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Statistical Applications in Genetics and Molecular Biology

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Volume 15, Issue 6 (Dec 2016)


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Tree-based quantitative trait mapping in the presence of external covariates

Katherine L. Thompson / Catherine R. Linnen / Laura Kubatko
  • Departments of Statistics and Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, United States of America
  • Other articles by this author:
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Published Online: 2016-11-22 | DOI: https://doi.org/10.1515/sagmb-2015-0107


A central goal in biological and biomedical sciences is to identify the molecular basis of variation in morphological and behavioral traits. Over the last decade, improvements in sequencing technologies coupled with the active development of association mapping methods have made it possible to link single nucleotide polymorphisms (SNPs) and quantitative traits. However, a major limitation of existing methods is that they are often unable to consider complex, but biologically-realistic, scenarios. Previous work showed that association mapping method performance can be improved by using the evolutionary history within each SNP to estimate the covariance structure among randomly-sampled individuals. Here, we propose a method that can be used to analyze a variety of data types, such as data including external covariates, while considering the evolutionary history among SNPs, providing an advantage over existing methods. Existing methods either do so at a computational cost, or fail to model these relationships altogether. By considering the broad-scale relationships among SNPs, the proposed approach is both computationally-feasible and informed by the evolutionary history among SNPs. We show that incorporating an approximate covariance structure during analysis of complex data sets increases performance in quantitative trait mapping, and apply the proposed method to deer mice data.

Keywords: coalescent theory; genome-wide association study (GWAS); phylogenetic covariance; quantitative trait mapping (QTM); single nucleotide polymorphisms (SNPs)


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About the article

Published Online: 2016-11-22

Published in Print: 2016-12-01

Funding Source: National Science Foundation

Award identifier / Grant number: DEB-1257739

The authors would like to thank Hopi Hoekstra for her gracious permission to allow us to re-analyze the deer mouse data. In addition, we would like to thank the University of Kentucky College of Arts & Sciences for the use of their computational cluster for simulation and real data analysis, as well as the University of Kentucky High Performance Computing Center for the use of the supercomputer for empirical data analysis. Lastly, we would like to thank the reviewers for their insightful feedback which greatly improved this manuscript. This material is based, in part, upon work supported by the National Science Foundation under Grant No. DEB-1257739 (to CRL).

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2015-0107.

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