Genetic Variants That Modulate Alzheimer's Disease Risk Deregulate Protein-Protein Correlations in the Gyrus Temporalis Medius
medrxiv(2025)
Delft University of Technology | Amsterdam UMC | Vrije Universiteit Amsterdam
Abstract
We conducted a comprehensive protein quantitative trait loci (pQTL) analysis on a unique set of Gyrus Temporalis Medius (GTM) samples obtained from 88 Alzheimer's Disease (AD) patients, 53 non-demented individuals, and 49 cognitively healthy centenarians. This investigation revealed 8,081 genetic variants associated with the abundance of 227 proteins, including several novel variant-protein links not identified in a prior pQTL study of the prefrontal cortex or expression QTL (eQTL) analysis across 12 brain regions (GTEx). Among all the AD risk variants tested for possible pQTL effects, only rs429358-T (which encodes the APOE4 allele) was significantly linked to higher APOE levels in the GTM, potentially explaining why this region is particularly prone to AD pathology. Further, through differential correlation analysis we identified AD risk variants linked to altered protein-protein correlations, specifically rs9381040 in TREML2, rs34173062 in SHARPIN, and rs11218343 near SORL1. Notably, DDX17 appears to play a protective role in individuals with the rs9381040-T/T genotype by tightly regulating synuclein levels. Collectively, these findings demonstrate that AD risk variants disrupt protein-protein interactions, highlighting a genetic basis for the coordinated modulation of protein networks in AD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No Applicable Funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Medical Ethics Committee of the Amsterdam UMC gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话