2024-12-04 コロンビア大学
<関連情報>
- https://www.publichealth.columbia.edu/news/computational-approach-identifies-alzheimers-related-biomarkers
- https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00329-X
アルツハイマー病のタンパク質を解読する AlphaFold3と統合された新しいメンデルランダム化法による3次元構造予測 Deciphering proteins in Alzheimer’s disease: A new Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction
Minhao Yao∙ Gary W. Miller∙ Badri N. Vardarajan∙ Andrea A. Baccarelli∙ Zijian Guo∙ Zhonghua Liu
Cell Genomics Published:December 4, 2024
DOI:https://doi.org/10.1016/j.xgen.2024.100700
Graphical abstract
Highlights
•MR-SPI is a new instrumental variable method for causal inference in proteomics data
•Seven plasma proteins have been identified as linked to Alzheimer’s disease
•AlphaFold3 predicts protein 3D structural changes caused by missense mutations
•Genetic-variant-induced structural changes may help identify future drug targets
Summary
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.
