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Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Author(s) -
Raffield Laura M.,
Dang Hong,
Pratte Katherine A.,
Jacobson Sean,
Gillenwater Lucas A.,
Ampleford Elizabeth,
Barjaktarevic Igor,
Basta Patricia,
Clish Clary B.,
Comellas Alejandro P.,
Cornell Elaine,
Curtis Jeffrey L.,
Doerschuk Claire,
Durda Peter,
Emson Claire,
Freeman Christine M.,
Guo Xiuqing,
Hastie Annette T.,
Hawkins Gregory A.,
Herrera Julio,
Johnson W. Craig,
Labaki Wassim W.,
Liu Yongmei,
Masters Brett,
Miller Michael,
Ortega Victor E.,
Papanicolaou George,
Peters Stephen,
Taylor Kent D.,
Rich Stephen S.,
Rotter Jerome I.,
Auer Paul,
Reiner Alex P.,
Tracy Russell P.,
Ngo Debby,
Gerszten Robert E.,
O'Neal Wanda K.,
Bowler Russell P.
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900278
Subject(s) - proteomics , quantitative proteomics , cohort , correlation , medicine , computational biology , multiplex , copd , proteome , spearman's rank correlation coefficient , bioinformatics , oncology , biology , statistics , mathematics , genetics , geometry , gene
Novel proteomics platforms, such as the aptamer‐based SOMAscan platform, can quantify large numbers of proteins efficiently and cost‐effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross‐assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from −0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population‐based Multi‐Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody‐based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta‐analyzing proteomics data across assays and studies.