BACKGROUND
Hepatic steatosis is a highly prevalent metabolic condition and a major contributor to chronic liver disease, particularly in Asian populations. This study was conducted to identify genetic variants associated with the severity of ultrasound-defined hepatic steatosis in a Taiwanese Han population and evaluate the variants' biological relevance through cross-ancestry meta-analysis and functional validation.
METHODS
We conducted a genome-wide association study (GWAS) of 133,895 individuals with data in the Genetic Biobank of China Medical University Hospital. Hepatic steatosis severity was graded on an ordinal scale (0-3) using standardised ultrasonographic criteria. The polygenic risk score (PRS) under continuous shrinkage (CS) approach was used to construct a PRS for genetic risk stratification across severity grades.
FINDINGS
In the GWAS, we identified 1,229 single-nucleotide polymorphisms associated with hepatic steatosis severity (P < 1 × 10⁻5), with the strongest signals discovered at the PNPLA3 locus (rs738408 and rs738409). The PRS-CS model exhibited strong discriminative performance (area under the receiver operating characteristic curve = 0.875) and a clear dose-response relationship with ultrasound-defined steatosis severity, substantially outperforming the risk model based on body mass index only. In vitro assays confirmed significantly increased lipid accumulation and intracellular triglyceride content in I148M-expressing hepatocytes under lipid-loading conditions.
INTERPRETATION
This study delineates the genetic landscape of ultrasound-defined hepatic steatosis severity in a large Taiwanese Han population and demonstrates the robust ability of the PRS to predict disease stage.
Blood lipids are heritable risk factors for cardiovascular disease (CVD), a leading cause of mortality worldwide. However, the genetic architecture of lipid traits and the performance of polygenic risk scores (PRSs) remain underexplored in East Asian (EAS) populations, including Taiwanese Han individuals.
We conducted genome-wide association studies and PRS analyses for five lipid traits: total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and the ratio of low-density lipoprotein cholesterol to total cholesterol. Lipid profile data were obtained from the China Medical University Hospital cohort. PRSs were evaluated on the basis of their correlations with measured lipid levels. To evaluate trans-ancestry PRS transferability, localized models were systematically compared against models derived from discovery-stage GWAS meta-analyses incorporating five ancestry groups from the Global Lipids Genetics Consortium. The performance of the PRS models in predicting lipid-related diseases was evaluated through receiver operating characteristic curve analyses.
The population-specific PRS models explained 11%–40% of the variance in lipid levels within the target cohort. Models leveraging global multiancestry GWAS meta-analysis weights revealed limited predictive performance (
r
2
= 0.04–0.19), whereas analyses incorporating EAS-specific data yielded higher correlations (
r
2
= 0.13–0.30), although these correlations did not exceed those derived from the hospital-based cohort alone. When combined with age and sex, the PRS models demonstrated strong predictive performance for coronary artery disease, atherosclerosis, and ischemic stroke, with area under the curve values of 0.910, 0.926, and 0.854, respectively.
Population-specific PRS models derived from a Taiwanese population outperformed meta-analysis-derived frameworks in predicting lipid levels and demonstrated substantial potential for predicting CVD risk, indicating the importance of ancestry-matched genetic studies in precision medicine.
Yu-Chia Chen, Ting-Yuan Liu, Chi-Chou Liao et al.· Frontiers in Bioinformatics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
