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Synergistic insights into human health from aptamer- and antibody-based proteomic profiling.
Pietzner, Maik; Wheeler, Eleanor; Carrasco-Zanini, Julia; Kerrison, Nicola D; Oerton, Erin; Koprulu, Mine; Luan, Jian'an; Hingorani, Aroon D; Williams, Steve A; Wareham, Nicholas J; Langenberg, Claudia.
Afiliação
  • Pietzner M; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Wheeler E; Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Carrasco-Zanini J; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Kerrison ND; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Oerton E; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Koprulu M; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Luan J; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Hingorani AD; MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
  • Williams SA; Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, WC1E 6BT, UK.
  • Wareham NJ; UCL BHF Research Accelerator Centre, London, UK.
  • Langenberg C; Health Data Research UK, London, UK.
Nat Commun ; 12(1): 6822, 2021 11 24.
Article em En | MEDLINE | ID: mdl-34819519
ABSTRACT
Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan® v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica / Locos de Características Quantitativas Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica / Locos de Características Quantitativas Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article