Genetic regulation of the plasma proteome and its link to cardiometabolic disease in Greenlandic Inuit
Bibliographic record
Abstract
Summary Background Circulating proteins play essential roles in numerous complex diseases, yet our understanding of the genetic influences on inflammation and cardiovascular-related proteins in non-European isolated populations remains sparse. Here, we aimed to characterise the genetic architecture of plasma protein biomarkers in the Greenlandic population. Methods Using combined data from Greenlandic population cohorts (n=3,707 individuals), including genotypes and plasma proteomics (177 proteins) from Olink Target 96 Inflammation and Cardiovascular II panels, we performed a protein quantitative trait loci (pQTL) study using a linear mixed model, accounting for relatedness and population structure. Findings Mapping of 177 plasma proteins in 3,707 adult Greenlandic individuals (mean age 47.9; 54.5% female) reveal 251 primary pQTLs, 235 additive (92 cis and 143 trans ) and 16 recessive (1 cis and 15 trans ), 48 secondary pQTLs, and 70 novel pQTLs (28%). We demonstrate a higher proportion of variance in protein levels explained in Greenlanders compared to Europeans from the UK Biobank (e.g. IL-27, IgGFcRII-b, IL-16, and Gal-9). We describe changes in expression of inflammation and cardiovascular-related proteins associated with known high impact Arctic-specific variants, including in CPT1A , TBC1D4 , HNF1A , LDLR , and PCSK9 . Interpretation These findings highlight the importance of genome-wide plasma proteomic analyses in Greenlanders, and diverse populations in general, with implications for biomarker and therapeutic target development. Funding Novo Nordisk Foundation, The Independent Research Fund Denmark, and Karen Elise Jensen Foundation. Research in context Evidence before this study Recent affinity-based proteomic studies have been performed in large European biobank-scale cohorts such as the UK Biobank and deCODE. Several smaller-scale studies have also been performed in isolated European populations, e.g. MANOLIS and Pomak (Hellenic), Orkney (Scotland), and Vis (Croatia). Studies in non-European populations are also beginning to emerge, including in the China Kadoorie Biobank. Studies performed in diverse populations can identify population-specific variants in genes implicated in regulating the expression of proteins, which may be causally linked to inflammation and cardiovascular disease. In particular, small and historically isolated populations, such as the Greenlandic population, are more likely to harbour common variants with larger effect sizes that may contribute to health and disease. Added value of this study This study reports 251 primary protein quantitative trait loci (pQTLs) associated with the abundance of 177 plasma proteins, 28% of which have not been previously reported. We identified 48 additional pQTLs in a secondary conditional analysis. We identified novel pQTLs that were common in Greenland, but rare globally (e.g. ST1A1, DCN). We found common pQTLs which explained a substantial proportion of variance (>30%) in protein abundance (e.g. IL-27, IgGFcRIIb, IL-16, Gal-9) when compared to Europeans. We examined differences in protein abundance in carriers of Arctic-specific variants (e.g. CPT1A, TBC1D4 , HNF1A , LDLR , PCSK9 ) which are implicated in lipid metabolism and cardiometabolic disease, revealing underlying biological mechanisms. Implications of all the available evidence Given that both genetics and the environment affect protein levels causally linked to disease, it is crucial to perform genome-wide association studies in smaller populations of diverse genetic ancestry to ensure equity in genetic discovery. Investigating the effect of previously identified Arctic-specific variants on protein expression revealed links to therapeutic targets for metabolic disease, which may have implications for the health care system in Greenland and beyond, including access to treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".