When possible, we excluded samples with any of the following: pregnancy (when complete blood count (CBC) done), acute medical/surgical illness (when CBC done), blood cancer, leukemia, lymphoma, chemotherapy, myelodysplastic syndrome, bone marrow transplant, congenital or hereditary anemia (e.g., hemoglobinopathy such as sickle cell anemia or thalassemia), HIV, end-stage kidney disease, dialysis, EPO treatment, splenectomy, cirrhosis and those with any of the following extreme measurements: WBC count > 100∗109/L with > 5% immature cell or blasts, WBC > 200∗109/L, Hemoglobin > 20 g/dL, Hematocrit > 60%, Platelet > 1000∗109/L. For the WBC subtypes (e.g., basophils count) we used the relative count, i.e., the total WBC count multiplied by the proportion for each cell type (e.g., basophils percentage). Raw phenotypes were regressed on age, age-squared, sex, principal components and cohort specific covariates (e.g., study center, cohort, etc) if needed, WBC related traits were log10 transformed before regression modeling. Residuals from the modeling were obtained and then inverse normalized for cohort level association analysis or GWAS. All cohorts followed the same exclusions and phenotype modeling except for UKBB and INTERVAL that had their procedure described elsewhere (Astle et al., 2016 (link)). The cohort level association analyses were then conducted using a linear mixed effects model in order to account for known or cryptic relatedness (e.g., BOLT-LMM (Loh et al., 2015 (link), 2018 (link)), EPACTS https://github.com/statgen/EPACTS and rvtests (Zhan et al., 2016 (link)) with the additive genetic model. Linear mixed effects models have been shown to effectively account for both population structure and inter-individual relatedness within the UK Biobank cohort, along with having increased discovery power over simple linear regression with principal components.
Other organizations :
Harvard–MIT Division of Health Sciences and Technology, MRC Biostatistics Unit, Montreal Heart Institute, National Heart Lung and Blood Institute, Framingham Heart Study, University of North Carolina at Chapel Hill, VA Boston Healthcare System, Baker Heart and Diabetes Institute, National Health Service, NHS Blood and Transplant, Osaka University, RIKEN Center for Integrative Medical Sciences, Icahn School of Medicine at Mount Sinai, University of Wisconsin–Milwaukee, Kyushu University, University of Washington, University of Bristol, University of Copenhagen, Novo Nordisk Foundation, Hasso Plattner Institute, University of Potsdam, Erasmus MC, National Institute on Aging, Atlanta VA Medical Center, Kaiser Permanente, Health Data Research UK, University of Ioannina, Centre International de Recherche sur le Cancer, Wake Forest University, NIHR Imperial Biomedical Research Centre, Imperial College London, Medical Research Council, UK Dementia Research Institute, Universitätsmedizin Greifswald, German Centre for Cardiovascular Research, University of Pennsylvania, Fred Hutch Cancer Center, Novo Nordisk (United Kingdom), Chinese National Human Genome Center, Shanghai Industrial Technology Institute, Technical University of Munich, Tampere University Hospital, Tampere University, The University of Tokyo, Massachusetts General Hospital, University of Colorado Anschutz Medical Campus, University of Colorado Denver, Fimlab (Finland), Frederiksberg Hospital, Center for Clinical Research and Prevention, Duke Medical Center, University of Virginia, University of Minnesota, Kaiser Permanente Washington Health Research Institute, Turku University Hospital, University of Turku, The Lundquist Institute, Harbor–UCLA Medical Center, University of Massachusetts Amherst, Stanford University, Université de Montréal, Mashhad University of Medical Sciences, Brigham and Women's Hospital, University of Oxford, Wellcome Centre for Human Genetics, University of Liège, European Bioinformatics Institute, John Radcliffe Hospital, The Alan Turing Institute, Turing Institute
Cohort specific covariates (e.g., study center, cohort, etc)
dependent variables
Raw phenotypes
WBC related traits
control variables
Pregnancy (when complete blood count (CBC) done)
Acute medical/surgical illness (when CBC done)
Blood cancer
Leukemia
Lymphoma
Chemotherapy
Myelodysplastic syndrome
Bone marrow transplant
Congenital or hereditary anemia (e.g., hemoglobinopathy such as sickle cell anemia or thalassemia)
End-stage kidney disease
Dialysis
EPO treatment
Splenectomy
Cirrhosis
WBC count > 100*10^9/L with > 5% immature cell or blasts
WBC > 200*10^9/L
Hemoglobin > 20 g/dL
Hematocrit > 60%
Platelet > 1000*10^9/L
Annotations
Based on most similar protocols
Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.
As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to
get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
