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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Using Ancestral Information to Inform Analyses of Complex Data Sets

Katherine L Thompson1, Richard Charnigo1,2* and Catherine R Linnen3

1Department of Statistics, University of Kentucky, USA

2Department of Biostatistics, University of Kentucky, USA

3Department of Biology, University of Kentucky, USA

*Corresponding Author:
Richard Charnigo
Department of Biostatistics, University of Kentucky
Room 203, Multidisciplinary Science Building, Lexington
Kentucky 40536, USA
Tel: 859-218-2072
E-mail: [email protected]

Received Date: November 01, 2013; Accepted Date: November 02, 2013; Published Date: November 05, 2013

Citation: Thompson KL, Charnigo R, Linnen CR (2013) Using Ancestral Information to Inform Analyses of Complex Data Sets. J Biomet Biostat 4:e126. doi:10.4172/2155-6180.1000e126

Copyright: © 2013 Thompson KL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Over the last decade, improvements in sequencing technologies coupled with active development of association mapping methods have made it possible to link genotypes and quantitative traits in humans. Despite substantial progress in the ability to generate and analyze large data sets, however, genotype-phenotype associations are often difficult to find, even in studies with large numbers of individuals and genetic markers. This is due, in part, to the fact that effects of individual loci can be small and/or dependent on genetic variation at other loci or the environment. Tree-based mapping, which uses the evolutionary relatedness of sampled individuals to gain information during association mapping, has the potential to significantly improve our ability to detect loci impacting human traits. However, current tree-based methods are too computationally intensive and inflexible to be of practical use. Here, we compare tree-based methods with more classical approaches for association mapping and discuss how the limitations of these newer methods might be addressed. Ultimately, these advances have the potential to advance our understanding of the molecular mechanisms underlying complex diseases.


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