Year: 2023
Type: article
Abstract: technically challenging to measure accurately.PR is typically measured with a platelet function assay or by light transmission aggregometry.These assays are time consuming and difficult to standardize.Consequently, genetic studies of PR have been hampered by small (N < 4,000) sample sizes, identifying, in aggregate, only 15 distinct PR-associated loci.A higher throughput, standardised signature of PR using platelet scattergrams from Sysmex haematology analysers would enable biobank-scale (N > 10,000) genetic studies of PR.Aims: Our aims were to (1) train a predictor of flow cytometry-measured PR from Sysmex platelet channel scattergrams in the Cambridge Platelet Function Cohort (PFC); (2) perform a genome-wide association study (GWAS) of this predicted phenotype in 29,806 blood donors in INTERVAL;(3) build a genetic score of PR and (4) assess whether this genetic score of predicted PR is associated with health outcomes in UK Biobank.Methods: We extracted features capturing the variation between Sysmex scattergrams.We fit a Lasso regression using data from the 533 participants in the PFC for which both Sysmex scattergrams and flow-derived platelet activation measurements were available.We predicted PR in response to ADP in INTERVAL from Sysmex data, performed a GWAS of predicted PR and built a genetic score.We performed survival analyses in UK Biobank to test for associations between the score and 524 health outcomes.Results: We identified 21 distinctly associated loci, only 6 of which were previously known.The score of PR was associated with thrombosis-related health outcomes.Conclusion(s): Sysmex scattergrams contain a signature of PR, allowing biobank scale studies of PR.We linked a genetic score of PR to thrombosis in a population cohort.Our approach provides a blueprint for studying the determinants of hard-to-measure but biologically important traits by phenotype imputation.
Institution: University of Utah
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,250
OpenAlex ID: W4388598633