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Assessing population differentiation and isolation from single‐nucleotide polymorphism data

Author

Listed:
  • George Nicholson
  • Albert V. Smith
  • Frosti Jónsson
  • Ómar Gústafsson
  • Kári Stefánsson
  • Peter Donnelly

Abstract

Summary. We introduce a new, hierarchical, model for single‐nucleotide polymorphism allele frequencies in a structured population, which is naturally fitted via Markov chain Monte Carlo methods. There is one parameter for each population, closely analogous to a population‐specific version of Wright's FST, which can be interpreted as measuring how isolated the relevant population has been. Our model includes the effects of single‐nucleotide polymorphism ascertainment and is motivated by population genetics considerations, explicitly in the transient setting after divergence of populations, rather than as the equilibrium of a stochastic model, as is traditionally the case. For the sizes of data set that we consider the method provides good parameter estimates and considerably outperforms estimation methods analogous to those currently used in practice. We apply the method to one new and one existing human data set, each with rather different characteristics—the first consisting of three rather close European populations; the second of four populations taken from across the globe. A novelty of our framework is that the fit of the underlying model can be assessed easily, and these results are encouraging for both data sets analysed. Our analysis suggests that Iceland is more differentiated than the other two European populations (France and Utah), a finding which is consistent with the historical record, but not obvious from comparisons of simple summary statistics.

Suggested Citation

  • George Nicholson & Albert V. Smith & Frosti Jónsson & Ómar Gústafsson & Kári Stefánsson & Peter Donnelly, 2002. "Assessing population differentiation and isolation from single‐nucleotide polymorphism data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 695-715, October.
  • Handle: RePEc:bla:jorssb:v:64:y:2002:i:4:p:695-715
    DOI: 10.1111/1467-9868.00357
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    Cited by:

    1. Rongwei Fu & Dipak K. Dey & Kent E. Holsinger, 2011. "A Beta-Mixture Model for Assessing Genetic Population Structure," Biometrics, The International Biometric Society, vol. 67(3), pages 1073-1082, September.
    2. Alejandro Ochoa & John D Storey, 2021. "Estimating FST and kinship for arbitrary population structures," PLOS Genetics, Public Library of Science, vol. 17(1), pages 1-36, January.
    3. Samanta, Suvajit & Li, Yi-Ju & Weir, Bruce S., 2009. "Drawing inferences about the coancestry coefficient," Theoretical Population Biology, Elsevier, vol. 75(4), pages 312-319.
    4. Mathieu Gautier & Toby Dylan Hocking & Jean-Louis Foulley, 2010. "A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-16, August.
    5. Davison, D. & Pritchard, J.K. & Coop, G., 2009. "An approximate likelihood for genetic data under a model with recombination and population splitting," Theoretical Population Biology, Elsevier, vol. 75(4), pages 331-345.
    6. Soraggi, Samuele & Wiuf, Carsten, 2019. "General theory for stochastic admixture graphs and F-statistics," Theoretical Population Biology, Elsevier, vol. 125(C), pages 56-66.
    7. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    8. Hobolth, Asger & Siren, Jukka, 2016. "The multivariate Wright–Fisher process with mutation: Moment-based analysis and inference using a hierarchical Beta model," Theoretical Population Biology, Elsevier, vol. 108(C), pages 36-50.

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