Genome-wide association study identifies three PNPLA3/SAMM50 SNPs associated with HCC development in non-viral liver disease

Xia-Rong Liu; Tsai-Hsuan Yang; Tung-Hung Su; Szu-Ching Yin; Yi-Ting Chen; Fen-Fang Chen; See-Tong Pang; Ming-Chih Hou; Yen-Chun Peng; Shun-Fa Yang; Peng-Ju Huang; Sing-Lian Lee; Ming Chen; Chih-Yang Huang; Ya-Hsuan Chang; Hsuan-Yu Chen; Hwai-I Yang; Ming-Lung Yu; Chien-Jen Chen; Jia-Horng Kao; Mei-Hsuan Lee

doi:10.1016/j.jhepr.2025.101673

. 2025 Nov 11;8(2):101673. doi: 10.1016/j.jhepr.2025.101673

Genome-wide association study identifies three PNPLA3/SAMM50 SNPs associated with HCC development in non-viral liver disease

Xia-Rong Liu ^1,^†, Tsai-Hsuan Yang ^1,^†, Tung-Hung Su ^2,³, Szu-Ching Yin ¹, Yi-Ting Chen ¹, Fen-Fang Chen ², See-Tong Pang ^4,⁵, Ming-Chih Hou ⁶, Yen-Chun Peng ^7,^8,⁹, Shun-Fa Yang ^10,¹¹, Peng-Ju Huang ^12,¹³, Sing-Lian Lee ¹⁴, Ming Chen ^15,^16,¹⁷, Chih-Yang Huang ^18,^19,^20,^21,²², Ya-Hsuan Chang ²³, Hsuan-Yu Chen ²⁴, Hwai-I Yang ²⁵, Ming-Lung Yu ^13,^26,²⁷, Chien-Jen Chen ²⁵, Jia-Horng Kao ^2,^3,²⁸, Mei-Hsuan Lee ^1,^27,^29,^⁎

¹Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

²Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan

³Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan

⁴Division of Urology, Department of Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan

⁵School of Medicine, Chang Gung University, Taoyuan, Taiwan

⁶Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

⁷Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan

⁸Department of Internal Medicine, Taichung Veterans General Hospital Chiayi Branch, Chiayi, Taiwan

⁹School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

¹⁰Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan

¹¹Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan

¹²Kaohsiung Municipal Siaogang Hospital, Kaohsiung, Taiwan

¹³College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

¹⁴Division of Endocrinology, Department of Internal Medicine, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan

¹⁵Department of Genomic Medicine and Center for Medical Genetics, Changhua Christian Hospital, Changhua, Taiwan

¹⁶Department of Obstetrics and Gynecology, Changhua Christian Hospital, Changhua, Taiwan

¹⁷Department of Medical Research, Changhua Christian Hospital, Changhua, Taiwan

¹⁸Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan

¹⁹Graduate Institute of Medical Science, China Medical University, Taichung, Taiwan

²⁰Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

²¹Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan

²²Center of General Education, Buddhist Tzu Chi Medical Foundation, Tzu Chi University of Science and Technology, Hualien, Taiwan

²³Institute of Molecular and Genomic Medicine, National Health Research Institute, Taiwan

²⁴Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

²⁵Genomics Research Center, Academia Sinica, Taipei, Taiwan

²⁶Hepatobiliary Division, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

²⁷Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan

²⁸Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, Taiwan

²⁹Advanced Therapeutics Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

^⁎

Corresponding author. Address: Institute of Clinical Medicine, National Yang Ming Chiao Tung University, 155 Li-Nong Street, Section 2, Beitou, Taipei 112, Taiwan; Tel.: +886-2-2826-7248, fax: +886-2-2820-5699. meihlee@nycu.edu.tw

^†

These authors contributed equally to this work

PMCID: PMC12857342 PMID: 41624488

Abstract

Background & Aims

Few genome-wide association studies have examined genetic variants associated with hepatocellular carcinoma (HCC) risk in individuals seronegative for HBsAg and anti-HCV, and the long-term impact of these variants remains uncertain.

Methods

This multi-stage study analyzed adults >30 years old who were seronegative for HBsAg and anti-HCV. In the genome-wide association study discovery phase, 765 HCC cases and 9,949 controls were analyzed for 308,693 SNPs, with significant SNPs confirmed in community-based (171 HCC cases, 684 controls) and hospital-based (470 HCC cases, 5,460 controls) validation sets. A cohort of 67,909 participants, followed from 2012 to 2021, was used to evaluate the long-term HCC risk associated with these variants.

Results

Ten SNPs in PNPLA3/SAMM50 were significantly associated with HCC risk (p <1.62 × 10^-7) and were in high linkage disequilibrium. Three SNPs (rs738409, rs2281135, rs2235776) were replicated and demonstrated strong associations with HCC, independent of steatosis. Over 267,238 person-years of follow-up, 32 new HCC cases occurred, with elevated risks observed in individuals homozygous for the risk genotypes: GG (rs738409), AA (rs2281135), and TT (rs2235776). The adjusted hazard ratios (95% CI) were 3.37 (1.32–8.63), 2.80 (1.06–7.37), and 2.64 (0.91–7.66), respectively. In allelic models, carriers of the risk allele had higher HCC risk, with adjusted hazard ratios (95% CI) of 1.88 (1.14–3.10) for rs738409, 1.68 (1.02–2.78) for rs2281135, and 1.61 (0.98–2.67) for rs2235776.

Conclusions

This study identified PNPLA3/SAMM50 variants significantly associated with long-term HCC risk in individuals without viral hepatitis. These findings highlight their potential utility as biomarkers for HCC risk stratification and underscore the need for further research into underlying mechanisms.

Impact and implications

Variants in the PNPLA3/SAMM50 locus were significantly associated with hepatocellular carcinoma (HCC) risk in individuals seronegative for HBsAg and anti-HCV. The stepwise increase in HCC risk with higher numbers of risk alleles suggests their potential utility as biomarkers to identify high-risk individuals without a history of chronic hepatitis B or C virus infection. These findings provide a foundation for genetic risk stratification and precision prevention strategies in populations where non-viral etiologies of HCC are becoming increasingly important.

Keywords: long-term risk, prospective cohort, biobank, Steatosis, susceptibility

Graphical abstract

Highlights

•
Multi-stage GWAS identified HCC risk variants in HBsAg and anti-HCV seronegative individuals.
•
PNPLA3/SAMM50 variants were associated with HCC, independent of steatotic liver disease.
•
Findings were validated in a hospital case-control and community cohort, showing robustness and relevance.
•
PNPLA3/SAMM50 were associated with long-term HCC risk and may guide non-viral risk stratification.

Introduction

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and a leading cause of cancer-related deaths, with over 50% of global HCC-related deaths occurring in the Asia-Pacific region.¹ Although chronic hepatitis B and C virus (HBV and HCV) infections are major contributors to HCC cases, its development is multifactorial.² With the widespread implementation of hepatitis B vaccination programs and the availability of highly effective antivirals for hepatitis C, the etiology of HCC is evolving. Increasingly, HCC cases are arising in individuals without chronic hepatitis B or C virus infections, presenting a growing public health challenge in the coming years.

Familial clustering of HCC³^,⁴ has suggested a substantial heritable component to its development, indicating that genetic predisposition is a significant determinant. HCC risk may be influenced by multiple genetic variants,⁵ making genome-wide association studies (GWAS) a powerful tool to identify common genetic variants linked to HCC susceptibility. However, most prior GWAS on HCC have primarily focused on populations with chronic HBV⁶^,⁷ or HCV[8], [9], [10], [11] infections. While some GWAS have investigated HCC unrelated to hepatitis B or C, these studies have often concentrated on alcohol-related HCC¹²^,¹³ or lacked clear definitions of HBV and HCV infection status,[14], [15], [16] leading to potential population heterogeneity. Consequently, GWAS specifically examining HCC risk among individuals without chronic HBV or HCV infections are limited, and the long-term impact of identified variants on HCC risk remained unclear.

To address this gap, we conducted a large-scale GWAS to identify genetic variants associated with HCC in individuals seronegative for hepatitis B surface antigen (HBsAg) and antibodies against hepatitis C virus (anti-HCV). We validated the identified single nucleotide polymorphisms (SNPs) in independent community- and hospital-based case-control studies. Finally, we conducted a follow-up study to assess the long-term impact of these SNPs on HCC.

Patients and methods

Study design and flow

This study included only participants aged 30 years or older who were seronegative for HBsAg and anti-HCV. Both case-control and prospective cohort designs were used, as illustrated in Fig. 1.

Fig. 1 — Study design and participant flow diagram.

HCC, hepatocellular carcinoma; HWE, Hardy-Weinberg equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism.

GWAS discovery phase

In the first stage, a GWAS was conducted to compare 765 HCC cases with 9,949 non-HCC controls. The HCC cases were sourced from the Taiwan Liver Cancer Network (TLCN), and the controls were drawn from the Taiwan Biobank.

Validation phase

In the second stage, SNPs identified as significantly associated with HCC risk in the GWAS phase were validated in two independent case-control studies: a community-based set (171 HCC cases and 684 controls) and a hospital-based set from the TPMI (Taiwan Precision Medicine Initiative) (470 HCC cases and 5,460 controls). Finally, we assessed the long-term risk of HCC associated with these SNPs in a prospective cohort of 67,909 Taiwan Biobank participants, all distinct from the controls used in the GWAS phase. This cohort was followed over time to determine HCC risk. Neither patients nor the public were involved in the design, conduct, reporting, or dissemination of this research. The study protocol was approved by the Institutional Review Board of National Yang Ming Chiao Tung University in Taipei, Taiwan.

TLCN

The TLCN is a national biobank comprising biological specimens from patients with HCC treated at major medical centers across Taiwan. Informed consent was obtained for sample collection, epidemiological data, abdominal ultrasound information, and virological analysis. Sample collection and handling were standardized, and all samples were stored at −80 °C until further analysis.⁸^,¹⁷

Taiwan Biobank

The Taiwan Biobank is a government-approved repository that enrolls participants from the general Taiwanese population. Collected data include personal and family history questionnaires, blood and urine biochemical tests, and biological samples such as DNA, plasma, and urine. To date, 189,108 participants have been enrolled, most of whom have undergone whole-genome SNP genotyping. Participants from the Taiwan Biobank were included as controls in the GWAS phase (n = 9,949) and as the prospective cohort in the replication phase (n = 67,909), with no overlap between the two groups.

To ascertain HCC cases and participants' status, we linked computerized data across three nationwide registries maintained by Taiwan's Ministry of Health and Welfare, covering the period from January 1, 2012, to December 31, 2021. HCC cases were identified using ICD codes 155 (ICD-9) and C22 (ICD-10) from the National Cancer Registry and confirmed through histopathology, imaging, or elevated serum alpha-fetoprotein (AFP) levels.

Community-based case-control validation set

The community-based cohort, previously described, comprised 18,541 individuals seronegative for HBsAg and anti-HCV from a baseline pool of 23,820 participants across seven Taiwanese townships.¹⁸^,¹⁹ Blood samples were collected, processed, and stored at −70 °C for analysis. During follow-up, 171 cases of HCC were identified, and control groups matched by age and sex in a 1:4 ratio were randomly selected. The SNPs identified in the GWAS phase were genotyped using the TaqMan genotyping assay (Applied Biosystems).

Hospital-based case-control validation set

The TPMI is a large-scale genetic project involving volunteers from 16 medical centers across Taiwan.²⁰ For this study, 470 HCC cases and 5,460 controls with sufficient clinical and genotypic data were included, with information on age, sex, BMI, and serum alanine aminotransferase (ALT) levels. Diabetes was identified using ICD codes 250 (ICD-9) and E11 (ICD-10). SNP genotyping was performed using an adapted SNP array from the Taiwan Biobank 2.0 Array Plate.

SNP genotyping and quality control

Genomic DNA was extracted from peripheral blood leukocytes using standard protocols. Genotyping for the GWAS discovery phase used the Axiom™ Genome-Wide TWB 2.0 Array (Thermo Fisher Scientific). Quality control (QC) steps removed SNPs with call rates <97%, minor allele frequencies <1%, or those violating Hardy-Weinberg equilibrium (p <10^-4). Samples with call rates <97%, discrepancies between self-reported and genetically determined sex, and heterozygosity rates more than three standard deviations from the mean were excluded. Identity-by-descent analysis removed cryptic relatedness between subject pairs with identity-by-descent scores >0.1875. After QC, 308,693 SNPs remained for analysis.

Statistical methods

Principal component analysis (PCA) was conducted to assess population structure, with the first two principal components plotted. Quantile-quantile (Q-Q) plots and the genomic inflation factor (λ) were used to assess the degree of population stratification. SNPs surpassing the Bonferroni-corrected significance threshold of 1.62 × 10^-7 were considered genome-wide significant. A Manhattan plot depicted the -log₁₀ (p values) for all SNPs passing QC. Further analysis and visualization were conducted using R and PLINK, with LocusZoom and HAPLOVIEW employed for regional association and linkage disequilibrium analysis. We selected three highly linked tag SNPs for validation analyses. Logistic regression models, adjusting for age, sex, BMI, serum ALT and AFP levels, smoking, and alcohol use, estimated odds ratios (ORs), and 95% CIs. Adjustments were made for potential population stratification using PCA. Ultrasound data provided steatosis status for all study participants (765 HCC and 9,949 controls) in the GWAS phase, allowing for stratified SNP-HCC association analysis by steatosis status. In the validation phase, BMI was used as a steatosis proxy in the hospital cohort.

In the prospective analysis, Cox proportional hazard models were used to evaluate the association between SNP genotypes and HCC risk over time. Kaplan-Meier survival curves and log-rank tests assessed cumulative HCC incidence across genotype groups. Hazard ratios (HRs) with 95% CIs quantified the associations between SNPs and HCC risk. Additionally, we integrated the three most significant SNPs identified and calculated the total number of risk alleles for each participant to estimate their cumulative risk for HCC. The Cochran-Armitage trend test was used to examine the dose-response relationship between the number of risk alleles and HCC risk across different genotypes. The proportional hazards assumption of the Cox models was tested and confirmed not to be violated. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC).

Results

GWAS of HCC in individuals seronegative for HBsAg and anti-HCV

In the GWAS discovery phase, HCC cases were older, more frequently male, and had higher BMI, serum ALT, and serum AFP levels than controls (<0.05). HCC cases also had a higher prevalence of smoking and alcohol consumption compared to controls (Table S1). Following quality control, 308,693 SNPs were retained for analysis across 765 HCC cases and 9,949 non-HCC controls. The Q-Q plot comparing observed vs. expected p values showed no early deviation, and the genomic inflation factor (λ = 1.046) suggested minimal inflation, indicating limited population stratification. PCA of the first two components further confirmed tight clustering of cases and controls, mitigating concerns about confounding from population structure. PCA of the first two components revealed that cases and controls were tightly clustered, further confirming that population structure was unlikely to confound the findings.

The Manhattan plot, using an additive genetic model, identified a cluster of ten genome-wide significant SNPs on chromosome 22, with p values below the Bonferroni-corrected significance threshold of 1.62 × 10^-7 (Fig. 2). Importantly, all ten SNPs identified on chromosome 22 remained significant under the conventional genome-wide threshold of p <5 × 10^-8⁴¹^,⁴² (Fig. 2), with p values ranging from 2.36 × 10^-8 to 6.27 × 10^-9. This confirms that the associations are robust even under the more stringent criterion. To evaluate the association between genotype frequencies and HCC risk, individuals with complete genotype data for all ten genome-wide significant SNPs on chromosome 22 were included in the final analysis, comprising 765 HCC cases and 9,949 controls. Genotype frequencies were compared between cases and controls (Table S2).

Fig. 2 — Manhattan plot of HCC-associated SNPs in individuals seronegative for HBsAg and anti-HCV.

p values were estimated using logistic regression models. The red line represents the conventional genome-wide significance threshold (*p =* 5×10^-8).

HCC, hepatocellular carcinoma; SNP, single nucleotide polymorphism.

HCC risk associated with PNPLA3/SAMM50 genotypes

Among the ten significant SNPs identified on chromosome 22, three – rs738409, rs2281135, and rs2253776 in the PNPLA3/SAMM50 gene – were selected for further analysis based on linkage disequilibrium patterns observed in HaploView (Fig. 2). The linkage disequilibrium plot demonstrated that these SNPs are in strong linkage disequilibrium (r² >0.8) with neighboring SNPs, suggesting that they belong to a risk locus spanning PNPLA3 and SAMM50.

All three selected SNPs exhibited similar trends in their association with HCC risk. In the GWAS discovery phase, each SNP showed a significant association with increased HCC risk across various genotypes in both crude and adjusted models (Table 1). For rs738409, the heterozygous (CG) and homozygous (GG) genotypes were linked to increased HCC risk relative to the reference (CC) genotype, with adjusted ORs of 1.09 (95% CI 0.86-1.39) and 1.89 (95% CI 1.41-2.55), respectively (p_trend <0.0001). Similarly, for rs2281135, the GA and AA genotypes demonstrated a dose-dependent increase in risk compared to the GG genotype, with adjusted ORs (95% CI) of 1.14 (0.90-1.45) and 1.81 (1.34-2.43) (p_trend = 0.0003). Likewise, for rs2253776 in SAMM50, the TC and TT genotypes indicated high risk compared to the AA genotype, with adjusted ORs (95% CI) of 1.16 (0.92-1.48) and 1.73 (1.28-2.33) (p_trend = 0.0008). Stratification analyses by ultrasound-determined steatosis status, given previous associations of these SNPs with steatotic liver, revealed consistent SNP-HCC associations regardless of steatosis status (Table S3).

Table 1.

Risk of HCC associated with SNPs in GWAS discovery and validation sets.

SNP	GWAS discovery phase		GWAS validation phase
	Study participants (765 HCC cases, 9,949 controls)		Community-based case-control study (171 HCC cases, 684 controls)		Hospital-based case-control study (470 HCC cases, 5,460 controls)
	Crude OR (95% CI)	Multivariate adjusted OR (95% CI)^†	Crude OR (95% CI)	Multivariate adjusted OR (95% CI)^§	Crude OR (95% CI)	Multivariate adjusted OR (95% CI)^¶
rs738409
CC	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
CG	1.12 (0.94-1.32)	1.09 (0.86-1.39)	1.38 (0.94-2.02)	1.34 (0.90-1.99)	1.07 (0.85-1.33)	1.08 (0.87-1.36)
GG	1.94 (1.58-2.37)	1.89 (1.41-2.55)	1.46 (0.89-2.41)	1.38 (0.82-2.33)	1.88 (1.46-2.42)	1.85 (1.43-2.40)
	p_trend <0.0001	p_trend <0.0001	p_trend = 0.0888	p_trend = 0.154	p_trend <0.0001	p_trend <0.0001
rs2281135
GG	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
GA	1.13 (0.95-1.34)	1.14 (0.90-1.45)	1.39 (0.95-2.04)	1.34 (0.90-1.99)	1.05 (0.84-1.32)	1.06 (0.84-1.33)
AA	1.88 (1.54-2.31)	1.81 (1.34-2.43)	1.33 (0.80-2.20)	1.26(0.75-2.12)	1.76 (1.36-2.26)	1.73 (1.34-2.25)
	p_trend <0.0001	p_trend = 0.0003	p_trend = 0.1672	p_trend = 0.266	p_trend <0.0001	p_trend <0.0001
rs2235776
CC	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
TC	1.16 (0.98-1.38)	1.16 (0.92-1.48)	1.62 (1.10-2.39)	1.54 (1.03-2.30)	1.14 (0.92-1.43)	1.15 (0.92-1.44)
TT	1.86 (1.51-2.29)	1.73 (1.28-2.33)	1.60 (0.95-2.70)	1.43 (0.83-2.47)	1.61 (1.24-2.09)	1.60 (1.23-2.09)
	p_trend <0.0001	p_trend = 0.0008	p_trend = 0.0332	p_trend = 0.099	p_trend <0.0001	p_trend <0.0001

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p_trend was calculate using the Cochran–Armitage test for trend. AFP, alpha-fetoprotein; ALT, alanine aminotransferase; HCC, hepatocellular carcinoma; OR, odds ratio.

^†

Adjusted for the first 20 principal components, age, sex, BMI, cigarette smoking, alcohol consumption, serum ALT levels, and serum AFP levels.

^§

Adjusted for age, sex, BMI, cigarette smoking, alcohol consumption, and serum ALT levels.

^¶

Adjusted for age, sex, BMI, serum ALT levels, and diabetes.

To assess whether the identified SNPs were associated with HCC independent of cirrhosis, we repeated the analyses restricted to participants with cirrhosis (Table S4). The associations remained significant (p <0.0001), suggesting that these variants represent HCC susceptibility loci rather than cirrhosis-related signals.

Cross-sectional validations of HCC-associated SNPs

Baseline characteristics of study participants in the community-based and hospital-based case-control validation sets are summarized in Table S4. In both sets, HCC cases had higher serum ALT levels and a greater history of smoking and alcohol use than controls (Table S4). We examined the magnitude of associations for the three target SNPs. The three SNPs in the hospital-based validation set showed significant associations with HCC, with adjusted ORs ranging from 1.60 to 1.85 for homozygous (Table 1). Stratified analyses, using BMI as a proxy for steatosis, revealed consistent associations between these SNPs and HCC in individuals with both BMI <24 kg/m² and BMI ≥24 kg/m² (Table S5). The associations remained significant across BMI strata, suggesting that the SNPs are associated with long-term HCC risk, independent of BMI. Although associations in the community-based validation set did not reach statistical significance, genotype patterns were consistent with those observed in the hospital-based set.

Long-term risk of HCC: Cumulative HCC risk by genotypes

To assess the long-term risk of HCC associated with three SNPs, we conducted a prospective validation study using data from the Taiwan Biobank. The baseline characteristics of the study population are displayed in Table S7. As illustrated in Fig. 3, the cumulative risks of HCC were stratified by the genotypes of rs738409, rs2281135, and rs2235776. Significant differences in the genotypes of these individual SNPs were observed in relation to HCC risk (p <0.001). In the GWAS discovery phase, we also observed a borderline cluster of SNPs in the HLA region. To further investigate this signal, we performed HLA imputation and examined the associations between imputed HLA genotypes and HCC risk; however, no significant associations were identified in the prospective validation analysis (Table S8).

Fig. 3 — Cumulative HCC risk stratified by genotypes in a prospective cohort (N = 67,909).

(A) rs738409. (B) rs2281135. (C) rs2235776. The overall p value was calculated using the log-rank test. HCC, hepatocellular carcinoma.

HCC risk stratified by genotypes after follow-up

After 267,238 person-years of follow-up, 32 newly developed HCC were identified among the 67,909 study participants. As shown in Table 2, participants with homozygous genotype GG for rs738409, AA for rs2281135, and TT for rs2235776 had significantly increased risks of HCC, with adjusted HRs of 3.37 (95% CI 1.32–8.63), 2.80 (95% CI 1.06–7.37), and 2.64 (95% CI 0.91–7.66), respectively, compared to the reference genotypes. As shown in Table 3, allelic models further corroborated these findings, indicating increased HCC risk for individuals carrying risk alleles. The adjusted HRs (95% CI) were 1.88 (1.14–3.10) for the G allele of rs738409 compared to C, 1.68 (1.02–2.78) for the A allele of rs2281135 compared to G, and 1.61 (0.98–2.67) for the T allele of rs2235776 compared to C. Among the three SNPs tested, the rs738409 risk allele exhibited the strongest associations with HCC risk.

Table 2.

Long-term HCC risk by genotypes of validated SNPs in a prospective cohort (N = 67,909).

SNP	Crude HR (95% CI)	Age-sex adjusted HR (95% CI)	Multivariate adjusted HR (95% CI)^¶
rs738409
CC	1.00 (reference)	1.00 (reference)	1.00 (reference)
GC	1.29 (0.54-3.11)	1.30 (0.54-3.13)	1.39 (0.57-3.38)
GG	3.53 (1.42-8.77)	3.70 (1.49-9.19)	3.37 (1.32-8.63)
	p_trend = 0.0087	p_trend = 0.0067	p_trend = 0.0144
rs2281135
GG	1.00 (reference)	1.00 (reference)	1.00 (reference)
AG	1.33 (0.56-3.16)	1.33 (0.56-3.18)	1.42 (0.59-3.44)
AA	2.91 (1.15-7.37)	3.04 (1.20-7.70)	2.80 (1.06-7.37)
	p_trend = 0.0291	p_trend = 0.0236	p_trend = 0.0423
rs2235776
CC	1.00 (reference)	1.00 (reference)	1.00 (reference)
TC	1.87 (0.78-4.51)	1.89 (0.79-4.56)	1.99 (0.82-4.87)
TT	2.75 (1.00-7.57)	2.91 (1.06-8.02)	2.64 (0.91-7.66)
	p_trend = 0.0461	p_trend = 0.0351	p_trend = 0.0600

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p_trend was calculated using the Cochran-Armitage test for trend. AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; HbA1c, glycated hemoglobin; HCC, hepatocellular carcinoma; HR, hazard ratio.

^¶

Adjusted for age, sex, BMI, cigarette smoking, alcohol consumption, serum ALT levels, serum AST levels, serum AFP levels, and HbA1c.

Table 3.

Allelic model for SNPs associated with HCC in a prospective cohort (N = 67,909).

SNP	AF in controls %	AF in cases %	Crude HR (95% CI)	p value	Age-sex adjusted HR (95% CI)	p value	Multivariate adjusted HR^¶ (95% CI)	p value
rs738409
C	61.4	45.3	1.00		1.00		1.00
G	38.6	54.7	1.93 (1.18-3.16)	0.0089	1.97 (1.20-3.22)	0.0071	1.88 (1.14-3.10)	0.0139
rs2281135
G	60.3	46.9	1.00		1.00		1.00
A	39.7	53.6	1.73 (1.06-2.82)	0.0294	1.76 (1.08-2.87)	0.0244	1.68 (1.02-2.78)	0.0422
rs2235776
C	60.7	48.4	1.00		1.00		1.00
T	39.3	51.6	1.65 (1.01-2.69)	0.0459	1.69 (1.04-2.76)	0.0361	1.61 (0.98-2.67)	0.0616

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HRs and p values were determined using Cox regression models. AF, allele frequency; ALT, alanine aminotransferase; AST, aspartate aminotransferase; HbA1c, glycated hemoglobin; HCC, hepatocellular carcinoma; HR, hazard ratio.

^¶

Adjusted for age, sex, BMI, cigarette smoking, alcohol consumption, serum ALT levels, serum AST levels, serum AFP levels, and HbA1c.

Discussion

We employed a multi-stage approach – starting with a GWAS for discovery, followed by validation in independent case-control sets and a prospective cohort – to enhance the reliability and reproducibility of our findings. This layered design helps minimize false positives and enhances the generalizability of the results. We identified a cluster of ten SNPs on chromosome 22, which are in high linkage disequilibrium, reinforcing the association between this region and HCC risk. Additionally, variants in PNPLA3/SAMM50 were linked to long-term HCC risk in individuals who were seronegative for both HBsAg and anti-HCV.

This study stratified participants based on the presence of steatotic liver disease, which had a prevalence of nearly 35% in the population.²¹ Variants in PNPLA3/SAMM50 were associated with steatotic liver susceptibility, suggesting that steatotic liver disease is an intermediate pathway in HCC development.²² Mediation analyses would help determine whether these variants independently contribute to HCC risk or if steatotic liver mediates this relationship.²³ However, in both the GWAS discovery set (with ultrasound-based fatty liver data) and the hospital validation set (using obesity as a surrogate), we found that PNPLA3/SAMM50 variants were significantly associated with HCC in participants with and without steatotic liver. These results indicate that the observed associations are not fully explained by fatty liver status. Notably, recent studies indicate that the PNPLA3 variant rs738409 influences fibrosis independently of steatotic liver status,²³^,²⁴ suggesting that pathways beyond lipid accumulation may be involved in fibrogenesis and HCC risk.²³

Our findings reveal that PNPLA3/SAMM50 variants are associated with HCC risk across both steatotic and non-steatotic liver subgroups, underscoring potential pleiotropic effects²⁵ where these genes may contribute to HCC through diverse biological pathways beyond steatotic liver. By including participants from community-based cohorts, we were able to examine associations in individuals without steatotic liver disease, who might not otherwise be identified in clinical settings. These results underscore the importance of investigating these variants in broader populations to deepen our understanding of HCC risk factors. This approach may lay the groundwork for future multi-factorial risk prediction models in populations seronegative for HBsAg and anti-HCV.

As a non-synonymous SNP, rs738409 in the PNPLA3 gene results in an isoleucine-to-methionine substitution (I148M).²⁶ This variant has been previously identified as a major susceptibility factor for metabolic dysfunction-associated steatotic liver disease (MASLD) in prior GWAS.[27], [28], [29], [30] The mutant protein exhibits reduced lipolytic activity, leading to increased triglyceride accumulation in liver cells. PNPLA3 encodes patatin-like phospholipase domain-containing protein 3, primarily expressed in adipose tissue and the liver, where it regulates lipid droplet turnover.³¹ It is considered the largest contributor to genetic variability of hepatic fat accumulation in the general population.²⁷ Notably, a GWAS on patients with histologically confirmed MASLD identified PNPLA3 as a marker for fibrosis severity,²⁹ suggesting a possible role in HCC development. Furthermore, the variant has been associated with cirrhosis risk,³² highlighting its potential involvement in liver cancer progression. Beyond fibrosis, emerging evidence suggests more direct oncogenic roles.³⁹ It demonstrated that lipid stimulation of hepatocytes carrying the 148M variant induces pro-inflammatory and pro-angiogenic chemokines, including IL-8 and CXCL1, which promote migration and angiogenesis, thereby linking PNPLA3 directly to tumor-promoting microenvironments. Consistent with this, our analysis showed that PNPLA3 variants remained associated with HCC risk even among patients with cirrhosis, implying that their effect on HCC is not fully mediated by cirrhosis and may reflect pleiotropic mechanisms beyond fibrosis progression.

The precise relationship between PNPLA3 variants and HCC risk remained incompletely understood. A meta-analysis of European patients with cirrhosis identified a significant association between the rs738409 variant and HCC, particularly in patients with alcohol-related liver disease.³³ Similarly, one case-control study reported that each copy of the rs738409 G allele was associated with an additive increase in HCC risk, with GG homozygotes exhibiting a fivefold higher risk.²² Longitudinal studies specifically assessing the variant’s contribution to HCC remained limited,[34], [35], [36] likely due to the relatively low HCC risk in patients with MASLD. Most studies aggregate multiple advanced liver-related outcomes, such as liver-specific death, transplantation, or decompensation, rather than focusing solely on incident HCC.[34], [35], [36] A recent prospective study found that PNPLA3 variants were associated with an increased risk of major adverse liver outcomes in patients with steatosis, especially those with advanced fibrosis.³⁴ While another prospective study found no definitive impact on HCC due to the small number of cases, it highlighted that women aged 50 or older with MASLD carrying the rs738409 GG genotype were at high risk of severe liver outcomes.³⁶ These findings are consistent with our results, where rs738409 and rs2281135 variants in PNPLA3 were associated with HCC in a dose-dependent manner based on the number of risk alleles carried. These findings suggest that the variant may serve as a genetic marker for identifying high-risk individuals, particularly those with advanced steatotic liver disease. Notably, a study of patients with alcohol-related cirrhosis reported that over 90% of individuals who carried SAMM50 variants also carried the PNPLA3 I148M allele, indicating that the PNPLA3 I148M locus is a stronger predictor of alcohol-related HCC risk than genetic variation in SAMM50.⁴⁰ Because these variants are in strong linkage disequilibrium, we examined their associations through both allelic and genotypic models, which consistently demonstrated that increasing numbers of risk alleles corresponded to a stepwise increase in HCC risk. Taken together, these findings underscore the robustness of the PNPLA3/SAMM50 locus as a susceptibility signal for HCC.

We also identified an association between the rs2235776 variant in the SAMM50 gene and HCC risk. This SNP is in strong linkage disequilibrium with PNPLA3/SAMM50, which are often described as part of a single genomic block influencing MASLD susceptibility.²⁸ Beyond PNPLA3, SAMM50 variants have been linked to HCC risk in recent GWAS.¹⁴^,¹⁶ SAMM50 encodes Sam50, a protein involved in the mitochondrial outer membrane, responsible for inserting β-barrel proteins. Sam50 has been implicated in mitochondrial dysfunction, and its loss in hepatocytes induces mitochondrial membrane remodeling, leading to mitochondrial DNA release, liver inflammation, and subsequent liver injury.³⁷

In comparison to previous GWAS on genetic variants and HCC risk,¹⁴^,¹⁶ our study uniquely focuses on individuals seronegative for HBsAg and anti-HCV, isolating risk factors not directly attributable to chronic hepatitis B or C infections in an Asian population undergoing a shift from viral to other etiologies. Previous studies,¹⁴^,¹⁶ such as those conducted in the US, included both hepatitis-related and unrelated HCC cases, potentially diluting associations specific to HCC risk outside of chronic hepatitis infections. Unlike prior studies, our multi-stage GWAS with prospective follow-up in a general population cohort allows us to assess cumulative HCC risk over time. Adjustments for BMI and lifestyle factors enhance our findings’ relevance to populations facing rising metabolic HCC risks, underscoring the need for region-specific longitudinal studies to clarify evolving HCC risk profiles.

To our knowledge, this is the largest GWAS focused on individuals seronegative for both HBsAg and anti-HCV. We rigorously determined the virological status of all participants through comprehensive virological testing, ensuring a well-defined population. The study populations were homogeneous, consisting of Taiwanese individuals, which minimized confounding due to genetic diversity. Given that SNP-phenotype associations are often modest, our large sample size ensured adequate statistical power to detect significant associations between SNPs and HCC risk. By leveraging large biobanks and national registries, we obtained robust estimates of genetic associations. Linking to Taiwan’s national cancer and death registries allowed us to comprehensively and accurately identify HCC cases. With a sufficiently long follow-up period, we were able to assess the impact of the identified SNPs on HCC occurrence, which has rarely been explored in previous studies. Our community-based cohort allowed for the assessment of SNP-HCC associations beyond individuals with advanced liver disease,³⁵ thereby enhancing the clinical relevance of our findings for screening relatively healthy individuals for early diagnosis biomarkers.

However, there are some limitations to consider. HBV is endemic in Taiwan, with most chronic carriers acquiring the virus via vertical transmission. Many individuals in this population are positive for anti-HB core antibodies,³⁸ which may influence the findings. In our GWAS, we also observed a borderline cluster of SNPs in the HLA region, suggesting a potential residual viral or immune effect. This may reflect occult HBV infection, although its prevalence in the general population is relatively low. To more definitively evaluate the role of HLA variants in HCC unrelated to chronic HBV or HCV infection, larger studies with greater statistical power will be needed. While our results are relevant to populations in Asia where HBV is prevalent, generalizability to regions with different viral epidemiology needs further evaluation. Additionally, we lacked image-based data to diagnose steatotic liver disease in this cohort, which limited our ability to stratify analyses by steatotic liver status.

In conclusion, our study identified PNPLA3/SAMM50 variants associated with increased HCC risk among individuals seronegative for HBsAg and anti-HCV. These variants were linked to a higher long-term HCC risk, with individuals carrying multiple risk alleles showing notably elevated risk. These findings offer valuable insights into potential biomarkers for HCC risk stratification, and further investigation is warranted to elucidate the underlying mechanisms of hepatocarcinogenesis associated with these variants.

Abbreviations

AFP, alpha-fetoprotein; ALT, alanine aminotransferase; GWAS, genome-wide association study; HCC, hepatocellular carcinoma; HBV, hepatitis B virus; HCV, hepatitis C virus; MASLD, metabolic dysfunction-associated steatotic liver disease; OR, odds ratio; PCA, principal component analysis; PNPLA3, patatin-like phospholipase domain-containing protein 3; SAMM50, sorting and assembly machinery component 50 homolog; TLCN, Taiwan Liver Cancer Network.

Authors’ contributions

Study concept and design: Mei-Hsuan Lee. Data acquisition: Mei-Hsuan Lee. Data analysis and interpretation: Tsai-Hsuan Yang, Xia-Rong Liu, Tung-Hung Su, Szu-Ching Yin, Fen-Fang Chen, and Mei-Hsuan Lee. Coordination and oversight of sample collection for the TPMI biobank: See-Tong Pang, Ming-Chih Hou, Yen-Chun Peng, Shun-Fa Yang, Peng-Ju Huang, Sing-Lian Lee, Ming Chen, and Chih-Yang Huang. Drafting of the manuscript: Tsai-Hsuan Yang, Xia-Rong Liu, Szu-Ching Yin, and Mei-Hsuan Lee. Critical revision of the manuscript for important intellectual content: Tsai-Hsuan Yang, Xia-Rong Liu, Tung-Hung Su, Szu-Ching Yin, Yi-Ting Chen, Fen-Fang Chen, See-Tong Pang, Ming-Chih Hou, Yen-Chun Peng, Shun-Fa Yang, Peng-Ju Huang, Sing-Lian Lee, Ming Chen, Chih-Yang Huang, Ya-Hsuan Chang, Hsuan-Yu Chen, Hwai-I Yang, Ming-Lung Yu, Chien-Jen Chen, Jia-Horng Kao, and Mei-Hsuan Lee. Funding acquisition and study supervision: Mei-Hsuan Lee.

Data availability

All or part of the data used in this research were authorized by and received from Taiwan Biobank (Authorization Code: TWBR11104-1) and Health and Welfare Data Science Center Database, Ministry of Health and Welfare (NHIRD_MOHW: H112006). Dr. Mei-Hsuan Lee applied for all data use. Other researchers may request the materials used through collaboration.

Financial support

This study was supported by the National Science and Technology Council, Taipei, Taiwan (grant: 112-2628-B-A49-007 and 113-2628-B-A49-012), the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and by the National Health Research Institute, Chunan, Taiwan (grant: NHRI-EX112-11117PI). None of the funding organizations contributed to the study design and delivery, data collection, management, analysis, and interpretation, data preparation and review, or manuscript approval.

Conflict of interest

The authors have no conflicts of interest to disclose.

Please refer to the accompanying ICMJE disclosure forms for further details.

Acknowledgments

We gratefully acknowledge the National Health Research Institute Biobank and its cooperative institutions for providing biological specimens and related clinical data (all de-identified) for this research. The National Health Research Institute Biobank is supported by grants from the Ministry of Health and Welfare, the Ministry of Science and Technology, and the National Health Research Institutes, Taiwan. We also extend our gratitude to all participants and investigators of the Taiwan Precision Medicine Initiative. This study was funded by Academia Sinica (Grant Nos. 40-05-GMM, AS-GC-110-MD02, and 236e-1100202) and the National Development Fund, Executive Yuan (Grant No. NSTC 111-3114-Y-001-001).

Footnotes

Author names in bold designate shared co-first authorship

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhepr.2025.101673.

Supplementary data

The following are the Supplementary data to this article:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

[bib1] 1.Mak L.Y., Liu K., Chirapongsathorn S., et al. Liver diseases and hepatocellular carcinoma in the Asia-Pacific region: burden, trends, challenges and future directions. Nat Rev Gastroenterol Hepatol. 2024;21(12):834–851. doi: 10.1038/s41575-024-00967-4. [DOI] [PubMed] [Google Scholar]

[bib2] 2.Vogel A., Meyer T., Sapisochin G., et al. Hepatocellular carcinoma. Lancet. 2022;400:1345–1362. doi: 10.1016/S0140-6736(22)01200-4. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Hassan M.M., Spitz M.R., Thomas M.B., et al. The association of family history of liver cancer with hepatocellular carcinoma: a case-control study in the United States. J Hepatol. 2009;50:334–341. doi: 10.1016/j.jhep.2008.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib4] 4.Turati F., Edefonti V., Talamini R., et al. Family history of liver cancer and hepatocellular carcinoma. Hepatology. 2012;55:1416–1425. doi: 10.1002/hep.24794. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Yang T.H., Chan C., Yang P.J., et al. Genetic susceptibility to hepatocellular carcinoma in patients with chronic hepatitis virus infection. Viruses. 2023;15 doi: 10.3390/v15020559. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Jiang D.K., Sun J., Cao G., et al. Genetic variants in STAT4 and HLA-DQ genes confer risk of hepatitis B virus-related hepatocellular carcinoma. Nat Genet. 2013;45:72–75. doi: 10.1038/ng.2483. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Zhang H., Zhai Y., Hu Z., et al. Genome-wide association study identifies 1p36.22 as a new susceptibility locus for hepatocellular carcinoma in chronic hepatitis B virus carriers. Nat Genet. 2010;42:755–758. doi: 10.1038/ng.638. [DOI] [PubMed] [Google Scholar]

[bib8] 8.Lee M.H., Huang Y.H., Chen H.Y., et al. Human leukocyte antigen variants and risk of hepatocellular carcinoma modified by hepatitis C virus genotypes: a genome-wide association study. Hepatology. 2018;67:651–661. doi: 10.1002/hep.29531. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Kumar V., Kato N., Urabe Y., et al. Genome-wide association study identifies a susceptibility locus for HCV-induced hepatocellular carcinoma. Nat Genet. 2011;43:455–458. doi: 10.1038/ng.809. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Matsuura K., Sawai H., Ikeo K., et al. Genome-wide association study identifies TLL1 variant associated with development of hepatocellular carcinoma after eradication of hepatitis C virus infection. Gastroenterology. 2017;152:1383–1394. doi: 10.1053/j.gastro.2017.01.041. [DOI] [PubMed] [Google Scholar]

[bib11] 11.Miki D., Ochi H., Hayes C.N., et al. Variation in the DEPDC5 locus is associated with progression to hepatocellular carcinoma in chronic hepatitis C virus carriers. Nat Genet. 2011;43:797–800. doi: 10.1038/ng.876. [DOI] [PubMed] [Google Scholar]

[bib12] 12.Kim K.Y., Kim J.O., Kim Y.S., et al. Genome-wide association of individual vulnerability with alcohol-associated liver disease: a Korean genome and epidemiology study. Hepatology. 2022;75:391–402. doi: 10.1002/hep.32115. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib13] 13.Trepo E., Caruso S., Yang J., et al. Common genetic variation in alcohol-related hepatocellular carcinoma: a case-control genome-wide association study. Lancet Oncol. 2022;23:161–171. doi: 10.1016/S1470-2045(21)00603-3. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Genome-wide association study identifies three PNPLA3/SAMM50 SNPs associated with HCC development in non-viral liver disease

Xia-Rong Liu

Tsai-Hsuan Yang

Tung-Hung Su

Szu-Ching Yin

Yi-Ting Chen

Fen-Fang Chen

See-Tong Pang

Ming-Chih Hou

Yen-Chun Peng

Shun-Fa Yang

Peng-Ju Huang

Sing-Lian Lee

Ming Chen

Chih-Yang Huang

Ya-Hsuan Chang

Hsuan-Yu Chen

Hwai-I Yang

Ming-Lung Yu

Chien-Jen Chen

Jia-Horng Kao

Mei-Hsuan Lee

Abstract

Background & Aims

Methods

Results

Conclusions

Impact and implications

Graphical abstract

Highlights

Introduction

Patients and methods

Study design and flow

Fig. 1.

GWAS discovery phase

Validation phase

TLCN

Taiwan Biobank

Community-based case-control validation set

Hospital-based case-control validation set

SNP genotyping and quality control

Statistical methods

Results

GWAS of HCC in individuals seronegative for HBsAg and anti-HCV

Fig. 2.

HCC risk associated with PNPLA3/SAMM50 genotypes

Table 1.

Cross-sectional validations of HCC-associated SNPs

Long-term risk of HCC: Cumulative HCC risk by genotypes

Fig. 3.

HCC risk stratified by genotypes after follow-up

Table 2.

Table 3.

Discussion

Abbreviations

Authors’ contributions

Data availability

Financial support

Conflict of interest

Acknowledgments

Footnotes

Supplementary data

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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