Yu-Chia Chen

Background: Variant interpretation in clinical genomics requires integration of population-specific allele frequencies, curated database annotations, and phenotype evidence. However, variants annotated as pathogenic or likely pathogenic in reference databases may have different allele frequencies across populations, and electronic medical record (EMR) data may provide useful but incomplete clinical context. Methods: In this study, we used the China Medical University Hospital Genetic Biobank (CMUH-GB) and linked EMRs to evaluate ClinVar-annotated candidate variants in a Taiwanese Han population. Genotyped array variants were filtered by quality control, mapped to ClinVar, and prioritized if annotated as pathogenic or likely pathogenic and observed with an alternative allele frequency greater than 0.0001 in CMUH-GB. Results: After an updated annotation review, EMR linkage, exclusion of known rare-disease cases or ineligible loci, and retention of variants with clinically relevant EMR phenotypes, 11 candidate variants were analyzed. These variants were located in SCN5A, KCNH2, FBP1, PAH, ACADS, TBX6, BRCA1, LDLR, GP6, and SLC4A11. Several candidate variants showed substantially higher allele frequencies in CMUH-GB and the Taiwan Biobank than reported in some external population datasets. Genotype–phenotype association analyses were performed using additive genetic models with covariate adjustment and Benjamini–Hochberg false discovery rate correction. No interpretable association remained statistically significant after correction. SCN5A rs794728912 models for long QT syndrome and cardiac conduction defects were not estimable because no cases were observed among alternative-allele carriers, resulting in sparse-event separation. A nominal GP6 association with coagulation defects did not remain significant after correction. These findings support population-specific reassessment prioritization of selected ClinVar-annotated variants but do not constitute formal ACMG/AMP reclassification. Conclusions: Our study highlights the value and limitations of integrating hospital-based genotyping data with EMR-derived phenotypes for ancestry-aware variant interpretation in underrepresented populations.

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The prevalence and phenotypes of AMD differ among populations, including between people in Taiwan and other regions. We performed a genome-wide association study to identify genetic variants and to develop genetic models to predict the risk of AMD development and progression in the Taiwanese population. In total, 4039 patients with AMD and 16,488 non-AMD controls (aged ≥ 65 years) were included. We identified 31 AMD-associated variants (p < 5 × 10⁻⁸) on chromosome 10q26, surrounding PLEKHA1-ARMS2-HTRA1. Two genetic models were constructed using the clump and threshold method. Model 1 included the single nucleotide polymorphism rs11200630 and showed a 1.31-fold increase in the risk of AMD per risk allele (95% confidence interval (CI) = 1.20–1.43, p < 0.001). In model 2, 1412 single-nucleotide polymorphisms were selected to construct a polygenic risk score (PRS). Individuals with the top 5% PRS had a 1.40-fold higher AMD risk compared with that of individuals with a PRS in the bottom quartile (95% CI = 1.04–1.89, p = 0.025). Moreover, the PRS in the upper quartile was related to a decreased age at AMD diagnosis by 0.62 years (95% CI = −1.15, −0.09, p = 0.023). Both genetic models provide useful predictive power for populations at high risk of AMD, affording a basis for identifying patients requiring close follow-up and early intervention.

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.