
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.11.24313439
preprint
1
Article
Identification of 16 novel Alzheimer’s disease susceptibility loci using multi-ancestry meta-analyses of clinical Alzheimer’s disease and AD-by-proxy cases from four whole genome sequencing datasets
Willett Julian Daniel Sunday http://orcid.org/0000-0002-8103-0329

Waqas Mohammad
Choi Younjung
Ngai Tiffany
Mullin Kristina
Tanzi Rudolph E. http://orcid.org/0000-0002-7032-1454

Prokopenko Dmitry
12 9 2024
2024.09.11.24313439https://creativecommons.org/licenses/by-nd/4.0/ This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
http://medrxiv.org/lookup/doi/10.1101/2024.09.11.24313439
nihpp-2024.09.11.24313439.pdf
Abstract

Alzheimer’s disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD, we identified 14 new loci - five common ( FBN2,/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1 ) and nine rare ( VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1) . Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci ( RPL23/LASP1 and CEBPA /AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.
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