Fig. 3

a Clusters of blood cells were identified using bs-OS-Seq profiling of IL13 and ORMDL3. bs-OS-seq was able to cluster different types of blood cells distinctly using methylation data available only for the two gene loci separately, emphasizing the importance of available reference data sets that can be used to correct for cell type variation in methylation patterns. Sorted blood cell populations (CD4 + T cells, CD8 + T-cells, CD19 + B cells, CD14 + monocytes, granulocytes, and neutrophils) and PBMCs from six healthy adult males (adult 1–6), and 22 whole blood samples from asthmatic and healthy school-aged children (1–22; healthy, mild asthma, severe asthma) were included. The whole blood samples from the children clustered readily in subgroups representing lymphocytic methylation pattern predominance, myeloid methylation pattern predominance, and neither. b Childhood asthma cases clustered with specific subgroups following the composition of blood cell types. Clustering based on DNA methylation profiling of IL13 and ORMDL3 revealed asthma subgroups that co-clustered with specific blood cell types. Subgrouping based on methylation profiles followed cellular composition in the whole blood samples of the school aged children with asthma. CD14 +  = CD14 + monocytes, CD19 +  = CD19 + B cells, CD4 +  = CD4 + T cells, CD8 +  = CD8 + T-cells, Gran = granulocytes, Neu = neutrophils, PBMC = peripheral blood mononuclear cells, whole_blood = whole blood, leuk = leukocytes, neu = neutrophils, eos = eosinophils, lymf = lymphocytes, mono = monocytes