A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases

Wei Liu, Mingyao Li, Wenxuan Deng, Ming Chen, Zihan Dong, Biqing Zhu, Zhaolong Yu, Daiwei Tang, Maor Sauler, Chen Lin, Louise V. Wain, Michael H. Cho, Naftali Kaminski, Hongyu Zhao

Abstract: Author summary Cell type proportions such as T cell proportions have been found to be potential disease progression indicator especially for cancer patients. However, cell type proportion changes can result from disease status, thus making it difficult to know whether those changed proportions are due to disease progression or the cause of disease status. Genetic components of cell type proportions, however, can potentially help to identify cell type proportions leading to different disease statuses. Here we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. In simulated data, cWAS showed a high statistical power in identifying disease-associated cell type associations with a well-controlled type-I error rate. Applying cWAS to breast cancer data, we found that blood CD8+ T cells may serve as the protective factor against breast cancer, i.e. high CD8+ T cell proportions lead to lower breast cancer risks and better prognostic condition. Overall, cWAS is a powerful tool to help identify disease-associated cell type proportions and potentially help in clinical research and practices.

External IDs:doi:10.1371/journal.pgen.1010825