Abstract

Quantitative trait loci (QTL) mapping of wood properties in conifer species has focused on single time point measurements or on trait means based on heterogeneous wood samples (e.g., increment cores), thus ignoring systematic within-tree trends. In this study, functional QTL mapping was performed for a set of important wood properties in increment cores from a 17-yr-old Scots pine (Pinus sylvestris L.) full-sib family with the aim of detecting wood trait QTL for general intercepts (means) and for linear slopes by increasing cambial age. Two multi-locus functional QTL analysis approaches were proposed and their performances were compared on trait datasets comprising 2 to 9 time points, 91 to 455 individual tree measurements and genotype datasets of amplified length polymorphisms (AFLP), and single nucleotide polymorphism (SNP) markers. The first method was a multilevel LASSO analysis whereby trend parameter estimation and QTL mapping were conducted consecutively; the second method was our Bayesian linear mixed model whereby trends and underlying genetic effects were estimated simultaneously. We also compared several different hypothesis testing methods under either the LASSO or the Bayesian framework to perform QTL inference. In total, five and four significant QTL were observed for the intercepts and slopes, respectively, across wood traits such as earlywood percentage, wood density, radial fiberwidth, and spiral grain angle. Four of these QTL were represented by candidate gene SNPs, thus providing promising targets for future research in QTL mapping and molecular function. Bayesian and LASSO methods both detected similar sets of QTL given datasets that comprised large numbers of individuals.

Keywords

functional QTL mapping; wood quality traits; Scots pine; multi-locus model; shrinkage estimation

Published in

G3
2014, Volume: 4, number: 12, pages: 2365-2379
Publisher: GENETICS SOCIETY AMERICA

SLU Authors

• Hallingbäck, Henrik

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

• Abrahamsson, Sara

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    
  • Forestry Research Institute of Sweden, Skogforsk

• Fries, Anders

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

• Andersson Gull, Bengt

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    
  • Forestry Research Institute of Sweden, Skogforsk

• Garcia Gil, Rosario

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Genetics
Forest Science


Publication identifier

DOI: https://doi.org/10.1534/g3.114.014068

Permanent link to this page (URI)

https://res.slu.se/id/publ/67165