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

Il Youp Kwak, Candace R. Moore, Edgar P. Spalding, Karl W. Broman

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)79-86
Number of pages8
JournalG3: Genes, Genomes, Genetics
Volume6
Issue number1
DOIs
StatePublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 Kwak et al.

Keywords

  • Function-valued traits
  • Growth curves
  • Model selection
  • Multivariate analysis
  • QTL

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