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Genetic Epidemiology of Multifactorial Diseases

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: 25 January 2026 | Viewed by 953

Special Issue Editors


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Guest Editor
Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41500 Larissa, Greece
Interests: genetic epidemiology; genetic association studies; diabetic nephropathy; gene ontology

E-Mail Website
Guest Editor
Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41500 Larissa, Greece
Interests: genetic epidemiology; genetic association studies; diabetic nephropathy; chronic kidney disease

Special Issue Information

Dear Colleagues,

Multifactorial diseases, such as cardiovascular disease, diabetes, neurodegenerative disorders, autoimmune diseases and many others, are driven by a combination of multiple environmental and genetic factors. Due to their high prevalence, multifactorial diseases represent a substantial burden for public health systems. Advances in high‑throughput genotyping, whole‑genome sequencing and multi‑omics integration have transformed our ability to dissect the genetic architecture of multifactorial diseases. Understanding their genetic causes and pathophysiological mechanisms are essential for developing novel therapies to target specific diseases. This Special Issue of Genes, titled “Genetic Epidemiology of Multifactorial Diseases”, will address the genetic architecture of multifactorial diseases. We welcome original research articles, brief reports, reviews and methodological papers that address (but are not limited to) the following topics:

  • Genetic association studies (GWASs and candidate gene studies);
  • Polygenic risk scoring and risk prediction models;
  • Gene–environment and gene–gene interactions;
  • Population and statistical genetics methods;
  • Functional genomics and multi‑omics integration;
  • Causal inference and Mendelian randomization studies;
  • Translational applications, precision medicine and public health impacts.

Dr. Maria Tziastoudi
Prof. Dr. Ioannis Stefanidis
Guest Editors

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Keywords

  • genetic association studies
  • polygenic risk scores
  • functional genomics
  • causal inference
  • mendelian randomization
  • translational applications

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Published Papers (1 paper)

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Research

15 pages, 829 KB  
Article
The Role of VEGFA in T2DM-Nephropathy: A Genetic Association Study and Meta-Analysis
by Maria Tziastoudi, Christos Cholevas, Constantinos Zorz, Efthimios Dardiotis, Evangelia E. Tsironi, Maria Divani, Theodoros Eleftheriadis and Ioannis Stefanidis
Genes 2025, 16(11), 1386; https://doi.org/10.3390/genes16111386 - 17 Nov 2025
Viewed by 538
Abstract
Background: Diabetic nephropathy (DN) is a leading cause of end-stage renal disease, arising from complex interactions between metabolic, hemodynamic, and genetic factors. Among candidate genes, vascular endothelial growth factor A (VEGFA) has been extensively investigated due to its role in [...] Read more.
Background: Diabetic nephropathy (DN) is a leading cause of end-stage renal disease, arising from complex interactions between metabolic, hemodynamic, and genetic factors. Among candidate genes, vascular endothelial growth factor A (VEGFA) has been extensively investigated due to its role in endothelial homeostasis and microvascular complications of diabetes. The present study aimed to examine the association of VEGFA polymorphisms with DN in a Greek population and to perform a comprehensive meta-analysis of available evidence. Methods: A case–control study was conducted, including 197 patients with type 2 diabetes mellitus (T2DM) and DN, 155 diabetic patients without nephropathy, and 246 healthy controls. Ten tagging single-nucleotide polymorphisms (SNPs) across VEGFA were genotyped. Statistical analyses employed the generalized odds ratio (ORG). To contextualize these findings, a meta-analysis of 13 eligible studies was performed, encompassing 7520 cases, 6951 diabetic controls, and 1718 healthy controls. Results: Of the tested variants in the present case–control study, only rs833070 was significantly associated with DN across all comparisons. Nine VEGFA variants were evaluated in meta-analysis, with rs2146323 showing a protective effect (allelic OR = 0.85; 95% CI: 0.76–0.95), while other variants yielded non-significant associations. Conclusions: Overall, the data suggest that VEGFA polymorphisms, particularly rs833070 and rs2146323, contribute to genetic susceptibility to DN, although population-specific differences and heterogeneity across studies remain substantial. Future research in large, ethnically diverse cohorts with functional analyses is warranted to clarify causal mechanisms and enable the integration of VEGFA genetic variation into risk stratification and personalized therapeutic strategies. Full article
(This article belongs to the Special Issue Genetic Epidemiology of Multifactorial Diseases)
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