The combination of genetic and protein expression data (“proteogenomics”) has long been recognized as a highly relevant and powerful combination of molecular analysis. In its simplest form, this approach can be used to calculate the percentage of variation in protein expression among individuals that is due to genetics. This is important information when analyzing the results of population cohorts etc. A good example of this was reported by Ulf Gyllensten’s group from Uppsala University, who described strong effects of genetic and lifestyle factors on protein biomarker variation in a population cohort from northern Sweden.
When cis-pQTL data is combined with phenotypic data in a Mendelian Randomization (MR) analysis, it provides extremely strong evidence that the protein plays a causal role in the disease or biological process being studied.
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More recently, proteogenomics is increasingly used to identify specific genetic loci that affect the levels of individual proteins. These associations are known as protein Quantative Trait Loci (pQTLs), and when located very close to the genetic locus encoding the affected protein (“cis-pQTL”), these are powerful tools for gaining actionable insights into disease biology and to drive new drug development. When cis-pQTL data is combined with phenotypic data in a Mendelian Randomization (MR) analysis, it provides extremely strong evidence that the protein plays a causal role in the disease or biological process being studied. This is particularly important for drug target identification, where causality is a prerequisite to take the protein into a drug discovery and development program but cannot be reliably determined from either genomics or proteomics alone.
This powerful approach is being rapidly adopted by the scientific community and has also resulted in a major international consortium dedicated to large-scale collaboration and pQTL data sharing. SCALLOP is an independent collaborative framework for the discovery and follow-up of genetic associations with proteins for researchers generating data using the Olink platform. The aim of the SCALLOP consortium is to identify novel molecular connections and protein biomarkers that are causal in diseases, and to date, 35 PIs from 28 research institutions have joined the effort, which now comprises summary level data for almost 70,000 patients and controls from 45 cohort studies.