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, 33 PIs from 28 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

 

Current work

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

Operations

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.

Repository browser

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).

Acronym Design Sample size Olink panels
Aristotle Atrial fibrillation 1500 3
ASAP Aortic valve surgery 573 5
BAMSE Childhood asthma 2000 1
Bialystok PLUS Population based/ CVD, CHD 899 5
BioFinder Dementia 1550 4
COSM-C Population based 4500 3
Dan-NICAD Stable coronary artery disease 1650 3
DIRECT Diabetes 3000 5
EpiHealth Prospective observational 2500 3
Estonian Biobank Population based 500 4
FENLAND Population based 500 15
Framingham Population based 520 5
HELIC MANOLIS Population based 1356 5
HELIC POMAK Population based 1537 5
I AM Frontier Deep phenotyping, Longitudinal 240 15
IMPROVE Prospective, metabolic syndrome 3403 1
INTERVAL Blood donors 5000 4
Kadoorie biobank Pancreatic cancer 1400 1
KARMA Incident breast cancer 1820 2
KORA F4 Population based 1050 1
LBC1936 Population aged 72 750 1
LifeLines Deep Population based 1200 1
Pfizer/ MadCam_ph2 Inflammatory bowel disease 200 3
MPP-RES Heart failure 1000 1
NSPHS Population isolate 1000 6
ORCADES Population isolate 1000 15
PIVUS Prospective observational 933 1
PRIDE Dementia 1500 14
PROCARDIS Coronary artery disease 900 1
PURE Epidemiological cohort 8000 15
Qmdiab Diabetes 320 2
RECOMBINE Rheumatoid arthritis 800 1
RISC Prospective observational 1000 9
Rotterdam Population based 250 1
Rotterdam Study-III cohort Population based 3500 2
SMCC/ SIMPLER Population based, women 5000 3
STABILITY Acute coronary syndrome 3000 2
STANLEY Bipolar disorder, depression 681 4
SWHS/ SMHS/ SCCS Population based 300 14
ULSAM Prospective observational 1000 9
VIS Population isolate 1000 3
WHI Clinical trial, mixed population, women 1400 5
1000IBD Crohn’s disease and ulcerative colitis 1000 1

 


pQTL publications and data from SCALLOP members

Smith-Byrne, K., Chen, Y., Kachuri, L., Kapoor, P. M., Guida, F., Zahed, H., … & Malarstig, A. (2021).  IL-18 and Lower Risk for Lung Cancer: Triangulated Evidence from Germline Predictions, Pre-Diagnostic Measurements, and Tumor Expression.  medRxiv 2021.03.26.21254400. Αrticle link


Klaric, L., Gisby, J. S., Papadaki, A., Muckian, M. D., Macdonald-Dunlop, E., Zhao, J. H., … & Peters, J. E. (2021).  Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19.  medRxiv 2021.04.01.21254789. Αrticle link


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