mapping quantitative trait loci underlying function-valued traits using functional principal component analysis and multi-trait mapping

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ID: 188159
2016
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Abstract
We previously proposed a simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative trait locus mapping is applied to these dimension-reduced data, either with a multi-trait method or by considering the traits individually and then taking the average or maximum LOD score across traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl.
Reference Key
kwak2016g3:mapping Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Il-Youp Kwak;Candace R. Moore;Edgar P. Spalding;Karl W. Broman
Journal separation and purification technology
Year 2016
DOI
10.1534/g3.115.024133
URL
http://g3journal.org/lookup/doi/10.1534/g3.115.024133
Keywords
qtl model selection growth curves

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