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G3 (Bethesda). 2015 Nov 3;6(1):79-86. doi: 10.1534/g3.115.024133.

Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping.

Author information

1
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.
2
Department of Botany, University of Wisconsin, Madison, Wisconsin 53706.
3
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706 kbroman@biostat.wisc.edu.

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.

KEYWORDS:

QTL; function-valued traits; growth curves; model selection; multivariate analysis

PMID:
26530421
PMCID:
PMC4704727
DOI:
10.1534/g3.115.024133
[Indexed for MEDLINE]
Free PMC Article

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