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Published on: November 10, 2023
Proteogenomic links to human metabolic diseases
Mine Koprulu1, Julia Carrasco-Zanini1, Eleanor Wheeler1
1MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
This study reveals new genetic links between plasma proteins and health outcomes, identifying potential therapeutic targets for metabolic diseases like type 2 diabetes. Integrating genomics and proteomics advances disease understanding.
Area of Science:
- Genomics
- Proteomics
- Metabolomics
- Systems Biology
Background:
- The plasma proteome acts as a crucial link between an individual's genome and their observable traits (phenome).
- Understanding these links can uncover novel disease mechanisms and identify therapeutic targets.
Purpose of the Study:
- To conduct a cis-focused proteogenomic analysis of plasma proteins.
- To identify novel protein quantitative trait loci (pQTLs) and their association with health outcomes.
- To explore the genetic regulation of plasma proteins and their role in metabolic diseases.
Main Methods:
- Analyzed 2,923 plasma proteins in 1,180 individuals using antibody-based assays.
- Performed a cis-focused proteogenomic analysis to identify protein quantitative trait loci (pQTLs).
- Investigated shared genetic regulation between pQTLs and 575 health outcomes.
Main Results:
- Identified 256 novel protein quantitative trait loci (pQTLs).
- Demonstrated shared genetic regulation for 224 cis-pQTLs with 575 health outcomes, including links to metabolic diseases.
- Improved causal gene assignment at 40% of overlapping risk loci.
- Observed convergence between cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 in lipoprotein metabolism.
Conclusions:
- Integrating proteomics and genomics, even at a moderate scale, is valuable for discovering disease mediators.
- Findings highlight potential therapeutic targets for metabolic diseases, such as gastrin-releasing peptide for type 2 diabetes.
- This approach advances the understanding of genotype-phenotype relationships and disease pathogenesis.
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