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Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities

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  • Ian J. Wilson
  • Michael E. Weale
  • David J. Balding

Abstract

Summary. We develop a flexible class of Metropolis–Hastings algorithms for drawing inferences about population histories and mutation rates from deoxyribonucleic acid (DNA) sequence data. Match probabilities for use in forensic identification are also obtained, which is particularly useful for mitochondrial DNA profiles. Our data augmentation approach, in which the ancestral DNA data are inferred at each node of the genealogical tree, simplifies likelihood calculations and permits a wide class of mutation models to be employed, so that many different types of DNA sequence data can be analysed within our framework. Moreover, simpler likelihood calculations imply greater freedom for generating tree proposals, so that algorithms with good mixing properties can be implemented. We incorporate the effects of demography by means of simple mechanisms for changes in population size and structure, and we estimate the corresponding demographic parameters, but we do not here allow for the effects of either recombination or selection. We illustrate our methods by application to four human DNA data sets, consisting of DNA sequences, short tandem repeat loci, single‐nucleotide polymorphism sites and insertion sites. Two of the data sets are drawn from the male‐specific Y‐chromosome, one from maternally inherited mitochondrial DNA and one from the β‐globin locus on chromosome 11.

Suggested Citation

  • Ian J. Wilson & Michael E. Weale & David J. Balding, 2003. "Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(2), pages 155-188, June.
    • Handle: RePEc:bla:jorssa:v:166:y:2003:i:2:p:155-188
    DOI: 10.1111/1467-985X.00264
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    References listed on IDEAS

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    1. Igor V. Ovchinnikov & Anders Götherström & Galina P. Romanova & Vitaliy M. Kharitonov & Kerstin Lidén & William Goodwin, 2000. "Molecular analysis of Neanderthal DNA from the northern Caucasus," Nature, Nature, vol. 404(6777), pages 490-493, March.
    2. Bob Mau & Michael A. Newton & Bret Larget, 1999. "Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods," Biometrics, The International Biometric Society, vol. 55(1), pages 1-12, March.
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    Cited by:

    1. Jenkins Paul A., 2012. "Stopping-Time Resampling and Population Genetic Inference under Coalescent Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-20, January.
    2. Tsukuda Koji & Mano Shuhei & Yamamoto Toshimichi, 2020. "Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-18, April.
    3. Heled, Joseph, 2012. "Sequence diversity under the multispecies coalescent with Yule process and constant population size," Theoretical Population Biology, Elsevier, vol. 81(2), pages 97-101.
    4. Ajay Shrestha & Ausif Mahmood, 2016. "Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture," Journal of Mathematics, Hindawi, vol. 2016, pages 1-10, April.
    5. Noa Chapal-Ilani & Yosef E Maruvka & Adam Spiro & Yitzhak Reizel & Rivka Adar & Liran I Shlush & Ehud Shapiro, 2013. "Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-17, November.

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