de Miguel et al. BMC Genomics 2014, 15:464
http://www.biomedcentral.com/1471-2164/15/464
RESEARCH ARTICLE
Open Access
Genetic control of functional traits related to
photosynthesis and water use efficiency in Pinus
pinaster Ait. drought response: integration of
genome annotation, allele association and QTL
detection for candidate gene identification
Marina de Miguel1,2, José-Antonio Cabezas1,2, Nuria de María1,2, David Sánchez-Gómez1, María-Ángeles Guevara1,2,
María-Dolores Vélez1,2, Enrique Sáez-Laguna1,2, Luis-Manuel Díaz1,2, Jose-Antonio Mancha1, María-Carmen Barbero1,2,
Carmen Collada2,3, Carmen Díaz-Sala4, Ismael Aranda1 and María-Teresa Cervera1,2*
Abstract
Background: Understanding molecular mechanisms that control photosynthesis and water use efficiency in
response to drought is crucial for plant species from dry areas. This study aimed to identify QTL for these traits in a
Mediterranean conifer and tested their stability under drought.
Results: High density linkage maps for Pinus pinaster were used in the detection of QTL for photosynthesis and
water use efficiency at three water irrigation regimes. A total of 28 significant and 27 suggestive QTL were found.
QTL detected for photochemical traits accounted for the higher percentage of phenotypic variance. Functional
annotation of genes within the QTL suggested 58 candidate genes for the analyzed traits. Allele association analysis
in selected candidate genes showed three SNPs located in a MYB transcription factor that were significantly
associated with efficiency of energy capture by open PSII reaction centers and specific leaf area.
Conclusions: The integration of QTL mapping of functional traits, genome annotation and allele association
yielded several candidate genes involved with molecular control of photosynthesis and water use efficiency in
response to drought in a conifer species. The results obtained highlight the importance of maintaining the integrity
of the photochemical machinery in P. pinaster drought response.
Keywords: Candidate gene, Drought, Genome annotation, Photochemistry, Photosynthesis, Pinus pinaster, QTL,
Water use efficiency
Background
Drought resistance is crucial for growth and survival of
species living in water scarce environments [1]. Unraveling the molecular mechanisms that control functional
traits, such as photosynthesis and water use efficiency in
response to drought, is especially relevant in view of its
implication in survival, growth and biomass production.
* Correspondence: cervera@inia.es
1
Departamento de Ecología y Genética Forestal, INIA-CIFOR. Ctra, de La
Coruña Km 7.5, 28040 Madrid, Spain
2
Unidad Mixta de Genómica y Ecofisiología Forestal, INIA/UPM, Madrid, Spain
Full list of author information is available at the end of the article
However, carbon uptake in response to drought is a
complex process with many mechanisms acting in coordination in final CO2 fixation [2]. From stomatal and
mesophyll resistances to diffusion of CO2 to biochemical
processes within chloroplast, complex mechanisms are
involved in net carbon fixation [2-5]. The functional
bases that control carbon uptake under water stress have
been largely studied [6,7], but less information is available about its genetic regulation.
Complex functional trait dissection can be achieved
through two approaches: association studies and QTL
(Quantitative Trait Loci) mapping [8]. The resolution
© 2014 de Miguel et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public
Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this
article, unless otherwise stated.
de Miguel et al. BMC Genomics 2014, 15:464
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power of association studies is higher than QTL mapping [9]. However, the rapid decay in linkage disequilibrium of conifers [10] makes the development of genome
wide association studies in these species laborious and
advocates in favor of candidate gene approaches [11].
In the past, identification of candidate genes underlying
QTL was difficult due to the use of anonymous markers
and limited sequences with functional information, and
thus restricted the approach to model plant species [12].
Nowadays, gene-based markers are easily developed and
much more functional information is available for a
wide range of organisms [13-18], allowing to integrate
functional annotation with QTL studies [19]. Moreover,
the development and application of high throughput
genotyping technologies have allowed the construction
of dense genetic maps [[20-27], http://dendrome.ucdavis.
edu/cmap/]. The use of highly saturated genetic maps
allows to narrow down the position of loci involved in
the genetic control of the targeted trait and the combination of high density gene based maps with functional
annotation allows to identify positional candidate genes
for these QTL [19,28]. Suggested candidate genes are
suitable for association studies that can validate markertrait associations [29]. Therefore, identification of positional candidate genes within QTL confidence intervals,
some of them with known function in other species, could
be considered as a preliminary step that contributes to
the detection of genes underlying traits of interest [30].
Additionally, QTL mapping allows the evaluation of the
genetic basis for potential adaptation in natural populations [31,32] and to extend the understanding of relationships between different morpho-functional traits [33]. The
identification of the main QTL involved in drought response could be a first step to develop marker assisted
selection (MAS) strategies for these traits [11].
Consequently, the detection of QTL involved in
photosynthesis and water use efficiency in the context
of drought response is a first attempt to understand the
genetic basis regulating the expression of these traits.
QTL studies on functional drought response have been
largely implemented for non-forest model species [34-39].
Some of these QTL studies in crop species have recently
identified genomic regions controlling photochemistry of
carbon uptake [40,41]. Breeding programs implemented in
crops have reported yield improvement associated with increased photosynthesis [42]. However, fewer QTL analyses
on functional drought response of forest tree species have
been performed [11,43-46] and to our knowledge none of
them has focused on the photochemical machinery.
QTL studies involve development of a segregant progeny
for target traits, phenotypic and molecular characterization
of the progeny and construction of genetic maps [47]. The
power to resolve the location of a QTL is related to the
size of the studied population and the mapping coverage
Page 2 of 19
[48]. Additionally, forest tree species are characterized by
long generation times which hinder development of backcross or three-generation pedigrees by controlled crosses.
On the other hand, replication of each genotype is needed
for a reliable phenotypic evaluation [49], especially when
working with physiological parameters that are extremely
sensible to environmental conditions [41,50,51].
Mediterranean species are particularly threatened by
drought [52-54], especially in the context of climatic
change predictions [55]. Pinus pinaster Ait. is an important conifer in Mediterranean region with a high ecological
and socio-economical value [56-58]. Although P. pinaster
shows evidence of drought adaptation [59,60], recurrent
or severe drought periods can limit its growth [61,62].
Understanding the molecular basis of drought tolerance
is of high importance for a suitable management of the
available genetic resources of P. pinaster in conservation,
afforestation or breeding programs. QTL and association
studies of drought tolerance traits have been developed in
several tree species, such as P. taeda [23,63,64], Populus
sp. [19,65,66] or Quercus robur [43,67]. Several QTL and
association studies in P. pinaster have analyzed the molecular basis of different processes related to growth or
wood quality traits [68-73], terpenes [74] and serotiny
[75]. However, to date only association studies based on a
few potential candidate genes [59,60] and one QTL study
have analyzed the molecular basis of drought tolerance in
P. pinaster [44].
The main objective of this work was to unravel the
genetic basis of different functional parameters related
to carbon uptake and water use efficiency in response to
drought for P. pinaster. For this purpose a QTL analysis
using vegetatively propagated genotypes in order to improve the reliability of phenotypic estimates was designed.
Several specific objectives were outlined: 1) construction
of dense gene-based linkage maps with functional information; 2) identification of genomic regions underlying
photosynthesis and water use efficiency in response to
drought through QTL analysis; and 3) identification of a
set of promising candidate genes in targeted genomic
regions that may be involved in the genetic regulation
of photosynthesis and water use efficiency in response
to drought.
Methods
Plant material, experimental setup and phenotypic
evaluation
Plant material, experimental setup and phenotypic evaluation are explained in detail in de Miguel et al. [76].
Briefly, 162 seedlings from a F1 full-sib family of P. pinaster obtained from a controlled cross between a male
parent (Oria6) from Oria, a natural population from
South-East Spain (37° 31 ’N 2° 21 ’W) and a female parent (Gal1056) from a breeding program established in
de Miguel et al. BMC Genomics 2014, 15:464
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Pontevedra, North-West Spain (42° 10 ’N 8° 30 ’W), were
vegetatively replicated and established in an incomplete
block design in a greenhouse at Instituto Nacional de
Investigación y Tecnología Agraria y Alimentaria (INIA).
Phenotypic evaluations were conducted on the 103 clones
for which at least three ramets were obtained. For phenotypic characterization three time-points of measurement
were carried out starting in October 2009. During the 1st
time-point, plants were watered close to full holding capacity. Then, watering was withdrawn and during the 2nd
time-point of measurement plants were left 7 days without
watering. The final third batch of measurements was carried out after plants have been 14 days without watering.
Net photosynthetic rate (An, μmol CO2m−2 s−1), stomatal
conductance to water vapour (gsw, molH2Om−2 s−1),
intrinsic water use efficiency (WUEi, μmol CO2
molH2O−1), specific leaf area (SLA, m2Kg−1), maximum
efficiency of photosystem II under light conditions
(Fv ’Fm’) and quantum yield (ΦPSII) were measured for
all plants. Chlorophyll fluorescence parameters were
measured following the procedure described in Cano
et al. [6].
In the 1st time-point of measurement four adult needles were collected for each plant, dried and ground
into a fine homogeneous powder. Carbon isotope composition was measured with a PDZ Europa ANCA-GSL
elemental analyzer interfaced to a PDZ Europa 20–20
continuous flow isotope ratio mass spectrometer (Sercon
Ltd., Cheshire, UK) at Stable Isotope Facility UC Davis,
California, USA. The isotopic composition of 13C (‰) was
expressed as [77]:
δ 13 C ¼
Rs − Rb
Rb 1000
Where Rs and Rb refer to the 13C/12C ratio in the sample
and in the Pee Dee Belemnite standard, respectively.
Broad-sense heritability estimates and genetic correlations were calculated for the analyzed traits according
to de Miguel et al. [76].
DNA extraction and marker genotyping
The mapping progeny was genotyped with nuclear microsatellites (single sequence repeats, nSSR), selective amplification of microsatellite polymorphic loci (SAMPL) and
single nucleotide polymorphism (SNP) markers. Different
DNA extraction methods were used in needles: a modified
protocol from Dellaporta et al. [78] for nSSRs, SAMPLs
and SNP array D (detailed below); the commercial kit
Invisorb DNA plants HTS 96 kit (Invitek GmbH, Berlin,
Germany) for SNP arrays A and C (detailed below) and
the commercial kit DNeasy Plant mini kit (Qiagen,
Düsseldorf, Germany) for SNP array B (detailed below).
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A total of twenty nine primer pairs designed for amplification of nSSR loci in P. pinaster and P. taeda [79,80]
were tested for their segregations in the mapping population and both progenitors and six progeny individuals
were genotyped. The whole mapping progeny was then
genotyped only for polymorphic microsatellite loci. PCRs
were performed in 10 μl containing 10 ng of DNA, 1x
PCR reaction buffer (Invitrogen, Grand Island, NY, USA),
250 μM of each dNTP (Invitrogen, Grand Island, NY,
USA), 0.25 U Taq polymerase (Invitrogen, Grand Island,
NY, USA), 4 mM MgCl2 (Invitrogen, Grand Island, NY,
USA) except for A6D12 where 2 mM MgCl2 was used,
0.2 μM of forward primer and 0.2 μM of reverse primer
labeled on its 5’ end with IRD800. The PCR profile used
was 94°C 4 min, 2 cycles of 94°C 45 s, 60°C 45 s, 72°C
45 s, 18 touchdown cycles of 94°C 45 s, 59.5°C 45 s
(−0.5°C/cycle), 72°C 45 s, 20 cycles 94°C 30 s, 50°C 30 s,
72°C 45 s and final extension at 72°C 5 min. PCR reactions
were carried out with a Perkin-Elmer GenAmp 9700 thermal cycler (Perkin Elmer Inc., Waltham, Massachusetts,
USA). Amplified products were separated on denaturing
gels containing 6% (w/v) acrylamide/bisacrylamide
(19:3), 7 M urea and 1 x TBE and visualized in a 4300
DNA Analyzer (LI-COR Biosciences, Lincoln, NE,
USA). Fragments were scored visually as codominant
markers.
SAMPL genotyping was performed as in de Miguel
et al. [81] with several modifications. Preamplification
was carried out using EcoRI + A / MseI + C primer combination. In order to identify the most informative selective primer combinations (those with a higher number of
informative polymorphic fragments) different primer combinations were tested using DNA from the progenitors
and 6 offspring. A total of five CATA/EcoRI and three
GATA/EcoRI primer combinations were used for the selective amplification. The whole mapping progeny was
then genotyped for the eight selected SAMPL primer
combinations. Primers CATA and GATA were IRDye
700 and IRDye 800 5’end labeled, respectively. Samples
were loaded into denaturing gels containing 16% (w/v)
Long Ranger® 50% (w/v) Gel Solution (Lonza, Basel,
Switzerland), 7 M urea and 1 x TBE. Fragment detection
was carried out on a 4300 DNA Analyzer (LI-COR Biosciences, Lincoln, NE, USA). Each gel was visually scored
twice independently by two different people.
In this study, four SNP genotyping assays were used,
three of which were Golden Gate assays (Illumina Inc.,
San Diego, CA, USA): SNP arrays A and C, which were
two different 1,536 BeadArray™ experiments; and SNP
array B, which was a 384 BeadXpress®. The SNP array D
was a 12 K Infinium assay (Illumina Inc., San Diego, CA,
USA). SNP arrays B and C were used to genotype the
whole mapping progeny, whereas A and D could be used
only on 83 and 70 progeny individuals, respectively. SNP
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array B was developed including many of the SNPs
targeted in array A [82] and 14 additional SNPs from
candidate genes for drought resistance [59] in order to
complete the information for a set of genes of special
interest (see de Miguel et al. [81] for further details).
When the same SNP was successfully genotyped in both
assays only the data of SNP array B was used because of
the higher number of individuals genotyped in this assay.
SNP array C was designed using a P. pinaster gene catalog
obtained from 454 sequencing of cDNA libraries constructed with different tissues from 9 siblings of the
mapping progeny submitted to different growing conditions (i.e. drought stress versus control plants; E SáezLaguna et al., unpublished). The genotyping of SNP
array C was developed at CNIO, Madrid, Spain. Finally,
SNP array D contained 10,593 SNPs identified from
unigene set “PineContig_v2” of P. pinaster [20]. The
four genotyping assays were carried out according to the
manufacturer’s instructions (Illumina Inc., San Diego, CA,
USA) and SNPs clusters revised manually with Illumina
Genome Studio v.1.9.4 software with a GenCall score cutoff of 0.15. SNP clusters were modified manually to refine
cluster positions when necessary. For the SNP array D
(12 K Infinium) SNPs with Gen-Train values lower than
0.25 were discarded, with values between 0.25 and 0.5
were manually scored and with values higher than 0.5
were automatically scored.
Additional file 1). Segregation ratios were tested using
χ2 test (p ≤ 0.01) after Bonferroni correction. Framework
maps for Gal1056, Oria6 and GxO were also built. For
this purpose, only the most informative markers with
very reliable positions and inter marker distance of circa
10 cM were kept. Total genome length was calculated
as the sum of all mapped marker intervals. Estimated genome length was determined from the partial linkage data
according to Hulbert et al. [86] modified by Chakravarti
et al. [87] (Method 3). To estimate genome length using
framework maps, a minimum LOD score of three was
chosen. Observed map coverage was calculated as the
ratio of total genome length to estimated genome length.
To estimate the number of different mapped genes a
BlastN was performed between gene sequences contained
in the different SNP genotyping arrays. Sequences with a
percentage of identity higher than 98% were considered
the same gene. To test whether the mapped genes
were evenly distributed between linkage groups χ2
tests (p < 0.05) were performed by comparing observed
and estimated numbers of genes per linkage group
(LG). The expected number of genes for each LG was
obtained by multiplying the ratio size of LG to total
genome length by the total number of mapped genes.
Linkage maps were compared with previously developed
P. pinaster maps [20,81,82] based on common SNPs
and SSRs.
Construction of dense linkage maps
QTL mapping
For the construction of two genetic maps, one for each
progenitor (Gal1056 and Oria6), the “two-way-pseudotestcross” mapping strategy was applied [83]. The consensus map for the cross, combining markers informative
for both parents, was also developed (GxO). Linkage
analyses and map estimations were performed using the
regression mapping algorithm implemented in the software JoinMap® v4.1 [84] with the CP population type
and using a recombination fraction < 0.35 and a LOD >
3 as mapping parameters. Map distances were calculated using Kosambi mapping function [85]. For map
building a goodness-of-fit jump threshold of 5 was
established. JoinMap suggests three genetic maps with
increasing number of markers (map1, map2 and map3).
In map2, new markers were added because more pair
wise data were available but statistical support is the
same as in map1. In map3, the remaining loci were
added by increasing the goodness-of fit jump threshold.
In these cases map2 was kept for further analyses. Mean
χ2 contribution to the goodness of fit and number of
double recombinants were inspected in order to remove
not reliably positioned markers from the estimated
maps. When a pair of markers was considered identical
based on the lack of recombination between them, only
one of the markers was selected for mapping (see
In order to avoid errors in marker order that may have
some impact on the precision and accuracy of QTL
placement, QTL analyses were performed using the
framework linkage maps. QTL detection was carried
out using the regression algorithm implemented in the
software MapQTL® v6.0 [88]. Interval mapping was applied followed by multiple QTL mapping (MQM) when
more than one QTL was found for a trait. Analyses were
performed using a mapping step size of one. The thresholds (95% and 99% confidence) for QTL significance were
determined using a chromosome and genome wide permutation test with 10,000 iterations. Support intervals for
the detected QTL were estimated based on the observed
decrease of LOD value in one and two units. QTL identified with only the 95% significance at chromosome level
were considered as suggestive of putative QTL. Each detected QTL received an identification name indicating the
measured trait, the time-point of measurement, the linkage group (LG) and the map (“f” and “m” for female and
male parents respectively and “i” for consensus map)
where the QTL was detected.
Candidate genes search
Functional annotation for gene based markers of SNP
arrays A, B and D were described by Chancerel et al.
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[20,82]. Functional annotation for SNP array C was obtained with Blast2GO software [89]. For high-scoring
segment pair (HSP) a restrictive E−20 e-value was chosen
finding in a Blastx search against a set of 88,516 reference proteins from UniProt (http://www.uniprot.org/).
In order to update annotation information for the sequences of the four SNP arrays that mapped to the
QTL, a second round of annotation was performed
using Blast2GO software with a restrictive E−25 e-value
for HSP, and annotation was completed with InterPro
(http://www.ebi.ac.uk/interpro/) and Kyoto Encyclopedia
of Genes and Genomes (KEGG, http://www.genome.jp/
kegg/) searches. For those genes within the significant
QTL confidence intervals (±2 LOD), functional annotations were queried to identify functional relationship between the positional candidate genes and each analyzed
trait. In order to cover all mapped genes, QTL confidence
intervals in framework maps were extrapolated to maps
with all the mapped markers.
Association between phenotypes and alleles at candidate loci was further studied by ANOVA using the traits
as dependent variables and the SNP genotypes as factors.
Thereof nineteen traits (seven different traits measured
at three water irrigation regimes, except δ13C measured
only at 1st time-point) and 73 SNPs located in 58 identified candidate genes were inspected. False discovery rate
(FDR) was calculated using the package qvalue. Association analyses were carried out in R version 2.15.2 (R
Development Core Team, 2012).
Results
Phenotypic evaluation
Descriptive statistics of all analyzed traits are shown in
Table 1. Almost all traits showed a close to normal distribution with low levels of skewness and kurtosis. Although
normal distribution is an assumption in interval mapping,
this method and MQM are quiet robust against deviations
from normality [88]. Water stress produced a decrease
in mean values for almost all variables except for WUEi
and SLA that showed higher and very similar mean
values, respectively, for the three time-points of measurement. Coefficients of variation were progressively
higher with the imposition of drought stress being gsw
the trait that showed the higher coefficient of variation
in the 3rd time-point of measurement.
Phenotypic correlations between the studied traits are
presented in Table 2. An was correlated with gsw and with
chlorophyll fluorescence parameters (Fv’Fm’ and ΦPSII).
The magnitude of the correlation coefficients was very
similar for the 1st time-point of measurement. However,
under drought stress An showed a higher correlation coefficient with chlorophyll fluorescence parameters than with
Table 1 Descriptive statistics of measured traits in the F1 full sib family Gal1056xOria6 (n = 103)
Trait
Time-point
Mean ± SD
Range
CV(%)
Skewness
Kurtosis
p-value
An
1
10.8 ± 1.3
7.9-15.2
12.4
0.65
0.46
0.04
2
10.1 ± 1.3
6.5-12.9
12.4
−0.21
0.16
0.63
3
5.9 ± 1.7
1.2-9.9
29.8
−0.18
−0.3
0.67
1
0.21 ± 0.03
0.141-0.301
15.9
0.47
0.09
0.04
2
0.146 ± 0.03
0.068-0.222
22.6
0.06
−0.44
0.57
3
0.07 ± 0.03
0.01-0.198
45.7
0.71
1.28
0.02
1
54.7 ± 9.8
32.5-81.8
17.9
0.7
0.52
0.003
2
79.9 ± 16.4
41.2-130.1
20.6
0.4
0.21
0.38
3
100.4 ± 27.8
44.8-179.4
27.7
0.52
0.34
0.05
δ13C
1
−29.6 ± 0.64
−31.1/-28
2.2
0.003
−0.09
0.92
SLA
1
7.3 ± 0.7
5.8-10.4
9.6
1.16
3.26
<0.001
gsw
WUEi
Fv'Fm'
ΦPSII
2
6.7 ± 0.6
5.6-8.6
9.4
0.55
−0.005
0.03
3
6.6 ± 0.6
5.3-8.8
9.5
0.68
0.94
0.02
1
0.618 ± 0.03
0.496-0.684
5.3
−0.71
1.16
0.02
2
0.575 ± 0.04
0.48-0.688
7.5
−0.32
−0.26
0.04
3
0.468 ± 0.04
0.361-0.591
8.5
0.14
0.17
0.94
1
0.21 ± 0.02
0.157-0.252
9.9
−0.44
0.03
0.09
2
0.213 ± 0.03
0.153-0.276
11.8
0.15
−0.43
0.63
3
0.16 ± 0.02
0.096-0.223
15.5
−0.008
−0.05
0.99
An = net photosynthetic rate (μmol CO2m−2 s−1); gsw = stomatal conductance to water vapour (molH2Om−2 s−1); WUEi = intrinsic water use efficiency (μmol CO2
molH2O−1); δ13C = isotopic composition of 13C (‰); SLA = specific leaf area (m2Kg−1); Fv’Fm’ = maximum efficiency of PSII under light conditions; ΦPSII = quantum
yield. Time-points of measurements correspond with: 1, well watered plants; 2, seven days without irrigation; 3, fourteen days without irrigation. SD stands for
standard deviation and CV for coefficient of variation. p-values were obtained from Shapiro- test to check normality.
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Table 2 Pearson correlation coefficients and statistical
significance for measured traits in the F1 full sib family
Gal1056xOria6 (n = 103)
Time-point Trait
gsw
1
0.42** 0.36**
An
gsw
WUEi
δ13C
SLA
Fv’Fm’ ΦPSII
n.s
0.23*
0.47**
−0.61** −0.51** 0.37** 0.24*
0.49**
WUEi
δ13C
n.s
n.s
0.37**
0.31**
0.21*
Fv’Fm’
An
gsw
0.52** n.s
−0.8**
-
0.58**
0.29** 0.5**
0.2*
0.21*
n.s
0.57** n.s
0.62**
-
Fv’Fm’
gsw
n.s
-
SLA
An
n.s
0.31**
WUEi
3
n.s
0.34** n.s
SLA
2
0.54**
0.24*
0.22*
n.s
0.39**
-
n.s
0.82**
0.77**
−0.65** -
n.s
0.49**
0.27**
n.s
WUEi
-
SLA
-
Fv’Fm’
-
n.s
n.s
n.s
n.s
0.73**
An = net photosynthetic rate (μmol CO2m−2 s−1); gsw = stomatal conductance to
water vapour (molH2Om−2 s−1); WUEi = intrinsic water use efficiency (μmol CO2
molH2O−1); δ13C = isotopic composition of 13C (‰); SLA = specific leaf area
(m2Kg−1); Fv’Fm’ = maximum efficiency of PSII under light conditions; ΦPSII =
quantum yield. Time-points of measurements correspond with: 1, well watered
plants; 2, seven days without irrigation; 3, fourteen days without irrigation.
*p < 0.05, **p < 0.01,***p < 0.001.
gsw. Besides, An and chlorophyll fluorescence parameters
showed a tight genetic correlation (see Additional file 2).
For WUEi and δ13C, a significant phenotypic (Table 2)
and broad sense genetic correlation (see Additional file 2)
was found. Both traits had higher phenotypic correlation
coefficients with gsw than with An. SLA was moderately
correlated with An, gsw, WUEi and δ13C for the 1st and 2nd
time-points of measurement (Table 2). Broad sense heritability estimates for the analyzed traits are presented
in Additional file 3. All of them presented moderate to
low values of heritability being the higher estimates for
gsw, WUEi and δ13C.
Highly saturated linkage maps
For Gal1056, Oria6 and consensus map, 17, 16 and 13
linkage groups (LG) were obtained, respectively (Table 3).
The three constructed genetic linkage maps had in total
2,107 markers representing 1,314 mapped genes (Table 3).
Genes were evenly distributed between linkage groups
(χ2 test p > 0.05 for the three linkage maps). Map coverage was 65–100% and average distance between two adjacent markers was smaller than 2 cM (Table 3). The
vast majority of markers with distorted segregations
were discarded because of insufficient linkage information to be mapped (Table 3). Out of the six distorted
markers, five mapped in the first 10 and 20 cM of LG 5 in
Oria6 and consensus maps, respectively (see Additional
files 4 and 5).
Through comparisons between both parental maps, as
well as with previously developed maps for P. pinaster
[20,81,82] based on 654 common markers, 12 groups
could be identified for the three maps, which is in agreement with the haploid number of chromosomes for the
species. Common markers among the different genetic
maps compared mapped always in the same homologous
LG excepting three markers (see Additional file 6): contigs FN696780 and AL749831 that mapped in LG 9 and
LG 4 in Chancerel et al. [20] and in LG 7 and LG 9 in
this study, respectively (see Additional files 4 and 5);
contig CT577280 that mapped in LG 7 and LG 4 in the
two different maps obtained in Chancerel et al. [20]
while in Gal1056 and the consensus map it was mapped
in LG 4.
For the 82% and 86% of contigs with more than one
mapped SNP, they mapped at less than 1 cM in Gal1056
and Oria6 respectively. There was a significant exception
for contig BX249015 that had one SNP mapped in LG 5
(BX249015-204) in Gal1056, Oria6 and the consensus
map and the other SNP mapped in LG 8 (BX249015289) in Oria6 and the consensus map (see Additional
files 4 and 5), whereas this contig was mapped in LG 5
in Chancerel et al. [20].
The consensus linkage map is available at Dendrome
(http://dendrome.ucdavis.edu/cmap/).
QTL detection
Of the 55 detected QTL (Table 4, Figures 1 and 2), 28
were highly significant QTL, whereas the remaining 27
could be considered as suggestive or putative QTL. QTL
were detected for all traits but the higher number of QTL
were detected for Fv ’Fm’ and ΦPSII (Table 4, Figures 1
and 2). The total phenotypic variance explained for a
single QTL ranged from 4.6% (WUEi) to 20.9% (Fv ’Fm’).
The higher percentage of total phenotypic variance explained by all the QTL detected for a trait in a timepoint of measurement was 44% (Fv ’Fm’).
Consequently, four QTL hotspots could be identified in
LG 5, LG 6, LG 7 and LG 12 (Table 4, Figures 1 and 2)
due to the co-localization of QTL for different traits
(Figures 1 and 2): SLA co-localized with gsw in LG 5
and LG 7; Fv ’Fm’ co-localized with WUEi in LG 5 and
LG 12 with δ13C in LG 6, with An in LG 6 and LG 12
and with ΦPSII in LG 7 and LG 12. QTL for SLA, Fv ’Fm’
and ΦPSII were detected for the three time-points of
measurement. Some of them co-localized in the same
region, such as the identified for SLA in LG 5, LG 7 and
LG 12 and for Fv ’Fm’ in LG 6 and LG 7. Co-localization
of QTL for the same traits at different levels of water
stress highlights the stability of QTL with the imposition
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Table 3 Mapping features of the two parental linkage maps (Gal1056 and Oria6) and consensus map for the cross
(GxO)
Mapping features
Gal1056
Oria6
Total number of available markers
1,539
1,574
2,601
SSRs loci
8
7
8
SAMPL loci
29
33
55
SNP loci
1,502
1,534
2,538
Total number of distorted markers
33 (2.1%)
36 (2.3%)
53 (2%)
Unlinked markers (%)
65 (4.2%)
78 (5%)
54 (2.1%)
Number of markers assigned to LG
a
GxO
1,474
1,496
2,547
SSRs loci
8
7
8
SAMPL loci
21
25
54
SNP loci
1,445
1,464
2,485
b
Number of positioned markers
1,026 (66.7%)
1,184 (75.2%)
1,810 (69.6%)
SSR loci
2 (25%)
3 (42.9%)
1 (12.5%)
SAMPL loci
12 (41.4%)
12 (36.4%)
22 (40%)
SNP loci
1,012 (67.4%)
1,169 (76.2%)
1,787 (70.4%)
c
Number of positioned genes
685
792
1,154
Number of distorted positioned markers
0
5
6
LG before alignments
17
16
13
Groups after alignments
12
12
12
Smallest LG before alignments
24 cM
28.7 cM
39.1 cM
Largest LG before alignments
141.9 cM
149.6 cM
165 cM
Average length LG ± SD before alignments (cM)
87.6 ± 42
92.9 ± 41.8
128.9 ± 31.8
Smallest group after alignments
76 cM
70.5 cM
116.1 cM
Largest groups after alignments
187.8 cM
149.6 cM
165 cM
Average length of a group ± SD after alignments (cM)
124.1 ± 26.9
123.9 ± 22.3
138.5 ± 17.1
Maximum distance between 2 adjacent markers
20 cM
28.8 cM
18.3 cM
Average distance between 2 adjacent markers ± SDd
1.92 ± 2.7
1.66 ± 2.6
1.24 ± 1.9
Observed map length (cM)
1,488.7
1,486.8
1,662.3
Estimated map length (cM)
2,337.7
1,479.7
2,378.2
Observed map coverage
64%
100%
69.9%
Estimated map coverage
100%
100%
100%
a
At p < 0.01 after Bonferroni correction for the number of markers.
Not positioned markers correspond to unlinked markers or markers which position could not be reliably estimated. Percentages calculated over the total number
of available markers.
c
Twenty one, 47 and 59 positioned contigs for Gal1056, Oria6 and GxO maps respectively, were not considered.
d
Identical markers whose position was the same because of the lack of recombination between them were not considered.
SD: Standard deviation.
b
of drought stress. QTL for An could only be detected for
the 2nd and 3rd time-point of measurement while QTL
for gsw and WUEi were only detected in the 1st and 2nd
time-point of measurement (Table 4).
Candidate gene identification
The 74% of the mapped sequences (991 out of 1,348)
were annotated. Gene annotations and co-localization
with the detected QTL lead to the identification of 58
positional candidate genes that could be involved in the
expression of the targeted traits (see Additional file 7).
Genes related with oxidative stress, ATPase family proteins or proteins of the light harvesting centers were found
in the confidence intervals of QTL for net photosynthesis
or chlorophyll fluorescence traits. Genes related with stomatal regulation, ABA signaling pathways or cell wall
composition were found in QTL for gsw and WUEi. Genes
expressed under drought conditions co-localized with
QTL identified in the 2nd or 3rd time-point of measurements but not in the first one, which could be pointing
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Table 4 Identified QTL in Gal1056, Oria6 and GxO maps
Trait
Time-point
Map
Total var.
LG
LOD
Sig.
Var.
Add. Eff.
Position (cM)
CI1LOD (cM)
CI
An
2
Oria6
23
4
2.7
**ch
10
+
91.4
77.1-107.4
69.7-112.4
6
2
*ch
7.1
-
18.5
5-44.5
0-79.5
AnS2LG6m
9
2.4
**ch
8.7
+
61.8
40-79.8
30-113.8
AnS2LG9m
gsw
δ C
SLA
Oria6
12
1.5
*ch
12
-
30.6
0-41
0-66.6
An_S3LG12m
Gal1056
18.7
7_1
1.9
**ch
7.4
+
0.0
0-25
0-27
gsw S1LG7_1f
12
2.1
*ch
8
+
132.2
119-145.2
95.1-152.1
gsw S1LG12f
GxO
37.8
7
3.5
**ch
10.7
37.5
27-47
5-65
gsw S1LG7i
10
4.1
**ch
12.5
16.1
5-40
5-55
gsw S1LG10i
12
3.9
**ch
12
114.5
103.1-125.5
90-131.6
gsw S1LG12i
1
1
1
5
2.4
**ch
10.1
GxO
Gal1056
24.1
5
3.9
**ch
14.4
11
2.8
*ch
10
50.1
32.6-55.1
10-75
gsw S2LG11i
Gal1056
15
7_1
1.2
*ch
4.7
-
6.0
0-27
0-27
WUEiS1LG7_1f
+
86.4
52.9-112.3
36.7-121.6
gsw S2LG5f
68.7
61.9-79.7
48.3-85.7
gsw S2LG5i
12
2.0
*ch
7.8
-
132.2
118-141.2
83.1-152.1
WUEiS1LG12f
Oria6
3_2
1.06
*ch
4.6
+
0
0-18.4
0-18.4
WUEiS1LG3_2m
Gal1056
5
2.1
*ch
9
-
103.3
87.4-116.3
13-130.6
WUEiS2LG5f
GxO
5
3
*ch
12.7
68.7
49.9-76.2
42.1-115.7
WUEiS2LG5i
Gal1056
6
2.3
*ch
9.7
34.3
22-56.4
11-79.8
δ13C S1LG6f
GxO
6
3.2
*ch
13.4
33.2
21.2-45.2
8-55.4
δ13C S1LG6i
Gal1056
16.2
GxO
+
5
1.9
*ch
7.5
+
53.4
29.4-86.4
0-130.6
SLAS1LG5f
12
2.2
*ch
8.8
-
10.1
0-38.1
0-58.2
SLAS1LG12f
12.9
0-53.8
0-76.1
SLAS1LG7i
46.9
20-72
3-96.9
SLAS2LG5f
7
3.1
*ch
13.1
2
Gal1056
18.6
5
2.5
**ch
9.7
12
2.6
**ch
9.8
-
10.1
0-34.1
0-51.1
SLAS2LG12f
3
Gal1056
16.8
5
2.5
**ch
9.7
+
46.9
17.5-55.9
12-83.9
SLAS3LG5f
+
GxO
Fv'Fm'
QTL id
AnS2LG4m
3
2
13
(cM)
1
2
WUEi
2LOD
1
2
Gal1056
+
7_1
2.2
**ch
8.5
7
3.1
*ch
13.1
0.0
0-8
0-25
SLAS3LG7_1f
44.8
29.6-87.7
0-101.5
SLAS3LG7i
7_2
2.9
*gw
12.1
6
1.9
*ch
7.4
-
5.5
0-35
0-54.4
Fv’Fm’S1LG7_2f
+
122.4
114.7-122.4
0-122.4
Fv’Fm’S1LG6m1
-
Oria6
18
6
2.7
**ch
10.3
GxO
28.2
3_2
3.5
**ch
12
7
3.7
**ch
12.9
90.3
67-120
40-145.3
Fv’Fm’S1LG7i
Gal1056
22.9
2_1
1.9
*ch
7
-
0.0
0-11
0-100.3
Fv’Fm’S2LG2_1f
5
2.7
**ch
10
+
86.5
71.9-99.4
20.5-114.4
Fv’Fm’S2LG5f
12
2.1
*ch
7.5
+
152.1
130.6-152.1
75.2-152.1
Fv’Fm’S2LG12f
Oria6
GxO
32.7
44
62.7
37.5-96.7
25.5-104.7
Fv’Fm’S1LG6m2
0
0-3
0-8
Fv’Fm’S1LG3_2i
1_2
2.6
**ch
8.2
-
45.1
30.2-66.1
24.5-67.8
Fv’Fm’S2LG1_2m
3_1
3.3
**gw
10.8
+
18.6
2-22.6
0-26.6
Fv’Fm’S2LG3_1m
6
2
*ch
6.4
-
62.7
38.3-85.7
0-101.7
Fv’Fm’S2LG6m
12
1.7
*ch
5.4
-
30.6
0-41.6
0-66.6
Fv’Fm’S2LG12m
6
4.1
**ch
11.4
59.3
55-77
50-95
Fv’Fm’S2LG6i
7
7
**gw
20.9
54.8
50-66
48-70
Fv’Fm’S2LG7i
9
3.6
*ch
9.6
71.3
65-80
60-85
Fv’Fm’S2LG9i
12
4.7
**gw
13.2
83.3
80-95
77-110
Fv’Fm’S2LG12i
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Table 4 Identified QTL in Gal1056, Oria6 and GxO maps (Continued)
3
ΦPSII
1
Oria6
6
2.1
*ch
9
GxO
29.1
6
3.7
**ch
13.3
7
4.5
*gw
15.6
Gal1056
20.1
2_2
1.8
*ch
6.8
4_1
2.7
**ch
10.1
+
3.3
7_2
1.8
*ch
6.5
-
37.4
-
Oria6
8_2
2.7
**ch
11.3
2
GxO
7
2.9
*ch
12.3
3
Gal1056
8
1.8
*ch
7.8
4
2
*ch
7.9
12
1.9
*ch
7.3
7
2.9
*ch
12.2
Oria6
15.1
GxO
-
-
34.3
0-43.3
0-68.7
Fv’Fm’S3LG6m
54.8
0-80
0-124.8
Fv’Fm’S3LG6i
63.7
55.8-71.6
54.5-81.6
Fv’Fm’S3LG7i
85.2
69.9-95.5
0-95.5
ΦPSII S1LG2_2f
0-22.3
0-34.3
ΦPSII S1LG4_1f
23.8-60.4
0-88.8
ΦPSII S1LG7_2f
79
74.1-79
60.8-79
ΦPSII S1LG8_2m
37.5
24.6-62.8
16.5-62.8
ΦPSII S2LG7i
-
99.6
78.5-110
0-124.5
ΦPSII S3LG8f
+
20.5
10.5-30.5
0-56.5
ΦPSII S3LG4m
-
12.7
0-30.7
0-66.5
ΦPSII S3LG12m
63.7
48.8-67.1
24.6-71.2
ΦPSII S3LG7i
Columns stand for trait names [An = net photosynthetic rate (μmol CO2m−2 s−1); gsw = stomatal conductance to water vapour (molH2Om−2 s−1); WUEi = intrinsic
water use efficiency (μmol CO2 molH2O−1); δ13C = isotopic composition of 13C (‰); SLA = specific leaf area (m2Kg−1), Fv’Fm’ = maximum efficiency of PSII under
light conditions; ΦPSII = quantum yield], time-points of measurements (1st stands for well watered, 2nd and 3rd for seven and 14 days without watering), genetic
map where the QTL was identified, total phenotypic variance explained (%) for all detected QTL for a given trait in a given time-point of measurements, linkage
group, maximum LOD score for mapped markers, level of significance (* <0.05, ** < 0.01, ch stands for chromosome and gw for genome wide level), total
phenotypic variance explained for each QTL (%), sign of the additive effect, position of the marker with the maximum LOD score, one LOD confidence interval,
two LOD confidence interval and QTL identification name.
out the induced drought functional response of P. pinaster. Other remarkable co-localizations were found
for two QTL for SLA with an enzyme involved in auxin
biosynthesis, or between a QTL for δ13C and a member
of the aquaporin family (see Additional file 7 for a detailed list of candidate genes). ANOVA test developed
for the 73 tested SNPs in candidate genes resulted in
43 significant associations with at least one of the analyzed traits (data not shown). After corrections using
the false discovery rate estimated, only three SNPs of
the gene MYB1 (m746, m747 and m751) remained significantly associated with Fv ’Fm’ measured in the 1st
time-point (well watered plants) and SLA measured in
the 3rd time-point (14 days without watering; Figure 3).
SNP m746 was located in an intron but m747 and
m751 were located in exon regions. The base substitution in SNP m747 was a non-synonymous change between a threonine (when a cytosine is present) and an
isoleucine (when a thymine is present) while in m751
was a synonymous change. SNPs m747 and m751 explained 14.4% and 12.6% of the phenotypic variance for
Fv’Fm’ measured in the 1st time-point and SLA measured
in the 3rd time-point respectively. SNP m746 explained 9%
of the total phenotypic variance for Fv’Fm’ measured in the
1st time-point.
Discussion
Highly saturated linkage maps
Combining different types of markers three highly dense
linkage maps were constructed. They include more than
1,000 genes scattered throughout the genome of P. pinaster and distributed in 12 groups that match the
chromosome number of the species. The aforementioned highly saturated maps, with less than 2 cM
mean distance between markers, are in the range of recently published linkage maps for other conifer species
[20,22,24,27,51]. Estimated map length was higher in
the female than in the male parent. Differences in genome length between parental maps are usually found in
conifer species [90-93] and it may be a consequence of
differences in the recombination rate between parental
trees [94,95].
The accuracy of the SNP genotyping assays previously
proved [20,82] has been confirmed in this study by genes
with more than one SNP that mapped in almost all cases
within a distance lower than 1 cM. The single exception
of contig BX249015 could be attributed to the existence
of two paralogous genes for this sequence placed in different LGs. Indeed, high levels of synteny and colinearity
were observed between female and male parental maps.
The fact that four out of the five distorted markers
mapped in the same region suggests that segregation distortion could be due to pre or post-zygotic selection rather
than to genotyping errors.
The construction of dense genetic maps for different
conifers provides additional tools for studying conifer
genomes organization and evolution at a finer scale
[27]. In addition, high density linkage maps can be used
to position scaffolds along linkage groups contributing
to the assembly of a reference genome sequence [24,96].
P. pinaster genome sequencing is currently in progress,
and it should be noted that Oria6, the male progenitor of
the mapping family, is the genotype from which the haploid line was selected and its DNA used as template [97].
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Figure 1 Graphical representation of the QTL identified on the parental (Gal1056 and Oria6) and consensus (GxO) framework linkage
maps (LGs 1 to 6). Only linkage groups for which QTL have been detected are presented. The one and two LOD confidence intervals are
indicated by squares and lines, respectively. Colored QTL are the significant QTL (significant at 99% at chromosome level or 95% at genome wide
level), with each color representing a different trait, and black QTL are the suggestive QTL (significant at 95% confidence at chromosome level).
An = net photosynthetic rate (μmol CO2m−2 s−1); gsw = stomatal conductance to water vapour (molH2Om−2 s−1); WUEi = intrinsic water use
efficiency (μmol CO2 molH2O−1); δ13C = isotopic composition of 13C (‰); SLA = specific leaf area (m2Kg−1); Fv’Fm’ = maximum efficiency of PSII
under light conditions; ΦPSII = quantum yield. S1, S2 and S3 stand for 1st time-point of measurement (well watered plants), 2nd time-point of
measurement (seven days without watering) and 3rd time-point of measurement (14 days without watering) respectively.
Additionally, development of dense genetic maps from
individuals belonging to two Spanish natural populations
(from Northwest coast and Southeast mountains) that
show high levels of genetic divergence with the Spanish
(from the Castilian Plateau; [81]) and French populations
(from Landes and Corsica; [20]), from which segregating
progenies have been previously mapped, is important to
explore the genetic organization and evolution of the species. Synteny and colinearity were highly conserved when
compared with 654 common markers with previous studies [20,81,82]. Only three discrepancies were found that
supposed just a 0.46% over all the common markers analyzed: Contig CT577280 was mapped in LG 7 and LG 4 in
two of three obtained maps for P. pinaster in Chancerel
et al. [20] and it was suggested the existence of two paralogous genes for this sequence. In this study the position of
CT577280 in LG 4 was confirmed. Contigs FN696780 and
AL749831 mapped in LG 9 and LG 4 in Chancerel et al.
[20] and in LG 7 and LG 9 in this study respectively,
which suggest also the existence of two paralogous genes
for these sequences.
The high level of synteny and colinearity observed
between the genetic maps developed for individuals
that belong to populations with very different genetic
backgrounds [98] points out the high reliability in the
marker order obtained. Thus, it is possible the development of a composite genetic map for the species by
integrating the genetic maps developed by de Miguel
et al. [81], Chancerel et al. [20] and those obtained in this
work, which is currently in progress. Parental maps are
the most accurate regarding both, marker order and
marker distances; since they have been constructed
through separated information of the meiosis occurred
in each progenitor. Accuracy is also related with the
presence of genotyping errors, missing values and segregation distortion in the molecular marker data used for
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Page 11 of 19
Figure 2 Graphical representation of the QTL identified on the parental (Gal1056 and Oria6) and consensus (GxO) framework linkage
maps (LGs 7 to 12). Only linkage groups for which QTL have been detected are presented. The one and two LOD confidence intervals are
indicated by squares and lines, respectively. Colored QTL are the significant QTL (significant at 99% at chromosome level or 95% at genome wide
level), with each color representing a different trait, and black QTL are the suggestive QTL (significant at 95% confidence at chromosome level).
An = net photosynthetic rate (μmol CO2m−2 s−1); gsw = stomatal conductance to water vapour (molH2Om−2 s−1); WUEi = intrinsic water use
efficiency (μmol CO2 molH2O−1); δ13C = isotopic composition of 13C (‰); SLA = specific leaf area (m2Kg−1); Fv’Fm’ = maximum efficiency of PSII
under light conditions; ΦPSII = quantum yield. S1, S2 and S3 stand for 1st time-point of measurement (well watered plants), 2nd time-point of
measurement (seven days without watering) and 3rd time-point of measurement (14 days without watering) respectively.
the construction of linkage maps [99]. In this study it
was achieved by the thorough genotypic data integrity
obtained by using highly stringent thresholds to consider SNPs for mapping. Also, the position of the SNPs
genotyped in fewer individuals (SNP array D) was validated through the comparison with previously developed
P. pinaster maps [20]. In addition, only a few distorted
markers have been mapped and almost all located in a
narrow region of a single linkage group (Oria6 and GxO
LG 5), which points towards a probable biological origin.
Even so, QTL analyses have been developed using framework linkage maps to minimize the problems that possible
errors in marker order could cause.
QTL detection
One of the main goals of this work was to identify QTL
for leaf functional traits related to photosynthesis and
water use efficiency in response to drought. QTL analysis
in forest tree species is challenging by its long generation
times which hinder the development of classical mapping
populations like backcross, F2 or recombinant inbred lines.
In order to overcome this shortcoming alternative strategies are usually developed for QTL detection in trees,
such as the two-way pseudo-testcross [83] used in this
study. In this work, two parents from contrasting populations in their drought response were selected to maximize
the variability of the F1 obtained progeny, at molecular
and functional levels. Although some recent QTL studies
in trees worked with larger progenies [51,100,101], the
162 obtained siblings in this study are in the range or
higher than other QTL analysis in trees [31,46,69,70,102].
On the other side, gas exchange parameters are extremely
sensible to variations in the environmental conditions. To
cope with the problem of environmental noise in phenotypic evaluation, different strategies have been used for
QTL analysis in the literature. For example the implementation of statistical and physiological models to adjust
phenotypic values for microclimatic differences [41,50] or
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Figure 3 Boxplots for SNPs in candidate gene MYB 1 significantly associated with traits. For each one of the three SNPs (gene name and
SNP position between brackets) is shown the p-value of ANOVA and false discovery rate (q-value) estimated for Fv’Fm’ (maximum efficiency of PSII
under light conditions) and SLA (specific leaf area). S1, S2 and S3 stand 1st, 2nd and 3rd time-point of measurements respectively.
de Miguel et al. BMC Genomics 2014, 15:464
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the development of inbred line populations for annual
species [103,104]. In this study, four vegetative copies of
each genotype were established in a completely randomized block design in a greenhouse. All these efforts allowed
the identification of significant and suggestive QTL for important traits related to water use efficiency and carbon
uptake in P. pinaster.
For all the analyzed traits several QTL were identified
with moderate effects rather than a single or few QTL
with large effect, as expected for complex functional traits.
The only exception was δ13C for which only one QTL
could be identified. For gas exchange parameters, the percentage of observed phenotypic variance explained when
taking into account all significant QTL detected in a single
trait in each time-point of measurements was in average
20%. In addition, some suggestive QTL for water use efficiency estimated from different approaches have been reported, although their existence should be further tested
using larger population sizes or analyzing their stability in
different genetic backgrounds [105]. For chlorophyll fluorescence parameters, a large number of QTL were identified. The significant QTL detected in each time-point of
measurement for Fv’Fm’ explained together up to 44% of
the observed phenotypic variance. The results achieved
point towards a tight genetic control of photochemical
traits, as previously described in crop species like maize
[103,106], barley [104] or soybean [40].
For δ13C only one QTL was detected in LG 6, in agreement with Brendel et al. [44] that found a QTL in the
same region of LG 6 for P. pinaster. No co-localization
of QTL for WUEi and δ13C was found in spite of the
significant phenotypic and genetic correlation between
both traits. In this study δ13C was measured only in the
1st time-point of measurement, thus its value probably
reflected the water use efficiency in well watered conditions. As the genotypes analyzed have showed high genetic variability in water use efficiency in response to
drought [76], it could be expected to observe higher
variation in δ13C in needles developed under water limiting conditions maintained in a long-lasting water stress
period [107-109]. Higher variability on δ13C would enhance the detection of QTL for this trait and it might be
possible to find other QTL as reported by Brendel et al.
[44], who found four significant and four suggestive
QTL. Differences in the number of detected QTL for
δ13C between both studies could also be explained because Brendel et al. [44] measured δ13C in tree growth
rings from 15 years-old trees while in this study δ13C
was measured in needles of one year-old seedlings.
Nevertheless, the co-localization of this QTL for δ13C
between two genetically unrelated progenies from wide
geographic origins (Landes x Landes versus Galicia x
Oria) and growing under different environmental conditions supports its stability.
Page 13 of 19
Interaction between QTL and environmental conditions
was tested performing the QTL analyses using three
different time-points of measurements corresponding
to different water irrigation regimes. In general, most
of the detected QTL were environment-specific, suggesting that genes are differentially activated during
maritime pine drought response [110]. Nevertheless,
several QTL for Fv ’Fm’, ΦPSII and SLA were less sensitive to environmental conditions and maintained the
same location with drought imposition, confirming the
stability of these QTL across different levels of water
stress endured by plants.
This way, four clusters of QTL were identified in LG 5,
LG 6, LG 7 and LG 12. Clustering of QTL could be related
with the pleiotropic effect of one or a few genes affecting
different traits rather to the existence of rich gene regions,
as genes were homogeneously distributed between LGs.
Chancerel et al. [20] detected higher number of genes in
LG 6 and LG 12 than in the other linkage groups, however
the maps developed in this study could not confirm these
results.
QTL for photosynthesis measured through gas exchange
and chlorophyll fluorescence parameters co-localized in
LG 6 and LG 12, accordingly with the high broad-sense
genetic correlation found between both traits. However,
additional and no co-localizing QTL were identified for
these traits in other LGs, suggesting that CO2 fixation and
electron transport were not entirely coupled, in agreement
with Gu et al. [41]. Uncoupling of these two processes
may be due to drought effects on stomatal conductance,
biochemical alterations of carbon fixation enzymes, or
photoinhibition affecting electron transport rate [7,8].
Under drought stress An and gsw showed a lower level
of phenotypic correlation while the correlation coefficient between An and Fv ’Fm’ or ΦPSII increased with
water stress, which suggests that under stomatal closure
the differences that can be observed between genotypes
in carbon fixation could be due to differences in electron transport through PSII rather than to differences
in stomatal conductance, as previously observed in
other species [106].
SLA showed a significant phenotypic and genetic
correlation with WUEi and Fv ’Fm’. The identification of
relationships between two traits using phenotypic correlations may not distinguish whether the traits could
be causally related or simply varying in association. However, the coincidence of QTL for two traits is strong evidence that they could be functionally related [36]. QTL
co-localization of SLA with WUEi and Fv ’Fm’ was found
in LG 5 and LG 7 pointing towards a strong interrelationship between SLA, WUEi and Fv ’Fm’. The aforementioned co-localization could indicate that plants with
lower SLA are more efficient in water use but had a
lower efficiency of electron transport through photosystem
de Miguel et al. BMC Genomics 2014, 15:464
http://www.biomedcentral.com/1471-2164/15/464
II that could be explained because of the higher importance of gsw over An in determining WUEi in this
species [76,111-113].
Most of the detected QTL were found only in one of
the two progenitors. The parental trees were selected
from two distant populations, showing high level of genetic differentiation, and with a different degree of drought
tolerance. Oria6 came from the southeast of Spain governed by a Mediterranean climate with long, hard and
frequent summer dry periods, while Gal1056 came from
the northwest of Spain where Atlantic climate is
present. Consequently, a higher degree of drought adaptation is expected in Oria6 than in Gal1056. Controlled
crosses performed with so different parental trees in
their response to drought are very useful to compare
QTL identified in individuals with different genetic
backgrounds.
Candidate genes within QTL
The identification of the gene or genes underlying a trait
has been described as one of the greatest challenge for
geneticists during this century [114]. The development
of high density linkage maps using gene-based markers
selected, in some cases, for their known implication in
drought response allowed the identification of potential
candidate genes for the quantitative multigenic traits
analyzed in this study. Due to the lack of sequence annotation, a considerable number of mapped sequences
showing high homology with cDNA sequences from
other conifers could not be functionally inspected. Thus,
some QTL with large effect had no obvious candidate
genes but hold great promise to identify unknown genes
underlying the corresponding processes in the future. For
other QTL, positional candidate genes with known
function in other species that were selected according
to their functional similarity with genes involved in processes related with the studied trait were identified. A
MIXTA-LIKE TRANSCRIPTION FACTOR (MYB) and
a HISTONE CHAPERONE were found at 25 and 12 cM
from the LOD peak of one of the four most clearly detected QTL, Fv ’Fm’S2LG7i. MYB transcription factors
are a wide group related with multiple physiological
processes such as photosynthesis signaling [115]. The
HISTONE CHAPERONE acts as a heat protection protein [116]. The increase of leaf temperature could be an
important consequence under drought stress conditions
due to reduced transpiration caused by stomatal closure.
In this sense, the gene encoding the MYB transcription
factor and the HISTONE CHAPERONE also co-localized
with Fv ’Fm’S3LG7i, both QTL measured under water
stress. Another gene of the MYB family encoding the
MYB 1 transcription factor, co-localized with several
QTL for Fv’ Fm’ and ΦPSII measured in well watered conditions (Fv’Fm’S1LG7i, Fv’Fm’S1LG7_2f, ΦPSII S1LG7_2f) and
Page 14 of 19
SLA measured under water stress (SLAS3LG7i). In this
sense, three SNPs positioned in MYB 1 gene resulted in a
significant association with Fv’Fm’ measured in the 1st
time-point of measurements (well watered plants) and
SLA measured in the 3rd time-point of measurements
(14 days without watering). Lepoittevin et al. [117] found
that the gene MYB 1 showed complete linkage disequilibrium in P. pinaster over a distance of 1,304 bp. Together
with their intron/exon location and base substitution
types, this points towards association of SNPs m751
and m746 with target traits could be the consequence
of genetic linkage with m747, that had higher chance
to influence Fv ’Fm’ and SLA. The expression of MYB
1 regulates genes of the phenylalanine pathway in
white spruce [118] and maritime pine [119]. Increase
of isoprenoid related compounds has been described
to be related with photoprotection mechanisms triggering under abiotic stresses [120]. In this respect,
some of the SNPs observed for MYB 1 at present
could be related with enhancing maintenance of photochemistry function as higher Fv ’Fm’ during drought.
These associations should be further validated analyzing,
i.e. specific nucleotide variants in a panel of unrelated
genotypes [121].
Several genes related with oxidative stress co-localized
with QTL for photosynthesis under water stress conditions
inferred both by gas exchange or chlorophyll fluorescence.
For example, 5 -ADENYLSULFATE REDUCTASE-LIKE
4-LIKE that was implicated in the cell redox homeostasis
[122], co-localized with QTL ΦPSIIS1LG8_2m; PROLYL 4HYDROXYLASE ALPHA SUBUNIT-LIKE PROTEIN
that has oxidoreductase activity [123], co-localized with
QTL AnS2LG9m; or CINNAMOYL- REDUCTASE 1LIKE and PEROXIREDOXIN- CHLOROPLASTIC-LIKE
that are enzymes from the flavonoid and phenylpropanoid biosynthesis pathways, respectively [124], were on
the confidence interval of QTL Fv ’Fm’S2LG1_2m. Overall, gene annotation seems to point out to an important
role of maintenance photochemical integrity machinery
in the drought response of P. pinaster.
Several genes that have been described to be related
with regulation of stomatal aperture were found in the
range of QTL for gsw and WUEi. For example, MALATE
DEHYDROGENASE catalyzes the reaction which converts malate to oxalacetate and a reduction in malate
before stomatal closure was observed [125,126]. Also,
PHOSPHOLIPASE C 3-LIKE is required for the control of stomatal aperture by ABA [127,128]. Genes encoding these enzymes co-localized with QTL gswS2LG5f,
WUEiS2LG5f, WUEiS2LG5i. AQUAPORIN NIP1-2-LIKE
co-localized with δ13CS1LG6i, which was found interesting because of the importance of aquaporins in determining the leaf water status [129] and the proved stability of
this QTL.
de Miguel et al. BMC Genomics 2014, 15:464
http://www.biomedcentral.com/1471-2164/15/464
Conclusions
The in-depth analysis of genetic control of the CO2 fixation process in response to drought was possible after
measuring different functional parameters using complementary techniques, such as gas exchange and chlorophyll fluorescence, that measure final carbon capacity
uptake. The use of maritime pine replicated genotypes
and a suitable experimental design have made possible
to identify genetic control for functional and morphological leaf traits, measured under three water irrigation
regimes as they are highly dependent on environmental
conditions. Several genomic regions implicated in the
genetic control of drought resistance traits have been
identified. The identification of potential candidate genes
leads this project a step beyond the simple detection of
QTL. Nonetheless, further association studies with proposed candidate genes are needed in order to validate
detected SNP marker-trait associations.
Additional files
Additional file 1: Identical markers based on recombination rate.
Not positioned markers correspond to unlinked markers or markers
which position could not be reliably estimated.
Additional file 2: Broad sense genetic correlations (±standard error)
between the analyzed traits.
Additional file 3: Broad sense heritability (estimate ± standard error).
Additional file 4: Parental linkage maps for Gal1056, Oria6 and
consensus map for both progenitors (GxO).
Additional file 5: Mapped markers in parental linkage maps for
Gal1056, Oria6 and consensus map for both progenitors (GxO).
Additional file 6: Marker order comparison with maps obtained by
Chancerel et al. [20].
Additional file 7: Candidate genes within QTL [130-180].
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MdM: SSR genotyping, phenotypic evaluation, genetic maps, QTL analysis,
candidate genes search and wrote the first draft of the manuscript. JAC:
genetic maps, QTL analysis and candidate genes search. NdM: SAMPL
genotyping, design of SNP array C and candidate genes search. DS-G:
phenotypic evaluation. MAG: SSR genotyping, SAMPL genotyping, design of
SNP array C and candidate genes search. MDV: SAMPL and SNP genotyping
and candidate genes search. ES-L: DNA extraction and functional
annotations. LD: SSR genotyping, SAMPL genotyping. JAM: phenotypic
evaluation. MCB: DNA extraction, SAMPL genotyping. CC: design of SNP array
C and candidate genes search. CD-S: design of SNP array C and candidate
genes search. M-TC and IA: conceived and designed the experiments and
collected funding. All authors have read and approved the final version of
the manuscript.
Acknowledgments
This work was supported by the Spanish projects MAPINSEQ (AGL2009-10496;
Spanish Ministry of Science and Innovation), PinCoxSeq (AGL2012-35175;
Ministry of Economy and Competitiveness) and the Plant-KBBE project
SUSTAINPINE (PLE2009-0016). The research leading to these results has also
received funding from the European Union’s Seventh Framework
Programme (FP7/2007-2013) under grant agreement n° 289841 (ProCoGen).
L Alté and S Ferrándiz are gratefully acknowledged for their assistance and
Rose Daniels for the revision of the quality of written English.
Page 15 of 19
Author details
1
Departamento de Ecología y Genética Forestal, INIA-CIFOR. Ctra, de La
Coruña Km 7.5, 28040 Madrid, Spain. 2Unidad Mixta de Genómica y
Ecofisiología Forestal, INIA/UPM, Madrid, Spain. 3ETSIM, Departamento de
Biotecnología, Ciudad Universitaria, s/n, 28040 Madrid, Spain. 4Departamento
de Ciencias de la Vida, Universidad de Alcalá, Ctra. de Barcelona Km 33.6,
28871 Alcalá de Henares, Madrid, Spain.
Received: 3 October 2013 Accepted: 5 June 2014
Published: 12 June 2014
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doi:10.1186/1471-2164-15-464
Cite this article as: de Miguel et al.: Genetic control of functional traits
related to photosynthesis and water use efficiency in Pinus pinaster Ait.
drought response: integration of genome annotation, allele association
and QTL detection for candidate gene identification. BMC Genomics
2014 15:464.
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