4.6 Article

Quantitative variability of 342 plasma proteins in a human twin population

Journal

MOLECULAR SYSTEMS BIOLOGY
Volume 11, Issue 2, Pages -

Publisher

WILEY
DOI: 10.15252/msb.20145728

Keywords

heritability; longitudinal variability; plasma biomarkers; SWATH-MS; twin study

Funding

  1. Swiss National Science Foundation [310030B_147086]
  2. SystemsX.ch (PhosphoNetPPM)
  3. European Research Council [ERC-2008-AdG 233226]
  4. Wellcome Trust
  5. EC
  6. National Institute for Health Research-BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust
  7. King's College London
  8. PRIME-XS (European Community, EC)
  9. Swiss National Science Foundation (SNF) [310030B_147086] Funding Source: Swiss National Science Foundation (SNF)

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The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2-7years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis-SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood-based biomarker studies.

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