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Biomedical subjects

Denis Plotnikov

Publications and source records attributed to Denis Plotnikov.

2 recordsLinked to original sources

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans

Nonuniform Association of Genetic Risk Scores for Intraocular Pressure.

IMPORTANCE: Elevated intraocular pressure (IOP) is a risk factor for primary open-angle glaucoma, and genetic risk scores hold promise as a tool for screening for ocular hypertension. However, genetic risk scores for IOP have a nonuniform association across the range of IOP, which reduces their accuracy. OBJECTIVE: To test the hypothesis that nonuniform behavior of genetic risk scores for IOP is associated with a specific type of genetic interaction. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional, post hoc genetic association studies were performed using linear and quantile regression in a sample of UK Biobank participants. Data were analyzed from January to September 2025. EXPOSURES: Ninety-eight genetic variants associated with IOP. MAIN OUTCOMES AND MEASURES: Tests were carried out for 98 genetic variants associated with IOP (P&#x2009;<&#x2009;5.0&#x2009;&#xd7;10-8) to examine (1) dominant or recessive genetic effects, (2) genotype&#x2009;&#xd7;&#x2009;genotype interactions, (3) genotype&#x2009;&#xd7;&#x2009;age interactions, and (4) genotype&#x2009;&#xd7;&#x2009;sex interactions. RESULTS: A total of 98&#x202f;235 participants (mean [SD] age, 58.1 [7.9] years; 52&#x202f;168 female [53.1%]) were included in this analysis. More variants exhibited genotype&#x2009;&#xd7;&#x2009;age interactions than expected by chance (14 of the 98 variants associated with IOP had at least nominal evidence of an interaction with age; P&#x2009;=&#x2009;3.76&#x2009;&#xd7;10-4). For 12 of these 14 variants, age increased rather than decreased the magnitude of the IOP vs genotype association. However, integrating age interactions into the genetic risk score construction process did not yield improved accuracy (incremental noninteraction model, R2&#x2009;=&#x2009;4.05; 95% CI, 3.82-4.31 and interaction model, R2&#x2009;=&#x2009;4.04; 95% CI, 3.80-4.27). There was little support for other types of genetic interaction. CONCLUSIONS AND RELEVANCE: In the current work, findings show minimal evidence that nonadditive allelic effects, genotype&#x2009;&#xd7;&#x2009;genotype interactions, and genotype&#x2009;&#xd7;&#x2009;sex interactions contributed to the nonuniform association of genetic variants with IOP across quantiles of IOP. Although a genetic risk score for IOP was more accurate in older vs younger individuals, efforts to account for genotype&#x2009;&#xd7;&#x2009;age interactions in genetic risk score construction did not improve accuracy. These findings suggest other factors, such as gene-environment interactions, contribute to the nonuniform relationship of genetic variants with IOP.

Humans