Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Bioinformatics
Phylogenetics of heterogeneous samples
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
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The random accumulation of variations in the human genome over time implicitly encodes a history of how human populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a challenging computational and statistical problem but has important applications both to basic research and to the discovery of genotype-phenotype correlations. In this study, we present a novel approach to inferring human evolutionary history from genetic variation data. Our approach uses the idea of consensus trees, a technique generally used to reconcile species trees from divergent gene trees, adapting it to the problem of finding the robust relationships within a set of intraspecies phylogenies derived from local regions of the genome. We assess the quality of the method on two large-scale genetic variation data sets: the HapMap Phase II and the Human Genome Diversity Project. Qualitative comparison to a consensus model of the evolution of modern human population groups shows that our inferences closely match our best current understanding of human evolutionary history. A further comparison with results of a leading method for the simpler problem of population substructure assignment verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to inferring the relationships among them.