CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.05 - Sept.-Oct.

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Issue No.05 - Sept.-Oct. (2013 vol.10)

pp: 1137-1149

Ming-Chi Tsai , CMU-Pitt PhD Program in Computational Biology, Pittsburgh

Guy Blelloch , Carnegie Mellon University, Pittsburgh

R. Ravi , Carnegie-Mellon University, Pittsburgh

Russell Schwartz , Carnegie Mellon University, Pittsburgh

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.98

ABSTRACT

Detecting and quantifying the timing and the genetic contributions of parental populations to a hybrid population is an important but challenging problem in reconstructing evolutionary histories from genetic variation data. With the advent of high throughput genotyping technologies, new methods suitable for large-scale data are especially needed. Furthermore, existing methods typically assume the assignment of individuals into subpopulations is known, when that itself is a difficult problem often unresolved for real data. Here, we propose a novel method that combines prior work for inferring nonreticulate population structures with an MCMC scheme for sampling over admixture scenarios to both identify population assignments and learn divergence times and admixture proportions for those populations using genome-scale admixed genetic variation data. We validated our method using coalescent simulations and a collection of real bovine and human variation data. On simulated sequences, our methods show better accuracy and faster runtime than leading competitive methods in estimating admixture fractions and divergence times. Analysis on the real data further shows our methods to be effective at matching our best current knowledge about the relevant populations.

INDEX TERMS

Sociology, Statistics, Bioinformatics, Genomics, Computational modeling,computations on discrete structures, Biology and genetics, graphs and networks, information theory

CITATION

Ming-Chi Tsai, Guy Blelloch, R. Ravi, Russell Schwartz, "Coalescent-Based Method for Learning Parameters of Admixture Events from Large-Scale Genetic Variation Data",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.10, no. 5, pp. 1137-1149, Sept.-Oct. 2013, doi:10.1109/TCBB.2013.98REFERENCES