The identification of new biomarkers for disease can provide a valuable indication of an individual’s risk of certain illnesses and even indicate the best treatments. This makes it an exciting area of research with the potential to radically improve our understanding of disease and to advance both the type and effectiveness of care.
OMICSPRED is a community resource that can simplify research by allowing the use of DNA to ascribe biomolecular traits. It can enhance analyses across domains and cohorts and enable large-scale data integration to help identify biomarkers, therapeutic targets and pathways.
Genetically-predicted levels of biomolecular traits are useful for investigating the molecular underpinnings of complex phenotypes. However fresh research was needed to build on its potential.
The team behind OMICSPRED aimed to develop an open resource of genetic scores for transcriptomic, proteomic and metabolomic traits. Their aim was to help researchers identify new biomarkers, bringing practical benefits in areas of work such as validation studies.
OMICSPRED results from a large collaborative effort supported by HDR UK (as part of its Multi-Omics Consortium) the University of Cambridge and the Baker Heart and Diabetes Institute. Among those leading the way were Dr Michael Inouye, Director of Research in Systems Genomics and Population Health in the University of Cambridge Department of Public Health and Primary Care.
Their approach has been to use a single cohort (INTERVAL), which consisted of 50,000 healthy blood donors, with extensive multi-omics data to train genetic scores using machine learning.
The portal allows users to explore and download the genetic scores for a wide range of biomolecular traits plus summary statistics of their associations with key traits and diseases in the UK Biobank.
Impact and outcomes
The team has been able to create models that allow researchers to use DNA to predict the levels of molecules that are risk markers for certain diseases (such as cardiovascular disease or heart attacks) that will be in someone’s blood.
This is relatively simple and inexpensive compared to measuring the actual amount of molecules in blood.
By providing genetic scores the team hopes to advance data integration efforts, new biomarker identification and causality analysis for protein, metabolites and other biomolecules.
There has already been an excellent community response with researchers saying they find OMICSPRED a useful resource.
As a “living resource” it will be further expanded with further validation, more scores and platforms.
A journal publication led by postdoctoral researcher Dr Yu Xu is being prepared which details the research that has given rise to OMICSPRED.
Dr Michael Inouye, said:
“Omicspred is something that will help support a lot of early stage research in discovering which molecules are important for which diseases and might represent good targets for drugs. People want new therapies and they want new predictive biomarkers that make their clinical care better. That’s exactly what the project is aiming to do.”
Dr Inouye firstname.lastname@example.org
Diabetes Data Science Catalyst Showcase
23 February 2023 at 2:00 pm
Join us at this webinar to hear about our Diabetes Data Science Catalyst's aims, insights and an update on our upcoming funding call.
Cardiac rehabilitation smart tech set to be trialled
1 February 2023
The use of smart technology to support the recovery of heart attack patients is to be assessed as part of a major new study, with input from the BHF Data Science Centre.