Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 42665426

Revealing the Association of LIG1 Genetic Variants With Pterygium Susceptibility and Demographic Characteristics in a Taiwanese Population.

Pei-Shin Hu·Ning-Yi Hsia·Mei-Chin Mong·Te-Chun Hsia·Jaw-Chyun Chen·Chia-Wen Tsai·DA-Tian Bau·Wen-Shin Chang
View original source ↗Export metadata ↓

Abstract

BACKGROUND/AIM: Pterygium is a common ocular surface disorder associated with long-term ultraviolet exposure and/or oxidative DNA damage. Accumulated evidence suggests that defects in DNA repair pathways may contribute to pterygium risk. DNA ligase I (LIG1), a key enzyme involved in DNA replication and base excision repair, has been involved in the etiology of several human diseases, including cancers. However, its role in pterygium has never been examined before. This study aimed at exploring the association between the LIG1 genotypes and pterygium risk in a Taiwanese population. PATIENTS AND METHODS: The hospital-based case-control study was conducted including 165 patients with pterygium and 320 age- and sex-matched non-pterygium controls. LIG1 rs20579 genotypes were accessed utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. Stratified analysis and the calculation of odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were used for evaluating the associations between genotypes and pterygium risk. RESULTS: Individuals carrying the homozygous variant AA genotype exhibited a significantly elevated risk of pterygium compared with those carrying the GG genotype (OR=2.74, 95% CI=1.17-6.43, p=0.0305). Under the recessive model, the AA genotype conferred a 2.65-fold elevated risk (95% CI=1.14-6.18, p=0.0350). The variant A allele was also associated with increased susceptibility (OR=1.46, 95% CI=1.03-2.07, p=0.0413). Stratified analyses revealed significant associations specifically among individuals aged ≥60 years (OR=5.64, 95% CI=1.67-19.04, p=0.0039) and males (OR=5.23, 95% CI=1.74-15.73, p=0.0034), but not those of younger ages or females. CONCLUSION: The LIG1 rs20579 genotype is significantly associated with pterygium susceptibility in Taiwanese individuals, particularly among elderly and male subjects. These findings support the involvement of impaired DNA repair machinery in pterygium pathogenesis and suggest that LIG1 rs20579 may serve as a novel genetic biomarker for risk assessment and early detection of pterygium.

Explore related subjects

HumansPterygiumMaleFemaleGenetic Predisposition to DiseaseTaiwanMiddle AgedDNA Ligase ATPPolymorphism, Single NucleotideGenotypeCase-Control StudiesAgedGenetic Association StudiesOdds RatioRisk FactorsGene FrequencyAllelesAdultDemographyPolymorphism, Restriction Fragment LengthAgegenotypeligase IpolymorphismpterygiumsexJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pei-Shin Hu, Ning-Yi Hsia, Mei-Chin Mong, Te-Chun Hsia, Jaw-Chyun Chen, Chia-Wen Tsai, DA-Tian Bau, Wen-Shin Chang. 2026. Revealing the Association of LIG1 Genetic Variants With Pterygium Susceptibility and Demographic Characteristics in a Taiwanese Population.. https://doi.org/10.21873/invivo.14422

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗