Genetic Analyses of Blood Cell Structure for Biological and Pharmacological Inference

Abstract

Thousands of genetic associations with phenotypes of blood cells are known, but few are with phenotypes relevant to cell function. We performed GWAS of 63 flow-cytometry phenotypes, including measures of cell granularity, nucleic acid content, and reactivity, in 39,656 participants in the INTERVAL study, identifying 2,172 variant-trait associations. These include associations mediated by functional cellular structures such as secretory granules, implicated in vascular, thrombotic, inflammatory and neoplastic diseases. By integrating our results with epigenetic data and with signals from molecular abundance/disease GWAS, we infer the hematopoietic origins of population phenotypic variation and identify the transcription factor FOG2 as a regulator of platelet α-granularity. We show how flow cytometry genetics can suggest cell types mediating complex disease risk and suggest efficacious drug targets, presenting Daclizumab/Vedolizumab in autoimmune disease as positive controls. Finally, we add to existing evidence supporting IL7/IL7-R as drug targets for multiple sclerosis.

Delayed bystander CD8 T cell activation, early immune pathology and persistent dysregulation characterise severe COVID-19

“…Reticulocyte numbers were measured using a Sysmex XN-1000 haematology analyser as previously described (Akbari et al, 2020).…”

Longitudinal analysis reveals that delayed bystander CD8+ T cell activation and early immune pathology distinguish severe COVID-19 from mild disease

“…Reticulocyte numbers were measured using a Sysmex XN-1000 hematology analyzer according to manufacturer instruction and as previously described (Akbari et al, 2020). Briefly, Sysmex technology uses three signals to define the physiological and structural properties of cells and to distinguish reticulocytes from the other blood cells: forward scatter, side scatter and side fluorescent light.…”

Higher body mass index raises immature platelet count: evidence from Mendelian randomization analyses

“…These measurements were pre-adjusted for technical covariates such as time between venipuncture and blood count, as well as instrument drift, seasonal and weekly variation [26]. Adjustment was performed with cyclic, thin-plate and P-splines in a generalized additive model (GAM) [27]. Resulting platelet trait values along with their units of measurement are reported in Table 1 .…”