Coding and regulatory variants are associated with serum protein levels and disease

Valur Emilsson,Thorarinn Jonmundsson,Vilmundur Gudnason,Lenore J Launer,John R Lamb,Elias F Gudmundsson,Lori L Jennings,Thor Aspelund,Mohd A Karim,Marjan Ilkov,Valborg Gudmundsdottir,Jan H Lindeman,James R Staley,Nicholas M Morton,Brynjolfur G Jonsson,Alexander Gudjonsson

doi:10.1038/s41467-022-28081-6

Abstract

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins’ emerging role as biomarkers and potential causative agents of a wide range of diseases.

Highlights

The x-axis of each box plot shows the genotypes for the corresponding protein-associated SNP, while the y-axis denotes the Box–Cox transformed, age, and sex-adjusted serum protein levels
In comparison to our companion GWAS paper[50] and using the same linkage disequilibrium (LD) threshold for novel associations, we find that 48.4% were exome-array-specific
We report here that many of the measured serum proteins under genetic control share genetics with a variety of clinical features, including major diseases arising from various body tissues

Summary

Introduction

The x-axis of each box plot shows the genotypes for the corresponding protein-associated SNP, while the y-axis denotes the Box–Cox transformed, age, and sex-adjusted serum protein levels. We demonstrate the estimated effects of the respective cis- and trans-acting genetic instruments on the serum TREM2 levels in AGES-RS (x-axis) and risk of LOAD through a GWAS by Kunkle et al.[32] (y-axis), using 21,982 LOAD cases and 41,944 controls.

Results

Conclusion