SCALLOP – genetics of the proteome
The SCALLOP consortium (Systematic and Combined AnaLysis of Olink Proteins) is a collaborative framework for discovery and follow-up of genetic associations with proteins on the Olink Proteomics platform. To date, 29 PIs from 24 research institutions have joined the effort, which now comprises summary level data for almost 65,000 patients or controls. SCALLOP welcomes new members.
For more information please contact Rena Siopi (SCALLOP project coordinator) and Anders Mälarstig (SCALLOP project chair).
For the latest news about SCALLOP – view our news page
Introduction from Anders Mälarstig
Each SCALLOP member works on human study collections from the general population, clinical trials or patients with certain diseases such as coronary artery disease, rheumatoid arthritis, bipolar disease, heart failure, dementias or metabolic syndrome.
The aim of the SCALLOP consortium is to identify novel molecular connections and protein biomarkers that are causal in diseases.
This work starts with identification of so called protein quantitative trait loci, pQTLs, which are robust connections between a gene variant and the levels of a protein.
There are two types of pQTLs:
- cis-pQTLs are variants that are proximal to the gene encoding the protein under study whereas trans-pQTLs are distal regulation of proteins via an often unknown path.
- Trans-pQTLs can provide unique insights of molecular connections in human biology.
Cis-pQTLs are strong instruments for determining if a protein biomarker for disease is causing disease or elevated or suppressed as a consequence of it. The SCALLOP consortium is currently underway with mapping novel pQTLs for several 100s of proteins in unprecedented sample sizes, something which will yield much deeper insights into the trans-regulation of plasma proteins than what has been possible to date.
Identify causal protein biomarkers
To be a member of the SCALLOP consortium you have to be the PI of a study collection with Olink proteomics and genome-wide genotyping data. We also expect members to sign up to the Consortium Agreement, which manages conduct and authorships. Download the Consortium Agreement here.
The leadership for subprojects within the SCALLOP consortium rotates and members can take new ideas and suggestions for additional subprojects to the monthly steering committee meetings.
SCALLOP uses a dedicated server for sharing of data. The server is set up under the Danish node of the TRYGGVE server structure. TRYGGVE allows sharing of sensitive data thanks to 2-step authorization procedures and high data security. Thanks to this structure SCALLOP is set up to move to individual-level data should the consortium wish to do so.
Please note: hover your mouse pointer over the entry in the “Olink panels” column to see the specific panels used in each case (may not work on mobile devices).
pQTL publications and data from SCALLOP members
Gisby, J., Clarke, C. L., Medjeral-Thomas, N., Malik, T. H., Papadaki, A., Mortimer, P. M., … & Peters, J. E. (2020). Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease. medRxiv 2020.11.05.20223289. Αrticle link
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Raffler, J., Kerrison, N. D., Oerton, E., … & Langenberg, C. (2020). Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nature communications, 11(1), 1-14. Αrticle link
Folkersen, L., Gustafsson, S., Wang, Q., Hansen, D. H., Hedman, Å. K., Schork, A., … & Mälarstig, A. (2020). Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nature metabolism, 2(10), 1135-1148. Αrticle link
Read more about the above SCALLOP pQTL CVD I study on: SCALLOP news post and KI news.
Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2020). Genetics meets proteomics: perspectives for large population-based studies. Nature Reviews Genetics, 1-19. Αrticle link
Bretherick, A. D., Canela-Xandri, O., Joshi, P. K., Clark, D. W., Rawlik, K., Boutin, T. S., … & Haley, C. (2020). Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS genetics, 16(7), e1008785. Αrticle link
Folkersen, L., Fauman, E., Sabater-Lleal, M., Strawbridge, R. J., Frånberg, M., Sennblad, B., … & Mälarstig, A. (2017). Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS genetics, 13(4), e1006706. Αrticle link
Enroth, S., Johansson, Å., Enroth, S. B., & Gyllensten, U. (2014). Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nature communications, 5(1), 1-11. Αrticle link