Evidence mapPaperPMID 42417591Full record

ArticleInvestigative ophthalmology & visual science2026

Whole-Exome Analysis Identifies Candidate Genes Associated With Diabetic Retinopathy.

Ning Li, Long Liu, Wen Sun, Di Liu, Haibin Li, Changwei Li, Xiao Wang, Jianguang Ji, Yalu Wen, Deqiang Zheng

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Article in Investigative ophthalmology & visual science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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10 authors.

Ning LiDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Long LiuDepartment of Health Statistics, School of Public Health, Binzhou Medical University, Yantai, Shandong, China.
Wen SunDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Di LiuDepartment of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
Haibin LiMedical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Changwei LiDepartment of Epidemiology, O'Donnell School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, United States.
Xiao WangCenter for Primary Health Care Research, Lund University, Region Skåne, Malmö, Sweden.
Jianguang JiDepartment of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
Yalu WenDepartment of Statistics, University of Auckland, Auckland, New Zealand.
Deqiang ZhengDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Despite adequate glycemic control, a proportion of patients develop diabetic retinopathy (DR), suggesting the contribution of other mechanisms. To explore genetic associations with DR, we conducted a whole-exome sequencing (WES) study of DR using a dual-control design in individuals of European ancestry. Methods: Leveraging UK Biobank WES data, we implemented a dual-control design to identify candidate genes associated with DR, comparing cases with both diabetes controls and the general population. Rare variant associations were tested at gene-level using SAIGE-GENE+, and common variants were analyzed via PLINK2. Significant findings were assessed through the exclusion of self-reported cases, leave-one-variant-out (LOVO) analysis, time-to-event analysis, transcriptomics analysis, virtual knockout experiments, proteomics, and protein-protein interaction (PPI) analysis. Results: Exome-wide gene-based association analysis identified FRZB (frizzled-related protein) as a candidate gene potentially associated with DR in comparisons with diabetic controls (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.04-1.15; P = 2.55 × 10-7). The observed association showed generally consistent patterns across several sensitivity analyses, including exclusion of self-reported cases, LOVO analyses, and Cox proportional hazards models. In addition, transcriptomic, proteomic, virtual knockout, and PPI analyses provided complementary exploratory evidence supporting a possible involvement of FRZB in DR-related biological processes. Additionally, single-variant analysis revealed two loci, FAM160A1 (4:151662612:C:G) and HK1 (10:69300854:A:G), associated with DR in the general population (OR = 1.82 and OR = 1.50, respectively). However, multiomics support for these signals was limited. Conclusions: This multilayered genetic study identified FRZB as a candidate gene potentially associated with DR. However, this finding should be interpreted cautiously, and further experimental studies and independent replication are needed to clarify the potential relevance of FRZB to DR.

Indexed as

Diabetic RetinopathyExome SequencingGenetic Predisposition to DiseaseAgedCase-Control StudiesFemaleGenome-Wide Association StudyHumansMaleMiddle AgedPolymorphism, Single NucleotideUK Biobank

Identifiers

PMID42417591
PMCPMC13355387

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