Genetic regulation of the plasma proteome and its link to cardiometabolic disease in Greenlandic Inuit
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».