer’s protocol as previously described [ 30 ,  57 ]. For each sample, 1 μg of genomic DNA was sheared into fragments with a mode length of about 200 bp on the Covaris (Covaris). Sequencing libraries were constructed with the Illumina TruSeq DNA sample Preparation Kit with 12 different indexed adapters (Illumina). Then, the target enrichment was performed with NimbleGen SeqCap EZ choice system according to the standard protocol (Roche Diagnostics). The libraries were sequenced on four runs of the Illumina MiSeq platform with 2×150-bp paired-end module (Illumina).\nThe reads containing the Illumina adapter sequences were trimmed by using Trimmomatic version 0.32 [ 58 ]. After the quality control step for excluding or trimming low quality sequences, the sequence reads were aligned to human reference genome (hg19) via BWA version 0.7 [ 59 ]. The aligned reads were processed for removal of PCR duplicates and erroneous reads by Picard tools version 1.111, and for local realignment and base quality recalibration by GATK version 3.2.2 [ 60 ,  61 ]. SNVs and indels were detected with the HaplotypeCaller and VariantRecalibrator of the GATK version 3.2.2 [ 60 ,  61 ].\nWe analyzed DNase-seq data generated by Duke University in the ENCODE consortium to explore the distribution of DHSs. The sequence alignment files in the BAM format were downloaded from the ENCODE public download repository at  http://genome.ucsc.edu/ENCODE/downloads.html . The peak call for the identification of regions in which aligned reads were significantly enriched was implemented via F-Seq version 1.84 with the use of background model for 20 bp sequences [ 62 ].\nEndometrial carcinoma cell lines (HEC251 [JCRB1141] and HEC265 [JCRB1142]) were grown in DMEM (Sigma-Aldrich) with 10% FBS. These cell lines were purchased from JCRB Cell Bank. We confirmed that HEC251 and HEC265 were heterozygous for rs17761446 by Sanger sequencing of the PCR product with the primer pair listed in  S3 Table .\nThe 3C libraries from HEC251 and HEC265 were generated with the established protocol ( S10A Fig ) [ 34 ,  63 ]. HEC251 and HEC265 cells (1×10 8  cells per experiment) were crosslinked with 1% formaldehyde for 12 min at room temperature followed by adding glycine to a final concentration of 0.125M to stop further crosslinking. The crosslinked cells were lysed with the buffer (10 mM Tris-HCl, pH = 8.0, 10 mM NaCl, 0.2% IGEPAL CA-630 Protease Inhibitor (EDTA free)) using the Dounce homogenizer. The cell pellets were treated with corresponding buffers to restriction enzymes and 1% SDS, and then incubated at 65°C for 10 min to remove non-crosslinked proteins from the DNA. Placed on ice, 10% Triton X-100 was added to quench the SDS. For digestion, we used “6 bp-cutter” restriction enzymes ( Eco RI or  Hin dIII) followed by incubation at 37°C overnight with rotation. T4 DNA ligase was used to create intra-molecule ligations. The crosslinks were degraded by proteinase K treatment at 65°C overnight followed by an additional 2 h incubation at 42°C. The 3C DNA was purified by two rounds of phenol-chloroform (pH = 7.9) method followed by five ethanol washes. The unidirectional primer design [ 63 ] was used for examining chromatin loops formed between the restriction fragment containing candidate causal SNPs and the consecutive restriction fragments around  ANRIL ,  CDKN2A , and  CDKN2B  ( S10B Fig ). The designed primers are shown in  S2 Table .\nThe workflow of the AS3C is illustrated in  S10 Fig . We used HEC251 and HEC265 cells that were heterozygous for rs17761446. By using  Hin dIII, we could design the primers whose product contained the ligation junction and the SNP site at tractable size (1,225 bp;  S11 Fig  and  S2 Table ). The PCR product was gel-purified and then subjected to transposase-based library construction with the Nextera DNA Sample Preparation Kit (Illumina), which allowed simultaneous DNA fragmentation and adaptor ligation. The DNA libraries were pooled according to their molar concentrations evaluated by the Agilent High Sensitivity DNA Kit and 2100 Bioanalyzer (Agilent Technologies). The DNA libraries were sequenced on the MiSeq platform with 2×150-bp paired-end module (Illumina).\nThe bioinformatics analysis in AS3C-seq is described in  S10C Fig . For the sequence reads aligned to “artificial chromosome” that was an exact sequence of the 3C PCR product, the number of paired-end read tag counts supporting reference and alternative alleles was measured. The allelic imbalance between two alleles of the SNP in the AS3C-seq reads was evaluated by assuming that allele-specific paired-end read tag counts follows a binomial distribution. For each experiment, the deviation from balanced allele-specific paired-end read tag counts (50:50) was examined by the binomial test (two-sided). When results from multiple experiments were combined, the likelihood ratio test was examined as follows:\n L R T = 2 log L ( π 1 ) L ( π 0 ) = 2 ∑ i [ n i A log π 1 π 0 + ( n i − n i A ) log 1 − π 1 1 − π 0 ] , \nwhere  LRT  statistic follows a chi-square distribution with degrees of freedom of 1,  π 0  and  π 1  are the proportion of allele-specific reads supporting “A” allele for the SNP site under the null and alternative hypotheses, respectively. For  i th experiment,  n i  is the total number of reads mapped to the SNP site and  n iA  is the number of reads supporting “A” allele. Under the null hypothesis,  π 0  is set to be 0.5.  π 1  is defined as a weighted proportion across experiments:  π 1 = ∑ i n i A / ∑ i n i .\n61-nt sequences of which candidate SNP sites were located in the middle were retrieved from hg19. Two sequences with reference or alternative allele were generated for each candidate SNP. The PWMs were constructed based on the HOCOMOCO database [ 36 ]. MotifSuite was used to detect motifs within the query sequences showing better fits to the HOCOMOCO PWMs over the background model for human genome [ 64 ].\nChIP-seq data in HeLa-S3 cells were downloaded from the ENCODE public download repository at  http://genome.ucsc.edu/ENCODE/downloads . Significant peaks for the bindings of TCF7L2, EP300, and TBP were determined based on the ENCODE narrow peak regions. The enrichment of a histone modification, H3K27ac, was defined as the ENCODE broad peak regions.\nChIP assays for HEC251 cells were performed with SimpleChIP Plus Enzymatic Chromatin IP Kit (Cell Signaling) according to the manufacturer’s protocol. Briefly, cells were crosslinked with 1% formaldehyde for 10 min at room temperature, then treated with micrococcal nuclease to obtain fragments with a length of approximately 150–900 bp. The fragmented chromatin was subjected to immunoprecipitation with antibodies against TCF7L2, EP300 RNA polymerase II, H3K27ac, and SOX4. Normal rabbit IgG was used as negative control. The purified immunoprecipitated chromatin, input chromatin, and mock-IP (IgG) was subjected to PCR amplification of the candidate SNP site by using oligonucleotides primers ( S3 Table ). The binding affinities were measured by real-time quantitative PCR with the KAPA SYBR FAST qPCR kit (KAPA Biosystems) on the 7900HT sequence detection system (Applied Biosystems). The binding affinity in the immunoprecipitated chromatin was normalized to that in the corresponding input chromatin as follows:  2 − ( C t I P − C t I n p u t ) . The enrichment of the normalized binding affinity for the candidate SNP site or positive control region was normalized by dividing by that for negative control region. We used  MYC  promoter and α satellite repeat element as positive and negative control regions, respectively ( S3 Table ). Details on the antibodies are shown in  S4 Table .\nThe allele-specific TF binding at rs17761446 was determined using the TaqMan-based allelic discrimination assay. We used TaqMan SNP genotyping assay for rs17761446 (Applied Biosystems, Assay ID: C__33349228_10). In order to measure allelic ratios for the immunoprecipitated and input chromatins from HEC251 cells, a standard curve of the VIC/FAM ratios for the samples with known genotypes was generated as follows. Calibration of the allelic discrimination assay was determined by mixing three pairs of DNA samples with GG and TT homozygous genotypes for rs17761446 at the following proportions: 50:50, 60:40, 70:30, 80:20, and 90:10. DNA samples with GG, GT, and TT genotypes were also analyzed in the calibration assay. The standard curve was constructed by regressing the log2 transformed ratios for the VIC/FAM intensity on the log 2 transformed ratios of the two alleles. The allelic imbalance was examined by comparing the VIC/FAM ratios between the immunoprecipitated and input chromatins with the paired  t -test (two-sided).\nWe performed the ASE analyses of rs17761446 for  ANRIL ,  CDKN2A , and  CDKN2B  by using eutopic endometrial tissues and endometrial carcinoma cell lines. The eutopic endometrial tissues were provided by patients with ovarian cysts who underwent a total hysterectomy. All the specimens were snap-frozen in liquid nitrogen and stored at −80°C. Six patients with ovarian cysts and two endometrial carcinoma cell lines (HEC251 and HEC265) that were heterozygous for rs17761446 were used for further analyses. We performed target re-sequencing of the 9p21 region with the same methods described in the previous section. The haplotype phasing for the genotype data of these 8 samples along with the 48 samples in the previous section was implemented via Beagle 4.0 [ 65 ]. We found that the five patients with ovarian cysts and two endometrial carcinoma cell lines harbored heterozygous genotypes both for rs17761446 and a SNP within  ANRIL  (rs10965215) with the same haplotypes. In total, six and seven samples were available for the ASE analyses by using rs3814960 and rs3217992 as transcribed SNPs in  CDKN2A  and  CDKN2B , respectively. LD structure for these SNPs are shown in  S23 Fig .\nRNA was extracted from eutopic endometrial tissues and endometrial carcinoma cell lines using AllPrep DNA/RNA Mini kit (QIAGEN) according to the manufacturer's instructions. 1 μg of the total RNA was reverse transcribed by using ReverTra Ace-α kit (Toyobo) with random primer. The synthesized cDNA was subject to PCR amplification by using oligonucleotide primers for the three 9p21 genes ( S3 Table ). The genomic DNA was also subjected to PCR amplification by using oligonucleotides primers ( S3 Table ), and used as control. The obtained PCR products were converted into indexed libraries by using NEBNext Ultra DNA Library Prep Kit for Illumina (New England Biolabs) followed by sequencing on the MiSeq platform with 250-bp paired-end module (Illumina). The generated reads were aligned to “artificial chromosomes”, which were exact sequences of the PCR products. The reads mapped with a high confidence (MAQ > 30) were used for the ASE analyses. The allele-specific paired-end tag counts were measured by using only high confidence base calls (base quality > 20) at the transcribed SNP positions. The allelic imbalance was examined by comparing the log 2 transformed ratios of the allele-specific paired-end tag counts between cDNA and genomic DNA with the paired  t -test (two-sided).\nCHIR (Tocris Bioscience) was dissolved in DMSO (Sigma). HEC251 cells were treated for 24 h with either DMSO (vehicle) or CHIR (5 μM), and then harvested for gene expression analysis. RNA was extracted from DMSO- and CHIR-treated HEC251 cells using AllPrep DNA/RNA Mini kit (QIAGEN) according to the manufacturer's instructions. 1 μg of the total RNA was reverse transcribed by using ReverTra Ace-αkit (Toyobo) with random primer. The expression levels were measured by real-time quantitative PCR with the KAPA SYBR FAST qPCR kit (KAPA Biosystems) on the 7900HT sequence detection system (Applied Biosystems). Three 9p21 genes ( ANRIL ,  CDKN2A , and  CDKN2B ) were evaluated. Expression was normalized to  ACTB . The oligonucleotides primers used are shown in  S3 Table . The fold change was calculated by dividing expression level in CHIR-treated cells by that in cells treated with vehicle only (DMSO). Six replicates were performed. Difference in gene expression level between the two treatments was examined by testing whether log2 transformed fold change was different from zero with one sample  t -test (two-sided).\nImmunofluorescence analysis was performed with CHIR- and DMSO-treated HEC251 cells. Cells grown on microscope slides were rinsed in Dulbecco’s PBS and then fixed in Bouin’s fixative (Sigma-Aldrich) for 5 min. The cells were incubated with the primary antibody against polyclonal β-catenin ( S4 Table ) over-night in a humidified chamber at 4°C. After washing, the cells were incubated with the secondary antibody (Alexa-Flour 488 goat anti-rabbit IgG; Life Technologies) in a humidified chamber for 1 h at room temperature. Finally, the cells were mounted with Vetashield HardSet Mounting Medium supplemented with DAPI (Vector Laboratories). Images acquisition was performed using Olympus FluoView ™  FV1200 (Olympus).\nOne reservation on allele specific functional genomics studies based on next generation sequencing technologies is mapping bias, in which reads with reference allele are more likely to be mapped than reads with alternative allele (i.e., bias toward preferential mapping to reference allele) [ 35 ]. Therefore, we addressed mapping bias on our AS3C-seq and ASE analyses by performing simulation and sensitivity analyses described as below.\n(i) Simulation analysis\nWe performed a simulation analysis for evaluating mapping bias by patterning after three studies [ 35 ,  66 ,  67 ]. For AS3C-seq, we generated single-end reads based on the human reference genome (hg19). For all possible segments overlapping rs17761446 or rs17834457 at a defined sequence length, we generated a set of reads containing reference and alternative alleles. We considered random sequencing errors in the simulation, in which each base in the reads was substituted to a different randomly selected base with a Bernoulli probability of 0.001, 0.005, and 0.01. The error rate was determined based on Loman et al. reporting that MiSeq produced 0.1 mismatches per 100 bases [ 68 ]. We considered three simulation scenarios of different sequence lengths (50, 75, and 100 bp). We arbitrarily assigned the best base quality score of 41 to each base of the simulated reads.\nrs17761446 and rs17834457 are closely located (76 bp apart) and in perfect LD ( r 2  = 1.0). We incorporated this fact into our simulation. When the sequence length was 100 bp, some of the sequence segments overlapped both of these SNPs. In such case, the haplotype structure observed in Japanese population was reflected in the simulation: reference T allele at rs17761446 resided on the same haplotype with reference C allele at rs17834457.\nThe simulated reads were mapped to the human reference genome rather than the artificial chromosome. The SNP sites were distant from a ligation junction between fragments containing the SNPs and  ANRIL  promoter (>700 bp), indicating that any single reads overlapping the SNPs did not reach to the ligation junction. Therefore, mapping to the reference genome are thought to be sufficient in order to evaluate effects of mapping bias at the SNP sites. BWA with default setting was used. For the reads with mapping quality > 30, we measured allele-specific read counts at the SNP sites and calculated proportion of the reads supporting reference allele. For each parameter combination, 10,000 simulations were carried out. For allele specific expression analyses, we conducted simulations by using the sequences of transcripts rather than genome.\nThe results of the simulations for allele specific chromatin interaction and allele specific expression analyses are shown in Figs  2D  and  4C , respectively. The results show that when the sequence length is shorter and the error rate is higher, degree of preferential mapping to reference allele increases. The averaged proportion of mapped reads supporting reference allele did not surpass 52%, even when the error rate is set to be 0.01 and the sequence length is 50 bp, in which the error rate is ten times as large as a literature value for Illumina platform [ 68 ]. When we consider more realistic scenarios (i.e., the error rate of 0.001 and 0.005), the average proportion of mapped reads supporting reference allele was at most 50.03% and 50.06%, respectively. The results of the simulation showed very subtle preferences toward reference alleles, indicating that the mapping bias expected by the simulations does not affect our results.\n(ii) Sensitivity analysis\nIn order to evaluate effects of mapping bias not covered by the simulation study, we performed a sensitivity analysis in which a base at SNP site in the reference sequence is changed to alternative allele. If a SNP site is susceptible to mapping bias, change from reference to alternative allele may diminish a preference to reference allele and exaggerate a preference to alternative allele. We aligned the reads generated by our allele specific chromatin interaction and allele specific expression analyses to the “original” and “changed” reference sequences with the same method. Then, differences in allelic imbalance were examined between two reference sequences.\nThe results of the sensitivity analyses for allele specific chromatin interaction and allele specific expression analyses are shown in Figs  2E  and  4D , respectively. For the SNP sites analyzed, differences in allelic imbalance were small between two reference sequences (at most 0.15%).\nAccording to the two analyses, we confirmed that mapping bias does not affect our results. Additionally, both of the significant results from our AS3C-seq and ASE exhibited increased proportions of alternative alleles. Therefore, it is most likely that the observed allelic imbalances in our study were not caused by mapping bias.\nSequence data for re-sequencing of 9p21 region has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001001741/EGAD00001001942. Nucleotide sequence data for AS3C-seq are available in the DDBJ Sequenced Read Archive under the accession numbers DRX051835 and DRX051836.\n\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\nA) Consistent PCR amplifications for the detection of chromatin interaction between the fragment containing candidate SNPs (rs17761446 and rs17834457) and the fragment containing the promoter of ANRIL in HEC251 and HEC265 cell lines. B) Sanger sequence for the PCR amplicon for the interacting fragments between candidate SNPs (rs17761446 and rs17834457) and the promoter of  ANRIL  verified the presence of the ligation junction of these two fragments.\n(PDF)\nClick here for additional data file.\nA) 3C library preparation. B) PCR amplification followed by high-throughput sequencing. C) Bioinformatics analysis.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\nA) VIC/FAM ratios for immunoprecipitated and input chromatins by TaqMan allelic discrimination assay. B) Allelic ratios for ChIP libraries determined based on the standard curve.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\nA) Structures of ANRIL transcripts amplified by three primer pairs. B) Fold expressions of CHIR-treated cells for transcripts of  ANRIL ,  CDKN2A  and  CDKN2B  relative to cells treated with vehicle only (DMSO).\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.\n(PDF)\nClick here for additional data file.","source_license":"CC0","license_restricted":false}