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Lookup NU author(s): Dr Howsun Jow
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WEpresent a new approach for estimating mixing between populations based on non-recombining markers, specifically Y-chromosome microsatellites. A Markov chain Monte Carlo (MCMC) Bayesian statistical approach is used to calculate the posterior probability distribution of population parameters of interest, including the effective population size and the time to most recent common ancestor (MRCA). To test whether two populations are homogeneously mixed we introduce a "mixing"statistic defined for each coalescent event that weights the contribution of that ancestor's descendants to the two subpopulations, and an associated population "purity"statistic. Using simulated data with low levels of migration between two populations, we demonstrate that our method is more sensitive than other commonly used distance-based methods such as RST and DSW. To illustrate our method, we analysed mixing between 11 pre-defined Chinese ethnic/regional populations, using 5 microsatellite markers from the non-recombining region of the Y-chromosome (NRY), demonstrating a significant clustering of a subset of subpopulations with a high mutual relative degree of mixing (homogeneous mixing with support >0.99). Our analysis suggests that there is a strong correlation between effective population size and mixing with other subpopulations. Thus, despite considerable mixing between these groups, the purity statistic still identifies significant heterogeneity, suggesting that periods of historical isolation continue to leave a recoverable signal despite modern introgression. © 2006 The Authors Journal compilation © 2006 University College London.
Author(s): Jow H, Amos W, Luo H, Zhang Y, Burroughs NJ
Publication type: Article
Publication status: Published
Journal: Annals of Human Genetics
Year: 2007
Volume: 71
Issue: 3
Pages: 407-420
ISSN (print): 0003-4800
ISSN (electronic): 1469-1809
Publisher: Wiley-Blackwell Publishing Ltd.
URL: http://dx.doi.org/10.1111/j.1469-1809.2006.00329.x
DOI: 10.1111/j.1469-1809.2006.00329.x
PubMed id: 17156098
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