Chi-Chou Liao

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.

We addressed the underrepresentation of non-European populations in genome-wide association studies (GWASs) by building HiGenome, a large-scale genetic resource for the Taiwanese Han population. Using a custom genotyping array, we integrated deidentified electronic medical records (2003 to 2021) with genomic data to enable GWASs, phenome-wide association studies, and polygenic risk score (PRS) analysis. Among 413,000 participants, 323,397 passed ancestry and quality control filtering. GWASs covered 1085 traits, focusing on diseases prevalent in Taiwan such as type 2 diabetes, chronic kidney disease, gout, and alcoholic liver damage. PRSs were calculated for 238 traits, with the strongest associations observed in musculoskeletal disorders. Incorporating PRS into clinical practice supports early risk prediction and personalized prevention. To further expand translational value, we also conducted pharmacogenomic analysis and human leukocyte antigen typing. HiGenome offers a large-scale genetic and clinical dataset from the Taiwanese Han population, supporting population-specific analyses and precision medicine development in East Asia. The hospital-based design enables continuous follow-up and longitudinal data expansion.

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.

Purpose: Alopecia areata (AA) is one of the most prevalent autoimmune diseases affecting humans. Given that hair follicles are immune-privileged, autoimmunity can result in disfiguring hair loss. However, the genetic basis for AA in the Taiwanese population remains unknown. Materials and Methods: A genome-wide association study was conducted using a cohort of 408 AA cases and 8167 controls. To link variants to gene relationships, we used 882 SNPs (P< 1E-05) within 74 genes that were associated with AA group to build the biological networks by IPA software. HLA diplotypes and haplotypes were analyzed using Attribute Bagging (HIBAG)-R package and chi-square analysis. Results: Seven single nucleotide polymorphisms (SNPs) including LINC02006 (rs531166736, rs187306735), APC (rs112800832_C_CAT), SRP19 (rs139948960, rs144784670), EGFLAM (rs16903975) and LDLRAD3 (rs79874564) were closely associated with the AA phenotype (P< 5E-08). Examination of biological networks revealed that these genomic areas are associated with antigen presentation signaling, B cell and T cell development, Th1 and Th2 activation pathways, Notch signaling, crosstalk signaling between dendritic cells and natural killer cells, and phagosome maturation. Based on human leukocyte antigen (HLA) genotype analysis, four HLA genotypes (HLA-B*15:01-*40:01, HLA-DQA1*01:02-*03:03, HLA-DQA1*01:02, and HLA-DQB1*02:01) were found to be associated with AA (adjusted p-value< 0.05). HLA-DQA1*01:02 is the most significantly related gene in the Taiwanese population (adjusted p-value = 2.09E-05). Conclusion: This study successfully identified susceptibility loci associated with AA in the Taiwanese population. These findings not only shed light on the origins of AA within the Taiwanese context but also contribute to a comprehensive understanding of the genetic factors influencing AA susceptibility.