Large-scale plasma proteomics comparisons through genetics and disease associations

This study used a high-throughput proteomics platform that measures thousands of proteins in plasma, combining genetic and phenotypic information to explore the possibility of bridging the gap between the genome and disease. The researchers conducted association studies on Olink Explore 3072 data from plasma samples obtained from more than 50,000 UK Biobank participants through the UK Biobank Pharma Proteomics Project, focusing on people of British, Irish, African, and South Asian ancestry. They also compared the results with a SomaScan v4 study from the plasma of 36,000 Icelanders, for 1,514 of whom Olink data were also available. The correlation between the two platforms was moderate, and it was shown that the genetic associations of certain proteins differed between the platforms1.

Specifically, how did they differ?

The study detected different genetic associations on the two platforms (Olink and SomaScan) and provided examples in which these differences could affect the conclusions drawn from integrating disease research with protein levels. These differences were especially apparent in the detection of cis protein quantitative trait loci (cis-pQTLs), where the Olink platform detected these cis-pQTLs at a higher rate than the SomaScan platform (72% vs. 43%). Furthermore, it was observed that the genetic associations of proteins differed across the platforms, showing that this could affect the conclusions of a study1.

Please explain cis protein quantitative trait loci (cis-pQTLs) in an easy-to-understand way

cis protein quantitative trait loci (cis-pQTLs) refer to gene variants (genetic variations) that regulate the quantity or activity of a particular protein. These variants are named “cis” because they are located nearby on the same chromosome as the gene that encodes that protein. cis-pQTLs can affect protein expression and thereby influence an individual’s phenotype and disease risk.

How are cis-pQTLs determined?

cis-pQTLs are identified using statistical methods, by comparing genetic information with data on protein quantity. Specifically, they examine the association between individual gene variants (such as SNPs) and the quantity of proteins encoded near those gene variants. If the association is statistically significant, the gene variant may be identified as a cis-pQTL. This process is carried out using an individual’s genetic data and proteomics (protein measurement) data, and it helps in understanding the extent to which the quantity of a particular protein is genetically regulated.

What is the difference between the Olink platform and the SomaScan platform

Olink and SomaScan use different technologies to perform plasma proteomics analysis. The study showed that the Olink platform detected cis-pQTLs at a higher rate than the SomaScan platform, and that the genetic associations of proteins differed between the platforms. These differences may affect the conclusions drawn from integrating protein levels with disease research1.

Specifically, how do they differ technically?

Olink and SomaScan each use different technologies to perform proteomics analysis. The technical differences are described below:

Detection technology:
- Olink: The Olink platform uses an antibody-based Proximity Extension Assay (PEA) technology. This uses pairs of antibodies that bind to a specific protein, thereby detecting and quantifying the protein1.
- SomaScan: On the other hand, the SomaScan platform uses aptamer-based technology (aptamers are short single-stranded DNA or RNA molecules that bind to a specific protein)2.
Measurement precision and analytical range:
- The Olink platform has been shown to have high specificity for target proteins and a large number of phenotypic associations3.
- On the other hand, the SomaScan platform is considered to have broad measurement precision and analytical range across the whole proteome3.
Correlation range:
- A wide range of correlation has been shown between Olink and SomaScan in evaluating protein expression. This means that because the two platforms use different technologies, the same protein may show different results4.

These differences can be a factor in deciding which platform to choose depending on the design and purpose of a study. They may also affect the interpretation of study results obtained using different platforms.

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