University of Oulu

Calleja-Rodriguez, A., Li, Z., Hallingbäck, H., Sillanpää, M., Wu, H., Abrahamsson, S., García-Gil, M. (2019) Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design. Journal of Theoretical Biology, 462 (), 283-292. doi:10.1016/j.jtbi.2018.11.007

Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design

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Author: Calleja-Rodriguez, Ainhoa1,2; Li, Zitong3,4; Hallingbäck, Henrik1,5;
Organizations: 1Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Science, Umeå SE-901 83, Sweden
2Skogforsk, Box 3, Sävar SE-91821, Sweden
3Melbourne Integrative Genomics and School of Mathematics and Statistics, the University of Melbourne, Parkville, Victoria 3010, Australia
4Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki FI-0 0 014, Finland
5Department of Plant Biology, Uppsala BioCenter, Linnean Centre for Plant Biology, Swedish University of Agricultural Science, Uppsala SE-75007, Sweden
6Department of Mathematical Sciences and Biocenter Oulu, University of Oulu, Oulu FI-90014, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Elsevier, 2018
Publish Date: 2019-03-08


In forest tree breeding, family-based Quantitative Trait Loci (QTL) studies are valuable as methods to dissect the complexity of a trait and as a source of candidate genes. In the field of conifer research, our study contributes to the evaluation of phenotypic and predicted breeding values for the identification of QTL linked to complex traits in a three-generation pedigree population in Scots pine (Pinus sylvestris L.). A total of 11 470 open pollinated F2-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two grand-parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and Estimated Breeding Values (EBV). Different levels of genotype-by-environment (G × E) effects among sites at different years, were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBV accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%. In those cases where the same marker was associated to a phenotypic or an ebvQTL, the ebvQTL always explained higher proportion of the variance, maybe due to the more accurate nature of Estimated Breeding Values (EBV). Two SNP-QTL showed pleiotropic effects for traits related with hardiness, seed, cone and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity reflected by the genotype- and QTL-by-environment effects.

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Series: Journal of theoretical biology
ISSN: 0022-5193
ISSN-E: 1095-8541
ISSN-L: 0022-5193
Volume: 462
Pages: 283 - 292
DOI: 10.1016/j.jtbi.2018.11.007
Type of Publication: A1 Journal article – refereed
Field of Science: 112 Statistics and probability
1184 Genetics, developmental biology, physiology
Funding: This study was supported by the Research School in Forest Genetics and Breeding at the Swedish University of Agricultural Sciences, Knut och Alice Wallenbergs foundation, EVOLTREE EU project and PROCOGEN EU project.
EU Grant Number: (289841) PROCOGEN - Promoting a functional and comparative understanding of the conifer genome- implementing applied aspects for more productive and adapted forests.
Copyright information: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.