Thermo Fisher Scientific, Precision Health Research, Singapore (PRECISE) and Nanyang Technological University Singapore (NTU Singapore) have identified more than 2,500 previously unreported genetic-protein associations through a collaboration combining large-scale genomic and health data with complementary protein analysis technologies. The initial findings, presented at the Human Proteome Organization (HUPO) World Congress, are based on analysis of approximately 10,000 participant samples from the PRECISE-SG100K study. The results offer new leads for investigating disease biology and identifying potential biomarkers and therapeutic targets.

The collaboration, announced in April, brings together Thermo Fisher’s Olink Proximity Extension Assay (PEA) platforms, Orbitrap Astral mass spectrometry system and Seer’s Proteograph Product Suite. The technologies provide complementary approaches to measuring proteins, enabling researchers to combine targeted analysis with broader, discovery-based proteomics.

Researchers measured approximately 8,800 protein groups in samples from the first 10,000 study participants. A subsequent genetic association analysis linked the levels of more than 4,200 proteins to nearby genetic variations. Of those associations, more than 2,500 had not been reported in the 12 reference studies used for comparison. The findings expand the pool of potential relationships between genetic variation and protein expression that researchers can investigate in future studies. The findings provide additional hypotheses for examining how genetic differences influence protein biology and how those relationships may relate to health and disease, particularly among Asian populations that have historically been underrepresented in large biomedical datasets.

John Chambers, Ph.D., chief scientific officer at PRECISE and principal investigator of the SG100K study, presented the results during a plenary session at the HUPO congress. Chambers is also a professor of cardiovascular epidemiology and program director for population and global health at NTU Singapore’s Lee Kong Chian School of Medicine.

A central component of the collaboration is the use of complementary analytical technologies to examine the human proteome at population scale. Thermo Fisher’s Orbitrap Astral mass spectrometry platform supports broad protein discovery, allowing researchers to measure thousands of proteins across large sample sets. Olink PEA technology uses proximity-based protein detection to deliver sensitive measurements of selected proteins, complementing the wider discovery capabilities of mass spectrometry. Seer’s Proteograph Product Suite adds another proteomics approach to the study’s analytical framework. Integrated with genomic and clinical information, these methods enable researchers to examine relationships between genetic variation, protein levels and health-related traits from different analytical perspectives. Combining the approaches addresses a key challenge in population-scale proteomics: obtaining sufficient analytical depth to identify previously unknown biological relationships while processing the large numbers of samples needed to characterize diverse populations.

“Researchers no longer have to choose between depth and scale,” says Karen Nelson, chief scientific officer at Thermo Fisher Scientific. She notes that combining discovery mass spectrometry, targeted protein analysis and genomic and health data can reveal previously unrecognized biology and support research into biomarkers and potential therapeutic strategies.

For drug developers, the emerging dataset could provide additional starting points for investigating disease-associated pathways and prioritizing candidate biomarkers or therapeutic targets. However, genetic-protein associations alone do not establish causality or demonstrate that a particular protein is a viable drug target. The results represent an initial set of findings rather than the completion of the broader study. Further investigation will be needed to establish the biological and clinical significance of individual associations and determine which may have applications in disease prediction, diagnosis or treatment development.

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