ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2017
Efficient computation of the joint sample frequency spectra for multiple populations.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Faster inference of complex demographic models from large allele frequency spectra.Bioinformatics (Oxford, England) · 2026Article
- Computing coalescence rates for complex demographies and sampling configurations.bioRxiv : the preprint server for biology · 2026Article
- A general framework for branch length estimation in Ancestral Recombination Graphs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A General Framework for Branch Length Estimation in Ancestral Recombination Graphs.bioRxiv : the preprint server for biology · 2025Article
- Accelerated Bayesian inference of population size history from recombining sequence data.Nature genetics · 2025Article
- Estimation of demography and mutation rates from one million haploid genomes.American journal of human genetics · 2025Article
- Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.Nature genetics · 2025Article
- Effective Population Size Estimation in Large Marine Populations: Considering Current Challenges and Opportunities When Simulating Large Data Sets With High-Density Genomic Information.Evolutionary applications · 2025Review
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- A structured coalescent model reveals deep ancestral structure shared by all modern humans.Nature genetics · 2025Article
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- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Conditional frequency spectra as a tool for studying selection on complex traits in biobanks.bioRxiv : the preprint server for biology · 2024Article
- Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.Molecular biology and evolution · 2024Article
- Computationally efficient demographic history inference from allele frequencies with supervised machine learning.bioRxiv : the preprint server for biology · 2024Article
- Exact Decoding of a Sequentially Markov Coalescent Model in Genetics.Journal of the American Statistical Association · 2024Article
- Article
- Methods for Assessing Population Relationships and History Using Genomic Data.Annual review of genomics and human genetics · 2023Review
- Scaling the Discrete-time Wright Fisher model to biobank-scale datasets.bioRxiv : the preprint server for biology · 2023Article
- The effect of mutation subtypes on the allele frequency spectrum and population genetics inference.G3 (Bethesda, Md.) · 2023Article
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3 authors.
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Abstract
A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences and provides a highly efficient dimensional reduction of large-scale population genomic variation data. Recently, there has been much interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. SFS-based inference methods require accurate computation of the expected SFS under a given demographic model. Although much methodological progress has been made, existing methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable accurate, efficient computation of the expected joint SFS for thousands of individuals sampled from hundreds of populations related by a complex demographic model with arbitrary population size histories (including piecewise-exponential growth). Our results are implemented in a new software package called
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