新しいAIアルゴリズムが自己免疫疾患の予測と治療を改善する可能性(New AI algorithm may improve autoimmune disease prediction and therapies)

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2024-05-20 ペンシルベニア州立大学(PennState)

ペンシルベニア州立大学の研究チームが開発した新しいAIアルゴリズム「EXPRESSO」は、自己免疫疾患の予測と新治療法の開発を促進します。このアルゴリズムは遺伝子の発現と調節を正確にモデル化し、既存の方法より26%多くの新しい遺伝子と特性の関連を発見しました。シングルセルの発現定量形質遺伝子座データや3Dゲノムデータ、エピジェネティクスを統合して14の自己免疫疾患を解析し、ビタミンKやメトホルミンなどの既存薬が新たな治療法として有効である可能性を示しました。研究チームは今後、実験室や臨床試験での検証を進める予定です。

<関連情報>

複合形質リスク遺伝子を理解するための単一細胞発現定量形質座位要約統計の統合 Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Lida Wang,Chachrit Khunsriraksakul,Havell Markus,Dieyi Chen,Fan Zhang,Fang Chen,Xiaowei Zhan,Laura Carrel,Dajiang. J. Liu & Bibo Jiang
Nature Communications  Published:20 May 2024
DOI:https://doi.org/10.1038/s41467-024-48143-1

新しいAIアルゴリズムが自己免疫疾患の予測と治療を改善する可能性(New AI algorithm may improve autoimmune disease prediction and therapies)

Abstract

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

医療・健康
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