medRxiv : the preprint server for health sciences · 2025
An Integrated Large-Scale Atlas of Protein Quantitative Trait Loci across Olink and SomaScan platforms.
СтатьяПрепринтНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Khunsriraksakul Chachrit, Zhang Fan, Wang Lida, Chen Siyuan, Markus Havell, Chen Dieyi, Shenoy Ganesh, Lopez-Silva Carolina, Jabboure Fayez, Kesaf Ayse E, Zhan Xiaowei, Melia Joanna, Hui Ken, Jiang Bibo, Liu Dajiang J
Аннотация
Protein quantitative trait loci (pQTLs) provide insight into the genetic regulation of protein expression and disease biology. We performed a large-scale cross-platform meta-analysis of plasma proteomics, integrating data from over 90,000 individuals across Olink and SomaScan platforms. This effort identified >30,000 sentinel pQTLs, with multi-trait approach (MTAG) markedly boosting both discovery and replication, despite the potential differences in the protein measurements of the two platforms. While both platforms captured well-known pleiotropic loci ( HLA, ABO, SH2B3 ), we also uncovered platform-specific associations, reflecting unique assay designs. We further established a comprehensive resource for transcriptome- and proteome-wide association studies (TWAS, PWAS), identifying >100,000 upstream regulators with strong replication. As a proof of concept, we applied this framework to inflammatory bowel disease (IBD), generating the largest to date GWAS directly comparing Crohn's disease (CD) and ulcerative colitis (UC). Integrative multi-omics analyses revealed divergent loci enriched in the NF- κ B signaling pathway and improved CD vs. UC classification when proteomic features were combined with polygenic risk scores (PRS). Our findings provide a comprehensive resource for plasma protein genetics and demonstrate the value of integrative multi-omics for disease subtype classification, with broad implications for precision medicine.