IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes—including cell-type specific measures of granularity, nucleic acid content and reactivity—in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types—variants in  The authors identify genetic variation associated with properties of the internal biological structures of blood cells in hundreds of genes and show how such discoveries can be used to improve understanding of cellular mechanisms causing disease. Meanwhile, high-power genome-wide association studies (GWAS) of blood cell traits have concentrated on phenotypes in classical complete blood counts (cCBCs)4. cCBCs are standard clinical reports, which include measurements of the counts (i.e. concentrations) and average volumes of various types of cell in the peripheral blood. However, cCBCs do not measure the properties of intracellular structures, which play important roles in many functional haematological processes. For example, pathologies of leukopoiesis that result in the absence of specific granules from neutrophils can cause immune dysfunction5, but are frequently accompanied by a normal neutrophil count and so cannot be detected by cCBCs.Blood cells play vital roles in human physiology, including in oxygen transport, in haemostasis, and in host defence. Many of the biological functions of blood cells, such as thrombotic aggregation and the killing of pathogens or the killing of virally infected cells, are mediated by proteins stored in cell granules that are released into the extracellular space in response to a stimulus. At present, the functional responses of blood cells to stimuli cannot be measured using high-throughput instruments. Consequently, genetic association studies of cell function traits have been limited to small studies  by a neutrophil is a measure of the cell’s intracellular organelle complexity, including its granule content. Neutrophil granularity is an important immune phenotype. Activated neutrophils are known to exhibit greater SSC than resting neutrophils20. By integrating the results of GWAS of ncCBC traits with the results of multi-omic and disease GWAS, we show how flow-cytometry phenotypes can be used to identify the secretory origins of proteins in the blood plasma and used to study the role of blood cells in mechanisms mediating disease risk variation.Here, we report the first large-scale GWAS of flow-cytometry measured non-classical CBC (ncCBC) traits Fig. . We call21. Eleven of the traits explicitly summarise cell-level measurements of intracellular complexity/granularity (SSC), sixteen of the traits explicitly summarise cell level measurements of cell nucleic acid content/membrane lipid content (SFL) and fifteen of the traits explicitly summarise cell-level measurements of cell morphology/volume (FSC)  variant-trait associations (Supplementary Data r2 > 0.8) based greedy clumping algorithm (Methods). This clustered the 2172 variant-trait associations into 849 clumps, 231 of which represented platelet signals (i.e. contained at least one platelet cell-trait associated variant), 211 of which represented red cell signals and 432 of which represented white cell signals —of the 432 white cell association signals were novel—i.e. the corresponding clump did not contain a variant in LD (r2 > 0.8) with a variant reported to be associated with a white cell phenotype by Vuckovic et al. or Chen et al.4 , iron homoeostasis for red cell traits  and chemotaxis and adhesion for myeloid white cell traits   Fig. .Fig. 4Su22, illustrating the potential for GWAS of ncCBC traits to identify genes critical to blood cell function. Specifically, we identified 35 associations with NE-SSC (neutrophil granularity), 30 associations with EO-SSC (eosinophil granularity), and twelve associations with MO-SSC (monocyte granularity) that did not correspond to any of the genetic associations with cCBC traits reported by Vuckovic et al. or Chen et al. , in exocytosis , as granule cargo , as lysozyme cargo , and in immune response , confirming that our approach has uncovered biologically relevant genes, including genes in molecular pathways modulating cellular complexity and granule formation.We identified genes, not previously found by GWAS of granulocyte traits, that code for various white cell granule proteins including Arylsulfatase B, Lactoperoxidase, RNase 3 and multiple defensins. These proteins have well understood roles in immunity23 to identify the subcellular localisation of proteins expressed by the genes identified by the neutrophil trait GWAS in a wide variety of organelles  of nine types of progenitor cell  and nine types of the mature cell 29.Many of the intracellular structures generating variation in flow-cytometry phenotypes have developmental origins in the immature precursors of peripheral blood cells. Granule formation, for example, is a cell-type specific process occurring at particular stages of cellular differentiation; the granules of platelets and the granules of granulocytes begin to form in megakaryoblasts and myeloblasts, respectively27. The relative enrichments of genetic variants associated with NE-SSC (neutrophil granularity) in the nucleosome-depleted regions of the hematopoietic stem cell (HSC) and the four types of myeloid progenitor cell  are consistent with a progressive increase in the accessibility of enhancers regulating granule formation during myeloid differentiation and point to an origin of these granules in lineage-committed myeloid progenitors. In monocytes, we observed an enrichment of MO-SFL (monocyte nucleic acid content) associated variants in the nucleosome-depleted regions of granulocyte-macrophage progenitor cells (GMP)—the differentiation phase of proliferation and cell division— and an enrichment of MO-SSC (granularity) in the nucleosome depleted regions of peripheral blood monocytes, suggesting that granularity may be regulated in the ultimate stages of monocyte differentiation before egress from the bone marrow.We observed patterns of enrichment in the open chromatin regions of the progenitor cell-types ancestral to neutrophils, eosinophils, monocytes, and lymphocytes  in at least one of the blood cell-types surveyed by the ncCBC phenotypes (megakaryocytes (MKs) and erythroblasts—the respective progenitors of platelets and red cells—and neutrophils, eosinophils, basophils, monocytes, CD4+ T cells, CD8+ T cells, naive B cells). We performed colocalisation analysis between the pQTL and our cytometry trait-associated conditionally significant variants and identified 61 and 1021 colocalisations (PP > 80%) with cis and trans pQTL, respectively  (Supplementary Data ARSB (PP = 99%), LY9 (PP = 98%), MPO (PP = 100%), PRTN3 (PP = 100%) and RNASE6 (PP = 99%) ZFPM2 (encoding the transcription factor Friend of GATA-2 or FOG2)  on variants in the ZFPM2 locus suggested a single conditionally significant association, and fine-mapping of the locus identified a single intronic SNP (rs6993770) as the most probable causal variant (PP = 95%). rs6993770 is located in a region of open chromatin (ATAC-seq) in MKs, which contains histone modifications indicative of a transcriptional enhancer  with rs6993770 were located in regions for which epigenetic data supported causality in MKs  identified an association in G2) Fig. , colocalG2) Fig. . RNA-seqMKs Fig. . Complemage Fig. 29. A steMKs Fig. .Fig. 5Th35. We identified new associations with the ncCBC traits IPF#, PLT-SSC, and PLT-FSC 30. Of the 215 associated proteins, 44 were localised to α-granules and 171 were not. The direction of the effect size estimates of Sun et al. imply that rs6993770-T reduces the mean plasma concentration of 40 (91%) of the 44 α-granule localised proteins but only 101 (59%) of the 171 proteins not localised to α-granules .There are well-known associations between rs6993770 and the four platelet cCBC traits: platelet count (PLT), volume (MPV), crit (PCT) and volume distribution width (PDW)les Fig. . This in2FPKM > 1). We estimated the additive allelic effect of rs6993770-T on plasma proteins to be 0.038 phenotypic standard deviations lower on average  in proteins localised to α-granules compared to other proteins, adjusting for gene expression in MKs  38 and assessed the evidence for colocalisation between genetic associations with ncCBC traits and disease risks (Supplementary Data 39. We found strong evidence (PP > 80%) for colocalisation between 73 of the variant-ncCBC trait associations—corresponding to 29 variant clumps—and at least one disease association -2 (IL2RA) and IL-7 (IL7R) and in the gene encoding IL-7 itself (IL7). IL-2 receptor α-chain (the product of IL2RA) is the target of the therapeutic antibody Daclizumab, which is known to be clinically effective in the treatment of MS42, but has been withdrawn due to severe side effects including encephalitis46.Eight variant clumps contained variants associated with lymphocyte traits that colocalised with genetic associations for the risk of multiple sclerosis (MS), coeliac disease, primary biliary cirrhosis, hay fever/rhinitis or coronary artery disease (Supplementary Data IL7R) is one possible target48, and genetic evidence suggests that a higher serum concentration of soluble IL-7R increases the risk of multiple sclerosis49. A non-synonymous coding variant in IL7RA (rs6897932-C) effects alternative splicing causing a two-fold increase in the skipping of exon 6 resulting in production of soluble IL-7R51. rs6897932-C is in high LD (r2 = 1) with rs11567705-C (AF = 72.5%) which is associated with increased reactive lymphocyte count . This association with RE-LYMPH# colocalised with increased risk of MS (PP = 95.7%). Thus our analysis suggests that the effect of soluble IL-7R on MS aetiology may be mediated by modulation of lymphocyte activation as measured by RE-LYMPH#, consistent with the role of IL-7R/IL-7 as an efficacious target for the treatment of MS50.Alternative therapeutic approaches to MS are required to ameliorate the risk that subsets of patients develop severe side effects on existing drugs. The IL-7 receptor  and these associations colocalised with lower expression of ITGA4 in monocytes  and higher inflammatory bowel disease (IBD) risk (PP = 98.2%). This finding is interesting because Vedolizumab was originally used to treat IBD53 and was assumed to reduce trafficking of α4β7-positive gut-specific T-helper lymphocyte by diminishing integrin interaction with the mucosal addressin cell adhesion molecule 1 (MadCAM-1)55. However, a recent study has suggested that the antibody also reduces the ability of monocytes to egress into the colonic mucosa as an alternative mechanism57 consistent with the results of our genetic analyses of monocyte flow-cytometry traits.Further examples demonstrate the ability of ncCBC GWAS associations to indicate which blood cell-types may mediate disease aetiology. For instance, rs2124440-A is associated with properties of monocytes  can suggest mediating tissue types. Consequently, there is a need to catalogue genetic associations with phenotypes measuring cell-type specific biological variation at different levels of complexity, from variation in molecular abundances to variation in the properties of organelles.Over the last decade, GWAS have identified thousands of genetic associations with risks of common complex diseases. A major motivation for these studies has been to improve our understanding of the molecular and cellular mechanisms underlying disease aetiology, in order to develop safe and effective pharmacological treatments. Unfortunately, only a fraction of the biological mechanisms underlying the genetic associations with disease risk that have been identified are presently understood. Sometimes even the mediating cell-types and tissues are unknown. One approach to understanding a genetic association with disease risk, is to consider the context of colocalizing genetic associations with other phenotypes that have been published in the literature or online catalogues, a so-called ‘phenoscan’ELANE, MPO, PRTN3). rs138303849-C, a variant upstream of PRTN3  is associated with increased NE-SSC (granularity), NE-SFL (nucleic acid content), NE-FSC (volume), and decreased NE-SSC-DW (SSC distribution width). The allele, which is known to be associated with an increased risk of a form of vasculitis characterised by autoantibodies against PR360, is an eQTL for PTRN3 in whole blood61. We recently showed that the risk allele is associated with increased PR3 concentration in plasma30. In the present analysis, we observed that the risk allele and the cis-pQTL for higher plasma PR3 levels all colocalise (PP = 99.9%) with the neutrophil ncCBC associations. This suggests that the vasculitis risk allele not only increases PRTN3 transcription and PR3 plasma protein levels, but also changes the structural and granular properties of neutrophils.We have studied a new class of flow-cytometry phenotypes, which can capture cell-type specific quantitative variation in the functional structures of blood cells, such as secretory granules. These phenotypes can be measured affordably in tens of thousands of study participants using clinical haematology analysers. GWAS analyses of these traits showed that they are heritable, have complex genetic architectures, and are affected by variation in genes implicated in a variety of molecular and cellular pathways. The broad biological sensitivity of flow-cytometry phenotypes means that their genetic associations have a coarser interpretation than those of many other intermediate quantitative phenotypes, such as molecular abundances . For example, cytometry measured ‘neutrophil granularity’ depends on the average number of granules in peripheral blood neutrophils, and the level of expression of genes coding for proteins in neutrophil granules , has shown that the transcription factor FOG2 is a probable regulator of platelet α-granularity, which influences the concentrations of a multitude of α-granule proteins in the blood plasma.We have shown more generally how genetic associations with the new traits can be used to interpret colocalizing associations with disease risks and provide evidence to support drug targets, through examples which are corroborated by existing evidence in the literature, including the role of lymphocyte activation in IL-7R/IL-7 mediated risk of MS and the role monocytes Our study has some limitations. Firstly, because participants in the INTERVAL cohort are overwhelmingly of European ancestry, we have not been able to study genetic variation extraneous to the European ancestry population. Secondly, because most of the ncCBC traits are not intended for clinical use, they are subject to greater technical variability than cCBC traits. Nevertheless, we are confident that we have been able to remove much of this variation statistically. Genetic studies, which seek to estimate differences in phenotypic averages between genotype groups, do not rely on the calibration of phenotypes against an absolute standard. However, investigators who wish to perform non-genetic studies of ncCBC traits should be aware of the potential for between instrument variation. Thirdly, because we have no information about the medications taken by the participants, we were unable to exclude the possibility of confounding due to differential prescribing by genotype. However, we expect that such confounding is unlikely, given the general good health of blood donors and the fact that most common genetic variants have a modest effect on disease risk and hence prescribing risk. Fourthly, because the ncCBC phenotypes are novel to genetics, we do not have access to a replication dataset. However, CBC trait associations identified in INTERVAL using the same analysis protocol have a high rate of replicability in other datasets (e.g. UK Biobank). This gives us confidence in our results, although the ultimate validation of any genetic association must rely on cellular and functional laboratory follow up. Finally, although flow-cytometry traits are able to capture aspects of biological variation related to cell function, they lack the clean biological interpretation of phenotypes designed to measure particular cellular mechanisms, such as the response of cells to perturbation by an agonist. In future, the development of efficient cell-type specific assays may enable large-scale population studies of isolated functional processes such as activation, degranulation and cell motility likely to play a role in the aetiologies of cardiovascular and immune disorders.21. The study was approved by the Cambridge East Research Ethics Committee and we complied with the relevant ethical regulations. Informed consent was obtained from all participants during recruitment. Individuals who have suffered recent illness or infection are ineligible to donate blood. Consequently, the participants were predominantly healthy at the time of recruitment. The participants completed a baseline survey which included questions about their lifestyle and state of health, including their smoking habits, their alcohol consumption habits, whether they suffered from doctor-diagnosed anaemia, their use of medication  and their menopausal status. At baseline, participants were randomly allocated to a sex specific trial arm, according to which men were asked to donate blood every twelve, ten or eight weeks and women were asked to donate blood every sixteen, fourteen or twelve weeks. Participants gave blood samples for research purposes at baseline and at a follow up visit approximately two years later. In the intervening period they were asked to donate blood according to the schedule of their allocated trial arm. The blood samples given for research were collected from a pouch attached to a standard blood collection unit. The samples for CBC analysis were stored in 3 ml EDTA tubes which were inverted three times before being transported in rigid boxes at ambient temperature, via three NHSBT holding sites , to UK Biocentre, Stockport, Greater Manchester. Here, extended clinical blood count reports, which include measurements of cCBC and ncCBC traits, were generated using a pair of Sysmex XN-1000 analysers. Respectively, 72% and 98% of samples were processed within 24 h and 48 h of venipuncture.The INTERVAL study was a randomised trial of approximately 45,000 blood donors aged eighteen years or older who were recruited at 25 NHSBT  static donor centres across EnglandRBC/PLT (red cell and platelet counts/volumes using Coulter’s impedance principle)HGB WNR (white cell and nucleated red cell flow-cytometry)WDF RET (reticulocyte and red cell flow-cytometry)PLT-F (platelet flow-cytometry).The Sysmex XN series of haematology analysers can be configured to use various combinations of seven measurement channels: an impedance channel, a photometric channel and five flow-cytometry channels. The analysers used for the INTERVAL study were configured to use the following six channels:The channels used to measure each of the 63 traits we studied are indicated in Supplementary Data The analyser generates an aliquot of blood for each measurement channel. The flow-cytometry channel aliquots are diluted  and pre-treated with channel-specific reagents which differentially lyse and perforate cells by type. The aliquots are then treated with channel specific reagents containing dyes that bind to nucleic acids in organelles and the nucleus. The PLT-F flow-cytometry channel uses a reagent containing an oxazine based dye (Fluorocell PLT), while the other flow-cytometry channels use reagents containing a polymethine dye . Each flow-cytometry channel measures three cell-level properties of each particle in the blood aliquot: side fluorescence, forward scatter and side scatter. Side fluorescence is a measure of cell nucleic acid content, but also depends on cell membrane lipid content; forward scatter is a measure of cell size; side scatter is a measure of cell granularity. The cell-level measurements are used by the analyser to classify cells by type . We also excluded measurements if they were made on a day for which fewer than ten measurements of the corresponding trait were available from the corresponding analyser, or if they were made on a day when the median of the measurements of the trait made by the analyser deviated substantially (more than 8 median absolute deviations) from the global median of the measurements made the analyser over the course of the study.Haematology analyser data contain both technically and biologically mediated variation. Where this variation has a non-genetic origin it is a source of noise, which reduces the power of GWAS analysis. We adjusted the data from each trait in two stages using generalised additive models (GAMs) to remove phenotypic variation likely to be extraneous. We identified variables to include as predictors in the GAMs at each stage by consulting haematologists and performing exploratory visual and linear regression analyses. In the first stage, we adjusted each trait to remove technical variation and variation explained by the time at which the blood sample was analysed. The latter variation includes variation due to machine drift, variation caused by machine calibration and variation due to seasonal changes in the physiology of donors. In the second stage, we adjusted each trait for various environmental and physiological factors thought likely to generate variation in blood traits, but unlikely to be colliders of genotype and phenotype. At each stage, we adjusted the traits differently according to whether they were directly measured by the analyser or derived by the analyser from the directly measured traits (Supplementary Data mgcv) to each trait, using splines to model the dependence of the trait on the covariates jointly. The models were based on the regression Eq. (i is an index for the observation (the blood donor);yi represents the measured trait value and a(yi) the value after its initial transformation;t(i) denotes the number of seconds between the start of the study and the measurement of the trait value;m(i) is a categorical variable with two levels, representing the haematology analyser used to record the measurement;tyear(i) is the number of seconds between midnight on 1st of January in the year the measurement was made and the time at which the measurement was made;tday(i) is the time of day that the measurement was made, in seconds after midnight;tven(i) is the time duration in seconds between venipuncture and the midnight immediately preceding the measurement;I(i) is a binary variable which indicates whether tven(i) was imputed (rather than observed), imputation was by the median values of the observed tven(i) variable;D(i) is a categorical variable with seven levels, representing the day of the week on which the measurement was made, the baseline category is monday;s represent a P-spline smoothing spline;c represents a cyclic smoothing spline;tp represents a thin plate spline smoothing spline;⊗ indicates a binary operator, which expands additively to generate main effects and interaction terms.In the first stage, we adjusted each transformed trait for the time duration between the start of the study and the analysis of the blood sample, the time duration between venipuncture and the analysis of the blood sample, the day of the week, the number of days between the start of the year and the day of the measurement, and the instrument used for the analyse. The adjustments were made by fitting a GAM  is the residual from the first stage regression model corresponding to observation i;i) is the age of the participant;age(i) is a categorical variable with five levels indicating the menopausal status of the participant, taking values in {post, pre, hysterectomy, male, NA};meno(i) is the weight of the participant (missing data were imputed by the population mean);wgt(i) is the height of the participant (missing data were imputed by the population mean);hgt(i) is a categorical variable with four levels indicating the drinking status of the participant, K = {previous, current, NA}, the baseline category is ‘never’;drk(i) is a categorical variable with six levels indicating the alcohol consumption rate of the participant, A = {rarely, 1 to 3 months, 1 to 2 weeks, 3 to 5 weeks, most days}, the baseline category is ‘never’;alc(i) is the number of pack-years the participant has smoked (missing data were imputed by the population mean);pckyrs(i) is a categorical variable with four levels indicating the smoking status of the participant, M = {previous, current, NA}, the baseline category is ‘never’;smk(i) is a categorical variable with six levels indicating the smoking frequency of the participant, F = {special occasions, rarely, occasional, most days, every day}, the baseline category is ‘never’;sfrq(i) is a categorical variable with six levels indicating the INTERVAL trial arm to which the participant was assigned (see ‘INTERVAL Study’ above), R = {M10, M12, F12, F14, F16}, the baseline category is ‘M8’;arm(i), hgt.na(i), pckyrs.na(i), evaluate to true if the participant has missing data respectively for weight, height, or pack years smoked and otherwise evaluate to false.wgt.na and the adjusted value was more than 3.5 median absolute deviations from the median of the distribution of such differences. We then removed data points lying more than 4.5 median absolute deviations from the median of the distribution of the residuals. We also performed multivariate outlier removal within groups of phenotypes corresponding to each cell-type, by computing the sum of squares of the leading the mean of each trait in males, pre-menopausal females and post-menopausal females adjusted for age  andthe effect of age on the mean of the trait while adjusting for sex and menopause.Although the covariation between the ncCBC traits and biological variables is likely to be of interest to researchers and clinicians, it is difficult to interpret the estimates of coefficients of the GAMs used to adjust the phenotypes. Consequently, we reperformed some of the visual Fig.  used to We also performed a multiple linear regression of each trait on sex, menopause status, age and BMI to estimate the effect of BMI on each trait while adjusting for sex and menopause status.DNA extraction was performed at LGC Genomics (UK) from buffy coat using a Kleargene method. Samples were packaged and shipped to Affymetrix  in 96-well barcoded plates, two wells of which were left empty for standard Affymetrix controls. A sample selection algorithm was used to ensure that the samples on each plate came from participants with a representative distribution of recruitment centre, recruitment date, regional hub and sex. A PicoGreen-based method was used to identify plates with a high proportion of low DNA concentration samples, which were replaced prior to genotyping. Genotyping was performed on the Affymetrix GeneTitan Multi-Channel Instrument according to the Affymetrix Axiom 2.0 Assay Automated Workflow. Genotypes were called in batches of approximately 50 plates (4800 samples) using the Axiom GT1 algorithm implemented in the Affymetrix Power Tools software package.2. Data from samples showing evidence of DNA contamination were excluded. Data from samples that were genetic duplicates, from samples corresponding to related groups of participants and from samples from non-European ancestry participants were identified and removed. Data from samples with discordant self-reported and genetically determined sex were removed. Genetic duplicates and relatives were identified using a methods-of-moments estimator for coefficients of pairwise relatedness. Participants were considered to be of European ancestry if their scores on the first two principal components of genetic variation fell below component-specific thresholds in a dataset combining the genotyped INTERVAL participants and genotyped individuals in the major population groups of the 1000 Genomes project. These thresholds were determined by considering the bivariate distribution of principal components scores of individuals self-declaring their ethnicity as ‘White’. Non-autosomal variants, variants with a low call rate, multi-allelic variants and variants showing significant  deviation from the Hardy-Weinberg equilibrium were removed. The data were phased using SHAPEIT3, following which the genotypes of 87,696,910 variants were imputed from the combined 1000 Genomes Phase 3-UK10K panel using the PBWT imputation algorithm on the Sanger Imputation Server (https://imputation.sanger.ac.uk)62.A full description of the quality control procedures applied to the INTERVAL genotype data can be read in the Methods section of Astle et al.63.We used BOLT-LMM version 2.3.1. to perform univariable (i.e. a single genetic variable) linear mixed model regression of each ncCBC phenotype on the imputed alternative allele dose at each of the 29.5 million genetic variants which passed quality control filters . We included as covariates, dummy variables indicating the donor clinic at which the blood sample was taken and the score vectors corresponding to the leading ten principal components of genetic variation in the study sample. The Genomic Control inflation factor λ-values ranged between 1.001 and 1.058 suggesting that the mixed model analysis and principal components covariates adequately adjusted for any confounding by relatedness or large scale population stratificationP-value < 8.31 × 10−9) associations64. We partitioned the set of genome-wide significant variants for each trait into the maximum number of subsets (‘blocks’) such that no pair of variants in distinct subsets were separated by fewer than 10 Mb. We implemented a version of Efroymson’s stepwise regression algorithm using the fastLM function in the R package RcppEigen and applied it to the variants in each block. We initialised the algorithm by specifying a linear regression model M, ‘the current model’ that included the covariates used in the univariable GWAS analysis (PC score vectors and clinic), but no genetic variants. We then performed iterations as follows:M,M with the variant to create M* and compare M* to M by a likelihood ratio test; compute the corresponding P-value.augment For each variant not currently included in P-value in the set of model comparisons performed in step 1. If that P-value is lower than 8.31×10−9 and does not have a LD r2 score higher than 0.9 with any variants already in the model then update M by adding the variant to the model. If no such variant exists, terminate the algorithm.Identify the variant corresponding to the lowest M,M by removing the variant to create M* and compare M to M* by a likelihood ratio test; compute the corresponding P-value.reduce For each variant currently included in P-value in the set of model comparisons performed in step 3. If that P-value is greater 8.31×10−9 then update M by removing the variant from the model and return to 3.Identify the variant corresponding to the greatest Return to 1For each trait, we performed stepwise multiple linear regression analyses to identify a parsimonious subset of genetic variants explaining the univariable genome-wide significant  to be the number of conditionally significant variants in the window. We set the prior standard deviation on the effect size (–prior-std option) to 0.08. We computed the linkage disequilibrium (LD) correlation structure of the variants in each window from the imputed genetic dataset used for the univariable GWAS analysis. We computed a 95% Bayesian credible set of variants for each trait-window as the minimal set of variants jointly covering at least 95% of the posterior probability of association.We performed statistical fine-mapping by applying FINEMAP 1.3.1r2 measure of LD to merge clumps.The stepwise model selection procedure of the conditional analysis was performed independently for each ncCBC trait. Consequently, a genetic variant causally associated with multiple ncCBC traits may generate association signals that are tagged by different conditionally significant variants for different traits. To identify variants likely to represent common association signals, we sought to partition the full set of conditionally significant variants into sets of variants connected by chains of high LD. We applied the greedy clumping algorithm in PLINK 1.90b3l, which assigns each variant to a distinct clump and then iteratively merges pairs of clumps if there exists a variant in each clump such that the pair of variants is in high LD. We used a threshold of 0.8 on the r2 > 0.8) with a conditionally significant variant reported by Vuckovic et al. or Chen et al.4. Following this, we performed an analysis at the cell-type  level. Each clump containing a variant associated with a trait of a given cell-type was declared to represent a novel association with that cell-type if it did not contain any variants in strong LD (r2 > 0.8) with a variant identified by the conditional analyses of Vuckovic et al. or Chen et al. to be associated with cCBC traits of the same cell-type  version 84 to annotate the conditionally significant variants with the gene symbols of enveloping or proximal (within 5 kb) transcripts , CD15+ neutrophils (n = 300) and CD14+ monocytes (n = 1490), CD4+ T-cells, CD8+ T-cells and CD19+ B-cells using genetic and transcriptomic data obtained from CEDAR, Cardiogenics, the Wellcome Trust Centre for Human Genetics (WTCHG), and BLUEPRINT. Unmeasured genotypes were imputed using the Haplotype Reference Consortium release 1.1 reference panel. These data contributed to an eQTL meta-analysis which has been published in Võsa et al.67. We performed colocalisation analysis between genetic associations from 35 cell-type matched ncCBC cytometry traits and the eQTL. Association signals from H-IPF, P-LCR, PLT-SSC, PLT-SFL, PLT-FSC, PLT-SSC-DW, PLT-SFL-DW, and PLT-FSC-DW were colocalised with eQTL of platelets. Association signals from NE-SSC, NE-SFL, NE-FSC, NE-SSC-DW, NE-SFL-DW, NE-FSC-DW, EO-SSC, EO-SFL, EO-FSC, EO-SSC-DW, EO-SFL-DW and EO-FSC-DW were colocalised with eQTL of CD15+ cells. Association signals from MO-SSC, MO-SFL, MO-FSC, MO-SSC-DW, MO-SFL-DW, and MO-FSC-DW were colocalised with eQTL from CD14+ cells. Finally, association signals from RE-LYMP#, RE-LYMP(L)%, RE-LYMP%, and LY-SSC, LY-SFL, LY-FSC, LY-SSC-DW, LY-SFL-DW, and LY-FSC-DW were colocalised with eQTL of CD4+, CD8+, and CD19+ cells.We applied r² > 0.8). Each analysis was performed using data from the recombination region of Berisa et al.68 containing the conditionally significant variant. If the region contained multiple conditionally significant variants of the ncCBC trait, we regressed the ncCBC trait on the conditionally significant variants that were not in strong LD (r² > 0.8) with the variant of interest and used the resulting residuals as a phenotype for univariable association tests, to obtain ncCBC association statistics for each variant in the region with the effect of secondary signals removed. We filtered the results to report only those which are ‘highly likely’ to represent true colocalisation: those with a posterior probability greater than 80%30 , which lack α-granules, to those of healthy controls (n = 5). Platelets were isolated, analysed by mass spectrometry, and normalised abundance values (NAVs) were calculated for each protein detected84. We performed two-sample Student’s t-tests  to test for a difference in the mean of the log NAVs of each protein between the two conditions. Proteins for which the test was significant  and for which the log-ratios were greater than 2 standard deviations from the median were considered to be differentially abundant. Differentially abundant proteins that were less abundant in GPS platelets than controls were assumed to be localised to the platelet α-granule.Firstly, we conducted a literature review to identify proteins localised to platelet α-granules by mass spectrometry2FPKM > 1)30. The estimated effect size corresponding to 215 of these proteins was sufficiently large to correspond to an unadjusted P-value < 1 × 10−3. 44 of these were localised to α-granules, of which 40 had negative estimated T-allelic effect sizes, while 171 were not localised to α-granules, of which 101 had negative T-allelic effect sizes. We performed a Fisher’s exact test for the corresponding 2×2 table.We extracted from the results of Sun et al. the statistics summarising the univariate associations of rs6993770 with 1456 proteins showing strong evidence for expression in MKs (RNA-seq log2FPKM > 1), we performed a linear regression of the estimated effect size of rs6993770-T on a dummy variable indicating α-granule localisation while adjusting for the abundance of the mRNA transcripts for the corresponding genes in MKs.Using a dataset in which each row corresponded to one of 1456 plasma proteins studied by Sun et al. and expressed in MKs  by CRISPR/Cas9 genome editing. The sgRNA (5’ GA GTC GAC AGC AAC TTC CAG 3’) was cloned into the lentiGuide-Puro (Addgene plasmid # 52963102). The sgRNA and Cas9 plasmids were co-transfected into a human iPSC line using the Lonza Nucleofector™ and the Human Stem Cell NucleofectorTM Kit 1 . Nucleofected cells were single cell sorted and knock-out variants were confirmed by Sanger sequencing . The first two days after infection, cells were cultured with BMP4 (BioTechne) and FGF2  to induce mesoderm. Eighteen days were allowed for the differentiation and maturation of MKs, during which flow-cytometry experiments were used to monitor the expression of CD41a  and CD42b  as measures of maturation. Three technical replicates were nested within each of three biological replicates: two knockouts and one wild-type.MKs were differentiated from human iPSCs  using the protocol described in Moreau et al.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1–8Reporting Summary