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A functional mechanism for a non-coding variant near AGTR2 associated with risk for preterm birth



Preterm birth (PTB), defined as delivery before 37 gestational weeks, imposes significant public health burdens. A recent maternal genome-wide association study of spontaneous PTB identified a noncoding locus near the angiotensin II receptor type 2 (AGTR2) gene. Genotype-Tissue Expression data revealed that alleles associated with decreased AGTR2 expression in the uterus were linked to an increased risk of PTB and shortened gestational duration. We hypothesized that a causative variant in this locus modifies AGTR2 expression by altering transcription factor (TF) binding.


To investigate this hypothesis, we performed bioinformatics analyses and functional characterizations at the implicated locus. Potential causal single nucleotide polymorphisms (SNPs) were prioritized, and allele-dependent binding of TFs was predicted. Reporter assays were employed to assess the enhancer activity of the top PTB-associated non-coding variant, rs7889204, and its impact on TF binding.


Our analyses revealed that rs7889204, a top PTB-associated non-coding genetic variant is one of the strongest eQTLs for the AGTR2 gene in uterine tissue samples. We observed differential binding of CEBPB (CCAAT enhancer binding protein beta) and HOXA10 (homeobox A10) to the alleles of rs7889204. Reporter assays demonstrated decreased enhancer activity for the rs7889204 risk “C” allele.


Collectively, these results demonstrate that decreased AGTR2 expression caused by reduced transcription factor binding increases the risk for PTB and suggest that enhancing AGTR2 activity may be a preventative measure in reducing PTB risk.

Peer Review reports


Preterm birth (< 37 completed gestational weeks) complicates approximately 10% of pregnancies in the USA [1]. Globally, preterm birth and its complications are the leading cause of mortality in children under 5 years of age [2]. Previous studies have demonstrated that there is a strong genetic component involved in gestational duration and preterm birth [3], though early studies failed to prove statistically significant correlations [4, 5]. These early studies either focused on a set of pre-selected genetic variants or target genes and/or were performed in small cohorts. Advances in genome-wide association studies (GWAS) in the past two decades make it possible to evaluate genetic variants associated with pregnancy complications at the whole genome level in large cohorts of usually more than 5000, providing powerful and robust associations [6, 7]. Our group has recently reported the first 6 genetic loci robustly associated and replicated for altering the risk of preterm birth in the human population [8]. In this study, we examine the AGTR2 (angiotensin II receptor, type 2) locus, a locus on the X chromosome strongly associated with not only gestational duration but also preterm birth risk as a dichotomous trait [8].

The AGTR2 locus is particularly compelling in that it has clear developmental roles in maternal physiology and is a potential druggable therapeutic target. AGTR2 protein has been implicated in a multitude of functions involving vasodilation, cell apoptosis, growth inhibition, natriuresis, nitric oxide production, and collagen synthesis [9,10,11]. Cell signaling through AGTR2 is poorly understood, but likely occurs through activation of protein phosphatases, activation of cGMP, and release of phospholipase A2 and arachidonic acid [12]. AGTR2 is expressed in the developing fetus and then rapidly decreases after birth in most tissues. It is predominantly expressed in the lung, uterus, and endometrium [13, 14]. Function of this gene in reproduction has been suggested in early placental development [15] and implantation or decidualization [16]. As the starting sample materials for both studies were either placental/decidual [15] or whole endometrial biopsies [16], the specific tissue/cell type of action has not yet been identified.

From our GWAS study, the index SNP lies more than 200 kb upstream of the AGTR2 gene and acts as an eQTL for the AGTR2 gene [8], suggesting that distal enhancer activity may be involved in the molecular mechanisms underlying AGTR2 gene expression. However, any variants in the linkage disequilibrium (LD) block with this index SNP can potentially be responsible for the detected association, whereas only a few of them are likely truly causal and functional [17, 18]. Functional studies can provide mechanistic insights by determining whether the distal acting non-coding variants disrupt the DNA binding of transcription factors, chromatin regulators, and/or nucleosomes in an allele-dependent manner [19]. It is also known that molecular mechanisms of action via non-coding genetic variants are highly cell- and tissue-specific [20,21,22,23]. To address these issues, we take advantage of recent advances in genomics, epigenomics, and transcriptomics that annotate the diverse gene regulatory elements [24,25,26,27,28]. These studies are often large scale, covering multiple tissues and/or cell types, and include multiple layers of genomic and epigenomic regulatory mechanisms. Mining of such data sets can provide useful insights for causal SNP prioritization and identification and identify potential regulators such as transcription factors and target tissue/cell types in which they operate.

Once a potential causal SNP and target gene have been identified in a relevant tissue/cell type, it is equally crucial to experimentally validate and further elucidate the mechanisms leading to human diseases. As not all human diseases can be recapitulated in vitro, a common approach to demonstrate the functionality of a particular non-coding variant is to (1) identify TFs with altered DNA binding activity via electromobility shift assays (EMSA) or DNA affinity precipitation assays (DAPA) [29]; and (2) create a construct harboring both alleles of the variant and test its ability to alter reporter gene expression [30].

Due to the complexity and uncertainty of the function of the AGTR2 gene, our study focuses on demonstrating the enhancer activity of the identified non-coding region in regulating AGTR2 expression, identifying the regulatory proteins involved in allelic regulation, and exploring the potential tissue/cell types in which this regulatory module operates. To this end, we sought to determine the likely causative variant in the noncoding region adjacent to AGTR2 and to determine the mechanistic effect of this variant on the regulation of AGTR2 expression. We conducted bioinformatic analysis using publicly available multi-omics datasets. This approach prioritized a single non-coding SNP, rs7889204, to be the most likely causal SNP. rs7889204 is a strong eQTL for the AGTR2 gene, especially in uterine tissue, with the major “T” (non-risk) allele associated with higher AGTR2 expression, and the minor “C” (risk) allele with lower AGTR2 expression. Our method also identified transcription factors with predicted allele-dependent binding at this SNP. Using EMSA, we validate that rs7889204 alters the binding of the CEBPB and HOXA10 TFs, both of which display stronger binding to the T non-risk allele compared to the C risk allele. Reporter assays demonstrate decreased enhancer activity for the risk allele at rs7889204. Collectively, these results support the potential utility of modulation of AGTR2 activity and/or its CEBPB and HOXA10 regulators for PTB prevention and therapy.


Cell line, culture, and induced decidualization

The cell line used in EMSA and luciferase assay was an immortalized human endometrium stromal fibroblast cell line (T HESC, Mor lab, Yale University, corresponding to ATCC CRL-4003). Culture and further decidualization conditions are described in [31] with minor modifications. Specifically, the decidualization medium is supplemented with 2% FBS, 0.5 mM cAMP(Sigma 23583–48-4), 1uM MPA (Sigma, M1629), 1*Pen/Strep and 0.24 g NaHCO3 per 200 mL DMEM medium.

Computational analysis to predict and prioritize functional SNPs

We performed linkage disequilibrium (LD) expansion using the PLINK software package (version 1.90b) with EU ancestry (LD r2 > 0.8). This resulted in a total of 49 candidate SNPs. We then annotated these SNPs with eQTL data. We collected functional genomic datasets including ENCODE [28], Cistrome [32], PAZAR [33], Re-Map [34], and NIH Roadmap Epigenomics [35]. All datasets were indexed by their genomic coordinates, which were used to intersect with the LD expanded list of SNPs of interest. To help prioritize functional SNPs, SNPs with overlapping TF ChIP-seq signals were scanned for the putative binding signals using TF binding models based on protein binding microarray data using the Cis-BP database. After cross reference of the above results, we identified rs7889204 as the candidate causal SNP and FOXA1, CEBPB, and HOXA10 as transcription factors with predicted differential allelic binding at this T/C SNP.

Genotype-Tissue Expression quantitative trait (eQTL) analysis

We examined the tissue-specific expression of AGTR2 using the Genotype-Tissue Expression (GTEx) Portal ( Significant single-tissue eQTLs for AGTR2 in all tissues were extracted from GTEx Analysis Release V8. The distribution of the eQTLs and the pairwise LD pattern were visually inspected using the GTEx Locus Browser (Fig. 2A). Single-tissue eQTLs for rs7889204 were extracted and examined using eQTL violin plot (Fig. 2B).

3D Genome Interaction Analysis using 3DIV

We used rs7889204 as the bait on the HiC page under the interaction visualization tab on the 3D Genome Interactive Viewer and Database [36] (3DIV) ( We followed the tutorial using default settings with the exception that we set the threshold on distance normalized interaction frequency to be 2.00. We started with a selection of all possible data sets and then ranked them by gene name. Chromatin looping interaction signal was found between this SNP and the promoter region of AGTR2 in a fibroblast cell context (Fig. 2C).

Electrophoretic mobility shift assay

Electrophoretic mobility shift assays (EMSAs) were performed as previously described [29, 30] to determine whether the candidate variant at the AGTR2 locus differentially affected the binding of the predicted transcription factors, namely Forkhead box protein A1 (FOXA1), CCAAT/enhancer-binding protein beta (CEBPB) and Homeobox protein A10 (HOXA10). Briefly, we prepared nuclear protein extracts as previously described with minor modifications [29]. Decidualized hESF cells were trypsinized, washed twice with cold PBS, counted, resuspended in PBS as 107 cells/ml, then centrifuged 3300 g for 2 min, 4 °C. PBS was removed, and 107 cells were resuspended in 400 μl CE buffer (10 mM HEPES (pH7.9), 10 mM KCl, 0.1 mM EDTA, 1 mM dithiothreitol (DTT), and 1X HALT protease/phosphatase inhibitor (ThermoFisher)), then incubated on ice for 15 min. Cells were mixed with 25 μl of 10% Nonidet P-40 by pipetting, and then nuclei were pelleted at maximum speed for 3 min, 4 °C. Nuclei were resuspended in 30 μl NE buffer (20 mM HEPES (pH7.9), 0.4 M NaCl, 1 mM EDTA, 1 mM DTT, 1X protease/phosphatase inhibitor) by vortexing, incubated on ice for 10 min, then centrifuged 3300 g for 2 min, 4 °C. Supernatants (nuclear proteins) were removed from pelleted debris and stored in small aliquots at − 80 °C. Protein concentration was determined by BCA protein assay (ThermoFisher). SNP rs7889204 was used to prepare 5′ IMR700 dye–labeled 35-bp duplexed oligonucleotide probes (Integrated DNA Technologies). SNP oligonucleotide sequences were obtained from the National Center for Biotechnology Information dbSNP database ( by using human genome build 37. The sequence of probes are as follows:

  • reference “T” allele is:


  • non-reference “C” allele oligo sequence is:


EMSA binding reactions contained 6 μg of nuclear extract, 100 fmol of the labeled probe, 2μL of 10 × binding buffer, 1 μg of Poly(dI-dC), 1μL of 40% glycerol, 2μL of 25 mmol/L dithiothreitol/2.5% Tween 20, 1μL of 1% NP-40, 1μL of 100 mM MgCl2, and ultrapure water (LI-COR Biosciences) in a total reaction volume of 20μL. Products were electrophoresed on native 5% Tris–Borate-EDTA precast polyacrylamide gels (Bio-Rad, Life Sciences, Hercules, Calif). Gels were prerun at 70 V/cm for 30 min in 0.5 × nondenaturing Tris–Borate-EDTA buffer, and samples were loaded, run for 60 min, and then imaged with an Odyssey CLx imager (LI-COR Biosciences). The hESF (human endometrium stromal fibroblast cell) cell extracts were supplemented with purified TF protein as above, as endogenous TF protein was not initially detected on EMSA. All candidate TF proteins examined were generated from an overexpression vector after nuclear lysate purification and tagging with a DDK motif (Origene). 50fmol fluorescent oligo DNAs were then added to the appropriate protein/binding mix and incubated for 20 min at room temp. For supershift assays, anti-DDK antibody (Origine #TA50011-100) was incubated with the nuclear lysate/binding buffer for 20 min prior to the addition and incubation with oligo DNA. 1 × orange loading dye (LI-COR kit) was added to samples, which were then resolved on (pre-cast, pre-run at 100 V for 60 min) 6% TBE gels (Novex, ThermoFisher) in 0.5 × TBE buffer for 80 min at 80 V (4 °C). Fluorescent bands were then imaged using a LI-COR chemiluminescent imaging system. EMSA experiments display representative panels of 2–3 replicates.

Luciferase assays

Luciferase reporter constructs were cloned by using the pNL1.1 nanoluciferase reporter vector (Promega, Madison, WI) as a backbone. For the AGTR2 promoter reporter construct, PCR primers were used to amplify a 1 kb sequence including the first 816 base pairs upstream of the transcription start site, and the region was subsequently cloned into the pNL1.1 backbone. For the putative AGTR2 regulatory GWAS locus upstream of the AGTR2 promoter, two complementary oligonucleotides were designed to create fragments for insertion into this backbone. The GWAS regulatory region was comprised of a 4.3-kb region surrounding rs7889204 (which includes three other SNPs(dbSNP_153), rs4478734 (which is an eQTL for AGTR2 in uterus and adrenal glands but not predicted to alter TF binding), rs9724136(no significant eQTL data in any tissue) and rs2310841(no entry in GTEx portal, not predicted to alter TF binding). The region was inserted into the pNL1.1 nanoluciferase reporter construct in front of the AGTR2 promoter. Site-directed mutagenesis was performed to generate a construct with all three non-reference SNPs and a construct with the non-reference (risk) allele of rs7889204 using the GeneArt Site-Directed Mutagenesis System (Invitrogen, Darmstadt, Germany). Sequences were confirmed by Sanger sequencing.

250 ng of nanoluciferase experimental constructs were transiently co-transfected with 250 ng of pGL3-control firefly luciferase reporter plasmid into endometrial fibroblast cells grown to ~ 90% confluence using Lipofectamine 3000 (L3000015, Thermo Fisher Scientific) in a 48-well cell culture plate using 500 ng of total plasmid per well. For decidualization, after 24 h, transfected cells were treated with cAMP, progesterone, and estradiol for 48 h as we have previously reported. Then cells were lysed and both nano and firefly luciferase activities were measured in the same sample by using the Nano-Glo® Luciferase Assay System (Promega, Madison, WI). The nanoluciferase measurement for each well was divided by the firefly luciferase measurement in order to account for cytotoxicity, small differences in cell number, and transfection efficiency. Each well was further normalized to the pNL1.1 nanoluciferase reporter backbone to control for baseline activity of the vector. Three independent experiments were performed, and three replicate samples were assayed in each experiment. Student’s t-test was performed to determine significant differences between the risk and non-risk alleles for rs7889204.


Prioritization of functional SNPs and predication of allele-dependent TF binding

The AGTR2 adjacent genetic association locus falls within a non-coding region of the human genome on chromosome X, which suggests that one or more of these variants might functionally act as enhancers by altering gene regulatory mechanisms. Although AGTR2 was the closest gene to this identified region and biologically plausible as the consequential gene affecting preterm birth as previously reviewed, the index SNP from the discovery phase was located over 200 kb from the AGTR2 transcription start site (TSS). As such, we first determined whether this GWAS locus interacts with the gene AGTR2 given the substantial distance between regions. It is promising to know that the locus falls in the same TAD (topologically associated chromatin domains) domain with the AGTR2 gene in human ESC (embryonic stem cells) and IMR90 cell lines [37]. TAD domains are known to be conserved among cell types and even different species. They are the unit of genome organization that allow for coordination of gene regulation programs and integration of transcriptional signals [38]. Next, a total of 49 SNPs were identified as close proxies in linkage disequilibrium (LD) with the index SNP from the discovery phase using criteria r2 > 0.8 using the PLINK software package (version 1.90b) [39] with European (EU) ancestry (Fig. 1 and Additional file 1, spreadsheet SNPs in LD). Out of these 49 SNPs, 35 are expression quantitative trait loci (eQTLs) for the AGTR2 gene in the uterus using data from the GTEx database [40] (Fig. 1 and Additional file 1, spreadsheet GTEx V7 eQTLs in uterus). Collectively, these data indicate that AGTR2 is a likely gene target explaining the non-coding GWAS association.

Fig. 1
figure 1

Strategic approach for SNP prioritization and transcription factor prediction. Our procedure started with an LD expansion of the GWAS-identified index SNP from the discovery phase using criteria r2 > 0.8 using the PLINK software package (version 1.90b) with EU ancestry. The resulting set of 49 SNPs was used for subsequent analysis to prioritize the causal SNP and to predict the differential binding of transcription factors. This analysis identified rs7889204 as the most likely causal SNP

We then used publicly available functional genomics datasets to prioritize the likely causal SNP(s) from the 35 SNPs identified in the previous analysis. The logic underlying this approach is the non-coding region harboring the causal SNP probably functions as an enhancer to regulate AGTR2 gene expression, and thus will be more likely to be marked with genetic and/or epigenetic marks that are typically associated with enhancers. We collected functional genomic datasets from a range of sources, including ENCODE [28], Cistrome [32], PAZAR [33], Re-Map [34], and NIH Roadmap Epigenomics [35]. All datasets were indexed by their genomic coordinates, which were used to intersect with the LD expanded list of SNPs of interest. We prioritized SNPs based on their overlap with both likely regulatory regions (e.g., regions with histone marks such as H3K27ac) and TF ChIP-seq datasets (Fig. 1 and Additional file 1, spreadsheets ChIP-seq no-TF overlaps and ChIP-seq TF overlaps). Collectively, rs7889204 emerged as the most likely functional candidate SNP (Fig. 1 and Additional file 1, spreadsheet ChIP-seq TF-based diff binding). Subsequent functional analysis thus focused on this potential causal SNP. A flowchart is provided (Fig. 1) that documents the complete process — results and intermediate output can be found in Additional file 1.

Differential TF binding analysis for rs7889204 performed using the CisBP database [41] yielded three strong candidates, all of which are predicted to bind more strongly to the T allele: CEBPB, HOXA, and FOX family members. We selected FOXA1 for experimental validation because the SNP is inside FOXA1 ChIP-seq peaks in multiple experiments, and CEBPB and HOXA10 were selected based on known biological roles.

rs7889204 is a tissue-specific eQTL for AGTR2

We next examined the effect of rs7889204 on AGTR2 gene expression using the GTEx dataset (Fig. 2). Figure 2A depicts the AGTR2 locus, showing the position of AGTR2 gene (marked in red) and the candidate SNP (middle panel, indicated by a red arrow). A zoomed-in view is also provided showing the effect of this SNP on the expression of the AGTR2 gene in different tissues including the adrenal gland, artery-aorta, lung, and uterus. This eQTL analysis suggests that the region harboring rs7889204 has the largest effect in uterine tissue, with higher AGTR2 expression observed for the reference (non-risk) “T” allele and lower expression for the non-reference (risk) “C” allele (Fig. 2B).

Fig. 2
figure 2

Rs7889204 likely acts by impacting AGTR2 expression level. A Locus view of rs7889204. Red line indicates the location of the AGTR2 gene on the X chromosome. The diamond in the zoomed view indicates the SNP rs7889204. B Violin plots demonstrating that rs7889204 is an eQTL for AGTR2, with the strongest effect in the uterus. C The rs7889204 locus physically interacts with the promoter region of AGTR2. Chromatin conformation analysis reveals a high distance normalized interaction frequency between the rs7889204 LD block (vertical red var) and the AGTR2 promoter. Higher peaks indicate stronger interactions. Data displayed were obtained from human fibroblasts. Analysis was performed using the 3D Genome Interactive Viewer and Database

rs7889204 forms chromatin loops to the promoter region of AGTR2

We next examined if the region surrounding this SNP has the potential to form enhancer-promoter interaction with AGTR2, which is essential to regulate target gene expression. We used rs7889204 as the bait on the 3D Genome Interactive Viewer and Database [36] (3DIV) ( We used default settings on the Hi-C page except we set the threshold on distance normalized interaction frequency to be 2.00. This analysis revealed that the regions harboring rs7889204 do indeed physically interact with the AGTR2 promoter in fibroblasts (Fig. 2C). Although experimental data do not currently exist for examining chromatin interactions between rs7889204 and AGTR2 in the context of preterm/gestational duration, these results suggest that such enhancer-promoter interactions are plausible in disease-relevant cell types.

Human AGTR2 expression and transcription factors in uterine tissue

Being the most abundant cell type in the endometrium, endometrial stromal cells undergo a dramatic morphological and functional differentiation during decidualization and are essential for the establishment of a successful pregnancy [42]. Analysis of existing Hi-C data also suggests that such enhancer-promotor interactions are plausible in a fibroblast cell context (Fig. 2C). However, we were not able to detect endogenous AGTR2 expression in the immortalized endometrium stromal fibroblast cell line (ATCC CRL-4003). It is also absent in the transcriptome study using this cell line before and after decidualization [31]. Gene expression profiling using microarray in the basal plate of the human maternal–fetal interface showed the expression of AGTR2 is higher in the 37–40 week samples than in the 14–18 week samples (P = 0.01, BH-FDR = 0.09) [43], indicating the dynamic expression pattern of this gene. We expanded our search to single-cell transcriptomic studies, but AGTR2 seems absent in the datasets we have checked [44,45,46,47]. E.g., re-analysis of single-cell data from the human early maternal–fetal interface showed AGTR2 is only present in 10 cells out of 70,325 cells (Additional file 2). Our attention was then directed to lung research, in which tissue shows biased AGTR2 expression. Re-analysis of single cell/single nuclei RNA-seq in human uterus tissue from this study [48] shows AGTR2 has low but highly selective and enriched expression in Stromal-2 cell populations as shown in Fig. 3, though the biological context of this data-driven unbiased cell type classification needs further investigation.

Fig. 3
figure 3

AGTR2 expression in human uterus cells. A Uniform Manifold Approximation and Projection (UMAP) representation of 23,110 cells from 2 human uterine samples (PMID: 32,603,599. GSM4035471 and GSM4035470). Eight cellular populations were identified and labeled using different colors. B Dot plot showing AGTR2 expression across 8 cell populations. AGTR2 has low but highly selective and enriched expression in Stromal-2 cell populations (frequency: 5.2%, enrichment fold: 3.3, P-value: 1E − 05). Node size represents gene expression frequency in a cell type. Node color represents the average gene expression in a cell type

The computational analysis suggests that the rs7889204 genomic locus might act as an enhancer to regulate AGTR2 gene expression in the uterine tissue. It further suggests that altered binding of HOXA10, CEBPB, and/or FOXA1 might explain the observed allele-dependent expression of AGTR2 in uterine tissue. Previous studies have shown that HOXA10 might play key roles in directing endometrial stromal cell differentiation and is key for normal implantation [49, 50]; CEBPB is expressed at implantation sites in human and non-human primates, where intense nuclear expression of CEBPB has been observed in decidual stromal cells [51]. No direct connection between FOXA1 and pregnancy has been previously reported. Previous transcriptomic studies in immortalized human endometrium stromal cells before and after induced decidualization show expression of both HOXA10 and CEBPB, but not FOXA1 [31]. This finding is also confirmed by the TaqMan gene expression assay by our group (Additional file 2).

In the following in vitro studies, we chose the immortalized endometrium stromal fibroblast cell line (ATCC CRL-4003) knowing the limitations of lack of detectable expression of AGTR2.

EMSA confirms allele-dependent binding of CEBPB and HOXA10 at rs7889204

Using an electrophoretic mobility shift assay (EMSA), we detected decreased binding of CEBPB to the risk “C” allele of rs7889204 as predicted using decidualized hESC (human endometrium stromal cell) nuclear lysate with recombinant CEBPB protein (Fig. 4). We also detected decreased binding of HOXA10 to the risk “C” allele of rs7889204 as predicted using decidualized hESC nuclear lysate with recombinant HOXA10 protein (Fig. S1). However, we did not detect substantial differences between the risk “C” and non-risk “T” alleles of rs7889204 using decidualized hESC nuclear lysate with recombinant FOXA1 protein (Fig. S2). Collectively, these results confirm the predictions of stronger binding of the HOXA10 and CEBPB transcription factors to the non-risk allele of rs7889204.

Fig. 4
figure 4

EMSA confirms allele-dependent binding of CEBPB at rs7889204. Panel A visually depicts the computational prediction that the rs7889204 T allele creates a stronger binding site for CCAAT Enhancer Binding Protein Beta (CEBPB). The sequence logo of the CEBPB binding motif (below) shows the DNA-binding preferences of CEBPB. Tall nucleotides above the dashed line indicate DNA bases that are preferred by CEBPB, whereas bases below the dashed line are disfavored. The y-axis indicates the relative free energies of binding for each nucleotide at each position. The height of each nucleotide can be interpreted as the free energy difference from the average (ΔΔG) in units of gas constant (R) and temperature (T). The sequence located in the AGTR2 locus is shown directly below the x-axis, with the T allele for rs7889204 at the bottom. The T allele changes the sequence from C (most disfavored) to T (most preferred). Panel B shows the experimental validation of allele-dependent binding of CEBPB to rs7889204 using an electrophoretic mobility shift assay (EMSA). Arrows indicate allele-dependent binding of CEBPB (bottom arrow) and a “super shift” of the protein-DNA complex induced by the binding of DDK tagged antibody (CEBPB tagged with DDK motif) to the complex (top arrow)

The risk “C” allele of rs7889204 has decreased enhancer activity

To determine whether the region surrounding rs7889204 possesses transcriptional enhancer activity and if the reference non-risk “T” and non-reference risk “C” alleles demonstrate differences in activity, we performed luciferase reporter experiments in both non-decidualized and decidualized hESC cells. The rs7889204 region and promoter region of AGTR2 were placed in front of the nano-luciferase gene. Nano-luciferase expression was responsive to the AGTR2 promoter both in non-decidualized and decidualized hESCs (Fig. 4); however, we observed greater expression in the AGTR2 promoter-containing pNL1.1 vector after decidualization (Fig. 5). Importantly, the regulatory region containing the risk “C” allele for rs7889204 demonstrated significantly less nano-luciferase expression compared to the reference allele reporter construct. This decrease in nano-luciferase expression for the non-reference allele of rs7889204 was not strongly dependent on cellular conditions, as a significant, albeit slightly, weaker decrease was also observed in non-decidualized endometrial cells (Fig. 5). These results are consistent with the reduced transcription factor binding observed in EMSA (Fig. 4), collectively revealing a mechanism involving weaker CEBPB and HOXA10 binding to the rs7889204 risk allele, leading to reduced AGTR2 expression.

Fig. 5
figure 5

The risk “C” allele of rs7889204 shows decreased reporter activity. A 4.3-kb region surrounding rs7889204 with either the reference (T) or non-reference (C) SNP haplotypes were inserted in front of a 1 kb sequence including the first 816 bases before the transcription start site of AGTR2 in a pNL1.1 nanoluciferase reporter construct. Nanoluciferase readings were normalized to the constitutive control for each replicate. Each bar represents the mean across three experimental replicates and the standard deviation


Preterm birth remains a significant healthcare burden on a global scale due to a lack of progress in mechanistic insights to develop preventative measures. Using bioinformatics and functional analyses, we identified a functional mechanism in which rs7889204 at the AGTR2 locus serves as part of a likely enhancer where the risk “C” allele shows decreased AGTR2 expression potentially via allele-dependent binding of the transcription factors CEBPB and HOXA10 (Fig. 6). The absence of evidence for the co-expression between AGTR2 and CEBPB and HOXA10 regulators is indicated by the dashed line, although it is still the prevailing scenario for such regulation.

Fig. 6
figure 6

Schematic illustration of rs7889204’s function as an allele-dependent enhancer for AGTR2. The T allele of the non-coding variant rs7889204 results in stronger binding of the CEBPB and HOXA10 transcription factors, with subsequent activation of the target gene AGTR2 in human endometrium stromal fibroblast cells. The C allele disrupts this interaction, resulting in down-regulation of the AGTR2 target gene

In this study, we prioritized SNPs by interrogating available data at the genomic level. We carefully designed our prioritization procedure using existing genomic and epigenomic data sets, which successfully prioritized rs7889204 as the top causal genetic variant. We further demonstrated that rs7889204 can regulate AGTR2 expression via the alteration of the activity of a likely enhancer, through allelic differential binding of the biologically relevant transcription factors CEBPB and HOXA10.

Our study has shown that the risk “C” allele of rs7889204 exhibits decreased transcription factor binding and enhancer activity in vitro. To confirm whether this finding leads to decreased AGTR2 expression in vivo, further studies are necessary. Unfortunately, endogenous AGTR2 expression was not detected in the cell lines used in this study (Additional file 2). Transcriptomic profiling using primary human endometrium biopsies in the proliferative phase also did not detect AGTR2 expression [16]. To eliminate cell type heterogeneity effects, we also explored two publicly available single-cell transcriptomics data sets, with one focusing on the human midluteal implantation window [44] and the other on human endometrium during the menstrual cycle [47]. Unfortunately, neither dataset showed reliable AGTR2 expression (Additional file 2).

Previous studies have reported that AGTR2 expression is specific to defined tissues during development including the endometrium, uterus, and lung [13, 14]. Western blotting and PCR analysis have suggested its possible presence in myometrium and uncertain expression in the human placenta and endometrium [12, 49]. Our single-cell RNA-seq analysis supports the expression of AGTR2 specifically in the endometrial stromal cell during pregnancy (Fig. 3) which is consistent with the likely cell context of effect for Wnt4 in modulation of gestational duration which we have previously described [8]. The expression of AGTR2 declines during pregnancy, with negligible levels detected in late pregnancy [12]. These findings suggest that AGTR2 expression is regulated spatially and temporally during pregnancy and further complicates its detection in human pregnancy though also lends credence to the notion that AGTR2 promotes gestational prolongation.

The restricted ability to detect AGTR2 expression throughout pregnancy is a limitation in our findings. While the absence of expression in the cell line we used is a limitation in interpretation, like many human pregnancy-related expression changes, we are only beginning to understand the context for their expression. These limitations highlight the importance of understanding tissue-specific regulation of AGTR2 expression and the need for further studies in animal models to elucidate the mechanism of AGTR2 regulation during pregnancy. The induction of specific primate genes in the context of decidual-placental interaction has been a challenging task despite providing all necessary signals. For instance, the presence of the human corticotropin-releasing hormone (CRH) has been well-documented in placental syncytiotrophoblast [52]. However, even in primary syncytialized trophoblasts, endogenous expression of CRH has not been detected. Instead, researchers had to create a novel mouse model by incorporating a bacterial artificial chromosome (BAC) with the human CRH gene and approximately 180 kb of flanking sequence into the genome of FVB/N mice [53], which showed placental expression of CRH. In the case of AGTR2, we believe the endometrial stromal cell line provides the permissive environment to allow the EMSA and luciferase studies we have performed to reveal mechanistic insights regarding rs7889204. Follow-up studies in animals will be the most meaningful next step for the future.

To gain a deeper understanding of the mechanism underlying AGTR2’s regulation of birth timing, additional research using animal models is a logical next step. Specifically, it is important to characterize AGTR2 expression throughout gestation in nonhuman primates and/or in AGTR2 BAC transgenic and gene-edited mice. The use of animal models will allow for the investigation of AGTR2 regulation at both the molecular and physiological levels, further validating the causal relationship between the non-coding SNP rs7889204 and PTB. New techniques in profiling multiple layers of genetic and epigenetic marks simultaneously [54] will also facilitate the identification of regulatory mechanisms and lead to better understanding of the interplay between genetic factors in preterm birth. Such studies could provide valuable insights into the complex interplay of factors involved in the timing of birth and may have important implications for the development of strategies to prevent preterm birth.

The potential for AGTR2 as a target for drug development has been demonstrated by the development of a number of agonists and antagonists. Our results further underscore the potential for modulation of AGTR2 and its regulatory transcription factors, CEBPB and HOXA10, as promising targets for the prevention and treatment of preterm birth.


This study is the first single locus post-GWAS functional study in preterm birth. Using a combination of experimental and computational tools, we linked a non-coding variant in the human genome to its target gene, AGTR2. Our results also will draw more attention to the study of AGTR2’s role in preterm birth. As a druggable target, our results will greatly assist the utility of AGTR2 modulation and its regulators for preterm birth prevention and therapy. This work will be of broad interest to the scientific community and spanning across many disciplines and especially in the areas of post-GWAS functional studies.

Availability of data and materials

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Additional Materials.



Angiotensin II receptor type 2


CCAAT enhancer binding protein beta


Corticotropin-releasing hormone


DNA affinity precipitation assays


Electromobility shift assays


Expression quantitative trait loci


Embryonic stem cells


Forkhead box protein A1


Genotype-Tissue Expression


Genome-wide association studies


Human endometrium stromal fibroblast cell


Homeobox A10


Linkage disequilibrium


Preterm birth


Single nucleotide polymorphisms


Topologically associated chromatin domains


Transcription factor


Transcription start site


  1. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK. Births: Final Data for 2018. Natl Vital Stat Rep. 2019;68:1–47.

    PubMed  Google Scholar 

  2. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the sustainable development goals. Lancet. 2016;388:3027–35.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Zhang G, Srivastava A, Bacelis J, Juodakis J, Jacobsson B, Muglia LJ. Genetic studies of gestational duration and preterm birth. Best Pract Res Clin Obstet Gynaecol. 2018;52:33–47.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. BJOG. 2000;107:375–81.

    Article  CAS  PubMed  Google Scholar 

  5. Kistka ZAF, Palomar L, Lee KA, Boslaugh SE, Wangler MF, Cole FS, et al. Racial disparity in the frequency of recurrence of preterm birth. Am J Obstet Gynecol. 2007;196:131.e1–6.

    Article  PubMed  Google Scholar 

  6. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–12.

    Article  CAS  PubMed  Google Scholar 

  7. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang G, Feenstra B, Bacelis J, Liu X, Muglia LM, Juodakis J, et al. Genetic associations with gestational duration and spontaneous preterm birth. N Engl J Med. 2017;377:1156–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lemarie CA, Schiffrin EL. The angiotensin II type 2 receptor in cardiovascular disease. J Renin Angiotensin Aldosterone Syst. 2010;11:19–31.

    Article  CAS  PubMed  Google Scholar 

  10. Qi Y, Katovich MJ. Is angiotensin II type 2 receptor a new therapeutic target for cardiovascular disease? Exp Physiol. 2014;99:933–4.

    Article  PubMed  Google Scholar 

  11. Chow BS, Allen TJ. Angiotensin II type 2 receptor (AT2R) in renal and cardiovascular disease. Clin Sci (Lond). 2016;130:1307–26.

    Article  CAS  PubMed  Google Scholar 

  12. Delforce SJ, Lumbers ER, de Corbisier Meaultsart C, Wang Y, Proietto A, Otton G, et al. Expression of renin-angiotensin system (RAS) components in endometrial cancer. Endocr Connect. 2017;6:9–19.

    Article  CAS  PubMed  Google Scholar 

  13. Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. 2014;13:397–406.

    Article  CAS  PubMed  Google Scholar 

  14. Duff MO, Olson S, Wei X, Garrett SC, Osman A, Bolisetty M, et al. Genome-wide identification of zero nucleotide recursive splicing in Drosophila. Nature. 2015;521:376–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tower CL, Lui S, Charlesworth NR, Smith SD, Aplin JD, Jones RL. Differential expression of angiotensin II type 1 and type 2 receptors at the maternal-fetal interface: potential roles in early placental development. Reproduction. 2010;140:931–42.

    Article  CAS  PubMed  Google Scholar 

  16. Petracco RG, Kong A, Grechukhina O, Krikun G, Taylor HS. Global gene expression profiling of proliferative phase endometrium reveals distinct functional subdivisions. Reprod Sci. 2012;19:1138–45.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19:491–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tak YG, Farnham PJ. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin. 2015;8:57.

  19. Schoenfelder S, Fraser P. Long-range enhancer-promoter contacts in gene expression control. Nat Rev Genet. 2019;20:437–55.

    Article  CAS  PubMed  Google Scholar 

  20. Zhu Y, Tazearslan C, Suh Y. Challenges and progress in interpretation of non-coding genetic variants associated with human disease. Exp Biol Med (Maywood). 2017;242:1325–34.

    Article  CAS  PubMed  Google Scholar 

  21. Nasrallah R, Imianowski CJ, Bossini-Castillo L, Grant FM, Dogan M, Placek L, et al. A distal enhancer at risk locus 11q13.5 promotes suppression of colitis by Treg cells. Nature. 2020;583:447–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W, Haugen C, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373:895–907.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim-Hellmuth S, Aguet F, Oliva M, Munoz-Aguirre M, Kasela S, Wucher V, et al. Cell type-specific genetic regulation of gene expression across human tissues. Science. 2020;369:6059.

  24. Yan J, Qiu Y, Ribeiro Dos Santos AM, Yin Y, Li YE, Vinckier N, et al. Systematic analysis of binding of transcription factors to noncoding variants. Nature. 2021.

  25. Boix CA, James BT, Park YP, Meuleman W, Kellis M. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Nature. 2021;590:300–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Stunnenberg HG, International Human Epigenome, Consortium, Hirst M. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell. 2016;167:1897.

    Article  CAS  PubMed  Google Scholar 

  27. Roadmap Epigenomics, Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.

    Article  Google Scholar 

  28. Consortium, Encode Project. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.

    Article  Google Scholar 

  29. Miller DE, Patel ZH, Lu X, Lynch AT, Weirauch MT, Kottyan LC. Screening for functional non-coding genetic variants using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA). J Vis Exp. 2016.

  30. Bernstein DI, Lummus ZL, Kesavalu B, Yao J, Kottyan L, Miller D, et al. Genetic variants with gene regulatory effects are associated with diisocyanate-induced asthma. J Allergy Clin Immunol. 2018;142:959–69.

    Article  CAS  PubMed  Google Scholar 

  31. Rytkonen KT, Erkenbrack EM, Poutanen M, Elo LL, Pavlicev M, Wagner GP. Decidualization of human endometrial stromal fibroblasts is a multiphasic process involving distinct transcriptional programs. Reprod Sci. 2019;26:323–36.

    Article  CAS  PubMed  Google Scholar 

  32. Liu T, Ortiz JA, Taing L, Meyer CA, Lee B, Zhang Y, et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 2011;12:R83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Portales-Casamar E, Arenillas D, Lim J, Swanson MI, Jiang S, McCallum A, et al. The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucleic Acids Res. 2009;37(Database issue):D54–60.

    Article  CAS  PubMed  Google Scholar 

  34. Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015;43:e27.

    Article  PubMed  Google Scholar 

  35. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH roadmap epigenomics mapping consortium. Nat Biotechnol. 2010;28:1045–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang DC, Jang I, Choi J, Kim MS, Lee AJ, Kim H, et al. 3DIV: A 3D-genome Interaction Viewer and database. Nucleic Acids Res. 2018;46:D52–7.

    Article  CAS  PubMed  Google Scholar 

  37. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485:376–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Campbell MJ. Tales from topographic oceans: topologically associated domains and cancer. Endocr Relat Cancer. 2019;26:R611–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Consortium, G. TEx. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  41. Lambert SA, Yang AWH, Sasse A, Cowley G, Albu M, Caddick MX, et al. Similarity regression predicts evolution of transcription factor sequence specificity. Nat Genet. 2019;51:981–9.

    Article  CAS  PubMed  Google Scholar 

  42. Salsano S, Quinonero A, Perez S, Gomez TG, Simon C, Dominguez F. Dynamic expression of PGRMC1 and SERBP1 in human endometrium: an implication in the human decidualization process. Fertil Steril. 2017;108:832.

    Article  CAS  PubMed  Google Scholar 

  43. Singh R, Yang H, Dalziel B, Asarnow D, Murad W, Foote D, et al. Towards human-computer synergetic analysis of large-scale biological data. BMC Bioinformatics. 2013;14(Suppl 14):S10.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lucas ES, Vrljicak P, Muter J, Diniz-da-Costa MM, Brighton PJ, Kong CS, et al. Recurrent pregnancy loss is associated with a pro-senescent decidual response during the peri-implantation window. Commun Biol. 2020;3:37.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Suryawanshi H, Morozov P, Straus A, Sahasrabudhe N, Max KEA, Garzia A, et al. A single-cell survey of the human first-trimester placenta and decidua. Sci Adv. 2018;4:eaau4788.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018;563:347–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang W, Vilella F, Alama P, Moreno I, Mignardi M, Isakova A, et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nat Med. 2020;26:1644–53.

    Article  CAS  PubMed  Google Scholar 

  48. Guo M, Yu JJ, Perl AK, Wikenheiser-Brokamp KA, Riccetti M, Zhang EY, et al. Single-cell transcriptomic analysis identifies a unique pulmonary lymphangioleiomyomatosis cell. Am J Respir Crit Care Med. 2020;202:1373–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zanatta A, Rocha AM, Carvalho FM, Pereira RMA, Taylor HS, Motta ELA, et al. The role of the Hoxa10/HOXA10 gene in the etiology of endometriosis and its related infertility: a review. J Assist Reprod Genet. 2010;27:701–10.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Parasar P, Sacha CR, Ng N, McGuirk ER, Chinthala S, Ozcan P, et al. Differentiating mouse embryonic stem cells express markers of human endometrium. Reprod Biol Endocrinol. 2017;15:52.

  51. Kannan A, Fazleabas AT, Bagchi IC, Bagchi MK. The transcription factor C/EBP beta is a marker of uterine receptivity and expressed at the implantation site in the primate. Reprod Sci. 2010;17:434–43.

    Article  CAS  PubMed  Google Scholar 

  52. Lockwood CJ, Radunovic N, Nastic D, Petkovic S, Aigner S, Berkowitz GS. Corticotropin-releasing hormone and related pituitary-adrenal axis hormones in fetal and maternal blood during the second half of pregnancy. J Perinat Med. 1996;24:243–51.

    Article  CAS  PubMed  Google Scholar 

  53. Dunn-Fletcher CE, Muglia LM, Pavlicev M, Wolf G, Sun M-A, Hu Y-C, et al. Anthropoid primate-specific retroviral element THE1B controls expression of CRH in placenta and alters gestation length. PLoS Biol. 2018;16:e2006337.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Fu H, Zheng H, Chen X, Weirauch MT, Muglia LJ, Wang L, et al. NOMe-HiC: joint profiling of genetic variant, DNA methylation, chromatin accessibility, and 3D genome in the same DNA molecule. Genome Biol. 2023;24:50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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We thank Drs. Y. Liu and Y. Xu for help with discussion and figure designs.


This study was supported by the March of Dimes Prematurity Research Center Ohio Collaborative and the Bill & Melinda Gates Foundation (OPP1113966 and OPP1175128). LJM was also supported by grants from NIH/NICHD (R01HD091527 and R21HD090196). GZ is supported by grants from NIH (1R01HD101669) and the Burroughs Wellcome Fund (10172896). MTW was supported on this study by grants from the NIH (U01 AI130830) and the Cincinnati Children’s Hospital Medical Center (ARC Award 53632 and Endowed Scholar award). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations



LJM, LW, RMR, and GZ designed the project; LW and RMR contributed equally to this project; LW, RMR, MC, DM, CF, AL, JR, XZ, and FK carried out the experiment; LW, XC, GZ, and JC analyzed the data; LW wrote the manuscript; GZ, LJM, and MTW reviewed and edited the manuscript; BJ and LK provided critical discussion. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Li Wang or Louis J. Muglia.

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Supplementary Information

Additional file 1.

 Prioritization of functional SNPs and predication of allele-dependent TF binding

Additional file 2.

 Human AGTR2 expression analysis.

Additional file 3: Fig. S1.

EMSA confirms allele-dependent binding of HOXA10 at rs7889204. Fig. S2. EMSA does not confirm allele-dependent binding of FOXA1 at rs7889204.

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Wang, L., Rossi, R.M., Chen, X. et al. A functional mechanism for a non-coding variant near AGTR2 associated with risk for preterm birth. BMC Med 21, 258 (2023).

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  • Functional studies
  • Preterm birth
  • Non-coding variant
  • AGTR2