Wölbert et al. established a strategy that integrated conserved cell-type-specificity with conservation of transcriptional levels to identify robust signature genes. By leveraging evolutionary conservation as an additional dimension of cell identity, this strategy would facilitate cell identity annotation across species, particularly in non-model organisms and rare cell populations.
Editor-in-Chief Laura Zahn asked Dr. Philip Awadalla from the University of Oxford to tell us about his research and his recent Cell Genomics paper, “Pre-diagnosis plasma cell-free DNA reveals early signatures of prostate and breast cancer risk up to eight years prior to clinical detection.”
Anderson et al. extend comparative brain multi-omics across nine mammals and zebrafish, combining sequence conservation and predicted enhancer-gene relationships with functional assays to examine how regulatory elements with different evolutionary histories contribute to brain function and disease risk.
The Somatic Mosaicism across Human Tissues Network has established complementary benchmarks for somatic mutation detection, extended mutation analysis into underexplored genomic regions, and highlighted chromatin states as potential records of cellular history. These studies show that detection performance depends on reference representation, benchmark quality, and experimental design, requiring careful calibration when integrating technologies.
Disease-associated loci identified by genome-wide association studies (GWASs) may induce effects by altering transcription factor (TF) binding. However, due to the scarcity of population-level TF-binding experiments, identifying these disrupted TFs remains challenging. To address this, we present the TFXcan framework, which correlates genetically predicted TF binding with phenotypes to nominate TF-tissue pairs that may regulate complex traits. Within this framework, we introduce Enpact, a deep learning-based approach that integrates Enformer and IMPACT to infer personalized TF-binding events. Leveraging Enpact, we correlate predicted binding profiles for 700 TF-tissue pairs with prostate cancer risk, identifying multiple implicated TFs at risk loci. Matrix decomposition further reveals clusters of TF-tissue pairs, termed "programs," acting as candidate drivers of disease risk. TFXcan represents the first method capable of predicting TF binding from genotype data at scale, enabling systematic identification of TFs and regulatory programs underlying complex traits.
