| Title: | Systematic comparison of affinity plasma proteomic technologies across multiple cohorts reveals generalizability of proteomic prediction |
| Journal: | Nature Communications |
| Published: | 8 Oct 2026 |
| DOI: | https://doi.org/10.1038/s41467-026-78443-7 |
| URL: | https://www.nature.com/articles/s41467-026-78443-7_reference.pdf |
Publication 21579
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Abstract
Emergence of biobank level proteomic datasets with extensive phenotype linkage has significantly improved our understanding of proteomic drivers in health and ushered in a flurry of disease risk prediction models. However, differences in cohorts and proteomic assays can significantly undermine transferability of proteomic models, severely limiting their general use. In this study, we generate and harmonize comparisons of two leading multiplex affinity-based proteomic platforms (Olink and SomaScan) across 11 independent cohorts. We find key factors contributing to platform disparities and establish a high-quality set of 757 protein probe-pairs consistently correlated across cohorts as a reference for new emerging studies. To investigate the impact on proteomic prediction, we train models to predict age, body mass index and 15 common diseases, evaluating performance across multiple independent cohorts and platforms. We find systematic deterioration in model performance across platforms, especially without filtering for probe correlations, and a U-shaped effect in model transferability reflecting a trade-off between cross-platform protein consistency and biological relevance. The optimal cross-platform correlation cut-off varied between traits, with 0.3-0.4 as most prevalent. Our study highlights practical considerations for assessing proteomic model performance across platforms and cohorts, offering guidance for robust protein biomarker development, disease prediction, and drug targeting.</p>
19 Authors
- Anthony Barente
- Zijie Zhao
- Zhili Zheng
- Chun Wu
- Kritika Singh
- Zhaoqing Wang
- Aisling O'Hara Hall
- Laurence Menard
- Isaac Neuhaus
- Mitja Kurki
- Aarno Palotie
- Mark Daly
- Sarah Finer
- David A. van Heel
- Maik Pietzner
- Claudia Langenberg
- Joseph Maranville
- Tai Wang
- Benjamin B. Sun
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health