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  5. High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

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Preprint
2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

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English
2025
DOI: 10.1101/2025.10.24.682486

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Elio Riboli
Elio Riboli

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Akash Bahai
He Wang
Pritesh Jain
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Abstract

Abstract We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations ( cis- pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

How to cite this publication

Akash Bahai, He Wang, Pritesh Jain, Lucas D. Ward, Alexander Boychenko, Zhihao Ding, James W. Frederiksen, Joanne Ngeow, Jimmy Lee, Elio Riboli, Jianjun Liu, Sebastian Maurer‐Stroh, Enrico Petretto, Ching‐Yu Cheng, Bernett Lee, Saumya Shekhar Jamuar, Nicolas Bertin, Claire Bellis, Max Lam, Weiling Zheng, Xueling Sim, Patrick Tan, John C. Chambers, Nilanjana Sadhu (2025). High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort. , DOI: https://doi.org/10.1101/2025.10.24.682486.

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Publication Details

Type

Preprint

Year

2025

Authors

24

Datasets

0

Total Files

0

DOI

https://doi.org/10.1101/2025.10.24.682486

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