Tree-based quantitative trait mapping in the presence of external covariates
Thompson Katherine L. (),
Linnen Catherine R. and
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Thompson Katherine L.: Department of Statistics, University of Kentucky, Lexington, KY, United States of America
Linnen Catherine R.: Department of Biology, University of Kentucky, Lexington, KY, United States of America
Kubatko Laura: Departments of Statistics and Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, United States of America
Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 6, 473-490
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) (search for similar items in EconPapers)
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