Detecting and quantifying clonal selection in somatic stem cells

As DNA variants accumulate in somatic stem cells, become selected or evolve neutrally, they may ultimately alter tissue function. When, and how, selection occurs in homeostatic tissues is incompletely understood. Here, we introduce SCIFER, a scalable method that identifies selection in an individual tissue, without requiring knowledge of the driver event. SCIFER also infers self-renewal and mutation dynamics of the tissue’s stem cells, and the size and age of selected clones. Probing bulk whole-genome sequencing data of nonmalignant human bone marrow and brain, we detected pervasive selection in both tissues. Selected clones in hematopoiesis, with or without known drivers, were initiated uniformly across life. In the brain, we found pre-malignant clones with glioma-initiating mutations and clones without known drivers. In contrast to hematopoiesis, selected clones in the brain originated preferentially from childhood to young adulthood. SCIFER is broadly applicable to renewing somatic tissues to detect and quantify selection. SCIFER detects clonal selection in whole-genome sequencing data using a population genetics model. Applied to a range of somatic tissues, SCIFER quantifies stem cell dynamics and infers clonal ages and sizes without requiring knowledge of driver events.