Deciphering Proteins in Alzheimer's Disease: A New Mendelian Randomization Method Integrated with AlphaFold3 for 3D Structure Prediction

Overview

Journal Cell Genom

Date 2024 Dec 5

PMID 39637861

Authors

Minhao Yao

Gary W Miller

Badri N Vardarajan

Andrea A Baccarelli

Zijian Guo

Zhonghua Liu

Affiliations

Soon will be listed here.

Abstract

Hidden confounding biases hinder identifying causal protein biomarkers for Alzheimer's disease in non-randomized studies. While Mendelian randomization (MR) can mitigate these biases using protein quantitative trait loci (pQTLs) as instrumental variables, some pQTLs violate core assumptions, leading to biased conclusions. To address this, we propose MR-SPI, a novel MR method that selects valid pQTL instruments using Leo Tolstoy's Anna Karenina principle and performs robust post-selection inference. Integrating MR-SPI with AlphaFold3, we developed a computational pipeline to identify causal protein biomarkers and predict 3D structural changes. Applied to genome-wide proteomics data from 54,306 UK Biobank participants and 455,258 subjects (71,880 cases and 383,378 controls) for a genome-wide association study of Alzheimer's disease, we identified seven proteins (TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55) with structural alterations due to missense mutations. These findings offer insights into the etiology and potential drug targets for Alzheimer's disease.

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