Plasma Proteome Variation and its Genetic Determinants in Children and Adolescents

Abstract

The levels of specific proteins in human blood are the most commonly used indicators of potential health-related problems. Understanding the genetic and other determinants of the human plasma proteome can aid in biomarker research and drug development. Diverse factors including genetics, age, sex, body mass index (BMI), growth and development including puberty can affect the circulating levels of proteins. Affinity-based proteomics can infer the relationship between blood protein levels and these factors at a large scale. Compared to these methods, mass spectrometry (MS)-based proteomics provides much higher specificity of identification and quantification, but existing studies are limited by small sample sizes or low numbers of quantified proteins. Here we aim to elucidate to which extent genomic variation affects plasma protein levels across diverse age ranges and cohort characteristics. Employing a streamlined and highly quantitative MS-based plasma proteomics workflow, we measured the plasma proteome of 2,147 children and adolescents. Levels of 90% of these proteins were significantly associated with age, sex, BMI or genetics. More than 1,000 protein quantitative trait loci (pQTLs) - a third of which were novel - regulated protein levels between a few percent and up to 30-fold. These replicated excellently in an independent cohort of 558 adults, with highly concordant effect sizes (Pearson r > 0.97). We developed a framework to eliminate artefactual pQTLs due to protein-altering variants, paving the way for large-scale interrogation of pQTLs using MS-based proteomics. Our data reveal unexpectedly extensive genetic impacts on plasma protein levels, consistent from childhood into adulthood. These findings have implications for biomarker research and drug development.

MS-Based Proteomics of Body Fluids: The End of the Beginning

“…In our experience, unbiased proteomics usually results in panels of biomarkers that individually have small fold-changes but in combination capture sufficient information in body fluid proteomes to resolve confounders and accurately diagnose disease states. This empirical finding is supported by the notion that the body fluid proteome is multi-dimensional and influenced by factors such as genetics ( 91 , 92 , 93 , 94 ), gender ( 91 ), age ( 95 ), lifestyle ( 92 , 96 ), prior disease or treatment ( 97 ) and that biomarker panels are inherently better suited for to resolve this complexity ( 2 ). Our recent experience furthermore suggests that these panels typically comprised 10 to 20 proteins that together allowed patient classification ( 16 , 17 , 18 , 19 ).…”

“…Similarly, identification of genetic effects on protein levels in body fluids termed protein quantitative trait loci (pQTL) typically requires large study collectives ( 113 ). In MS-based proteomics, larger study sizes have drastically raised the number of identified pQTLs and there is a higher pQTL identification rate compared to similar affinity binder proteomics studies ( 94 ).

Fig.

…”

Helicobacter pyloriseropositivity associates with hyperglycemia, but not obesity in Danish children and adolescents

“…Participant genotyping was carried out as described previously (64,65). Out of the 1703 included participants, 1379 had been genotyped.…”

Non-linear genetic regulation of the blood plasma proteome

“…Finally, we aimed to replicate our analyses by training models using data generated by a different proteomics technology. We therefore, as above, retrained models using a Danish cohort of obese children measured using mass spectrometry based (MS) proteomics (The HOLBAEK Study) 10 .…”