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Meta-Analysis
. 2024 Jul 1;80(1):87-101.
doi: 10.1097/HEP.0000000000000800. Epub 2024 Feb 20.

Genome-wide association study identifies high-impact susceptibility loci for HCC in North America

Affiliations
Meta-Analysis

Genome-wide association study identifies high-impact susceptibility loci for HCC in North America

Manal M Hassan et al. Hepatology. .

Abstract

Background and aims: Despite the substantial impact of environmental factors, individuals with a family history of liver cancer have an increased risk for HCC. However, genetic factors have not been studied systematically by genome-wide approaches in large numbers of individuals from European descent populations (EDP).

Approach and results: We conducted a 2-stage genome-wide association study (GWAS) on HCC not affected by HBV infections. A total of 1872 HCC cases and 2907 controls were included in the discovery stage, and 1200 HCC cases and 1832 controls in the validation. We analyzed the discovery and validation samples separately and then conducted a meta-analysis. All analyses were conducted in the presence and absence of HCV. The liability-scale heritability was 24.4% for overall HCC. Five regions with significant ORs (95% CI) were identified for nonviral HCC: 3p22.1, MOBP , rs9842969, (0.51, [0.40-0.65]); 5p15.33, TERT , rs2242652, (0.70, (0.62-0.79]); 19q13.11, TM6SF2 , rs58542926, (1.49, [1.29-1.72]); 19p13.11 MAU2 , rs58489806, (1.53, (1.33-1.75]); and 22q13.31, PNPLA3 , rs738409, (1.66, [1.51-1.83]). One region was identified for HCV-induced HCC: 6p21.31, human leukocyte antigen DQ beta 1, rs9275224, (0.79, [0.74-0.84]). A combination of homozygous variants of PNPLA3 and TERT showing a 6.5-fold higher risk for nonviral-related HCC compared to individuals lacking these genotypes. This observation suggests that gene-gene interactions may identify individuals at elevated risk for developing HCC.

Conclusions: Our GWAS highlights novel genetic susceptibility of nonviral HCC among European descent populations from North America with substantial heritability. Selected genetic influences were observed for HCV-positive HCC. Our findings indicate the importance of genetic susceptibility to HCC development.

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Conflict of interest statement

Robin Kate Kelley advises and received grants from Agios, AstraZeneca, Excelixis, Ipsen, and Merck. She advises Compass, Kinnate, Regeneron, and Tyra Biosciences. She received grants from Bayer, Bristol Myers Squibb, Eli Lilly, EMD Serono, Genentech, Loxo Oncology, Partner Therapeutics, Roche, QED, Relay Therapeutics, Servier, Surface Oncology, and Taiho. Jian-Min Yuan received grants from the National Institutes of Health. R. Mark Ghobrial consults for TransMedics. Yuko Kono advises Lantheus Medical Imaging. She received grants from Bracco Diagnostics and Canon Medical Systems. Dimpy P. Shah received grants from Anthos, AstraZeneca, and Guardant. Mindie H. Nguyen advises and received grants from Exact, Gilead, and Intercept. She received grants from AstraZeneca, CurveBio, Delfi, Enanta, Innogen, Pfizer, and Vir. Geoffrey Liu advises and received grants from AstraZeneca, Pfizer, and Takeda. He advises EMD Serono, Jazz, Merck, and Novartis. Richard Kim consults and is on the speakers’ bureau for AstraZeneca. He consults for AbbVie, Eisai, Excelixis, Ipsen, Pfizer, and Taiho. He is on the speakers’ bureau for Incyte. Ernest Hawk received grants from Exact. Alison P. Klein received grants from the National Cancer Institute. Mikayla A. Schmidt is employed by the Mayo Clinic. Saira A. Khaderi consults for AstraZeneca. Ahmed O. Kaseb consults, advises, and received grants from Bristol Myers Squibb, Eisai, Exelixis, Genentech, Merck, and Roche. He received grants from Adaptimmune and Tvardi. Lewis R. Roberts advises and received grants from Bayer, Exact, and Gilead. He advises Grail, QED Therapeutics, and Tavec. He received grants from Ariad, BTG/Boston Scientific, Fujifilm, Glycotest, RedHill, and Target. The remaining authors have no conflicts to report.

Figures

None
Graphical abstract
FIGURE 1
FIGURE 1
Manhattan plots and quantile-quantile (Q-Q) plots for the genome-wide association study results of HCC. (A) All populations; (B) non-HCV–induced HCC; (C) HCV-induced HCC. X-axis represents SNP positions across the entire genome by chromosome, and the y-axis is the negative logarithm p-value, -log10 (p). The red horizontal line denotes p=5 × 10−8. For Q-Q plots, the x-axis represents Expected -log10 (p), and the y-axis is Observed -log10 (p) of each SNP. Abbreviation: SNP, single-nucleotide polymorphism.
FIGURE 2
FIGURE 2
Regional visualization of the GWAS of –log10 of the p-value by genomic locations: (A) PNPLA3 in overall HCC (rs738408); (B) MAU2 in overall HCC (rs58489806); (C) MOBP in overall HCC (rs9842969); (D) TERT in overall HCC (rs2242652); (E) PNPLA3 in non-HCV–induced HCC (rs738408); (F) HLA−DQB1 in HCV-induced HCC (rs9275224). In all figures, each dot represents a SNP, with the location depicted on the corresponding genes at the bottom. Abbreviations: GWAS, genome-wide association study; HLA−DQB1, human leukocyte antigen DQ beta 1; MAU2, sister chromatid cohesion factor; MOBP, myelin-associated oligodendrocyte basic protein; PNPLA3, patatin-like phospholipase domain-containing 3; SNP, single-nucleotide polymorphism; TERT, telomerase reverse transcriptase.
FIGURE 3
FIGURE 3
Graphical display (Forest Plot) of the estimated ORs (95% CIs) of the significant SNPs stratified by the main nongenetic risk factors for HCC, including alcohol drinking, type 2 diabetes mellitus, cigarette smoking, and HCV infection. Abbreviation: SNP, single-nucleotide polymorphism.
FIGURE 4
FIGURE 4
Integrative multi-omnics annotation analysis: (A) Plot for epigenetic annotation values. Instead of plotting raw values, for example, H3K27ac peaks, we showed the percentiles of scores of each attribute when ranked against findings across the entire genome. A higher percentile indicates a more functional value. The large red circle is an epigenetic PC that integrates all epigenetic annotation values, including some that were not plotted. (B) Plot for evolutionary conservation annotations. These annotations use data from many different organisms to describe whether certain genetic sequences are conserved through many evolutionary processes. More conserved sequences are believed to carry more functional importance. For instance, the mamPhCons annotation shows the level of conservation in mammals according to the phastCons algorithm. The large red circle is a PC (conservation PC) that integrates all conservation annotation values, including some not shown. We can see that rs58542926 is highly prioritized by these conservation annotations. Abbreviation: PC, principal component.

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