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PreprintCC BYbioRxiv

Stairway Plot v3: Nonparametric Demographic Inference from the Site Frequency Spectrum at Biobank Scale

  • X. Liu1
  1. 1University of South Florida
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Abstract

Inferring the history of effective population size (Ne) from genomic data is fundamental to population genetics, but model-flexible methods have not kept pace with biobank-scale samples. We present Stairway Plot v3, which infers a nonparametric Ne (t) trajectory with bootstrap confidence intervals directly from the site frequency spectrum (SFS). By recasting the optimization as a convex problem and combining inferences across multiple sub-sampled projections, the method scales to 100,000 haploid sequences on commodity CPU hardware without a GPU. Larger samples sharpen the resolution of recent demography, and we apply the method to five human populations including 54,302 Japanese individuals, to our knowledge the largest sample used for nonparametric SFS-based Ne (t) inference.

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Cite

BibTeXRIS
BibTeX
@misc{liu2026stairway,
  title = {{Stairway Plot v3: Nonparametric Demographic Inference from the Site Frequency Spectrum at Biobank Scale}},
  author = {X. Liu},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.16.751895},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.16.751895v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Stairway Plot v3: Nonparametric Demographic Inference from the Site Frequency Spectrum at Biobank Scale
AU  - X. Liu
PY  - 2026
DA  - 2026/09/16
PB  - bioRxiv
DO  - 10.64898/2026.09.16.751895
UR  - https://www.biorxiv.org/content/10.64898/2026.09.16.751895v1
AB  - Inferring the history of effective population size (Ne) from genomic data is fundamental to population genetics, but model-flexible methods have not kept pace with biobank-scale samples. We present Stairway Plot v3, which infers a nonparametric Ne (t) trajectory with bootstrap confidence intervals directly from the site frequency spectrum (SFS). By recasting the optimization as a convex problem and combining inferences across multiple sub-sampled projections, the method scales to 100,000 haploid sequences on commodity CPU hardware without a GPU. Larger samples sharpen the resolution of recent demography, and we apply the method to five human populations including 54,302 Japanese individuals, to our knowledge the largest sample used for nonparametric SFS-based Ne (t) inference.
ER  -