Tree Physiology 35, 1000–1006
doi:10.1093/treephys/tpv050
Letter
José Antonio Cabezas1,2,†, Santiago C. González-Martínez1,3,4,†, Carmen Collada2,5,†,
María Angeles Guevara1,2, Christophe Boury3,4, Nuria de María1,2, Emmanuelle Eveno3,4,
Ismael Aranda1,2, Pauline H. Garnier-Géré3,4, Jean Brach3,4, Ricardo Alía1,2, Christophe Plomion3,4
and María Teresa Cervera1,2,6
of Forest Ecology and Genetics, INIA-CIFOR, 28040 Madrid, Spain; 2Unidad Mixta de Genómica y Ecofisiología Forestal, INIA/UPM, 28040 Madrid, Spain;
UMR1202 BIOGECO, F-33610 Cestas, France; 4University of Bordeaux, UMR1202 BIOGECO, F-33170 Talence, France; 5Departamento de Biotecnología, ETSIM, Ciudad
Universitaria s/n 28040 Madrid, Spain; 6Corresponding author (cervera@inia.es)
1Department
3INRA,
Received December 1, 2014; accepted April 24, 2015; published online June 20, 2015; handling Editor Ron Sederoff
We have carried out a candidate-gene-based association genetic study in Pinus pinaster Aiton and evaluated the predictive performance for genetic merit gain of the most significantly associated genes and single nucleotide polymorphisms (SNPs). We used a
second generation 384-SNP array enriched with candidate genes for growth and wood properties to genotype mother trees collected
in 20 natural populations covering most of the European distribution of the species. Phenotypic data for total height, polycyclism,
root-collar diameter and biomass were obtained from a replicated provenance-progeny trial located in two sites with contrasting
environments (Atlantic vs Mediterranean climate). General linear models identified strong associations between growth traits (total
height and polycyclism) and four SNPs from the korrigan candidate gene, after multiple testing corrections using false discovery rate.
The combined genomic breeding value predictions assessed for the four associated korrigan SNPs by ridge regression-best linear
unbiased prediction (RR-BLUP) and cross-validation accounted for up to 8 and 15% of the phenotypic variance for height and polycyclic growth, respectively, and did not improve adding SNPs from other growth-related candidate genes. For root-collar diameter
and total biomass, they accounted for 1.6 and 1.1% of the phenotypic variance, respectively, but increased to 15 and 4.1% when
other SNPs from lp3.1, lp3.3 and cad were included in RR-BLUP models. These results point towards a desirable integration of candidate-gene studies as a means to pre-select relevant markers, and aid genomic selection in maritime pine breeding programs.
Keywords: association genetics, candidate gene, genomic selection, maritime pine, SNP, wood formation.
Global demand for timber and fibers derived from wood is continuously increasing over the years and it has been estimated
that wood production from planted forests may increase considerably during the next decades (Carle and Holmgren 2008).
Consequently, interest for breeding programs has revived in
many forest tree species, including maritime pine (Pinus pinaster
Aiton), for which biomass production and stem form are the
†These
main breeding targets. Maritime pine is the most important
source of softwood in south-western Europe. Genetic improvement of pines is costly and time-consuming, since a single
breeding cycle generally extends for over two decades (White
and Carson 2004), as most traits cannot be accurately evaluated at early stages. Genomic selection (GS) holds great promise to accelerate tree breeding, by shortening breeding cycles
authors contributed equally to this work.
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Nucleotide polymorphisms in a pine ortholog of the Arabidopsis
degrading enzyme cellulase KORRIGAN are associated with early
growth performance in Pinus pinaster
Association genetics for growth in maritime pine 1001
genes were selected from nucleotide diversity (Le Dantec et al.
2004, Pot et al. 2005, Eveno et al. 2008, Grivet et al. 2011)
and gene expression (Dubos et al. 2003, Dubos and Plomion
2003, Le Provost et al. 2003) studies in P. pinaster and in other
conifers (Lorenz et al. 2011), and positional candidate genes
co-localizing with trait-QTLs (Chagné et al. 2003, Markussen
et al. 2003, Pot et al. 2006). Studied trees were collected in 20
natural populations covering most of the distribution of the species (see Figure S1 available as Supplementary Data at Tree
Physiology Online). Maritime pine survived the last glaciations in
multiple refugia and several extant gene pools have been
described in the species using neutral molecular markers (Bucci
et al. 2007, Santos-del-Blanco et al. 2012, Jaramillo-Correa
et al. 2015). Although population stratification leads to confounding effects in association analysis, increasing false-positive
rates, these effects can be corrected incorporating the population structure to the model. Following Yu et al. (2006), we used
STRUCTURE software (Pritchard et al. 2000) to detect the number of differentiated gene pools (totalling six, in agreement with
the studies cited above) and compute the individual ancestry
proportions allocated to each of them (i.e., the Q matrix) based
on the 302 successfully genotyped SNPs (see Table S1 available as Supplementary Data at Tree Physiology Online).
Phenotypic data were obtained from a replicated provenanceprogeny trial located in two sites with contrasting environments:
Cestas (44°44′N, 00°46′W), a humid site in south-western
France; and Cálcena (41°3′N, 01°43′W), a very dry site in
north-eastern Spain (see Figure S1 available as Supplementary
Data at Tree Physiology Online). Four growth-related traits were
measured: total height, polycyclism, root-collar diameter and
total dry biomass (see Materials and methods available as Supplementary Data at Tree Physiology Online). Best linear unbiased
predictors (BLUPs) were computed for each trait using REML (see
Table S3 available as Supplementary Data at Tree Physiology
Online). Genetic correlations for growth traits across sites were not
significant (see Table S4 available as Supplementary Data at Tree
Physiology Online), thus they were analyzed separately for each
provenance-progeny trial (as in González-Martínez et al. 2008).
Single-marker association analyses were based on general
linear models (GLMs) using STRUCTURE Q matrix as covariate
to correct for population structure and permutation (106 permutations) to obtain unbiased P-values. Significant associations for
growth traits after multiple testing corrections using false discovery rate (FDR) were only found in the humid site (Cestas), probably due to extreme environmental stress (expressed also by
high mortality, ∼42%) in the dry site (Table 1). Four silent SNPs
(two synonymous and two intronic) from one candidate gene,
korrigan, had q-values (i.e., equivalent to significance values corrected for multiple testing) below 0.05 for height and polycyclic
growth, each explaining 3–4% of the phenotypic variance
(Table 1 and Figure 1). These SNPs were not in Hardy–Weinberg
equilibrium (HWE) but it has been shown that HWE deviations
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and increasing selection accuracy (reviewed by Isik 2014). Early
identification of individuals carrying the desired allele combinations would allow breeders to grow larger breeding populations,
decrease maintenance and evaluation costs and select elite
genotypes to be crossed in the next breeding cycle before they
express the desired phenotype.
Recent years have brought spectacular progress in the development of genotyping technologies for the identification of single
nucleotide polymorphisms (SNPs). However, the implementation
of genome-wide approaches to capture linkage disequilibrium
(LD) between markers and quantitative trait nucleotides in natural or breeding populations of these species has been hampered
by the combined effect of two factors. On one hand, by the rapid
decay of LD typically found in most genes studied so far in forest
trees (in the order of a few hundreds of base pairs, reviewed by
Thavamanikumar et al. 2013). On the other hand, by the large
size of conifer genomes, usually >10 Gb C−1 (reviewed in
MacKay et al. 2012). Thus, association genetics and genomic
prediction approaches in conifers have so far only relied on a
limited set of SNPs (from a few dozen selected in candidate
genes to a few thousand resulting from wider survey of polymorphisms within genes). Recently, proof-of-concept on GS carried
out with low population sizes in pine (Resende et al. 2012b),
spruce (Beaulieu et al. 2014) and eucalyptus (Resende et al.
2012) have proved to be successful for predicting breeding values. In this context, it has been suggested that pre-selection of
markers based on prior information in terms of positional, expressional and functional candidate genes as well as knowledge
about gene networks to take non-additive effects into account,
could help improve the predictive ability of statistical models
(Schulz-Streeck et al. 2011, Resende et al. 2012b).
In the last decade, family-based QTL mapping has identified a
few loci significantly associated with growth traits in maritime
pine, albeit controlling small proportions of the total phenotypic
variation (Brendel et al. 2002, Chagné et al. 2003, Markussen
et al. 2003, Pot et al. 2006). To date, only two association studies have been developed for these traits although none evaluating a wide-range sample of the species [see Lepoittevin et al.
(2012) for Aquitaine breeding population and Budde et al.
(2014) for natural populations in eastern Spain]. In the path
towards the development of genomic tools and strategies to
complement current and future breeding programs for growth
traits and biomass production in maritime pine, we carried out a
candidate-gene-based association genetic study in this species.
Furthermore, we evaluated and tested the predictive performance for genetic merit gain of the most significantly associated
genes and SNPs. We used a 384-SNP array (Illumina BeadXpress, San Diego, USA; conversion rate of 78.65%), enriched
with candidate genes for growth and wood properties (see
Table S1 available as Supplementary Data at Tree Physiology
Online) to genotype a collection of 394 trees (see Table S2
available as Supplementary Data at Tree Physiology Online). The
1002 Cabezas et al.
Table 1. Single-marker and haplogroup association for growth-related traits. Only significant SNPs after multiple testing corrections using FDR (q-values
<0.05) are reported. All significant SNPs were found in the korrigan gene. Alleles contributing the favorable effect at each locus are indicated in bold.
Marker names (with ‘m’ prefix) and linkage groups (LG) as reported in Chancerel et al. (2011). Haplogroups as defined in Figure 1. MAF, minimum
allele frequency; N, sample size.
SNP ID/
haplogroup
SNP
motif
546
987
1395
1499
C/G
T/C
A/G
A/G
SNP site
annotation
nc
syn
syn
nc
LG
MAF
N
Marker effects
Polycyclic growth
Height at age 3 years
12F
0.47
0.47
0.45
0.47
0.46
322
322
322
321
322
F
P
R2
17.839
17.839
19.142
18.229
17.834
4.20E−05
4.20E−05
1.20E−05
3.00E−05
4.59E−08
0.038
0.038
0.041
0.039
0.038
F
P
R2
17.349
17.349
19.864
18.031
17.346
1.20E−05
1.20E−05
2.00E−06
6.00E−06
7.13E−08
0.031
0.031
0.035
0.032
0.031
Figure 1. Schematic representation of the korrigan gene showing the positions of genotyped SNPs, SNPs associated with growth traits (following
Table 1) and the five identified haplotypes (A–E). A horizontal line indicates the two haplogroups formed by the four SNPs associated with growth.
have little effect on the overall rate of false positives (Chan et al.
2008). Although all four SNPs were almost in full LD (see Figure
S2 available as Supplementary Data at Tree Physiology Online),
best associations were found with korrigan-1395 SNP for both,
polycyclic shoot growth (q-value = 0.0045) and height
(q-value = 0.0136). Five korrigan haplotypes (named A–E, see
Figure 1) could be identified by studying the phase relationships
among the 12 SNPs genotyped in this gene (including the four
significant ones), using PHASE v2.1 (Stephens et al. 2001).
These same five haplotypes have been previously detected by
Pot et al. (2005) using Sanger sequencing of haploid tissue
(i.e., megagametophytes). The four significantly associated
SNPs differentiate two haplogroups, one of them combining the
alleles contributing the favorable effect on three very similar haplotypes. An association test developed using the same previous
model but based on haplogroups led to highly significant results
after multiple testing corrections by FDR (Table 1, Figure S3
available as Supplementary Data at Tree Physiology Online), with
similar estimated effects to individual SNPs.
We further investigated the utility of the detected associations
for breeding purposes using the studied sample as a training
population for genomic prediction. The very stringent corrections used in the association analyses to remove false positives
(due to population structure, multiple testing issues, etc.) would
have likely removed a substantial number of true-positives
Tree Physiology Volume 35, 2015
(Atwell et al. 2010). Thus, for each targeted trait, SNPs from
well-known functional candidate genes with significant P-values
but with q-values >0.05 were also considered, if by doing so
prediction accuracy increased. Genomic breeding value predictions (GEBVs) were estimated by ridge regression-best linear
unbiased prediction (RR-BLUP), using the R-based package rrBLUP (Endelman 2011) (Figure 2). Regarding height and polycyclic shoot growth, the combined GEBVs for the four korrigan
SNPs associated with these traits were similar to those from
models including all genotyped korrigan SNPs (12 SNPs), and
accounted for ∼8 and ∼15% of height and polycyclic growth
phenotypic variance, respectively. Adding SNPs from other
growth-related candidate genes did not improve the models.
Regarding root-collar diameter and total biomass, the four korrigan
SNPs accounted for only 1.6 and 1.1% of the phenotypic variance. However, these values increased to 15 and 4.1% when
other SNPs from korrigan and those from lp3.1, lp3.3 and cad
(three other candidate genes for growth and wood properties)
were included in RR-BLUP models (most complete model
included a total of 19 SNPs: 12 from korrigan, two from lp3.1,
two from lp3.3 and three from cad). The predictive power of
these SNPs for height, polycyclic growth and root-collar diameter were confirmed using a threefold cross-validation strategy
(see Table S5 available as Supplementary Data at Tree Physiology
Online). For total biomass, however, the predictive power was
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snp203
m734
m727
snp236
haplogroup
SNP position in
GenBank
accession
JN013967
Association genetics for growth in maritime pine 1003
found to be variable, as expected from the low overall variance
explained for this trait.
Our association analysis allowed confirmation of previous results
from QTL mapping experiments in maritime pine and expression
analyses in this and other conifer species. The molecular function of
korrigan is not fully known, but it is a membrane-bound endo-1,4β-d-glucanase involved in cellulose biosynthesis that appears to be
part of the primary cell wall cellulose synthase complex in the
plasma membrane [reviewed by Molhoj (2002), Somerville
(2006), Taylor (2008), Vain et al. (2014)]. In Arabidopsis thaliana
(L.) Heynh, mutations in KORRIGAN1 have been shown to impair
cellulose biosynthesis, resulting in defective cell elongation and
dwarfism (Vain et al. 2014). Population genetic analyses pointed to
this gene as a potential target of natural and/or artificial selection in
P. pinaster and Pinus radiata D. Don (Pot et al. 2005). Moreover, our
results support the co-localization of korrigan with a wood-related
QTL in a family-based P. pinaster mapping pedigree (Pot et al.
2006).
Cad (cinnamyl alcohol dehydrogenase) is a key enzyme in the
lignin biosynthesis pathway. It catalyzes the final step in the synthesis of monolignols by converting cinnamaldehydes to cinnamyl
alcohols before oxidative polymerization in the cell wall. This gene
presents natural mutations that produce abnormal lignin (Ralph
et al. 1997). In the Arabidopsis double mutant Atcad4/Atcad5,
the content of lignin in the stem is reduced by 60% with respect
to the wild type and the level of coniferyl and sinapyl alcohol is
reduced by 94% (Sibout et al. 2005). A 5% reduction of lignin
was also detected in a Pinus taeda L. natural cad mutant (Ralph
et al. 1997). However, loss of function or down-regulation of cad
did not lead to gross morphological variations in Nicotiana tabacum L., Medicago truncatula Gaertn., Populus or Pinus (MacKay
et al. 1995, Lapierre et al. 1999, 2004, Kim et al. 2003, Ralph
et al. 2008, Zhao et al. 2013). Rather, they resulted in a red coloration of xylem tissue because of enrichment in coniferyl aldehyde and sinapyl aldehyde. The associations found in our study
between growth-related variables and cad allelic variation in maritime pine are in agreement with those found in other tree species,
such as Neolamarckia cadamba Robx. with wood density (Tchin
et al. 2012) or P. taeda with wood specific gravity (involving various non-synonymous mutations; González-Martínez et al. 2007).
Lp3 is a small gene family encoding for nuclear water-deficitinduced proteins highly homologus to asr (abcisic acid stress
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Figure 2. Correlation of GEBVs based on RR-BLUP and BLUPs obtained in the humid site (Cestas) for all data combined (see Table S5 available as
Supplementary Data at Tree Physiology Online for a threefold cross-validation strategy showing similar predictive power). (a) 12 SNPs from korrigan
gene and height at age 3 years; (b) 12 SNPs from korrigan gene and polycyclic growth; (c) 19 SNPs from korrigan (12 SNPs), lp3.1 (2 SNPs), lp3.3
(2 SNPs) and cad (3 SNPs) genes and root-collar diameter; (d) 19 SNPs from korrigan (12 SNPs), lp3.1 (2 SNPs), lp3.3 (2 SNPs) and cad (3 SNPs)
genes and total biomass. Linear trends are also shown.
1004 Cabezas et al.
Tree Physiology Volume 35, 2015
15 and 4.1% for root-collar diameter and total biomass when
using 19 SNPs from four genes. A cross-validation strategy
showed robust predictive power for all traits except biomass.
These results point towards a desirable integration of candidategene studies (as a means to pre-select relevant marker) and the
possible use of genomic prediction for maritime pine breeding in
the near future.
Supplementary data
Supplementary data for this article are available at Tree Physiology
Online.
Acknowledgments
The authors thank the experimental Units of INRA Pierroton and
INIA for trial establishment and trait measurements. Experimental
data from Cálcena used in this research is part of the Spanish
Network of Genetic Trials (GENFORED, http://www.genfored.
es). We thank all persons and institutions linked to the establishment and maintenance of field trials used in this study.
Conflict of interest
None declared.
Funding
This study was supported by funding from the Treesnips European Union project (no. 836501) and the BOOST-SNP French
project (no. 07PFTV002).
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