Nanoparticle Enrichment Mass-Spectrometry Proteomics Identifies Protein Altering Variants for Precise pQTL Mapping

Genetic variation in coding regions of the genome can change a protein’s structure in a way that affects how it interacts with other molecules. This poses a challenge in pQTL studies using affinity-based methods: the goal of pQTL studies is to find genetic variants that affect protein abundance levels; however, if a protein variant affects the epitope of an affinity reagent, and this protein variant is correlated with nearby genomic variants, it leads to erroneous detection of pQTLs.

In order to perform robust pQTL studies, which are typically performed in hundreds to tens of thousands of samples, scalability is key. Prior to the launch of Seer’s Proteograph™ Product Suite, it was not possible to do this using deep unbiased proteomics, which overcomes the shortcoming of affinity-based methods (e.g., variability with epitope effect that can result in false pQTL conclusions).

As proof-of-concept, the value of pQTL analysis using proteomics information at the peptide-level tied to genomics information at the nucleotide level, is demonstrated in the first such study undertaken and published in Nature Communications.

Genomics researchers at Weill Cornell Medicine-Qatar harnessed the Proteograph workflow to perform and improve pQTL analysis, offering important new insights into genetics-based drug discovery for the treatment of diseases. What they uncovered in the process were crucial genetic variants linked to GIP and ApoB levels relevant to conditions like Type 2 diabetes and cardiovascular disease.

The Study Design

This study introduces a new approach to understanding how genetic alterations influence proteins. Using the Proteograph™ Product Suite, researchers performed pQTL analysis to account for protein altering variants, enhance protein quantification, and explore the impact of genetic variations on protein levels and human health. By leveraging the Proteograph workflow, they were able to measure peptides originating from protein-altering variants, allowing them to be able to:

• Identify protein variants not measured by affinity-based methods.
• Account for epitope effects, the effects of structural changes.
• Enhance MS-based protein quantification.

The Results

From a cohort of 345 individuals, ~3,000 proteins and >18,000 peptides were successfully detected, presenting unparalleled visibility into the proteome. Through this process, researchers:

• Confirmed a subset of cis-pQTLs previously identified by affinity-based methods.
• Identified novel cis signal corresponding to putatively trans-pQTLs identified by affinity-based methods.
• Reported additional pQTLs not previously reported by affinity-based methods.

In the end, significant enhancements in pQTL analysis were made, shortcomings of affinity-based approaches that can affect pQTL outcomes were highlighted, and protein-altering variants that can have clinical relevance, such as those linked to GIP and ApoB levels that play a role in conditions like Type 2 diabetes and cardiovascular disease, were identified in blood.