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Genetic and Genomic Insights in Kidney Diseases: Mechanisms, Diagnosis and...
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Genetic and Genomic Insights in Kidney Diseases: Mechanisms, Diagnosis and Therapeutics

A Special Issue of Genes (ISSN 2073-4425) belonging to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 25 October 2026 | Viewed by 350

Editors

Dr. Maria Amicone

E-Mail Website
Guest Editor
Unità di Nefrologia, Dipartimento di Sanità Pubblica, Università di Napoli “Federico II”, Via Pansini 5, 80131 Naples, Italy
Interests: ADPKD; kidney disease; nephronophtisis; kidney stones; CKD
Dr. Ersilia Nigro

E-Mail Website
Guest Editor
Dipartimento di Scienze e Tecnologie Ambientali Biologiche e Farmaceutiche (DISTABiF), Università della Campania “Vanvitelli”, Via G. Vivaldi 42, 81100 Caserta, Italy
Interests: human biology; cell biology; microbiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Genetic kidney diseases encompass a broad spectrum of inherited disorders affecting renal structure and function, including cystic, glomerular, and tubulointerstitial conditions. These diseases represent a significant cause of chronic kidney disease and are characterized by remarkable genetic and phenotypic heterogeneity.

This Special Issue aims to provide a comprehensive overview of the genetic architecture and molecular mechanisms underlying inherited kidney diseases, promoting integration between basic science, translational research, and clinical practice.

The identification of disease-causing genes and the development of advanced sequencing technologies have progressively transformed the diagnosis and classification of hereditary kidney disorders, enabling more precise patient stratification.

Recent advances in multi-omics approaches, gene editing technologies, and novel disease models are reshaping our understanding of pathogenesis and opening new avenues for targeted and personalized therapies.

We welcome original research articles, reviews, and short communications that address genetics, molecular mechanisms, disease modeling, diagnostics, biomarkers, and innovative therapeutic strategies across the spectrum of inherited kidney diseases, including cystic disorders.

Dr. Maria Amicone
Dr. Ersilia Nigro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cystic kidney disease
  • ADPKD
  • kidney stones
  • nephronophtisis
  • ADTKD
  • cystinuria
  • primary hyperoxaluria
  • NGS
  • MLPA
  • CGH array

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Published Papers (1 paper)

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Research

32 pages, 14553 KB  
Article
Multi-Omics Mendelian Randomization Maps Lipid-Related Brain Cell-Type Associations with Kidney Disease
by Jiani Deng, Boning Cao, Fengjie Zheng, Sinan Ai, Yaoxian Wang and Xu Wang
Genes 2026, 17(9), 1097; https://doi.org/10.3390/genes17091097 - 11 Sep 2026
Abstract
Background: Cellular heterogeneity limits causal inference in complex diseases. We applied an established cell-type-stratified Mendelian randomization (csMR) framework, originally developed by Hao et al. (2024), to investigate whether lipid phenotypes affect kidney disease through distinct brain cell populations, extended by a proteome-wide mediation [...] Read more.
Background: Cellular heterogeneity limits causal inference in complex diseases. We applied an established cell-type-stratified Mendelian randomization (csMR) framework, originally developed by Hao et al. (2024), to investigate whether lipid phenotypes affect kidney disease through distinct brain cell populations, extended by a proteome-wide mediation component. Methods: Using Bayesian colocalization (posterior probability of hypothesis 4 [PPH4], ≥0.8) and multidimensional instrumental variable selection, we evaluated cell-type-stratified MR-supported associations of five lipid traits across ten brain cell or tissue strata with kidney disease risk. Instrumental variables were derived from published genome-wide association studies and human brain single-cell expression quantitative trait locus (eQTL) maps. An exploratory proteomic mediation analysis using the Difference Method was performed with UKB-PPP as the discovery resource and deCODE as the external validation resource, with mediation signals interpreted as hypothesis-generating. For the primary csMR analysis, multiple testing was corrected separately within each lipid trait using a Bonferroni threshold of 3.33 × 10−4, corresponding to 150 cell/tissue-by-outcome tests per lipid trait. Results: In csMR analyses, cholesterol-related lipids showed cell-type-stratified associations with kidney disease, most prominently in oligodendrocyte-related analyses (max β on the log-odds scale = 1.025). LDL-related associations with broad chronic glomerular disease were most evident in excitatory neuron-related analyses. Dual-cohort proteomic analysis prioritized plasma proteins, including SNAP29 and ICAM4, as candidate protein-associated signals for exploratory mediation analysis. These findings represent genetic colocalization and Mendelian randomization-supported associations, not experimentally established mechanisms, and should be interpreted as hypothesis-generating. Conclusions: This study provides a cell-type-resolved genetic and proteomic association map linking lipid traits, brain cell strata, and kidney disease outcomes, offering hypotheses for future experimental validation. Full article
(This article belongs to the Special Issue Genetic and Genomic Insights in Kidney Diseases: Mechanisms, Diagnosis and Therapeutics)
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