Genomic atlas of the human plasma proteome
By Benjamin B. Sun, J. Maranville, J. Peters, D. Stacey, James R. Staley, James A. Blackshaw, Stephen Burgess, T. Jiang, Ellie Paige, P. Surendran and 23 more. Nature, 2018
1,884 citations recorded by the source corpus.
Subfields: Genetics, Hematology
View Paper: https://www.semanticscholar.org/paper/07427e8ace3d5797a22f217752e62dc082aa0c93
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- George Davey Smith
- Stephen Burgess
- Gibran Hemani
- J. Bowden
- Michael Holmes
- S. Thompson
- Philip C. Haycock
- Adam S. Butterworth
In Hematology
Citations in the corpus
- Plasma proteomic associations with genetics and health in the UK Biobank (2023, background): “6b, Supplementary Information and Supplementary Tables 17 and 18).”
- Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets (2023, neutral): “Recent years have seen a rapid increase in both the number and the size of pQTL studies, transforming our understanding of the genetic architecture of the circulating proteome 1–11 .”
- Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics (2020, background): “Molecular phenotype quantitative trait loci data (QTLs) includes protein QTL (pQTL) data of 2994 plasma proteins assessed in 3301 individuals of European descent (9).”
- Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. (2020, positive): “We used the GSMR method ( 29 ) to perform Mendelian randomization (MR) using near-independent [linkage disequilibrium (LD) R 2 = 0.05, where R 2 is the coefficient of determination] cis-pQTLs for IL17RA .”
- Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases (2019, methodology): “241 As another test of heterogeneity across studies, where the same protein was 242 measured in two or more studies, we performed colocalization analysis of each pQTL (in one 243 study) against the same pQTL (in another study) for the two studies in which we had access 244 to full summary results (S”
- The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, background): “Results were filtered to include only those with posterior probability for colocalization higher than 80% resulting in a set of colocalised loci which are considered ‘highly likely’ to be colocalized (Sun et al., 2018).”