Menu
Currency
Research Publication

Fast pairwise coalescence enables gene-resolution scans for recent selection in diverse human populations

Korfmann, K., Mathieson, S., Gauderman, W. J. et al.

5 Authors
2026-05-22 Published
5 Views
Scroll to explore
Chapter I

Publication Details

Comprehensive information about this research publication

Authors

KK
Korfmann, K.
MS
Mathieson, S.
GW
Gauderman, W. J.
CJ
Choupan, J.
HM
Herting, M.
Chapter II

Abstract

Summary of the research findings

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 12 per-site posterior evaluations), it yields a gene-level TMRCA landscape of 17,823 autosomal protein-coding genes after masking for segmental duplications. The scan recovers well-known sweeps ( LCT, SLC24A5, EDAR, FADS1, HERC2, ABCC11 ) and, combined with a depleted-to-enriched variant-class profile, resolves haplotype-block signals down to the gene level. Of seven case studies, two are developed in the main text — GRK2 / ADRBK1 (chr11q13.2; SAS+EUR) and TREML1 / TREM2 (chr6p21.1) — and the remaining five ( IFIH1 chr2q24/IBS, CCDC92 chr12q24/CDX, SLC6A15 chr12q21/CHS, BPIFA2 chr20q11/GIH, CLEC6A chr12p13/CDX) are presented in the Supplementary Information (SI). Notably, TREML1 / TREM2 is a shared out-of-Africa signal — ranked below the within-population 1% tail in 16 of 19 non-African 1000 Genomes panels that PopHumanScan and five landmark haplotype-based scans miss. A previous 10 kb-windowed-mean iHS scan dilutes the cluster of extreme sites packed inside the ∼5 kb gene bodies, while our own gene-level iHS independently recovers the locus in three South Asian panels (BEB, STU, ITU; top 0.4% genome-wide). We cross-validate the seven cases against the 9.7 million per-variant selection posteriors from a recent West-Eurasian ancient DNA scan. BPIFA2 is detected concordantly ( s ≈ 1.8% per generation). GRK2 and CCDC92 reach detection threshold in flanking variants but not within their own gene bodies, while the TREML1 / TREM2 cluster falls below it. To calibrate novelty, we review the candidate landscape against an expanded eight-catalog set spanning curated haplotype scans, the largest current West-Eurasian ancient-DNA leads, and a recent 26-population iHS refinement; the vast majority of our loci overlap at least one prior entry, and only a handful — including TREML1 / TREM2 — remain unflagged. The contributions of this work are gene-level resolution, systematic ancient DNA cross-validation, and a reusable TMRCA landscape that complements aDNA panels.

Listen to This Research

A two-host conversation exploring the key findings of this publication

Fast pairwise coalescence enables gene-resolution scans for recent selection in diverse human populations
Two-Host Conversation
Chapter III

Analysis

Comprehensive review of ancestry and genetic findings

Important Disclaimer: This review has been performed semi-automatically and is provided for informational purposes only. While we strive for accuracy, this analysis may contain errors, omissions, or misinterpretations of the original research. DNA Genics disclaims all liability for any inaccuracies, errors, or consequences arising from the use of this information. Users should independently verify all information and consult original research publications before making any decisions based on this content. This analysis is not intended as a substitute for professional scientific review or medical advice.

Summary

Key Findings

Ancestry Insights

Traits Analysis

Historical Context

Scientific Assessment