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Realizing the potential of population-scale proteogenomics

Population health

In the UK Biobank Pharma Proteomics Project (UKB-PPP), 13 biopharmaceutical companies generated new proteomic data from accessing the UK Biobank. Using the Olink Explore platform, they measured around 3,000 proteins covering all major biological pathways in more than 54,000 UKB participant samples.

Two major articles, published in Nature by consortium members, have reported on a series of impressive initial findings from the project that demonstrate the astonishing power of combined genomic and proteomic analysis at this unprecedented scale. They also describe a unique open-access data resource for the wider scientific community that will empower a plethora of new discoveries and insights. A third related article from deCODE Genetics compares the genetic associations with protein expression identified in the UKB-PPP dataset with those obtained using the SomaScan platform to analyze a large Icelandic cohort, finding compelling genetic corroboration for the specificity of Olink assays.

Plasma proteomic associations with genetics and health in the UK Biobank

Nature, Sun et al., 2023

This article from the UKB-PPP consortium provides a first detailed summary of the data obtained from a GWAS-based proteogenomic analysis and protein quantitative trait loci (pQTL) mapping, identifying over 14,000 primarily novel genetic associations with protein expression levels. These findings were analyzed in the context of several different diseases, illustrating the unprecedented breadth and depth of this dataset to help elucidate biological mechanisms, identify actionable new biomarkers, and accelerate drug development.

The study constructs an updated genetic atlas of the plasma proteome, reveals novel biological insights into prevalent illnesses, and provides the scientific community with an open-access, population-scale proteomics resource
Sun et al., 2023

Rare variant associations with plasma protein levels in the UK Biobank

Nature, Dhindsa et al., 2023

Authored primarily by consortium members from AstraZeneca, this study used the dataset described in the flagship paper by Sun et al. to study genetic associations of rare protein-coding variants with protein expression using an exome-wide association study (ExWAS) approach. Variant-level analysis revealed over 4,400 rare pQTLS, while aggregated gene-level analysis identified ~2,000 gene-protein associations. This study illustrates the importance of studying rare variants in the context of proteogenomics and their impact on biological outcomes. It also highlights the crucial need for large-scale proteogenomic studies to empower such discoveries.

We highlighted several examples of how this protein-coding pQTL atlas can address drug discovery and clinical pipeline challenges. We anticipate that this resource will provide novel insights into protein regulatory networks, upstream trans regulators of target genes whose inhibition could increase target protein levels, target safety assessments and drug repositioning opportunities
Dhindsa et al., 2023


Read groundbreaking studies showcasing how next-generation proteomics enables genetic assessment of protein causality and novel drug targets, enhancing genomics’ link to phenotype.

Large-scale plasma proteomics comparisons through genetics and disease associations

Nature, Eldjarn et al., 2022

This study from deCODE Genetics/Amgen compared proteogenomic data from the UKB-PPP dataset with that obtained using the SomaScan aptamer-based platform in a cohort of 36,000 Icelandic people. A large number of genetic associations with protein levels were found in both studies, but the correlation between the platforms was only moderate.  Most significantly, 72% of all proteins measured using Olink had associated cis-pQTLs (providing strong genetic corroboration of target specificity), while cis-pQTLs were found for only 43% of proteins measured using SomaScan.

... the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%)
Eldjarn et al., 2022