This paper presents a method for inferring changes in the distribution of white blood cell types between different human populations, such as cases and controls, using DNA methylation signatures. The approach is guided by an external validation set consisting of methylation profiles from purified white blood cell components. The method is validated through a cell mixture reconstruction experiment and applied to several data sets, including those from head and neck squamous cell carcinoma (HNSCC) and ovarian cancer studies. The results are consistent with prior biological findings, validating the approach. The method promises new opportunities for large-scale immunological studies of disease states and exposures, particularly in the context of immune-mediated responses. The authors also discuss potential sources of bias and provide a detailed analysis of their finite-sample statistical properties through extensive simulation studies.This paper presents a method for inferring changes in the distribution of white blood cell types between different human populations, such as cases and controls, using DNA methylation signatures. The approach is guided by an external validation set consisting of methylation profiles from purified white blood cell components. The method is validated through a cell mixture reconstruction experiment and applied to several data sets, including those from head and neck squamous cell carcinoma (HNSCC) and ovarian cancer studies. The results are consistent with prior biological findings, validating the approach. The method promises new opportunities for large-scale immunological studies of disease states and exposures, particularly in the context of immune-mediated responses. The authors also discuss potential sources of bias and provide a detailed analysis of their finite-sample statistical properties through extensive simulation studies.