Biobank-scale genetic characterization of Alzheimer’s disease and related dementias across diverse ancestries

Abstract Alzheimer’s disease and related dementias (AD/ADRDs) pose a significant global public health challenge. To effectively implement personalized therapeutic interventions on a global scale, it is essential to identify disease-causing, risk, and resilience factors across diverse ancestral backg...

Full description

Saved in:
Bibliographic Details
Main Authors: Marzieh Khani, Fulya Akçimen, Spencer M. Grant, Suleyman Can Akerman, Paul Suhwan Lee, Faraz Faghri, Hampton Leonard, Jonggeol Jeffrey Kim, Mary B. Makarious, Mathew J. Koretsky, Jeffrey D. Rothstein, Cornelis Blauwendraat, Mike A. Nalls, Andrew Singleton, Sara Bandres-Ciga
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-62108-y
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Alzheimer’s disease and related dementias (AD/ADRDs) pose a significant global public health challenge. To effectively implement personalized therapeutic interventions on a global scale, it is essential to identify disease-causing, risk, and resilience factors across diverse ancestral backgrounds. This study leveraged biobank-scale data to conduct a large multi-ancestry whole-genome sequencing characterization of AD/ADRDs. We thoroughly explored the role of protein-coding and splicing variants from key genes associated with AD/ADRDs across 11 ancestries, utilizing data from five distinct biobanks, including a total of 25,001 cases and 93,542 controls. We compiled the most extensive catalog of known and novel genetic variation in AD/ADRDs in a global context, providing clinical insights into their genetic-phenotypic correlations. A thorough assessment of APOE revealed ancestry-driven modulation of APOE-associated AD/ADRDs, as well as disease-modifying effects conferred by several variants among APOE ε4 carriers. Finally, we present an accessible and user-friendly platform to support future ADRD research ( https://niacard.shinyapps.io/MAMBARD_browser/ ).
ISSN:2041-1723