Genome-wide genetic studies have revealed that the majority of common and rare variants associated with complex human diseases are located in non-coding regions. A significant proportion of non-coding causal variants are hypothesized to reside within transcription factor (TF) binding sites located in regulatory open chromatin regions, such as promoters and enhancers. These variants can influence gene expression, thereby contributing to disease etiology. Accurate genome-wide identification of TF binding sites in disease-relevant cell types is therefore critical for pinpointing non-coding causal variants. Conventional functional genomic techniques, such as ChIP-seq, DNase-seq, and ATAC-seq, exhibit inherent limitations. ChIP-seq analyzes only one TF at a time and offers low resolution. Both ChIP-seq and DNase-seq are incapable of detecting TF binding in specific cell types within heterogeneous samples. Furthermore, DNase-seq and ATAC-seq display low sensitivity to detect TF binding. To overcome these challenges, we have recently developed a novel genome-wide, single-cell, and single-molecule TF footprinting method named as FOOtprinting with DeamInasE (FOODIE) (He et al., 2024). This technique enables the detection of TF footprints with near single-base resolution at the single-cell level, offering unprecedented insight into cell type-specific regulatory landscapes within heterogeneous tissues. The primary objective of this project is to leverage the FOODIE technique to generate high-resolution single-cell TF footprinting data across multiple human tissues, with a particular focus on brain tissues. By integrating these data with the large-scale human genetic variant data from the UK Biobank, we aim to identify a large number of novel non-coding causal variants associated with complex diseases, such as Alzheimer’s disease and various forms of cancer. The discovered variants may serve as potential therapeutic targets for complex human diseases.
