ArticlebioRxiv : the preprint server for biology2026
Fast pairwise coalescence enables gene-resolution scans for recent selection in diverse human populations.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Identifying the genetic changes that shaped recent human adaptation depends on our ability to detect selection from genomic data. Summary statistics from haplotype scans have been widely used for that purpose, aggregating genetic signal over windows, though resolution is limited by linkage and their power may diminish as sweeps approach fixation, as in the case of the integrated haplotype score (iHS). Ancient DNA based scans recover signal by analysing time-series trajectories, but the majority of human populations fall outside the geographic range of any existing ancient DNA dataset. Pairwise coalescence times provide a way to complement statistics and can be applied to any modern cohort, yet computing them densely enough at cohort scale poses a computational challenge due to the quadratic growth in the number of haplotype pairs. We introduce gamma_smc_cu, a GPU implementation of the Gamma-SMC algorithm (Schweiger and Durbin, 2023) for pairwise time-to-the-most-recent-common-ancestor (TMRCA) inference. Applied to the 1000 Genomes Project (3,202 phased samples, corresponding to 6,404 haplotypes; 829,638 within-population pairs across 26 populations and five different continental ancestries; ~ 10
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