mStruct: a new admixture model for inference of population structure in light of both genetic admixing and allele mutations

Authors:
Suyash Shringarpure;Eric P. Xing
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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Abstract

Traditional methods for analyzing population structure, such as the Structure program, ignore the influence of mutational effects. We propose mStruct, an admixture of population-specific mixtures of inheritance models, that addresses the task of structure inference and mutation estimation jointly through a hierarchical Bayesian framework, and a variational algorithm for inference. We validated our method on synthetic data, and used it to analyze the HGDP-CEPH cell line panel of microsatellites used in (Rosenberg et al., 2002) and the HGDP SNP data used in (Conrad et al., 2006). A comparison of the structural maps of world populations estimated by mStruct and Structure is presented, and we also report potentially interesting mutation patterns in world populations estimated by mStruct, which is not possible by Structure.