Cross-ancestry comparison of aptamer and antibody protein measures

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30% of the signals on each platform are likely driven by protein-altering variants. We highlight 80 proteins that correlate differently across ancestry groups likely in part due to differing protein-altering variant frequencies by ancestry. Furthermore, adjustment for protein-altering variants with opposite directions of effect by platform improves inter-platform protein measure correlation and results in more concordant genetic and phenotypic associations. Hence, protein-altering variants need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

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Publication Details

Journal
Nature Communications
Published
2026-01-22
DOI
https://doi.org/10.1038/s41467-025-67814-1
Citations
1
Primary Topic
Advanced Biosensing Techniques and Applications
Type
article
Field-Weighted Citation Impact
22.24

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article

Cross-ancestry comparison of aptamer and antibody protein measures

Josyf C. Mychaleckyj, Peter Durda, D. S. Katz, MARY R. ROONEY et al.
1 citations
Nature Communications
Advanced Biosensing Techniques and Applications
22.24
article

Cross-ancestry comparison of aptamer and antibody protein measures

Josyf C. Mychaleckyj, Peter Durda, D. S. Katz, MARY R. ROONEY, J. Coresh, A. Shah, JINGSHA CHEN, Clary B. Clish, Catherine L. Debban, Michael Mi, Peifeng Ruan, C. Johnson, K Pratte, Elaine S Cornell, Margaret Doyle, Rajat Deo, Quan Sun, Peter R. Ganz, K.E. Young, D. G. Gill, Francois Aguet, James S. Floyd, Usman A. Tahir, Jia Wen, Bing Yu, Jerome I. Rotter, Ruth F. Dubin, Alisa Manning, Laura M. Raffield, James B. Meigs, Xiuqing Guo, Alexis C. Wood, Thomas Blackwell, Ron C. Hoogeveen, Clary B. Clish, Ani Manichaikul, Yun Li, Brucy M. Psaty, Lynette Ekunwe, Robert E. Gerszten, Stephen S. Rich, Russell P. Tracy, Alexander P. Reiner, Nels C. Olson, James G. Wilson, Magdalena Sevilla-Gonzalez, Jayna C. Nicholas, Daniel E. Cruz, Russell P. Bowler, K. Alaine Broadaway
article en
1 citations

Abstract

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30% of the signals on each platform are likely driven by protein-altering variants. We highlight 80 proteins that correlate differently across ancestry groups likely in part due to differing protein-altering variant frequencies by ancestry. Furthermore, adjustment for protein-altering variants with opposite directions of effect by platform improves inter-platform protein measure correlation and results in more concordant genetic and phenotypic associations. Hence, protein-altering variants need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

Nature CommunicationsVol. 17(1)
Broad Institute (US), University of Vermont (US), University of North Carolina at Chapel Hill (US), Beth Israel Deaconess Medical Center (US), Cleveland Clinic (US), Children's Hospital of Philadelphia (US), Harvard University (US), Johns Hopkins University (US), University of California, San Francisco (US), Baylor College of Medicine (US), University of Washington (US), University of Michigan (US), UCLA Medical Center (US), Jackson Memorial Hospital (US), University of Mississippi Medical Center (US), National Jewish Health (US), Massachusetts General Hospital (US), Fred Hutch Cancer Center (US), Southwestern Medical Center (US), Stanford Medicine (US), Children's Nutrition Research Center at Baylor College of Medicine (US), Optimal Solutions (United States) (US), Harbor–UCLA Medical Center (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), University of Pennsylvania (US), The University of Texas Southwestern Medical Center (US), The University of Texas Health Science Center at Houston (US), University of Colorado Denver (US), Stanford University (US)
Broad Institute, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences
Openalex Percentile: Top 2%
Advanced Biosensing Techniques and Applications
22.24
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