Multitrait GWAS and functional validation reveal genetic loci for gastric cancer

Domain: biology
Published: 2026-03-15
Editorial outcome: major_revision

Abstract
Gastric cancer is a leading cause of cancer-related death, particularly in East Asia, yet its genetic basis remains incompletely understood. Using genome-wide association study data from BioBank Japan, we show that gastric cancer shares significant genetic associations with 25 other phenotypes, most notably cardiovascular conditions such as angina pectoris and myocardial infarction. Mendelian randomization supports a causal role of these traits in gastric cancer risk. Multitrait analysis of GWAS reveals a novel pleiotropic locus at 12q22 (rs12814712) that is shared between gastric cancer and cardiovascular traits. Functional experiments demonstrate that the risk allele at this locus reduces transcriptional activity and downregulates the expression of nearby genes, particularly VEZT. In gastric cancer cells, overexpression of VEZT and NR2C1 suppress proliferation and migration, whereas their knockdown promotes malignancy. Our analysis identifies rs12814712 as a novel susceptibility locus for gastric cancer and underscores its regulatory role in disease-related gene networks.

Introduction
According to global cancer statistics for 2022, gastric cancer ranks fifth in terms of both incidence and mortality worldwide, accounting for 968,350 new cases and 659,853 deaths, respectively 1. The incidence of gastric cancer varies regionally, with higher rates in East Asia and Eastern Europe and lower rates in Northern Europe and North America 1, which are likely influenced by environmental, lifestyle, and genetic factors. Empirical studies have shown that gastric cancer is the result of a complex interaction of genetic and environmental risk factors 2. Risk factors encompass a spectrum ranging from Helicobacter pylori (HP) infection to dietary habits such as the consumption of pickled foods, as well as behaviours such as alcohol consumption and tobacco use. Among these, HP infection emerges as the most influential determinant. Moreover, genetic studies, including twin and genome-wide association studies (GWASs), have revealed a substantial genetic contribution to gastric cancer susceptibility, with heritability estimates of approximately 20.9% in the Chinese population and 22% in Nordic twins 3,4.

Gastric cancer displays substantial polygenicity, with East Asian populations showing the identification of at least 15 risk loci through GWAS. For instance, in a two-stage GWAS in Japan, a notable association was found between an intronic SNP (rs2976392) within the PSCA gene and diffuse-type gastric cancer 5. Similarly, researchers at Nanjing Medical University reported genetic variations at loci 1p35.2 and 6p22.1 in the Chinese population, which correlated with increased gastric cancer risk 6. Moreover, they elucidated the roles of PRKAA1 at the 5p13.1 locus and NOC3L at the 10q23.33 locus in gastric tumour pathogenesis 7. In contrast, only the 11q22.3 locus was identified in Europeans 8. Furthermore, several genes within these risk loci, such as MUC1 (1q22), SPOCD1 (1p35.2), PSCA (8q24.3), PRKAA1 (5p13.1), BTN3A2 (6p22.1), and NOC3L (10q23.33), have been functionally validated as susceptibility genes for gastric cancer 5-7,9.

Recent advances in statistical methodologies for GWAS analyses have made it possible to conduct joint analyses of traits that share genetic architecture 10. Through meta-analyses of GWAS summary statistics for genetically correlated traits, multitrait analysis of GWAS (MTAG) not only enhances statistical power but also identifies pleiotropic genetic variants that influence interconnected traits. This cross-trait analysis approach has proven successful in numerous cancer research studies. Additionally, it has been applied to the investigation of seven upper gastrointestinal disorders 11. However, to the best of our knowledge, no multitrait GWAS analysis specifically targeting gastric cancer has been conducted to date.

In this study, we identified multiple novel pleiotropic loci associated with gastric cancer using GWAS data from the BioBank Japan (BBJ) 12. Notably, we observed that several genes adjacent to these loci might be involved in gastric cancer pathogenesis, as documented in the literature. We collected gastric cancer samples and conducted functional experiments to elucidate the regulatory relationships between the identified loci and their neighbouring genes, as well as to explore the potential roles of candidate genes in the aetiology of gastric cancer.

Results
In this study, we utilized GWAS data from BBJ and identified multiple new pleiotropic loci associated with gastric cancer using MTAG. Notably, we observed that several genes near the novel 12q22 locus might be involved in the pathogenesis of gastric cancer. Consequently, we collected clinical gastric cancer samples and conducted functional experiments to elucidate the potential regulatory relationships between the identified loci and their neighbouring genes and to explore the potential roles of candidate genes in the aetiology of gastric cancer. An overview of our study is provided in Fig. 1.

Genetic correlations between gastric cancer and associated phenotypes
We analysed GWAS data from BBJ to explore the genetic correlation between gastric cancer and 95 phenotypes, including diseases, biomarkers, and medication usage, in East Asians. Following multiple testing correction, our analysis revealed a significant positive genetic correlation between gastric cancer and 25 cardiovascular-related traits, including diseases like angina pectoris (AP, rg = 0.4611, PFDR = 0.0001), myocardial infarction (MI, rg = 0.3415, PFDR = 0.0009), blood pressure measurements such as diastolic blood pressure (DBP, rg = 0.2549, PFDR = 0.0028), systolic blood pressure (SBP, rg = 0.2421, PFDR = 0.001), and mean arterial pressure (MAP, rg = 0.2612, PFDR = 0.0009), as well as the use of cardiovascular drugs like calcium channel blockers (CCBUM, rg = 0.3561, PFDR = 4.56 × 10-7), renin-angiotensin system inhibitors (AARSUM, rg = 0.4309, PFDR = 1.41 × 10-8), HMG-CoA reductase inhibitors (HCRIUM, rg = 0.3786, PFDR = 1.82 × 10-6) and salicylic acid and derivatives (SADUM, rg = 0.4115, PFDR = 0.0002). Additionally, positive genetic correlations were identified between gastric cancer and other cancer types, including lung cancer (LC, rg = 0.7442, PFDR = 0.0058) and colorectal cancer (CRC, rg = 0.3043, PFDR = 0.002) (Supplementary Table S1, Fig. 2).

Causal Impact of Cardiovascular-Related Factors on Gastric Cancer
To further investigate the relationship between cardiovascular traits and gastric cancer, we conducted Mendelian randomization (MR) analysis. The inverse-variance weighted (IVW) method revealed that after false discovery rate (FDR) correction, eight cardiovascular-related factors were significantly associated with an increased risk of gastric cancer, including AP (OR 1.289, 95% CI 1.060–1.568; PFDR = 0.0192), stable angina pectoris (SAP, OR 1.408, 95% CI 1.243–1.596; PFDR = 5.56 × 10-7), and MI (OR 1.179, 95% CI 1.094–1.270; PFDR = 5.36 × 10-5). Additionally, antithrombotic agents (AAUM, OR 1.829; 95% CI 1.343-2.493; PFDR = 3.61 × 10-4), beta-blocking agents (BBAUM, OR 1.478, 95% CI 1.108-1.972; PFDR = 0.0159), SADUM (OR 1.799, 95% CI 1.412-2.293; PFDR = 9.61× 10-6), CCBUM (OR 1.719, 95% CI 1.203-2.456; PFDR = 0.00679), and HCRIUM (OR 1.368, 95% CI 1.266-1.477; PFDR = 2.52 × 10-14) were used (Supplementary Data 1). Sensitivity analyses further confirmed the majority of these associations (Supplementary Data 1). Additionally, the MR‒Egger intercept test suggested potential horizontal pleiotropy for HCRIUM (PFDR for intercept < 0.05). Conversely, the IVW analysis demonstrated that gastric cancer might increase the risk of 13 cardiovascular-related factors, with the exception of vasodilators used in cardiac disease use measurement (VUCDUM). Detailed results from the five MR analysis methods are presented in Supplementary Data 1.

Cell type-specific enrichment of SNP heritability
In our analysis of 96 phenotypes, including gastric cancer, we further partitioned SNP heritability on the basis of six chromatin marks and nine cell types. We found that gastric cancer was enriched across all six chromatin marks, specifically in stomach tissues or cell types. The enrichment patterns of cardiovascular-related traits, such as blood pressure, were similar across all the chromatin markers, predominantly in the kidney, adrenal glands, heart, atria, and aorta. Additionally, we observed significant genetic enrichment for diabetes-related traits in gastrointestinal tissues (such as the small intestine, stomach, and colon) and the pancreas. Interestingly, angina pectoris and myocardial infarction displayed strong similarities only in the H3K9AC chromatin mark, with enrichment in cardiovascular-related tissues or cell types such as the right ventricle, right atrium, and left ventricle (Supplementary Fig. S1).

Multitrait analysis of gastric cancer and corresponding risk factors
Motivated by the findings from genetic correlation analyses, we conducted a comprehensive investigation into potential genetic factors shared between gastric cancer and its genetically correlated traits, aiming to identify novel susceptibility loci associated with gastric cancer risk. Guided by the 25 traits significantly correlated with gastric cancer (LDSC PFDR < 0.05), we performed a joint analysis using MTAG to identify SNPs that reached genome-wide significance (P < 5×10-8). Our analysis revealed 76 signals among gastric cancer and 25 traits in East Asians, including 41 previously reported loci, 26 novel findings, and 9 located within the MHC region (Supplementary Data 2). Notably, a novel locus at 12q22 (rs12814712) was identified in the MTAG analyses that linked gastric cancer with AP, MI, and SADUM (Fig. 3A-C). Considering that 25 phenotypes were analysed—albeit not fully independent—we additionally applied a Bonferroni-corrected threshold to account for multiple trait testing (P < 2×10⁻⁹). Under this stricter criterion, rs12814712 remained significantly associated with gastric cancer and MI. To further investigate this genomic region, we employed LocusZoom 13 to visualize the genetic associations, which highlighted a distinct signal surrounding the NDUFA12-VEZT locus across all four traits (Fig. 4A-D). We subsequently utilized LocusCompare 14 to assess the colocalization of these associated signals at 12q22, which demonstrated strong colocalization among gastric cancer, angina pectoris (AP), myocardial infarction (MI), and salicylic acid and derivatives use measurement (SADUM) (Fig. 5A-C). To further validate this association, we evaluated rs12814712 in an independent GWAS in a Korean cohort. A similar direction of effect was observed (BETA = 0.066), which was consistent with the results of the GC-BBJ analysis (BETA = 0.079; P = 7.65×10⁻⁵) (Supplementary Data 2). However, owing to the limited number of cases (n = 378), the association did not reach statistical significance. Interestingly, a nearby SNP, rs9634181, exhibited a stronger association (BETA = 0.19, P = 0.012) and was in weak linkage disequilibrium with rs12814712 (R² = 0.1; Supplementary Fig. S2A). Similarly, in the European-ancestry Million Veteran Program (MVP) cohort (n = 1,152), a nominally significant association was observed with a nearby SNP, rs183275771 (BETA = 0.081, P = 0.005), which was in linkage disequilibrium with rs12814712 (R² = 0.2; Supplementary Fig. S2B). These results suggested that increasing the sample size might yield statistically significant replication.

Additionally, we have summarized previously reported susceptibility loci for gastric cancer identified through GWAS in Table 1.

Expression Quantitative Trait Locus (eQTL) Analysis of the Gastric Cancer Risk SNP rs12814712 and Neighbouring Genes in the Chinese Population
We next analysed a cohort of 46 gastric cancer specimens using Sanger sequencing. This experimental validation was designed to assess whether the locus of interest could affect gene expression in gastric cancer. Among these samples, the AG variant of rs12814712 was found in 34.8% (16 cases), while the GG variant was observed in 65.2% (30 cases) (Fig. 6A). The detailed clinical characteristics of these 46 samples are described in Supplementary Table S2. The aforementioned signal at 12q22 (rs12814712) in gastric cancer covered four genes, namely, NDUFA12, FGD6, NR2C1, and VEZT. Multiple lines of evidence suggest that these genes may be involved in the pathogenesis of various cancers, including gastric cancer 15-20. To identify the genes potentially regulated by rs12814712 in gastric cancer, we used RT‒qPCR to compare the expression levels of four genes near rs12814712—NDUFA12, FGD6, NR2C1, and VEZT—in gastric cancer tissues and adjacent noncancerous tissues between the AG and GG groups. The expression levels of FGD6, VEZT, and NR2C1 were significantly lower in both adjacent nontumour tissues and gastric cancer tissues from patients with the AG genotype of rs12814712 than in those from patients with the GG genotype (P < 0.05; Fig. 6B and 6C). These findings suggested a potential relationship between the rs12814712 genotype and the regulation of these genes in both nontumour and tumour contexts.

Potential Regulatory Effect of rs12814712(G>A) on the Transcriptional Activity of Neighbouring Genes
To investigate whether rs12814712 had a cis-regulatory function, we first determined its genomic location. It was located in the 12q22 region, downstream of NDUFA12, NR2C1 and FGD6, and upstream of VEZT (Fig. 4). On the basis of its distribution between these genes, we designed two different luciferase reporter assay systems to evaluate the potential regulatory role of this region: (1) A 2001-bp genomic fragment containing the rs12814712 locus (wild-type WT‑G or mutant MUT‑A) and its flanking sequences (GRCh38/hg38 coordinates: chr12:94965934–94967934) was cloned upstream of the SV40 promoter to detect whether this fragment possesses enhancer-like activity (Supplementary Fig. S3A), and (2) the same fragment was inserted into the downstream end of the luciferase gene in the pmirGLO vector to examine its potential to regulate reporter gene expression in a downstream context (Supplementary Fig. S3B). Notably, this study aimed to functionally validate the region containing rs12814712 as an independent noncoding fragment to explore whether it could exert cis-regulatory effects at the transcriptional level. In the enhancer reporter system, the MUT (A) allele significantly reduced luciferase activity (P < 0.05), suggesting that this fragment possessed enhancer-like activity and that the A allele impaired this function (Supplementary Fig. S3C). In contrast, no significant difference was observed between the two alleles (WT and MUT) in the pmirGLO system, which indicated that the fragment did not exhibit significant posttranscriptional regulatory effects under these conditions (Supplementary Fig. S3D). Given that our GWAS signal was located upstream of VEZT and downstream of NR2C1, FGD6, and NDUFA12, this finding supported our eQTL results, indicating a regulatory relationship between the rs12814712 A allele and the expression of VEZT. Taken together, these results suggested that the genomic region containing rs12814712 might play a role in cis-regulation with enhancer-like activity.

Association of genes near rs12814712 with proliferation and migration capacity in gastric cancer
To investigate the role of potential target genes near rs12814712 in gastric cancer, we conducted a series of in vitro experiments using AGS and MKN45 gastric cancer cell lines. NDUFA12, FGD6, NR2C1, and VEZT overexpression models were established via plasmid transfection, with successful overexpression confirmed by RT–qPCR (Fig. 7A). Cell viability assays indicated that overexpression of NR2C1 and VEZT significantly reduced cell proliferation, while NDUFA12 and FGD6 had no notable effect (Fig. 7B). This antiproliferative effect was further supported by the results of EdU assays, which revealed a significant reduction in the percentage of proliferating NR2C1- and VEZT-overexpressing cells but not in that of NDUFA12- and FGD6-overexpressing cells (Fig. 7C). Consistently, the results of the colony formation assays revealed that NR2C1 and VEZT markedly inhibited the colony-forming ability of both cell lines (Fig. 7D). Migration assays, including wound healing and Transwell assays, demonstrated that overexpression of NR2C1 and VEZT significantly impaired cell migration, whereas NDUFA12 and FGD6 did not affect migratory capacity (Fig. 8A and B).

To further validate the tumour-suppressive roles of VEZT and NR2C1, we performed gene knockdown experiments using two independent short hairpin RNAs (shRNAs) targeting each gene (Supplementary Fig. S4A). Silencing of either VEZT or NR2C1 significantly increased gastric cancer cell proliferation, as demonstrated by cell growth and colony formation assays (Supplementary Fig. S4B–D), and enhanced cell migration capacity (Supplementary Fig. S5A–B), which was consistent with the results observed in the overexpression experiments.

Methods
Ethical compliance
All research involving human participants complied with all relevant ethical regulations. The study protocol (YXLL-KY-2022(068)) was approved by the Institutional Review Board of the First Affiliated Hospital of Shandong First Medical University (Jinan, China). Written informed consent was obtained from all participants, and the study was conducted in accordance with the principles of the Declaration of Helsinki.

GWAS data
The GWAS data for gastric cancer in East Asian populations included 167,122 individuals, including 7,921 cases and 159,201 controls. Additionally, we obtained summary statistics for 95 phenotypes (including diseases, biomarkers, and medication usage) with case numbers greater than 3,000 from the 220 phenotypes available in the BBJ database 36. In BBJ, genotyping was conducted using Illumina arrays, and genotype imputation was performed using the 1000 Genomes Phase 3 reference panel combined with Japanese whole-genome sequencing data. Variants with an imputation of Rsq ≥ 0.7 were retained. Phenotypes were derived from curated medical records, ATC-coded medication data, and biomarker measurements, with appropriate outlier removal and medication-specific adjustments. A GWAS for binary traits employed SAIGE to account for sample relatedness and case‒control imbalance, whereas BOLT-LMM was used for quantitative traits with adjustments for age, sex, and principal components. A comprehensive overview of the included GWAS datasets from East Asian populations is provided in Supplementary Data 3. To validate our findings, we utilized two independent cohorts: the Korean Genome and Epidemiology Study (KoGES) cohort, comprising 378 gastric cancer cases and 71,920 controls of East Asian ancestry, and the Million Veteran Program (MVP) cohort, comprising 1,152 cases and 449,540 controls of predominantly European ancestry.

Global genetic correlation analysis
Genetic correlation (rg) between each pair of diseases or traits was assessed using LD score regression (LDSC) with GWAS summary statistics 37. The equation used in the LDSC was as follows: 𝑬[𝜷𝒋𝜸𝒋] = 𝑵𝒔𝒓𝒈𝒍𝒋 / 𝑴 + 𝑵𝟏𝑵𝟐𝒓𝒈𝒍𝒋 / 𝑴 √𝑵𝟏𝑵𝟐, where 𝛽𝑗 and 𝛾𝑗 represent the effect sizes of SNP j on the two traits being tested, 𝑁1 and 𝑁2 are the sample sizes for the two traits, 𝑁𝑠 is the number of overlapping samples between the two traits, r is the phenotypic correlation in the overlapping samples, and 𝑙𝑗 is the LD score. In line with the authors’ recommendations, we used the default LDSC regression weights and limited the analysis to SNPs with an MAF > 5% in the reference panel for estimation.

In this analysis, precalculated linkage disequilibrium scores for HapMap3 SNPs were utilized. These scores were derived from individuals of Asian ancestry from the 1000 Genomes Project. SNP markers with an imputation INFO score less than 0.9 were excluded from the analysis. LDSC was also employed to estimate the SNP-based heritability of the examined traits 37.

Cell type-specific enrichment of SNP heritability
Stratified LDSC (S-LDSC) was employed in this study to identify specific functional categories or cell types that significantly affect the heritability of the traits under investigation. The baseline v1.1 model, which contains 53 functional annotations, including gene regions, conserved elements, and regulatory features, was used as the reference in all S-LDSC analyses to account for known sources of heritability 38. The annotation data used in this analysis were obtained from the Roadmap project and included information on six chromatin marks (DHS, H3K27ac, H3K36me3, H3K4me1, H3K4me3, and H3K9ac) across a diverse set of 88 cell types or tissues. These annotations were subsequently used to partition the SNP heritability of each trait. Additionally, the cell type annotations were grouped into nine categories, namely, adipose, central nervous system (CNS), digestive system, cardiovascular, musculoskeletal and connective tissue, immune and blood, liver, pancreas, and others. We visualized the annotation-specific enrichment values using a colour-scaled heatmap generated by the ComplexHeatmap package (v2.12.1) in R 4.2.3. The hierarchical clustering of annotations was performed on the basis of Euclidean distance metrics with complete linkage agglomeration, implementing the default clustering algorithm (hclust) within the ComplexHeatmap framework.

Multitrait analysis of GWAS (MTAG)
MTAG was employed to explore the genetic associations between gastric cancer and each of the other phenotypes separately by leveraging combined GWAS summary statistics from gastric cancer and one additional trait in each analysis 10. This approach enhances the ability to detect genetic loci associated with correlated traits by addressing sample overlap and incomplete genetic correlation. For the MTAG analysis, we used default parameters, including a genetic correlation prior of N (0,1), and applied cross-trait LD score regression intercepts to account for residual sample overlap. In the initial step of MTAG, variants were filtered by excluding noncommon SNPs, duplicated SNPs, or those with strand ambiguity. The pairwise genetic correlation between traits was subsequently estimated using LDSC, and these estimates were then used to calibrate the variance‒covariance matrix of the random effect component. Following calibration, a random-effects meta-analysis was performed by MTAG to generate SNP-level summary statistics. The suggestive significance threshold (P < 0.01) in single-trait GWAS was selected to capture potentially meaningful associations while maintaining stringency, as these signals were further validated by genome-wide significance in multitrait analysis. Pleiotropic SNPs that achieved genome-wide significance (P< 5×10–8) in the multitrait analysis and suggestive significance (P< 0.01) in the single-trait GWAS were prioritized. The major histocompatibility complex (MHC) region was treated as one locus because of its complex LD structure.

Two-sample Mendelian Randomization
In the two-sample Mendelian randomization (MR) analysis, we implemented five widely recognized statistical methods: inverse-variance weighted (IVW), MR‒Egger, weighted median, simple mode, and weighted mode. The IVW method, which served as the primary analytical approach, was applied under a multiplicative random effects model to estimate the causal association between genetic predisposition to gastric cancer and cardiovascular-related risk factors identified through the LDSC. The IVW method assumes no intercept term and employs the inverse of the variance as weights to optimize model fitting. To address potential pleiotropic effects, sensitivity analyses were conducted using the MR‒Egger method 39, which incorporates a weighted linear regression framework to detect and adjust for horizontal pleiotropy among the instrumental variables (IVs). The results are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs), and statistical significance was defined as P < 0.05. All MR analyses were executed using the TwoSampleMR package in R software (version 4.0.2), ensuring robust and reproducible causal inference 40.

Patients and tissue samples
Human tissue samples for this study were obtained from a prospective cohort of 46 patients who underwent radical gastrectomy at the First Affiliated Hospital of Shandong First Medical University (Jinan, China) in 2023. For each patient, paired samples of primary gastric cancer tissue and matched adjacent non-tumourous gastric mucosa were collected. The cohort included 33 male and 13 female patients. The genotypic distribution of the rs12814712 variant was as follows: AG genotype (9 males, 7 females) and GG genotype (24 males, 6 females). The participant age distribution is provided in Supplementary Table S2. Sex was determined according to hospital medical records. Although sex was not incorporated as a biological variable in the primary genetic association study design, all clinical data were sex-disaggregated in the source material.

Written informed consent was obtained from all participants for the use of their tissue samples and associated clinical data for research purposes. No financial compensation was provided. Following collection, genomic DNA and total RNA were extracted from portions of the tissue samples for downstream analyses. The original tissue samples were not completely consumed during this process. Residual tissue materials (aliquots and tissue blocks) were cryopreserved and permanently stored at –80 °C in the institutional biobank of the First Affiliated Hospital of Shandong First Medical University.

These deidentified biospecimens and associated clinical data remain available for academic validation and collaborative research. Access requests can be submitted with a scientifically justified proposal and completion of a material transfer agreement (MTA). All such requests should be directed to the corresponding author, Guangyong Zhang (guangyongzhang@hotmail.com), for review and facilitation.

PCR-Sanger sequencing of rs12814712
We collected 25 mg of cancer tissue from each of the 46 gastric cancer patients and extracted genomic DNA using the Ezup Column Animal Genomic DNA Purification Kit (B518251; Sangon Biotech, China). The extracted genomic DNA was amplified by PCR, and the PCR amplification primers for the rs12814712 locus were as follows: forward primer (5’ to 3’): TCATACTATGGGCAACTTGGCAC; reverse primer (5’ to 3’): CGGCAGCCCAGAACAGTAAA. The PCR reaction mix included the following components: 10x Taq buffer (with MgCl2), 10 µM dNTPs (mix), forward and reverse primers at 10 μM, 1 μl of template genomic DNA, 5 U/μl Taq DNA polymerase, and sterile water added to a final volume of 25 µL. After amplification, the PCR products were purified by agarose gel electrophoresis, and the sequence of the PCR amplification products was detected by Sanger sequencing.

Quantitative Real-Time PCR
Total RNA was extracted from frozen gastric cancer tissue or cell lines with TRIzol reagent (G3013; Servicebio, Wuhan, China). Single-strand complementary DNA (cDNA) was synthesized with a ReverTra Ace qPCR RT Kit (FSQ-101; TOYOBO Co., Ltd., Osaka, Japan). For quantitative real-time PCR, SYBR Green Real-time PCR Master Mix (QPK-201; TOYOBO Co., Ltd.) was used following the manufacturer’s instructions. The total PCR volume was 20 μl, containing 0.1 μl of each primer. The target-specific primers used were designed by Huada Gene (Shenzhen, China), and the specific sequences are listed in Supplementary Table S3.

Cell culture
The HEK293T, AGS, and MKN45 cell lines were obtained from Procell (Wuhan, China) and cultured in DMEM (for HEK293T), Ham's F-12K (for AGS), or RPMI-1640 (for MKN45) medium (all from Gibco, Carlsbad, CA, USA), each supplemented with 10% foetal bovine serum (FBS; Gibco) and 1% penicillin/streptomycin (Gibco). The cells were incubated at 37°C in 5% CO₂. The identity of all the cell lines was verified by short tandem repeat (STR) analysis, and the cell lines were routinely screened for mycoplasma contamination.

Plasmid Constructs
For luciferase assay-related plasmids, the 2001-bp genomic fragment containing the rs12814712 (G>A) site and 1000-bp flanking sequences was cloned and inserted into 1) the pGL3-promoter vector (E1761; Promega, Madison, WI, USA) to assess its potential enhancer-like activity and 2) the pmirGLO vector (E1330; Promega, Madison, WI, USA) to simulate its regulatory ability in a downstream context (fragment sequences are shown in Supplementary Data 4). The SV40 promoter sequence was used in these two plasmids to determine promoter function (Supplementary Fig. S3A-B).

For gene loss- and gain-of-function assays, plasmids were constructed by LST Biotech (Shandong Co., Ltd., Jinan, China). For gene knockdown experiments, short hairpin RNA (shRNA) sequences targeting NR2C1 and VEZT (Supplementary Table S4) were cloned and inserted into the pLKO.1-EGFP vector. For gene overexpression, the full-length coding sequences of NDUFA12, FGD6, NR2C1, and VEZT were cloned and inserted into the corresponding expression vectors. Information on the sequences and vectors is provided in Supplementary Table S5. The newly constructed plasmids generated in this study are available from the corresponding author upon reasonable request for academic research.

Luciferase Assays
HEK293T and AGS cells (1×10⁵/well) were taken in 96-well plates and were transfected after 18 h of incubation, pGL3 constructs were transfected with 0.1 μg test plasmid plus 0.01 μg pRL-TK plasmid, and pmirGLO constructs were transfected with test plasmid alone. Luciferase activity was measured using a Dual-Luciferase® Reporter Assay System kit (E1910; Promega, Madison, WI, USA): cells were lysed with 20 μL of PLB (15 min of RT shaking), after which firefly (luciferase assay reagent II) and Renilla (Stop & Glo) luminescence was sequentially measured. Relative activity was calculated as the Firefly/Renilla ratio. The experiments were repeated in triplicate.

Cell transfection
Cells were seeded in 6-well plates (5 × 105 cells/well) and incubated for 18 h. Then, overexpression plasmids for the indicated genes (NDUFA12, FGD6, NR2C1, and VEZT) and knockdown plasmids for the indicated genes (NR2C1 and VEZT) were transfected using Lipofectamine 2000 (11668030; Invitrogen, Carlsbad, CA, USA) and Opti-MEM™ I (31985062; Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. After the cells were incubated for 8 h, the medium was replaced with Ham's F-12K (for AGS) or 1640 medium (for MKN45) supplemented with 10% FBS. The sequences of the shRNAs for NR2C1 and VEZT are listed in Supplementary Table S4.

Cellular viability assay
Approximately 24 hours after transfection, 5000 cells were seeded into 96-well plates. Cell viability was then evaluated at 0, 24, and 48 hours using the Cell Counting Kit-8 (CCK-8) assay (CK04, Dojindo Laboratories, Kumamoto, Japan), following the methods described previously 41. After a 2-hour incubation, cellular viability was assessed using a microplate reader, and the absorbance was measured at a wavelength of 450 nm.

EdU staining
In accordance with the manufacturer's instructions, cell proliferation was assessed using an EdU staining kit (C0071S; Beyotime, Shanghai, China). Cells (1 × 104) in each group were incubated with EdU buffer (10 μM) at 37°C for 2 hours. The supernatant was then discarded, and the cells were washed twice with PBS, followed by fixation with paraformaldehyde for 20 min. Afterwards, the mixture was incubated with freshly prepared cocktail buffer. To calculate the proportion of EdU-positive cells, the number of fluorescently stained cells was divided by the number of DAPI-stained cells. The nuclei were then stained with DAPI (C1006; Beyotime, Shanghai, China). The cells were observed and photographed under a fluorescence microscope. EdU was excited by light at a wavelength of 488 nm to emit green fluorescence, while DAPI was excited by light at a wavelength of 340 nm to emit blue fluorescence.

Colony formation assay
Twenty-four hours after transfection, the transfected gastric cancer cells (1 × 103) were seeded into 6-well plates. After an additional 14 days of culture, the cells were fixed and stained with 1% crystal violet (Solarbio, Beijing, China). After being stained with crystal violet for 20 min, the colonies formed were counted.

Transwell assay
Transwell assays were conducted to evaluate the migration and invasion of AGS and MKN45 cells. For the migration assay, cells (4 × 104) were suspended in serum-free medium (120 μL) and added to the upper chamber. Medium containing 10% FBS (750 μL) was added to the lower chamber to induce cell migration. The cells that migrated were washed three times with PBS, fixed with 4% paraformaldehyde for 30 minutes, and stained with 1% crystal violet. Images were observed at 100× magnification.

Wound healing assay
Cells (6 × 105) were cultured on 6-well plates. A pipette tip was subsequently used to scratch the bottom of the plate. Free cells were washed away with PBS buffer. The migration distance was observed at 0 h and 48 h.

Statistics & Reproducibility
The overall study design involved computational analysis of population-level genetic data (GWAS) followed by experimental validation in independent patient tissues and cell line models. No statistical method was used to predetermine sample size for the GWAS analyses, as these utilized all available data from the specified biobanks (BBJ, KoGES, MVP). The sample size for the patient tissue cohort (n=46) was based on availability during the study period. No data were excluded from the GWAS summary statistics analyses. For cellular and molecular experiments, sample sizes were determined on the basis of common practices in the field to ensure adequate power for detecting effects, and all experiments were performed with at least three biological replicates. The experiments were not randomized. The investigators were not blinded to allocation during the experiments or outcome assessment. Statistical analyses were conducted using SPSS software (version 22.0) and GraphPad Prism (version 8.0). Data from cellular and molecular experiments are presented as the mean ± standard deviation (SD). Group differences were evaluated using one-way ANOVA. Significance levels in the figures are denoted as follows: *P < 0.05, **P < 0.01, and ***P < 0.001. To control for multiple comparisons in the genome-wide analyses (genetic correlation via LDSC, S-LDSC, and Mendelian randomization), Benjamini–Hochberg false discovery rate (FDR) correction was applied, with significance defined as PFDR < 0.05.

Data availability
The GWAS summary statistics data used in this study are available in the GWAS Catalogue under the accession codes summarized in Supplementary Data 3 [https://www.ebi.ac.uk/gwas/]. The individual-level genotype and gene expression data generated in this study are available under restricted access to protect patient privacy and comply with ethical regulations; access can be obtained by submitting a formal request to the corresponding author, Dr. Guangyong Zhang (guangyongzhang@hotmail.com), for academic, noncommercial research purposes, which will then be reviewed by the Ethics Committee of The First Affiliated Hospital of Shandong First Medical University. A completed data transfer agreement may be needed. The raw individual-level clinical data are protected and are not available because of data privacy laws. Source data are provided with this paper.

Code availability
We conducted our analyses using a variety of publicly available software tools. The computational environment consisted of Python (v2.7.18), Perl (v5.16.3), and R (v4.2.3). Genetic correlation analyses between gastric cancer and 95 traits were conducted using LDSC v1.0.1, with subsequent S-LDSC to evaluate cell type-specific SNP heritability enrichment. Bidirectional Mendelian randomization was implemented through the TwoSampleMR R package (v0.6.4). For the multitrait GWAS meta-analysis of gastric cancer and its 25 genetically correlated traits, we employed MTAG v1.0.8. The custom scripts used in this study are deposited in Zenodo and are publicly accessible at https://zenodo.org/records/18085131.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study analyzed East Asian genetic data, unveiling association between gastric cancer and other conditions, particularly\ncardiovascular diseases. Employing sophisticated multi-trait analysis (MTAG), the authors made the significant discovery of\na novel shared genetic locus, rs12814712 at 12q22, implicating it in both gastric cancer and heart conditions. Intriguingly,\nthis SNP modulates the expression of nearby genes, including VEZT and NR2C1. Compelling functional experiments\nconfirmed that VEZT and NR2C1 act as tumor suppressors, inhibiting gastric cancer cell proliferation and migration. Despite\nthis, the risk allele was paradoxically linked to increased suppressor expression in tumors and worse clinical outcomes\n(lymph node metastasis), suggesting complex regulation. Transcriptomic analysis further connected the risk allele to\nfundamental chemical carcinogenesis pathways. This research identifies VEZT as a promising causal gene, offering\nvaluable new insights into shared disease mechanisms and potential therapeutic avenues.\nThe authors mentioned that they analyzed GWAS data from BBJ to explore the genetic correlation between gastric cancer\nand 95 phenotypes. However, with a total of 220 phenotypes available in the BBJ data, could you please explain how these\n95 were chosen? Also, why was gastric polyp—an important phenotype related to gastric cancer—excluded?\nIn the abstract, the authors mentioned, “our genetic correlation analysis identified significant associations between gastric\ncancer and 25 phenotypes”. But it seems like no correction for multiple comparisons was used. Could you run a multiple\ncomparison correction (like FDR)? If not, maybe tweak the phrasing to “nominally significant” for traits like MI and blood\npressure. Also, for the MR results, if multiple traits were tested, consider noting which results remain significant after\nadjusting for the number of traits tested (or state that the MR analyses were exploratory for each trait). This will give readers\nconfidence that the results are statistically robust and not just false positives. Plus, could you please soften the language of\nline 51 “Mendelian randomization further supported causal relationships between cardiovascular traits”? Genetic correlation\nisn’t the same as causal relationship.\nThe authors conducted a comprehensive investigation into potential shared genetic factors between gastric cancer and its\ngenetically correlated traits, including AP, MI and the use of salicylic acid and its derivatives (SADUM), to identify novel\nsusceptibility loci associated with gastric cancer risk. However, since SADUM was not found to be genetically correlated\nwith gastric cancer in earlier genetic correlation or MR analyses, it’s a bit unclear how and why this phenotype was chosen\nfor further analysis.\nThe authors identified a novel locus, rs1281471, and performed additional functional experiments to explore its role. But in\nSupplementary Data 3, another SNP, rs13306206, also showed significant association with gastric cancer and the same\nthree phenotypes, and it was annotated as new. Could you please clarify why this SNP was not discussed in the main\nmanuscript?\nThe authors reported genetic correlations between gastric cancer and 25 phenotypes; however, none of these phenotypes\nwere collected or analyzed in the 46 patients included in the experimental work. Could you explain this or maybe add some\ncomparison?\nMinor concerns/suggestions:\nMore detail on GWAS QC (e.g., MAF filters, population structure handling) and the rationale for the suggestive p-value\nthreshold used in MTAG prioritization would enhance reproducibility.\nCould you provide more specific parameters for bioinformatics tools (LDSC, S-LDSC, MTAG, RNA-seq\nalignment/quantification/DE analysis) which is needed for transparency and reproducibility?\nThe authors mentioned that alternative splicing events were identified, is there any related results from alternative splicing?", "reviewer_2": "In this study, Ding H et al. used publicly available data from Biobank Japan to identify diseases and Phenotypes associated\nwith gastric cancer. Furthermore, MTAG analysis identified several novel regions commonly associated with these\nphenotypes and Gasctric cancer. The authors also indicated that one of the novel regions, rs12814712, correlates with\nneighboring gene expression, and that cell proliferation and migration are altered by gene overexpression in gastric cancer\ncell lines. These results may be valuable data for elucidating the pathogenic mechanism of gastric cancer and for drug\ndiscovery.\nHowever, the results of this genome analysis are based on publicly available data, and the correlation between diseases\nhas already been analyzed in a previous paper (Sakaue et al, 2022). Because there are no independent samples for\nvalidation, discovery of new GC loci alone would not have enough impact. In addition, molecular mechanism how\nrs12814712 regulates mRNA of nearby genes and the biological mechanisms of VEZT in gastric carcinogenesis, are\nunclear. Overall, the study lacks novelty and is not appropriate for the publication in Nature Communications.\nThe following are my major concerns\n1. In the MTAG analysis, gastric cancer and multiple diseases/ phenotypes were analyzed. Therefore, 5x10-8 is insufficient\nas a correction for multiple testing.\n2. The analysis was performed using data from Biobank Japan only, and there were an overlap of samples. I recommend to\nvalidate these results using independent samples.\n3. Helicobacter Pylori (HP) infection is an important environmental factor of gastric cancer. It is important to know whether\nthese risk genetic factors is related to HP infection or to development of cancer after HP infection.\n4. In the analysis of tissue samples, GG and AG are compared, but it is desirable to include AA genotypes in the analysis.\n5. Genes rs12814712 are increased or decreased in gastric cancer tissues?\n6. The effects of SNP on mRNA expression were about 2-fold or less based on the results in human tissues. However in the\ncell line experiments, mRNA expression in overexpressing cells was more than 30 times higher than those in control cells.\nTherefore, results shown in cell growth and migration assay might be artifact due to extremely high expression of proteins. It\nis necessary to verify the effect of genome editing or gene knockdown to evaluate the physiological function of\nVEZT etc in gastric cancer cell lines.", "reviewer_3": "This is an interesting study, adding to understanding of gastric cancer risk through GWAS, multi-trait analyses and some\nfunctional work. I have some questions to clarify analysis reporting and some concerns for the functional analysis.\nThe Mendelian randomization analyses consistently state that cardiovascular traits are associated with increased gastric\ncancer risk. However, this is at odds with the ORs provided, that are all below 1, indicating reduced risk. Can the authors\nplease clarify the direction of effect presented? If the ORs are correct, they would also not align with the positive correlation\nobserved using LDSR.\nAlthough promising, MTAG results should be interpreted with caution due to high chance of false positive reporting – where\nloci are associated due to the strong influence of other included factors, rather than the focal trait. The authors should make\nthis clear in the limitation of the manuscript. Usually there would be an attempt to replicate findings in another dataset. There\nare several publicly available GWAS datasets for gastric cancer, have the authors accessed any to evaluate whether this\nloci displays any evidence of an association with gastric cancer, or whether it is in the same direction? The allele frequency\nfor rs12814712 looks pretty stable across all ethnicities so I strongly encourage the authors to do this.\nThe negative genetic correlation observed between gastric cancer and lung and colorectal cancer seems surprising, and\ndeserves to be highlighted in the discussion. Although gastric cancer wasn’t included, a large multi-cancer analysis (12 solid\ntumors) that performed pairwise genetic correlations found largely all cancers were positively correlated, albeit not all\nsignificantly (Lindstrom et al JNCI 2023).\nThe eQTL analysis and other analyses performed using the tissue cohort is very weak, comprising only 46 samples. The\nauthors don’t appear to adequately acknowledge the sample size limitation. It is difficult to draw meaningful results from\nthese data. Again, given the stable allele frequency across ethnicities, the authors should attempt validation using another\ncohort, such as TCGA.\nDue to the above, the evidence linking the variant to these genes is rather weak, would suggest that a luciferase promoter\nreporter gene assay would be a better method to provide evidence for SNP functionality.\nThe samples don’t appear to be matched but collected prospectively. Indeed, there is a significant difference in those that\nhad lymph node metastasis. Was there any adjustment for this in the differential expression analysis? It is a strong call to\nsuggest that >140 genes were significantly differentially expressed due to a risk variant which would normally have a small\neffect on risk? It would appear more likely to me that the DE genes are more likely representing lymph node metastasis, in\ngeneral and perhaps nothing to do with the SNP. On a different note, were your potential target genes at your locus\ndifferentially expressed?\nFurthermore, the association between the genotype and lymph node metastasis seems likely to be spurious, given the small\nnumber of samples collected.\nLines 299-301: the authors mention requiring validation in East Asians due to varying LD patterns, however they haven’t\nprovided any comparison of the LD pattern across populations in at this locus.\nThe direction of effect for the risk variant and gene effects requires clarification. The authors observed the gastric cancer risk\nallele associates with increased expression of their target genes, however expression of two of these genes (VEZT and\nNR2C1) displayed anti-cancer effects (inhibition of proliferation and migration). This is not consistent with the eQTL effect.\nOther Qs\nAre results throughout manuscript adjusted for multiple testing (e.g. MR analysis, gene correlations, stratified LDSC)?\nWhat was the criteria used for a trait to be included in the MTAG analysis? E.g. rg threshold, p-value)\nLine 117 notes that 12q22 is “previously documented”, is this a typo? Line 182 then calls the locus “novel”\nThe GG_4 sample in the PCA plot is some distance from the other samples. Is there something different about this particular\nsample?\nWere Mendelian randomization analyses performed bidirectionally?\nWas the baseline model used in the stratified LDSR analysis? Could you please annotate which model used?\nA table of results for the 15 known gastric cancer loci + plus the new loci identified in this study would be useful – with\nassociated alleles, ORs, CIs, p-values (before/after MTAG)"}, "author_response": "Point-by-point responses to Reviewers' comments\nWe express our gratitude to the editor and the reviewers for their meticulous evaluation of our\nmanuscript and for their valuable suggestions. In response to the feedback provided, we have\nmade thorough revisions to the manuscript to comprehensively address each of the reviewers'\ncomments. Below is a detailed point-by-point response:\nReviewer #1 (Remarks to the Author):\nThis study analyzed East Asian genetic data, unveiling association between gastric cancer and other\nconditions, particularly cardiovascular diseases. Employing sophisticated multi-trait analysis\n(MTAG), the authors made the significant discovery of a novel shared genetic locus, rs12814712 at\n12q22, implicating it in both gastric cancer and heart conditions. Intriguingly, this SNP modulates\nthe expression of nearby genes, including VEZT and NR2C1. Compelling functional experiments\nconfirmed that VEZT and NR2C1 act as tumor suppressors, inhibiting gastric cancer cell\nproliferation and migration. Despite this, the risk allele was paradoxically linked to increased\nsuppressor expression in tumors and worse clinical outcomes (lymph node metastasis), suggesting\ncomplex regulation. Transcriptomic analysis further connected the risk allele to fundamental\nchemical carcinogenesis pathways. This research identifies VEZT as a promising causal gene,\noffering valuable new insights into shared disease mechanisms and potential therapeutic avenues.\nWe sincerely thank you for the constructive comments. Your feedback has helped us improve the\nclarity and presentation of the manuscript, and we have carefully addressed all comments in our\nrevision.\n1. The authors mentioned that they analyzed GWAS data from BBJ to explore the genetic correlation\nbetween gastric cancer and 95 phenotypes. However, with a total of 220 phenotypes available in\nthe BBJ data, could you please explain how these 95 were chosen? Also, why was gastric polyp—\nan important phenotype related to gastric cancer—excluded?\nReply: Thank you for pointing this out. To clarify, we selected 95 phenotypes from the BBJ dataset\nbased on a predefined inclusion criterion requiring a minimum of 3,000 cases per phenotype. This\nthreshold was applied to ensure sufficient statistical power for reliable genetic correlation\nestimation and downstream multi-trait analyses. We have now included this criterion in the\nMethods section for clarity (Page 17-18, Lines 369-372):\n“Additionally, we obtained summary statistics for 95 phenotypes (including diseases,\nbiomarkers, and medication usage) with case numbers greater than 3,000 from the 220\nphenotypes available in the BBJ database.”\nRegarding gastric polyp, while we agree that it is a clinically relevant phenotype, it was\nexcluded because it had only 1,337 cases and therefore did not meet our inclusion threshold.\n2. In the abstract, the authors mentioned, “our genetic correlation analysis identified significant\nassociations between gastric cancer and 25 phenotypes”. But it seems like no correction for multiple\ncomparisons was used. Could you run a multiple comparison correction (like FDR)? If not, maybe\ntweak the phrasing to “nominally significant” for traits like MI and blood pressure. Also, for the MR\nresults, if multiple traits were tested, consider noting which results remain significant after\nadjusting for the number of traits tested (or state that the MR analyses were exploratory for each\ntrait). This will give readers confidence that the results are statistically robust and not just false\npositives. Plus, could you please soften the language of line 51 “Mendelian randomization further\nsupported causal relationships between cardiovascular traits”? Genetic correlation isn’t the same\nas causal relationship.\nReply: We appreciate the reviewer's insightful comments. We have now applied false discovery\nrate (FDR) correction to all P-values in both the MR and LDSC results. In the \"Methods\" section\nunder \"Quantification and statistical analysis,\" we have added the following statement: \"To control\nfor multiple comparisons across genetic correlation and causal analyses (LDSC, S-LDSC and MR),\nwe implemented Benjamini-Hochberg false discovery rate (FDR) correction, with significance\ndefined as P < 0.05.\" (Page 27-28, Lines 593-596) To improve clarity in reporting the results, we\nFDR\nhave updated the abstract and the \"Results\" sections—specifically in \"Genetic correlations\nbetween gastric cancer and associated phenotypes\" and \"Causal Impact of Cardiovascular-Related\nFactors on Gastric Cancer\"—by explicitly labeling P-values as P . (Page 3, Lines 48-49; Page 6-7,\nFDR\nLines 121-149) Additionally, to ensure precision, we have revised the phrasing in line 51 as\nsuggested by the reviewer, now stating: \"Mendelian randomization analyses provided further\nevidence for genetic associations between gastric cancer and cardiovascular traits.\" (Page 3, Lines\n49-51)\n3. The authors conducted a comprehensive investigation into potential shared genetic factors\nbetween gastric cancer and its genetically correlated traits, including AP, MI and the use of salicylic\nacid and its derivatives (SADUM), to identify novel susceptibility loci associated with gastric cancer\nrisk. However, since SADUM was not found to be genetically correlated with gastric cancer in earlier\ngenetic correlation or MR analyses, it’s a bit unclear how and why this phenotype was chosen\nfor further analysis.\nReply: We thank the reviewer for raising this important point. The genetic correlation between\ngastric cancer and the use of salicylic acid and its derivatives (SADUM) was supported by our LDSC\nanalysis, as shown in Figure 2 and Supplementary Table S1. However, this finding was not explicitly\ndescribed in the previous version of the manuscript. We have now added the following sentence\nto the Results section to clarify: \" Following multiple testing correction, our analysis revealed a\nsignificant positive genetic correlation between gastric cancer and cardiovascular-related traits,\nincluding diseases like angina pectoris (AP, r = 0.4611, P = 0.0001), …… and salicylic acid and\ng FDR\nderivatives (SADUM, r = 0.4115, P = 0.0002).\" (Page 6-7, Lines 123-133) Additionally, recognizing\ng FDR\nthat our initial MR analysis required further refinement, we have now conducted a more\ncomprehensive bidirectional Mendelian randomization investigation between gastric cancer and\ncardiovascular-related traits. This includes updated MR results for the SADUM–gastric cancer\nassociation, now presented in the revised manuscript as follows: \"including AP (OR 1.289, 95% CI\n1.060-1.568; P = 0.0192), ……., SADUM (OR 1.799, 95% CI 1.412-2.293; P = 9.61× 10-6), CCBUM\nFDR FDR\n(OR 1.719, 95% CI 1.203-2.456; P = 0.00679), and HCRIUM (OR 1.368, 95% CI 1.266-1.477; P\nFDR FDR\n= 2.52 × 10-14) were also implicated (Supplementary Data 1).\" (Page 7, Lines 140-150) We hope this\nclarification and additional analysis better support the inclusion of SADUM in our follow-up\ninvestigations.\n4. The authors identified a novel locus, rs12814712, and performed additional functional\nexperiments to explore its role. But in Supplementary Data 3, another SNP, rs13306206, also\nshowed significant association with gastric cancer and the same three phenotypes, and it was\nannotated as new. Could you please clarify why this SNP was not discussed in the main\nmanuscript?\nReply: We appreciate the reviewer’s comment. Although rs13306206 also reached genome-wide\nsignificance and was annotated as a novel signal in Supplementary Data 3, our main manuscript\nfocuses on rs12814712 due to its higher allele frequency (~15% in East Asians), clearer non-coding\nregulatory function, and robust supporting evidence from eQTL and functional experiments.\nIn contrast, rs13306206 is a low-frequency missense variant in APOB (MAF ~1% in East Asians),\nand currently annotated as “likely benign” in ClinVar. Due to its rarity, it is challenging to evaluate\nits regulatory effects through standard eQTL datasets, and its biological mechanism, whether\nthrough APOB loss of function or local regulatory influence, remains uncertain. We consider this\nlocus of potential interest but beyond the mechanistic scope of the current study.\nWe now briefly acknowledge rs13306206 in the Discussion to clarify this point (Page 15, Lines\n304-309).\n5. The authors reported genetic correlations between gastric cancer and 25 phenotypes; however,\nnone of these phenotypes were collected or analyzed in the 46 patients included in the experimental\nwork. Could you explain this or maybe add some comparison?\nReply: We thank the reviewer for this thoughtful comment. The genetic correlation analyses were\nperformed using GWAS summary statistics from large-scale population cohorts to investigate the\nshared genetic basis between gastric cancer and other traits. These analyses were independent of\nthe 46 experimental samples. The primary objective of the experimental validation in the 46\npatients was not to examine phenotypic correlations but to evaluate whether the locus of interest\nexerts regulatory effects on gene expression in gastric tissues. We have added a clarifying\nstatement in the results sections to emphasize this distinction and avoid confusion. (Page 10, Lines\n203-204).\nMinor concerns/suggestions:\n6. More detail on GWAS QC (e.g., MAF filters, population structure handling) and the rationale for\nthe suggestive p-value threshold used in MTAG prioritization would enhance reproducibility.\nReply: We thank the reviewer for this helpful suggestion. In response, we have revised the Methods\nsection to include additional details to improve reproducibility:\nIn the “GWAS data” section, we now provide expanded information on quality control and\nanalytical procedures:\n“In BBJ, genotyping was conducted using Illumina arrays, and genotype imputation was\nperformed using the 1000 Genomes Phase 3 reference panel combined with Japanese whole-\ngenome sequencing data. Variants with imputation Rsq ≥ 0.7 were retained. Phenotypes were\nderived from curated medical records, ATC-coded medication data, and biomarker measurements,\nwith appropriate outlier removal and medication-specific adjustments. GWAS for binary traits\nemployed SAIGE to account for sample relatedness and case-control imbalance, while BOLT-LMM\nwas used for quantitative traits with adjustments for age, sex, and principal components.” (Page\n17-18, Lines 365-376)\nIn the “Multi-trait analysis of GWAS (MTAG)” section, we have clarified the rationale for our\nsignificance threshold: “The suggestive significance threshold (P < 0.01) in single-trait GWAS was\nchosen to balance sensitivity and specificity in identifying potential signals, which were\nsubsequently evaluated for genome-wide significance in the multi-trait analysis.” (Page 21, Lines\n429–432)\n7. Could you provide more specific parameters for bioinformatics tools (LDSC, S-LDSC, MTAG, RNA-\nseq alignment/quantification/DE analysis) which is needed for transparency and reproducibility?\nReply: We appreciate the reviewer's suggestion to enhance methodological transparency. We have\nnow included detailed parameter specifications for all bioinformatics analyses throughout the\nMethods section. (Page 18, Lines 391-393; Page 19, Lines 413-417; Page20, Lines 420-435; Page\n26-27, Lines 573-586)\n8. The authors mentioned that alternative splicing events were identified, is there any related\nresults from alternative splicing?\nReply: We thank the reviewer for highlighting this point. Although alternative splicing was initially\nconsidered during our study design, it was not included in the final analysis pipeline. To avoid\nconfusion and ensure the manuscript accurately represents the analyses conducted, we have\nremoved all references to alternative splicing and focused solely on gene-level expression results.\nReviewer #2 (Remarks to the Author):\nIn this study, Ding H et al. used publicly available data from Biobank Japan to identify diseases and\nPhenotypes associated with gastric cancer. Furthermore, MTAG analysis identified several novel\nregions commonly associated with these phenotypes and Gastric cancer. The authors also indicated\nthat one of the novel regions, rs12814712, correlates with neighboring gene expression, and that\ncell proliferation and migration are altered by gene overexpression in gastric cancer cell lines. These\nresults may be valuable data for elucidating the pathogenic mechanism of gastric cancer and for\ndrug discovery.\nHowever, the results of this genome analysis are based on publicly available data, and the\ncorrelation between diseases has already been analyzed in a previous paper (Sakaue et al, 2022).\nBecause there are no independent samples for validation, discovery of new GC loci alone would not\nhave enough impact. In addition, molecular mechanism how rs12814712 regulates mRNA of\nneighboring genes and the biological mechanisms of VEZT in gastric carcinogenesis, are unclear. Overall,\nthe study lacks novelty and is not appropriate for the publication in Nature Communications.\nThank you for your thoughtful and rigorous assessment. We truly appreciate your critical\nperspective and have carefully revised the manuscript to clarify our approach and improve the\noverall presentation. We gave particular attention to your comments, as they raised important\nconceptual and methodological points, and we have addressed each of them in detail. In addition,\nwe also considered suggestions from the other reviewers and made several supplementary\nrevisions,including new experiments, to further strengthen the study. We believe these collective\nefforts have substantially improved the manuscript, and we sincerely hope it now meets your\nexpectations.\nThe following are my major concerns\n1. In the MTAG analysis, gastric cancer and multiple diseases/phenotypes were analyzed. Therefore,\n5x10-8 is insufficient as a correction for multiple testing.\nReply: Thank you for raising this important point. In our analysis, we adopted the conventional\ngenome-wide significance threshold of P < 5×10⁻⁸, consistent with prior MTAG studies, as this\nthreshold accounts for the number of independent variants tested and has been widely used in the\nfield. We understand and agree with the reviewer’s concern regarding multiple testing, particularly\nsince we included 25 phenotypes in the MTAG framework. However, these traits are not fully\nindependent due to substantial genetic correlations among them, and applying a Bonferroni\ncorrection assuming full independence would likely be overly conservative. Importantly, even\nunder this stricter correction (P < 2×10⁻⁹), rs12814712 remains significant in the analysis between\ngastric cancer and MI. We have reported both thresholds in the revised manuscript, along with a\nbrief explanation in the text (Page 9, Lines 179-182).\n2. The analysis was performed using data from Biobank Japan only, and there were an overlap of\nsamples. I recommend to validate these results using independent samples.\nReply: Thank you for pointing out the issue of overlapping samples across traits. We acknowledge\nthat partial sample overlap exists in the Biobank Japan dataset. To address this, we used the Multi-\nTrait Analysis of GWAS (MTAG) framework, which explicitly models and accounts for sample\noverlap by incorporating the covariance structure of GWAS summary statistics. Specifically, MTAG\nestimates the trait-specific SNP effect sizes by leveraging the genetic covariance matrix across traits\nand adjusts the standard errors accordingly, even in the presence of overlapping individuals. This\nfeature of MTAG allows for unbiased estimation and proper control of false-positive rates under\nrealistic overlap scenarios, as demonstrated in the original MTAG publication (Turley et al., Nat\nGenet 2018). We have added a corresponding statement in the Methods section to clarify this\npoint (Page 20, Lines 421-424).\nWhile MTAG accounts for sample overlap in its model, we agree that replication in\nindependent cohorts is critical for validating the findings. However, currently available genome-\nwide association study (GWAS) summary statistics for gastric cancer in East Asian populations\nremain limited. To evaluate the rs12814712 signal identified in the BBJ cohort, we examined an\nindependent Korean dataset comprising 378 gastric cancer cases. We observed a consistent\ndirection of effect (BETA = 0.066) with the GC-BBJ result (BETA = 0.079), although the association\ndid not reach statistical significance, likely due to limited power. Interestingly, a nearby SNP,\nrs9634181 (in weak linkage disequilibrium with rs12814712, R2=0.1), showed a strong and\nsignificant association (BETA = 0.19, P = 0.012) in the Korean dataset. These findings are now\ndiscussed in the revised manuscript (Page 9-10, Lines 188-198; Page 18, Lines 377-379), where we\nalso acknowledge the limited sample size of available replication datasets as a current limitation.\nFurther validation will be pursued as additional independent cohorts become available (Page 17,\nLines 339-344).\n3. Helicobacter Pylori (HP) infection is an important environmental factor of gastric cancer. It is\nimportant to know whether these risk genetic factors is related to HP infection or to development\nof cancer after HP infection.\nReply: We appreciate this insightful comment. We fully agree that distinguishing between genetic\nsusceptibility to Helicobacter pylori infection and to gastric cancer progression following infection\nis important for understanding disease mechanisms. However, HP infection status was not available\nin the Biobank Japan dataset used in our analysis, and we were therefore unable to directly assess\nthis relationship. We have now added a statement to the Discussion to acknowledge this limitation\nand to emphasize the need for future studies incorporating both genetic and HP infection data to\ndisentangle these effects. (Page 17, Lines 344-347)\n4. In the analysis of tissue samples, GG and AG are compared, but it is desirable to include AA\ngenotypes in the analysis.\nReply: Thank you for this insightful comment. We agree that including all three genotypes would\nprovide a more complete assessment of potential dose-dependent effects. However, in our tissue\nsample cohort, individuals with the AA genotype at rs12814712 were extremely rare, and none\nwere available for expression analysis. We have now explained this limitation in (Page 17, Lines\n347-351), and we also acknowledge it in the Discussion. We appreciate the reviewer’s point and\nagree that future studies with larger sample sizes will be important for evaluating genotype–\nphenotype relationships across all genotypic categories.\n5. Genes rs12814712 are increased or decreased in gastric cancer tissues?\nReply: Thank you for your comment and for pointing out the inconsistency in our previous\ndescription. We have corrected the text in the revised manuscript. The revised text states:\n“Expression levels of FGD6, VEZT, and NR2C1 were significantly lower in both adjacent non-tumor\ntissues and gastric cancer tissues of patients with the AG genotype of rs12814712 compared to\nthose with the GG genotype (P < 0.05, Fig. 6B and 6C).” (Page 10-11, Lines 214-217)\n6. The effects of SNP on mRNA expression were about 2-fold or less based on the results in human\ntissues. However in the cell line experiments, mRNA expression in overexpressing cells was more\nthan 30 times higher than those in control cells. Therefore, results shown in cell growth and\nmigration assay might be artifact due to extremely high expression of proteins. It is necessary to\nverify the effect of genome editing or gene knockdown to evaluate the physiological function of\nVEZT etc in gastric cancer cell lines.\nReply: Thank you for this important comment. We fully agree that overexpression systems may\nresult in non-physiological expression levels, which could affect the interpretation of functional\nassays. In response to this suggestion, we conducted additional gene knockdown experiments\nusing two independent shRNAs targeting each gene (shVEZT and shNR2C1). These experiments\nshow that knockdown of either gene significantly promotes gastric cancer cell proliferation and\nmigration, further supporting their tumor-suppressive roles. We have included these new results\nin the revised manuscript (Page 3 Lines 61; Page 12 Lines 253-262; Page 16 Lines 340-342;\nSupplementary Figure S4). We appreciate the reviewer’s suggestion, which greatly helped\nstrengthen the functional aspect of our study.\nReviewer #3 (Remarks to the Author):\nThis is an interesting study, adding to understanding of gastric cancer risk through GWAS, multi-\ntrait analyses and some functional work. I have some questions to clarify analysis reporting and\nsome concerns for the functional analysis.\nReply: We sincerely thank you for your comments. We have carefully reviewed the points you\nraised and made corresponding revisions to the manuscript to improve its clarity and overall\npresentation.\n1. The Mendelian randomization analyses consistently state that cardiovascular traits are\nassociated with increased gastric cancer risk. However, this is at odds with the ORs provided, that\nare all below 1, indicating reduced risk. Can the authors please clarify the direction of effect\npresented? If the ORs are correct, they would also not align with the positive correlation observed\nusing LDSR.\nReply: Thank you for your thorough review and careful attention to the details of our manuscript.\nUpon review, we identified an error in the directionality description of the Mendelian\nrandomization (MR) results in the initial submission. We have re-examined the raw data and\nrepeated the bidirectional MR analyses (see Page 7, Lines 136–146 and Supplementary Data 1).\nThe corrected results now indicate that cardiovascular traits are positively associated with gastric\ncancer risk (OR > 1), consistent with the positive genetic correlation observed in the LDSC analysis.\nWe apologize for the confusion and have revised all relevant figures, tables, and text in the\nmanuscript accordingly.\n2. Although promising, MTAG results should be interpreted with caution due to high chance of false\npositive reporting – where loci are associated due to the strong influence of other included factors,\nrather than the focal trait. The authors should make this clear in the limitation of the manuscript.\nUsually there would be an attempt to replicate findings in another dataset. There are several\npublicly available GWAS datasets for gastric cancer, have the authors accessed any to evaluate\nwhether this loci displays any evidence of an association with gastric cancer, or whether it is in the\nsame direction? The allele frequency for looks pretty stable across all ethnicities so I strongly\nencourage the authors to do this.\nReply: We sincerely appreciate the reviewer’s thoughtful comments and detailed suggestions. We\nfully agree that MTAG results should be interpreted with caution given the potential for false\npositives driven by genetically correlated traits. In the revised manuscript, we have explicitly\nacknowledged this limitation in the Discussion section. (Page 16, Lines 339-344)\nRegarding replication efforts, we appreciate the reviewer’s suggestion and have examined\navailable GWAS datasets for gastric cancer in East Asian populations. While publicly accessible\nsummary statistics remain limited, we attempted validation of the BBJ-identified variant\nrs12814712 using the Korean Genome and Epidemiology Study (KoGES), which is currently the\nlargest available East Asian dataset. Although the number of gastric cancer cases in KoGES is\nrelatively small (n = 378), we observed a consistent direction of effect (positive beta in both studies;\nBBJ: BETA = 0.079, KoGES: BETA = 0.066). In addition, we identified a nearby SNP, rs9634181, which\nshows weak linkage disequilibrium with rs12814712 (R² = 0.1) and exhibited a statistically\nsignificant association with gastric cancer in KoGES (BETA = 0.19, P = 0.012). These findings have\nbeen included in the revised manuscript (Page 10–11, Lines 199–209). (Page 9-10, Lines 188-198)\n3. The negative genetic correlation observed between gastric cancer and lung and colorectal cancer\nseems surprising, and deserves to be highlighted in the discussion. Although gastric cancer wasn’t\nincluded, a large multi-cancer analysis (12 solid tumors) that performed pairwise genetic\ncorrelations found largely all cancers were positively correlated, albeit not all significantly\n(Lindstrom et al JNCI 2023).\nReply: Thank you for your careful review and for pointing out this important issue, as well as for\nreferencing the relevant work by Lindstrom et al. (JNCI 2023). We revisited our analysis in light of\nyour comment and found that our initial interpretation was incorrect. As shown in Supplementary\nTable S1 and Figure 2, gastric cancer in fact exhibits positive genetic correlations with both lung\ncancer (r = 0.7442, P = 0.0058) and colorectal cancer (r = 0.3043, P = 0.002). These results\ng FDR g FDR\nare consistent with the general pattern reported by Lindstrom et al., where most solid tumors\nshowed positive pairwise genetic correlations. We have corrected the relevant description in the\nmanuscript (Page 7, Lines 129-132) and thank the reviewer again for the careful observation that\nled to this clarification.\n4. The eQTL analysis and other analyses performed using the tissue cohort is very weak, comprising\nonly 46 samples. The authors don’t appear to adequately acknowledge the sample size limitation.\nIt is difficult to draw meaningful results from these data. Again, given the stable allele frequency\nacross ethnicities, the authors should attempt validation using another cohort, such as TCGA.\nDue to the above, the evidence linking the variant to these genes is rather weak, would suggest that a\nluciferase promoter reporter gene assay would be a better method to provide evidence for SNP\nfunctionality.\nReply: Thank you for your insightful comments. We agree that the eQTL and related analyses based\non the 46-sample tissue cohort are limited in power, and we have now clearly acknowledged this\nlimitation in the revised manuscript (Page 17, Lines 347-351). In an effort to validate the findings\nusing independent data, we examined publicly available resources, including the TCGA data portal.\nHowever, we found that genotype-level data for gastric cancer are currently not available through\nTCGA, which prevented us from performing further validation using this cohort.\nTo address the functional relevance of rs12814712, we conducted luciferase reporter assays as\nsuggested. Specifically, we constructed plasmids to evaluate potential enhancer activity for VEZT\nand 3’UTR regulatory effects for NR2C1, FGD6, and NDUFA12. The results showed that rs12814712\nmay modulate VEZT transcription via enhancer-like activity, providing additional evidence for a\nregulatory role at this locus. These experiments and findings have been added to the revised\nmanuscript (Page 3 Lines 57-59; Page 12, Lines 253-262; Page 15, Lines 321-323; Page 23-24, Lines\n502-514; Supplementary Figure S3).\n5. The samples don’t appear to be matched but collected prospectively. Indeed, there is a significant\ndifference in those that had lymph node metastasis. Was there any adjustment for this in the\ndifferential expression analysis? It is a strong call to suggest that >140 genes were significantly\ndifferentially expressed due to a risk variant which would normally have a small effect on risk?\nIt would appear more likely to me that the DE genes are more likely representing lymph node\nmetastasis, in general and perhaps nothing to do with the SNP. On a different note, were your\npotential target genes at your locus differentially expressed?\nReply: Thank you for your insightful and constructive comment. We fully agree with your concern\nthat the observed differential expression may reflect underlying differences in lymph node\nmetastasis rather than effects driven by the risk variant itself. Upon re-examination, we found that\nwhile VEZT expression was reduced in the AG group (P = 0.0774), and NR2C1 showed a similar\ndownward trend (P = 0.1364). These results did not reach statistical significance, which may in part\nreflect the limited number of available samples or other uncontrolled factors.\nImportantly, we acknowledge that the differential expression analysis was performed using\nedgeR, which does not support direct covariate adjustment for clinical variables such as lymph\nnode status. While we initially attempted to control for this by including only patients with lymph\nnode metastasis in both groups, we now recognize that this approach may not be sufficient to\neliminate confounding, especially given the imbalance in metastatic subtypes (e.g., N1 vs N3).\nIn light of these limitations, we have removed this section from the main Results and now\npresent the findings in the Supplementary Note for transparency. In the revised text, we have\nexplicitly stated the potential confounding factors and clarified that these exploratory results\nshould be interpreted with caution. (Page 13, Lines 255-258; Supplementary Note)\n6. Furthermore, the association between the genotype and lymph node metastasis seems likely to\nbe spurious, given the small number of samples collected.\nReply: Thank you for this comment. We agree with the reviewer that the initial interpretation\nmay have overstated the findings given the limited sample size, and we have taken steps to\naddress this concern. Specifically, we refined the analysis of lymph node metastasis by evaluating\nN0, N1, N2, and N3 categories separately, rather than combining N1+N2+N3. A chi-squared test\nshowed no significant association (P = 0.1817). Accordingly, we have moved the original Table 1\nto Supplementary Table S2 and added the following description to the Results section under “SNP\nrs12814712 and Neighboring Genes in the Chinese Population”: “The detailed clinical\ncharacteristics of these 46 samples are described in Supplementary Table S2.” (Page 10, Lines\n206-207)\nIn addition, we have removed the section titled “Association Analyses of rs12814712\nGenotypes with Clinical Characteristics” from the Results to avoid potential overinterpretation.\n7. Lines 299-301: the authors mention requiring validation in East Asians due to varying LD patterns,\nhowever they haven’t provided any comparison of the LD pattern across populations in at this locus.\nReply: Thank you for pointing this out. We compared the linkage disequilibrium (LD) patterns across\nthe 10 kb region surrounding rs12814712 in both East Asian and European populations using Phase\n3 data from the 1000 Genomes Project via Ensembl. The results have been added as\nSupplementary Figure S7 in the revised manuscript. (Page 15, Lines 304; Supplementary Figure S7)\n8. The direction of effect for the risk variant and gene effects requires clarification. The authors\nobserved the gastric cancer risk allele associates with increased expression of their target genes,\nhowever expression of two of these genes (VEZT and NR2C1) displayed anti-cancer effects\n(inhibition of proliferation and migration). This is not consistent with the eQTL effect.\nReply: Thank you for your comment. The discrepancy was due to a misstatement in the earlier\nversion, which has now been addressed and corrected in the manuscript. The revised text now\nstates: “Expression levels of FGD6, VEZT, and NR2C1 were significantly lower in both adjacent non-\ntumor tissues and gastric cancer tissues of patients with the AG genotype of rs12814712 compared\nto those with the GG genotype (P < 0.05, Fig. 6B and 6C).” (Page 11, Lines 214-217)\nOther Qs\n9. Are results throughout manuscript adjusted for multiple testing (e.g. MR analysis, gene\ncorrelations, stratified LDSC)?\nReply: Thank you for your question. All relevant analyses, including MR analysis, gene correlations,\nand stratified LDSC, were adjusted for multiple testing. The corrected P values are indicated in the\nResults. In addition, we have added the following statement to the Quantification and Statistical\nAnalysis section of the Methods:\n“To control for multiple comparisons across genetic correlation and causal analyses (LDSC, S-\nLDSC and MR), we implemented Benjamini-Hochberg false discovery rate (FDR) correction, with\nsignificance defined as P < 0.05.” (Page 27-28, Lines 591-594)\nFDR\n10. What was the criteria used for a trait to be included in the MTAG analysis? E.g. rg threshold, p-\nvalue)\nReply: Thank you for the question. Traits included in the MTAG analysis were selected based on\nsignificant genetic correlations with gastric cancer, defined as LDSC P < 0.05. To clarify this, we\nFDR\nhave added the following statement to the Results section under “Multi-trait analysis of gastric\ncancer and corresponding risk factors”: “Guided by the 25 traits significantly correlated with\ngastric cancer (LDSC P < 0.05), we performed a joint analysis using MTAG to identify SNPs\nFDR\nreaching genome-wide significance (P < 5×10⁻⁸).”(Page 9, Lines 172-174)\n11. Line 117 notes that 12q22 is “previously documented”, is this a typo? Line 182 then calls the\nlocus “novel”\nReply: Thank you for pointing this out. The inconsistency was due to a typo. We have corrected the\ndescription on Page 6, Line 109 to ensure consistency, and the 12q22 locus is now clearly referred\nto as novel throughout the manuscript.\n12. The GG_4 sample in the PCA plot is some distance from the other samples. Is there something\ndifferent about this particular sample?\nReply: We sincerely appreciate the reviewer’s careful inspection and insightful observation. As\nnoted in the manuscript, the GG group exhibited greater dispersion in the PCA plot (Supplementary\nFigure S6A), and GG_4 appears to be an outlier within this group. We have verified that GG_4\nshares the same genotype and was processed under identical experimental and sequencing\nconditions as the other GG samples, with no apparent quality control issues. Given that these are\ncancer tissue samples, such variability is not uncommon and may reflect the inherent complexity\nand heterogeneity of tumor biology. Therefore, we opted to retain GG_4 to capture the full\nbiological spectrum within the GG group.\n13. Were Mendelian randomization analyses performed bidirectionally?\nReply: We thank the reviewer for this important comment. In our initial analysis, we only assessed\nthe causal effects of cardiovascular-related traits on gastric cancer. In response to the reviewer’s\nsuggestion, we have now performed bidirectional Mendelian randomization analyses by\nadditionally testing the potential causal impact of gastric cancer on cardiovascular traits. The\nResults section, specifically the subsection titled “Causal Impact of Cardiovascular-Related Factors\non Gastric Cancer”, has been revised accordingly to reflect these updated findings (Page 7-8, Lines\n136-151, Supplementary Data 1), with detailed results provided in Supplementary Data 1.\n14. Was the baseline model used in the stratified LDSR analysis? Could you please annotate which\nmodel used?\nReply: Thank you for pointing out this out. Yes, the baseline v1.1 model was used in all stratified\nLDSC (S-LDSC) analyses. To clarify this, we have added the following description to the “Cell-type-\nspecific enrichment of SNP heritability” section in the Methods:\n“The baseline v1.1 model, which contains 53 functional annotations including gene regions,\nconserved elements, and regulatory features, was used as the reference in all S-LDSC analyses to\naccount for known sources of heritability.” (Page 19, Lines 400-403)\n15. A table of results for the 15 known gastric cancer loci + plus the new loci identified in this study\nwould be useful – with associated alleles, ORs, CIs, p-values (before/after MTAG)\nReply: Thank you for your suggestion. As requested, we have compiled a table (Table 1)\nsummarizing all currently known gastric cancer loci as well as the newly identified loci in this study,\nincluding associated alleles, ORs, CIs, and p-values (before and after MTAG). We have also added\nthe following description at the end of the “Multi-trait analysis of gastric cancer and corresponding\nrisk factors” section in the Results:\n“In addition, we have summarized the susceptibility variants for gastric cancer identified\nthrough previous GWASs and this study (Table 1).” (Page 10, Lines 196-198)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript includes sufficient details and effectively addresses the previous concerns. It can now be considered for acceptance.", "reviewer_2": "The authors clearly addressed to the comments to the reviewer, and I considered that this paper is acceptable for nature communications.", "reviewer_3": "I appreciate the authors work in addressing my previous comments. I have a few additional queries.\n1. I am happy that the authors have now included limitations with interpretation of MTAG results in the paper and recognize they have now included KoGES as a replication set. However, I’m not clear why they have not assessed the effect of rs12814712 in any other population. The freq of this SNP is reasonable across most populations and should be testable by GWAS (freq A = 0.186 (EAS), 0.092 (EUR) in 1000G, similar in gnomad).\n2. Further details are required to understand/clarify the luciferase promoter reporter gene assays performed. It is not clear to me what pGL3 vector was used in the experiment – was it pGL3-promoter as stated in the manuscript (no supplier provided, no indication of the promoter) or pGL3 promoter basic as stated in the supp figure? Was a minimal promoter cloned into vector, or was it a constitutively active promoter? Or, did the authors clone the VEZT promoter (and if so, what was the sequence)? If the VEZT promoter was cloned, why not the other two genes as well in separate experiments? If these were performed, results should be provided. It is concerning that the activity of MUT in Supp Fig S3A is lower than the pGL3 promoter vector and suggests perhaps a poor plasma prep. For analysis, were the results log transformed to ensure normal distribution for ANOVA?\n3. For the pmirGLO basic experiment, there is no indication that specific 3’UTRs for NR2C1, FGD6 or NDUFA12 were cloned, but just the SNP-region. The SNP region is not in the 3’UTR for any of these genes so it is unclear why this experiment was performed.\n4. I appreciate that the author shave moved the DE analysis to the supplementary note. However, I don’t understand the statement that edgeR cannot adjust for clinical variables. edgeR uses a GLM framework so should support direct covariate adjustment for clinical or experimental variables such as lymph node status and other clinical features, if provided appropriately in the design matrix. I don’t agree that this is analysis is an appropriate way to investigate transcriptional impact of rs12814712 as stated in the supplementary note and wonder if it should be included."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe revised manuscript includes sufficient details and effectively addresses\nthe previous concerns. It can now be considered for acceptance.\nResponse: Thank you for your positive feedback. We’re glad the revised\nmanuscript addresses the concerns and meets your requirements.\nReviewer #2 (Remarks to the Author):\nThe authors clearly addressed to the comments to the reviewer, and I\nconsidered that this paper is acceptable for nature communications.\nResponse: Thank you very much for your valuable feedback. We’re\npleased that the revisions have addressed your concerns.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors work in addressing my previous comments. I have\na few additional queries.\n1. I am happy that the authors have now included limitations with\ninterpretation of MTAG results in the paper and recognize they have now\nincluded KoGES as a replication set. However, I’m not clear why they have\nnot assessed the effect of rs12814712 in any other population. The freq of\nthis SNP is reasonable across most populations and should be testable by\nGWAS (freq A = 0.186 (EAS), 0.092 (EUR) in 1000G, similar in gnomad).\nResponse: Thank you for this insightful comment. To address this point,\nwe examined European-ancestry data from the Million Veteran Program\n(MVP) GWAS on gastric cancer, and found evidence supporting the results\nobserved in East Asian populations. We have incorporated these findings\ninto the main text (Page 10, Lines 195-199, Supplementary Fig. S2B), as\ndetailed below: \"Similarly, in the European-ancestry Million Veteran\nProgram (MVP) cohort (n = 1,152), a nominally significant association was\nobserved with a nearby SNP, rs183275771 (BETA = 0.081, P = 0.005),\nwhich is in linkage disequilibrium with rs12814712 (R² = 0.2;\nSupplementary Fig. S2B).\" Furthermore, the methodological details for\nthis analysis have been included in the Methods section (Page 19, Lines\n396-400): \"To validate our findings, we utilized two independent cohorts:\nthe Korean Genome and Epidemiology Study (KoGES) cohort, comprising\n378 gastric cancer cases and 71,920 controls of East Asian ancestry, and\nthe Million Veteran Program (MVP) cohort, comprising 1,152 cases and\n449,540 controls of predominantly European ancestry. \"\n2. Further details are required to understand/clarify the luciferase promoter\nreporter gene assays performed. It is not clear to me what pGL3 vector was\nused in the experiment – was it pGL3-promoter as stated in the manuscript\n(no supplier provided, no indication of the promoter) or pGL3 promoter\nbasic as stated in the supp figure? Was a minimal promoter cloned into\nvector, or was it a constitutively active promoter? Or, did the authors clone\nthe VEZT promoter (and if so, what was the sequence)? If the VEZT\npromoter was cloned, why not the other two genes as well in separate\nexperiments? If these were performed, results should be provided. It is\nconcerning that the activity of MUT in Supp Fig S3A is lower than the\npGL3 promoter vector and suggests perhaps a poor plasma prep. For\nanalysis, were the results log transformed to ensure normal distribution for\nANOVA?\nResponse: Thank you for your insightful questions and for your careful\nreading of the manuscript. We are pleased to have the opportunity to clarify\nour experimental design and results, and we appreciate your feedback,\nwhich has helped us improve the manuscript. The relevant sections in the\nmanuscript (Results and Methods) have been revised (Page 11-12, Lines\n227-252; Page 24-25, Lines 520-533) to provide greater clarity. Please find\nbelow our point-by-point responses:\nVector Used and Promoter Details:\nWe used the pGL3-Promoter Vector (Cat. E1761, Promega, Madison, WI,\nUSA) for the luciferase reporter assay shown in Panel A. This vector\ncontains an SV40 promoter downstream of the multiple cloning site, which\ndrives the expression of the firefly luciferase reporter gene (Supplementary\nFig. S3A). The genomic fragment containing rs12814712 was cloned\nupstream of this SV40 promoter to test for potential enhancer-like activity.\nAdditionally, we used the pmirGLO vector (Cat. E1330, Promega,\nMadison, WI, USA), which also contains an SV40 promoter and the\nluciferase reporter gene. This vector was used to test the post-\ntranscriptional regulatory activity of the rs12814712 region. Unlike the\npGL3-Promoter vector, the rs12814712 SNP region was cloned\ndownstream of the Luciferase gene to evaluate its effect on luciferase\nactivity. The plasmid maps and experimental details for both vectors are\nprovided in Supplementary Fig. S3A and B. We have now explicitly stated\nthe suppliers and the promoters in the revised Methods section (Page 24-\n25, Lines 520-533).\nClarification on Promoter Cloning:\nWe did not clone the promoters of VEZT, NR2C1, FGD6, or NDUFA12.\nThe purpose of this experiment was to test whether the genomic region\ncontaining rs12814712 itself possesses intrinsic regulatory activity,\nindependent of any specific gene promoter.\nObservation on Mutant (MUT) Activity in pGL3 System:\nThe luciferase activity of the MUT (A) allele in the pGL3-Promoter system\nis lower than that of the empty pGL3-Promoter vector (Supplementary Fig.\nS3C) in HEK293T cells. While the lower activity of the MUT allele is\nobserved in HEK293T cells, it is important to note that in AGS cells, the\nactivity is similar between the MUT and pGL3 promoter vector. We have\nalso thoroughly checked the plasmid preparations and confirmed no issues\nwith the quality of the preparation. Therefore, this difference in activity\nbetween cell lines is likely due to normal biological variation rather than\nany technical issue.\nAt the same time, it is also possible that the MUT (A) allele disrupts or\nimpairs enhancer function, reducing activity back toward (or slightly below)\nthe baseline level of the SV40 promoter. Both explanations are consistent\nwith the observed data and support the interpretation that the A allele\naffects regulatory activity in this genomic region.\nStatistical Analysis and Data Transformation:\nThank you for raising this important statistical point. The raw luciferase\nactivity data (Firefly/Renilla ratio) were not log-transformed prior to\nANOVA. However, upon re-examination of our data distribution, we\nconfirmed that the residuals of the ANOVA model met the assumptions of\nnormality and homogeneity of variances. The statistical significance (P <\n0.05) was robust. Nevertheless, we have now re-analyzed the data using\nlog-transformed values and confirmed that the conclusion remains\nunchanged: the MUT (A) allele significantly reduces luciferase activity\ncompared to the WT (G) allele in the pGL3-SV40 enhancer assay (P <\n0.05). Here, we present the results of reanalysis using log2 transformation\nfor data with large variations (Figure R1). However, after reviewing the\nliterature, we found that the majority of similar studies have used a linear\nformat for presenting luciferase assay data (PMID: 35534546, 39122710),\nand therefore we opted to present the data in a linear format as well.\nFigure R1. (A) Relative luciferase activity (log2-transformed) of the WT\n(G) and MUT (A) alleles in the pGL3-Promoter-based enhancer assay,\nmeasured in HEK293T and AGS cell lines. (B) Relative luciferase activity\n(log2-transformed) of the WT (G) and MUT (A) alleles in the pmirGLO-\nbased 3' context assay, measured in HEK293T and AGS cell lines. Data\nwere analyzed by ANOVA. Significance denoted as ∗∗P< 0.01 and ∗∗∗P<\n0.001. ns, not significant.\n3. For the pmirGLO basic experiment, there is no indication that specific\n3’UTRs for NR2C1, FGD6 or NDUFA12 were cloned, but just the SNP-\nregion. The SNP region is not in the 3’UTR for any of these genes so it is\nunclear why this experiment was performed.\nResponse: Thank you for this insightful comment. The purpose of cloning\nthe fragment into the pmirGLO vector (Cat. E1330, Promega, Madison,\nWI, USA) was to broadly test whether the rs12814712 SNP region could\nexert post-transcriptional regulatory activity when placed in a 3' context\n(Supplementary Fig. S3B). This experiment was not designed to\nspecifically target the 3’UTRs of NR2C1, FGD6, or NDUFA12, but rather\nto explore the potential of this non-coding region to function in post-\ntranscriptional regulation more generally.\nThe results were unequivocally negative, showing no significant difference\nbetween the alleles in this system. This suggests that the rs12814712 locus\ndoes not function as a strong post-transcriptional regulator under the tested\nconditions. We have revised the manuscript accordingly (Page 11-12, Lines\n227-252).\n4. I appreciate that the author shave moved the DE analysis to the\nsupplementary note. However, I don’t understand the statement that edgeR\ncannot adjust for clinical variables. edgeR uses a GLM framework so\nshould support direct covariate adjustment for clinical or experimental\nvariables such as lymph node status and other clinical features, if provided\nappropriately in the design matrix. I don’t agree that this is analysis is an\nappropriate way to investigate transcriptional impact of rs12814712 as\nstated in the supplementary note and wonder if it should be included.\nResponse: Thank you very much for your thoughtful and constructive\nfeedback. We sincerely appreciate your comment regarding the use of\nedgeR for differential expression analysis. Upon reviewing the manuscript,\nwe agree that the statement in the supplementary note suggesting that\nedgeR cannot adjust for clinical variables was incorrect. As you rightly\npointed out, edgeR, which operates within a GLM framework, does indeed\nallow for the inclusion of clinical covariates such as lymph node status and\nother variables when appropriately incorporated into the design matrix.\nRegarding the analysis of the transcriptional impact of rs12814712, we\ngreatly value your perspective. After further reflection on your comments,\nwe agree that the substantial transcriptional variability observed in tumor\nsamples may make it difficult to draw meaningful conclusions regarding\nthe effects of a single genetic variant like rs12814712. In the context of\ntumor data, this type of analysis could be biologically challenging and\npotentially misleading, as the complex tumor microenvironment can\nconfound the interpretation. We now believe that such an analysis may be\nmore appropriate in normal tissue samples, where the transcriptional\nlandscape is less influenced by the extensive changes associated with\ncancer. In light of this, we have decided to remove the analysis from the\nmanuscript to avoid any misinterpretation and ensure the focus remains on\nthe most relevant aspects of our study.\nWe truly appreciate your careful review and valuable suggestions, which\nhave helped us strengthen the clarity and scientific rigor of the manuscript.\nThank you again for your helpful guidance."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I appreciate the authors attention to my concerns, which have now been suitably addressed. I am happy to recommend acceptance of this manuscript."}, "author_response": "Response: Thank you very much for your positive evaluation and for\nrecommending our manuscript for acceptance."}]

Decision letter