- May 9, 2026
- Zhi Yu, et al., Mass General Hospital
Topic/Product:
CNS Disease Panel 120, Neurology
Disease Area:
Population Health
Sample Type:
Plasma
Abstract
High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses’ Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB’s original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.
Authors & Affiliations
Linke Li* ¹˒², Ahmed Alaa* ³˒⁴, Youxin Tan²˒⁵, Ilker Demirel⁶, Samuel Friedman⁷, Qiayi Zha²˒⁵, Russell Tracy⁸, Kent D. Taylor⁹, Bing Yu¹⁰, Christie M. Ballantyne¹¹, Rajat Deo¹², Ruth Dubin¹³, Michael Y. Tsai¹⁴, Gina M. Peloso¹⁵, Jennifer Brody¹⁶, Tom Austin¹⁶, Bruce M. Psaty¹⁶, Jayna Nicholas¹⁷, Laura M. Raffield¹⁷, Usman Tahir¹⁸, Josef Coresh¹⁹, Whitney Hornsby²˒²⁰, Andrew Chan¹˒²¹, Stephen S. Rich²², Jerome I. Rotter⁹, Peter Ganz²³, Robert Gerszten²⁰, Anthony Philippakis²⁴, Pradeep Natarajan ²˒²⁰˒²¹, and Zhi Yu** ¹˒²˒²¹
¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
² Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
³ Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
⁴ University of California, San Francisco, San Francisco, CA, USA
⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
⁶ Massachusetts Institute of Technology, Cambridge, MA, USA
⁷ ML4H, Broad Institute of Harvard and MIT, Cambridge, MA, USA
⁸ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
⁹ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
¹⁰ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
¹¹ Department of Medicine, Baylor College of Medicine, Houston, TX, USA
¹² Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
¹³ Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
¹⁴ Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
¹⁵ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
¹⁶ Cardiovascular Health Research Unit, Department of Medicine & Department of Epidemiology, University of Washington, Seattle, WA, USA
¹⁷ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
¹⁸ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
¹⁹ Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
²⁰ Heart and Vascular Institute, Mass General Brigham, Boston, MA, USA
²¹ Department of Medicine, Harvard Medical School, Boston, MA, USA
²² Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA
²³ Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California San Francisco, San Francisco, CA, USA
²⁴ Google Ventures, Cambridge, MA, USA
