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 underlying driver event. Moreover, SCIFER infers the self-renewal and mutation dynamics of the tissues stem cells, and, if selection is present, the size and growth rate of the largest selected clone. We benchmark SCIFER with published data and then probe bone marrow of 22 non-leukemic individuals for clonal hematopoiesis (CH), identifying CH with known and unknown driver events. Unexpectedly, we find accelerated division of all stem cells in CH, compared to age-matched non-CH individuals, suggesting that the bone marrow environment alters stem cell dynamics in individuals with CH. SCIFER is broadly applicable to renewing somatic tissues to detect and quantify selection.