← brief for 2026-09-14 · Slowing decline

Integrating machine learning and GWAS for variant prioritization in the INCIPE cohort highlights ABC transporter genes in chronic kidney disease

Key takeaways

Researchers combined a genome-wide scan with machine learning to sort through gene variants in the INCIPE study group and pick out the ones most likely to matter in kidney disease. The strongest signals pointed to ABC transporter genes, which make the pumps that move drugs and waste in and out of cells. This is a genetic association study, so it points to genes worth investigating, not proof that these genes cause kidney disease. No treatment comes out of this yet.

Who did this work

Dramane D (Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona , ,) · Treccani M (Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona , ,) · Veschetti L (Infections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute , ,) · Koffi N (Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona , ,) · Patuzzo C (Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona , ,) · Ferraro P (Division of Nephrology, Department of Medicine, University of Verona and Azienda Ospedaliera Universitaria Integrata , ,) · Gambaro G (Division of Nephrology, Department of Medicine, University of Verona and Azienda Ospedaliera Universitaria Integrata , ,) · Noel D (Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie Génétique, Université Peleforo Gon Coulibaly , ,) · Malerba G (GM Lab, Department of Surgical Sciences, Dentistry, Paediatrics and Gynaecology, University of Verona , ,)

Source

Paper · Europe PMC, CKD · 2026-09-14
https://europepmc.org/article/PMC/PMC13571820