Abstract
Inference of ancestral information in recently admixed populations, in which every individual is composed of a mixed ancestry (e.g., African Americans in the US), is a challenging problem. Several previous model-based approaches have used hidden Markov models (HMM) to model the problem, however, the Markov Chain Monte Carlo (MCMC) algorithms underlying these models converge slowly on realistic datasets. While retaining the HMM as a model, we show that a combination of an accurate fast initialization and a local hill-climb in likelihood results in significantly improved estimates of ancestry. We studied this approach in two scenarios—the inference of locus-specific ancestries in a population that is assumed to originate from two unknown ancestral populations, and the inference of allele frequencies in one ancestral population given those in another.
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References
Bonnen, P., Pe’er, I., Plenge, R., Salit, J., Lowe, J., Shapero, M., Lifton, R., Breslow, J., Daly, M., Reich, D., et al.: Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia. Nat. Genet. 38, 214–217 (2006)
Price, A., Patterson, N., Plenge, R., Weinblatt, M., Shadick, N., Reich, D.: Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904–909 (2006)
Pritchard, J., Stephens, M., Donnelly, P.: Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000)
Falush, D., Stephens, M., Pritchard, J.K.: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003)
Patterson, N., Hattangadi, N., Lane, B., Lohmueller, K.E., Hafler, D.A., Oksenberg, J.R., Hauser, S.L., Smith, M.W., O’Brien, S.J., Altshuler, D., Daly, M.J., et al.: Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet. 74, 979–1000 (2004)
Hoggart, C., Shriver, M., Kittles, R., Clayton, D., McKeigue, P.: Design and analysis of admixture mapping studies. Am. J. Hum. Genet. 74, 965–978 (2004)
Tang, H., Coram, M., Wang, P., Zhu, X., Risch, N.: Reconstructing genetic ancestry blocks in admixed individuals. Am. J. Hum. Genet. 79, 1–12 (2006)
Sankararaman, S., Sridhar, S., Kimmel, G., Halperin, E.: Estimating local ancestry in admixed populations. American Journal of Human Genetics (to appear)
Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing. Prentice-Hall, Upper Saddle River (2001)
http://www.affymetrix.com/products/arrays/specific/500k.affx
Nachman, M., Crowell, S.: Estimate of the mutation rate per nucleotide in humans. Genetics 156, 297–304 (2000)
Tian, C., Hinds, D.A., Shigeta, R., Kittles, R., Ballinger, D.G., Seldin, M.F.: A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping. Am. J. Hum. Genet. 79, 640–649 (2006)
Brooks, S.P., Gelman, A.: General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7(4), 434–455 (1998)
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© 2008 Springer-Verlag Berlin Heidelberg
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Sankararaman, S., Kimmel, G., Halperin, E., Jordan, M.I. (2008). On the Inference of Ancestries in Admixed Populations. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_37
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DOI: https://doi.org/10.1007/978-3-540-78839-3_37
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