Identification of cis-regulatory variation influencing protein abundance levels in human plasma

NIHR Biomedical Research Centre for Mental Health, South London, UK.
Human Molecular Genetics (Impact Factor: 6.39). 05/2012; 21(16):3719-26. DOI: 10.1093/hmg/dds186
Source: PubMed


Proteins are central to almost all cellular processes, and dysregulation of expression and function is associated with a range of disorders. A number of studies in human have recently shown that genetic factors significantly contribute gene expression variation. In contrast, very little is known about the genetic basis of variation in protein abundance in man. Here, we assayed the abundance levels of proteins in plasma from 96 elderly Europeans using a new aptamer-based proteomic technology and performed genome-wide local (cis-) regulatory association analysis to identify protein quantitative trait loci (pQTL). We detected robust cis-associations for 60 proteins at a false discovery rate of 5%. The most highly significant single nucleotide polymorphism detected was rs7021589 (false discovery rate, 2.5 × 10(-12)), mapped within the gene coding sequence of Tenascin C (TNC). Importantly, we identified evidence of cis-regulatory variation for 20 previously disease-associated genes encoding protein, including variants with strong evidence of disease association show significant association with protein abundance levels. These results demonstrate that common genetic variants contribute to the differences in protein abundance levels in human plasma. Identification of pQTLs will significantly enhance our ability to discover and comprehend the biological and functional consequences of loci identified from genome-wide association study of complex traits. This is the first large-scale genetic association study of proteins in plasma measured using a novel, highly multiplexed slow off-rate modified aptamer (SOMAmer) proteomic platform.

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Available from: Sally Nelson, Jul 01, 2014
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    • "The dataset contained 42 (that is, about 40% of) protein biomarker analytes whose measurement has been approved by US Food and Drug Administration (FDA) for clinical purpose (hereafter, clinically assayed proteins) assayed in blood (Anderson, 2010). It compares favorably to prior multisample human plasma studies regarding analytical depth (Melzer et al, 2008; Kato et al, 2011; Lourdusamy et al, 2012; Johansson et al, 2013), particularly considering that the analytical time was a mere 2.5 h per sample and consumed only 0.015 ll of plasma per SWATH injection, and significantly exceeds the previous studies in terms of reproducibility and quantitative accuracy. We next sought to assess the properties of the SWATH data. "
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    ABSTRACT: 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 2-7 years, 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. © 2015 The Authors. Published under the terms of the CC BY 4.0 license.
    Molecular Systems Biology 02/2015; 11(2). DOI:10.15252/msb.20145728 · 10.87 Impact Factor
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    • "The first genome-wide genetic analysis on gene expression was performed in haploid yeast segregants [15] and this proof-of-concept analysis demonstrated a widespread genetic effect on gene expression. Subsequent studies were carried out in many different organisms, including humans [16] [17] [18] [19] [20], and on other molecular levels, such as proteins [21] [22] [23], metabolites [24] [25] [26] and methylation [27] [28]. These studies have greatly increased our knowledge of the functional consequences of genetic variants. "
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    Biochimica et Biophysica Acta 05/2014; 1842(10). DOI:10.1016/j.bbadis.2014.04.025 · 4.66 Impact Factor
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    Alzheimer's & dementia: the journal of the Alzheimer's Association 10/2011; 8(1 Suppl):S1-68. DOI:10.1016/j.jalz.2011.09.172 · 12.41 Impact Factor
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