Blood cells are crucial to human physiology, with functions in oxygen transport, infection
control, and wound healing. Molecular mechanisms endogenous to blood cells have
been implicated in the aetiologies of cancer, infection and inflammatory and immune
disorders. The genetic determinants of blood cell function have not been comprehensively
characterised, because it is too difficult to perform direct assays of cell function in large
population samples. High-throughput flow cytometry can be used to measure functionally
relevant phenotypes such as cell granulation, nucleic acid content, and cell size. Many
of these phenotypes are important for the diagnosis of diseases such as sepsis, Szary
disease, toxic granulation, and myelodysplastic syndromes, or correlate with assessments
of cell morphology from blood smear images. Here, I report the results of my genome-
wide association study of 63 previously genetically unstudied blood cell flow cytometry
phenotypes. I have identified associated variants in loci containing genes coding for
established drug targets with known roles in white cell function and immunity. I have
colocalised the association signals with blood cell transcriptomic, blood proteomic, and
disease risk, identifying possible causal roles for molecular mechanisms endogenous to white
cells in the aetiology of a range of immune disorders, including atopic dermatitis, multiple
sclerosis and celiac disease. My results have utility in drug design and therapeutic target
selection, demonstrated by examples including the replication of the mechanism of action
of Daclizumab, a treatment for multiple sclerosis, and evidence for the role of IL-18R1 in
aetiology of celiac disease. Furthermore, mendelian randomisation analyses suggest a causal
role for blood cell flow cytometry phenotypes in the aetiology of coronary artery disease,
lung cancer, and asthma. In addition to my work on flow cytometry traits, I report a major
contribution to the largest ever GWAS meta-analysis of routine clinical haematological
phenotypes, including 563,085 individuals. I performed primary and conditional analyses,
identifying parsimonious sets of independently associated variants. This is the largest
genome-wide association study study of clinical haematological phenotypes to date and
identifies 7,122 association signals.British Heart Foundatio