Cellular and Molecular Biology

E-ISSN : 1165-158X / P-ISSN : 0145-5680

www.cellmolbiol.org
Original Research
Association analysis of tolerance to dieback phenomena and trunk form using ISSR
markers in Quercus brantii
Abdol-Reza Shamari1, Ali-Ashraf Mehrabi2*, Abbas Maleki1, Ali Rostami1
1
Department agronomy and Plant Breeding, Science and Research Branch, Islamic Azad University, Ilam, Iran
2
Department of agronomy and Plant Breeding, University of Ilam, Ilam, Iran

Correspondence to: a.mehrabi@ilam.ac.ir

Received September 15, 2018; Accepted October 25, 2018; Published October 30, 2018
Doi: http://dx.doi.org/10.14715/cmb/2018.64.13.22
Copyright: © 2018 by the C.M.B. Association. All rights reserved.

Abstract: Oak decline is a complex syndrome in which several damaging agents interact and bring about a serious dieback in tree condition. Genetic diversity
is a key factor for better adoption of natural populations to environmental stresses. The objective of this research was to identify the association of polymorphism
patterns of different reproducible genomic Inter simple sequence repeats (ISSR markers) to level of dieback phenomena and also growth type in 18 different stands
of Persian oak in central Zagros region. Totally, 180 trees were sampled and evaluated for growth type, tree diameter at breast height (DBH) and level of tree die-
back. Genomic DNAs extracted of fresh leaves amplified using 15 multi-locus ISSR primers. The population structure determined using the Bayesian model-based
clustering method implemented in STRUCTURE software by Monte Carlo Markov Chain (MCMC) method. Five distinct sub-populations (K=5) determined by
the log likelihood of the data. Genome wide association study (GWAS) performed using the generalized linear model (GLM) and the mixed linear model (MLM)
with Kinship matrix. Informative alleles recognized for level of tolerance to dieback and tree growth type traits. It was observed a significant co-segregation for
phenotypic data and some of amplified fragments. Identification of these informative DNA markers can be utilized for pre-screening of high quality oak seedlings
in early growth stages and better management in restoration of damaged stands.

Key words: Diversity; ISSR; Oak decline; Polymorphism; Tree growth type.

Introduction genome mapping and evolutionary biology (10, 11).

Most important economic traits of horticultural and
Quercus brantii Lindl. is known as Persian oak, forest trees, such as wood properties, resistance to biotic
Brant's oak and Zagros oak. This forest tree species co- and abiotic stresses, fruit quality and biomass traits, are
vers more than 50 percent of the Zagros forest area (1). controlled by polygenes. Thus, quantitative genetic stra-
Persian oaks have been affected by a condition known tegies have been used to identify genes controlling these
as chronic oak dieback or decline from 2000 (2, 3). This traits (12). Association mapping, also known as linkage
disorder is widespread, prolonged and complex. The disequilibrium (LD) mapping, is a valuable approach to
causes of the condition often involve abiotic factors for overcome limitations of linkage mapping or pedigree-
example poor soils, recurrent drought, high winds, dis- based quantitative trait loci (QTL) mapping. In AM,
turbed environments and air pollutants. Biotic agents genotypic and phenotypic correlations are investigated
that cause this disease include insects and fungi that are in unrelated individuals. This methodology takes advan-
destructive to weakened trees (4). tage of both LD and historical recombination present
The breeding programs of forest trees is greatly li- within the gene pool of an organism, thus utilizing a
mited because of their long lifespan and the fact that broader reference population. AM has been used in mo-
most quantitative traits cannot be assessed until a seed- del species with available genomic resources. Recently,
ling has matured physiologically. Marker-assisted selec- this approach has been applied to dissecting quantitative
tion (MAS) is a technology which may help to overcome traits in many forest and fruit trees (13).
the barriers to genetic improvement of forest species by The objectives of this study was to assess the genetic
accelerating the selection process (5). structure in Persian oak population using ISSR marker
Molecular markers are valuable tools in the cha- and also the potential of Genetic association mapping to
racterization and assessment of genetic variation wit- investigate the genetic control of tolerance to dieback
hin and between plant species and populations (6, 7). phenomena in Q. brantii and also the growth type of
It has been demonstrated that different DNA markers stem in this species in natural stands of this forest tree.
might reveal various classes of variation in genome (8,
9). Inter simple sequence repeat (ISSR)-PCR is a tech- Materials and Methods
nique uses the microsatellite DNA sequences as primers
to generate highly polymorphic multi-locus markers. Plant materials and sampling area
ISSR markers are highly polymorphic and are useful 180 trees were sampled from central Zagros oak
in studies on genetics of populations, tagging of genes, forests include three western provinces in Iran (Ker-
116
Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

prepared a scale from 0 to ten. Each number equals to

ten percent.

DNA extraction
100mg fresh leaf tissue of each genotype used for
DNA extraction based on Doyle & Doyle (1987) pro-
tocol (15). DNA quality and quantity examined using
Figure 1. Geographical location of sampling sites in three central 0.8% agarose gel electrophoresis and spectrophoto-
Zagros regions also the systematic sampling strategy used for col- metry, respectively.
lection of plant materials.
PCR Reactions
manshah, Ilam and Lorestan) during March till May, 15 high reproducible polymorph ISSR primers
2016. Six different populations per each province which (table 2) have been used to conduct PCR reactions in
previously reported as damaged areas selected. At least 20ul volume include: 1.5ul DNA, 20ul PCR buffer 10x,
50 km was the distance between stands. A systematic 1.8ul MgCl2 (20mM), 0.4um dNTP (1mM), 1.2ul pri-
strategy carried out for selection 10 trees per stand so mer (10pM), 0.3ul Taq DNA Polymerase (5unit). PCR
that the first tree selected randomly and the next ones cycles carried out using a Bio-Rad c1000 thermal cy-
have 200 m distance with each other (Figure 1). Sam- cler. The PCR program was: 94 °C as the primary dena-
pling area per stand was approximately 30 hectares. turation: 10cycles (touchdown) with 0.5 °C per cycle
Fresh leaves collected from each tree and wrapped in decrease: 25 Normal PCR cycles.: Denaturation (94 °C
aluminum foils and transferred to nitrogen tank. All for 30sec): Annealing (Primer specific Tm °C for 45sec):
trees evaluated for some phenotypic traits like diame- Extension (72 °C for 2min). Final Extension time was 2
ter at breast height, the percentage of died shoots and °C for 7min.
growth form. There was at least some fresh weight on
tree and no one of them was totally dead. The criterion Electrophoresis
for oak decline is the proportion of dry dead shoots to Agarose gel 2% electrophoresis conducted to sepa-
all shoots of tree which measured as percent value. We rate amplified products in each reaction. Ethidium Bro-
Table 1. Geographical characteristics of 18 natural stands of Q. brantii located at central Zagros.
Sample
NO Region Stand Abbreviation Elevation (m) Geographic location
number
47" 21´ 34° N
1 Kermanshah- Gilangharb Kalkosh K1 10 1386
33 06 46 E
85 06 34 N
2 Kermanshah - Sarpol Zahab Imamieh K2 10 1390
89 27 46 E
33 06 34 N
3 Kermanshah- Gilangharb Avalviar K3 10 1310
95 03 46 E
66 13 34 N
4 Ilam – Ivan Sarab I1 10 1586
90 40 46 E
63 14 34 N
5 Ilam – Ilam Tajarian I2 10 1064
32 02 46 E
40 08 34 N
6 Ilam – Shirvan Kalilali I3 10 1215
46 10 46 E
54 16 33 N
7 Ilam – Chardavol Mamd-Gholi I4 10 948
42 07 47 E
78 05 33 N
8 Lorestan-Kuhdasht Bwineh L1 10 1012
38 20 47 E
64 48 33 N
9 Lorestan-Kuhdasht Komeil-Malmir L2 10 1191
09 30 46 E
49 42 33 N
10 Lorestan dowreh Chegeni Benarkooh L3 10 1256
42 33 46 E
91 42 33 N
11 Lorestan-Khorramabad Shourab L4 10 1155
97 19 46 E
82 44 33 N
12 Lorestan-Poldokhtar Chameshahran L5 10 1318
92 21 46 E
32 39 33 N
13 Lorestan-Kuhdasht Dargonbad L6 10 917
57 07 47 E
37 30 33 N
14 Ilam – Abdanan Dinarkooh I5 10 1277
58 31 47 E
82 31 33 N
15 Ilam – Badrah Haranmar I6 10 1291
49 52 47 E
06 26 33 N
16 Kermanshah - Kermanshah Charzebar K4 10 1213
97 11 48 E
64 20 33 N
17 Kermanshah - Dalahu Sorkhalizheh K5 10 903
60 55 47 E
57 40 33 N
18 Kermanshah - Islamabadgarb Aliabad K6 10 944
48 05 47 E

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Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

Table 2. The characteristics of the ISSR primers and amplified fragments in PCR reactions.
NO Primer Sequence Tm (°C) Number of alleles Fragment Size PIC- Value
1 UBC 840 5´- GAGAGAGAGAGAGAGAYT-3´ 53 8 1700- 250 0.121
2 UBC 836 5´-AGAGAGAGAGAGAGACYA-3´ 53 11 2000 - 250 0.259
3 ISSR 17 5´-CACACACACACACACAG-3´ 52 10 2000 - 400 0.294
4 UBC 809 5´-AGAGAGAGAGAGAGAGG-3´ 52 9 1000 - 200 0.326
5 ISSR 155 5´-TGT GTGTGT GTG TGT GGG-3´ 56 11 1500 - 300 0.288
6 ISSR 165 5´-AGGAGAGAGAGAGAGCC-3´ 56 14 1400 - 250 0.401
7 UBC 895 5´-AGAGTTGGTAGCTCTTGATC-3´ 56 11 1600 - 390 0.052
8 UBC 841 5´-GAGAGAGAGAGAGAGAYC-3´ 55 7 1500 - 250 0.274
9 UBC 842 5´-GAGAGAGAGAGAGAGAYG-3´ 55 9 1800 - 150 0.305
10 UBC 835 5´-AGAGAGAGAGAGAGACYC-3´ 55 10 1600 - 300 0.224
11 ISSR 08 5´-GAGAGAGAGAGAGAGAT-3´ 50 14 1600 - 300 0.379
12 UBC 807 5´-AGAGAGAGAGAGAGAGT-3´ 50 11 1600 - 300 0.425
13 UBC 810 5´-GAGAGAGAGAGAGAGAT-3´ 50 13 1400 - 350 0.360
14 UBC 814 5´-CTC TCT CTCTCTCTCTA -3´ 50 4 1500 - 350 0.246
15 ISSR 16 5´- GAGAGAGAGAGAGAG -3´ 50 17 1400- 300 0.210
Table 3. Diversity measures calculated based on 157 detected alleles for 15 ISSR loci.
Population Sample No Shannon Index (I)± SE Heterogeny (He)± SE PolymorphicAlleles (%)
I1 10 0.308 ± 0.022 0.211 ± 0.016 65.61
I2 10 0.291 ± 0.021 0.199 ± 0.016 63.06
I3 10 0.311 ± 0.021 0.212 ± 0.016 65.15
I4 10 0.313 ± 0.022 0.216 ± 0.016 64.97
I5 10 0.301 ± 0.022 0.208 ± 0.017 63.69
I6 10 0.280 ± 0.021 0.190 ± 0.015 62.42
K1 10 0.286 ± 0.022 0.200 ± 0.017 57.96
K2 10 0.319 ± 0.021 0.219 ± 0.016 68.15
K3 10 0.291 ± 0.022 0.200 ± 0.016 62.42
K4 10 0.203 ± 0.020 0.136 ± 0.014 47.13
K5 10 0.221 ± 0.021 0.152 ± 0.016 45.86
K6 10 0.240 ± 0.022 0.165 ± 0.016 50.32
L1 10 0.309 ± 0.021 0.212 ± 0.016 66.24
L2 10 0.296 ± 0.021 0.202 ± 0.016 64.97
L3 10 0.300 ± 0.021 0.206 ± 0.016 63.06
L4 10 0.285 ± 0.021 0.194 ± 0.015 61.75
L5 10 0.271 ± 0.022 0.188 ± 0.016 55.41
L6 10 0.285 ± 0.023 0.200 ± 0.017 55.05
Total 180 0.284 ± 0.005 0.195 ± 0.004 61.1
I= Ilam, K= Kermanshah, L= Lorestan
mide staining used for detecting the fragments. Pres- performed using the GenAlex 6.5 software (18). 3.
ence and absence of each fragment scored as 1/0 for all Frequency pattern of amplified alleles by STRUCTURE
genotypes (supplementary data file). software (19), with the length of the burn-in period of
100,000 followed by 10,000 Monte Carlo Markov Chain
Molecular and Statistical Analyses (MCMC) replicates. The admixture model and correla-
polymorphism information content (PIC) calculated ted alleles frequencies were considered in the analysis.
based on Anderson (1987) equation (14 using the fol- The number of hypothetical subgroups (K) was set from
lowing formula: PICi = 1-ΣP2ij, where Pij is the frequen- 1 to 10 and three independent runs were made for each
cy of the jth allele in ith marker (Table 2). K. Optimal number of K determined by the log likeli-
hood of the data [Ln P(D)] in STRUCTURE output and
Population structure and relative kinship ΔK based on the rate of the change in Ln P(D) between
The genetic structure of the evaluated populations successive K values (19); The pairwise kinship coeffi-
was analyzed using three different methods: 1. Principal cients among the genotypes estimated by the TASSEL
coordinate analysis (PCoA) on dissimilarity coefficients program (20).
among natural populations and 2. Hierarchical cluster
analysis of genotypes and also the populations using Association analysis
UPGMA algorithm. Association between genotyping and phenotyping
PCoA based on the Nei's genetic distance (17) data implemented using GLM (General Linear Model)
Cell Mol Biol (Noisy le Grand) 2018 | Volume 64 | Issue 13 118
Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

Figure 2. Bi-plot of 180 genotypes based on 157 alleles amplified

for 15 ISSRs markers.

Figure 5. Grouping 18 populations evaluated from three central

Zagros geographical regions using the UPGMA algorithm (Five
groups are distinguished by cut off line).

Figure 3. Bi-plot of 18 Persian oak stands based on 157 alleles

amplified for 15 ISSRs markers.

Figure 6. the posterior probabilities lnP(D) calculated for different

Ks and determining the optimum K by calculating DeltaK from
Dividing Mean lnP(D) to mean of SD[lnP(D)].

between the studied regions has been calculated (Table

5). The most genetic distance obtained for Kermanshah
stands. Since the low heterogeneous stand (K4) and
highest one (K2) are in this region, this result was ex-
pectable (Table 3). Despite the low rate of genetic hete-
Figure 4. Grouping of genotypes by a UPGMA dendrogram rogeny (He) within stands, there is a noticeable diffe-
constructed based on Nei distance matrix. rentiation between stands (Fig 4 and Table 4). Analysis
of molecular variance showed a significant variation
and MLM (Mixed Linear Model) procedures. All asso- between 18 assessed stands. The amount of this specific
ciation analysis carried out by TASSEL program. variation was 22 percent and the rest one was shared in
all stands (Table 6). Also here the molecular variance
Results among different geographical regions calculated and,
the highest variation obtained for Ilam province (Table
The most important molecular diversity criteria cal- 7).
culated based on 157 polymorph fragments amplified at Principle coordinate analysis for all genotypes (Fi-
15 ISSR marker loci across all 180 genotypes. Shannon gure 2) as well as stands (Figure 3) applied to find out
index varied of 0.203 to 0.319. Genetic heterogeneity the pattern of distribution of them and presence of struc-
ranged from 0.136 to 0.219. Also total polymorphism ture and sub-population in these oak forests. Based on
per population was different from 45.86% to 68.15% illustrated plots there is distinct subpopulations across
(Table 3). Mean of all calculated criteria was high for evaluated materials. Although, make a decision on num-
Ilam population, old natural oak forests scattered across ber of subpopulations based on the biplot of genotype
north and north-eastern regions of this province (Figure was no possible, but five subpopulations could be reco-
1, Table 1). gnized based on the biplot of stands.
Genetic distance among all evaluated stands, pres- Hierarchical clustering carried to determine more
ented in table 4. Average genetic distances within and exact number of sub-populations in studied regions.
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 Abdol-Reza Shamari et al.
Cell Mol Biol (Noisy le Grand) 2018 | Volume 64 | Issue 13

Table 4 Genetic similarity (above the diagonal) and genetic distance (below the diagonal) calculated based on 157 detected alleles for 15 ISSR primer (Nei, 1987).
  I1 I2 I3 I4 I5 I6 K1 K2 K3 K4 K5 K6 L1 L2 L3 L4 L5 L6
I1 … 0.963 0.942 0.939 0.968 0.935 0.056 0.049 0.042 0.921 0.929 0.912 0.953 0.958 0.966 0.956 0.941 0.951
I2 0.038 … 0.958 0.937 0.923 0.910 0.079 0.067 0.084 0.911 0.922 0.899 0.937 0.915 0.953 0.965 0.946 0.934
I3 0.06 0.043 … 0.930 0.918 0.925 0.059 0.063 0.089 0.907 0.913 0.907 0.918 0.918 0.953 0.955 0.945 0.917
I4 0.062 0.065 0.073 … 0.926 0.913 0.066 0.049 0.063 0.873 0.883 0.888 0.968 0.936 0.929 0.92 0.926 0.942
I5 0.032 0.080 0.086 0.077 … 0.973 0.062 0.062 0.052 0.929 0.930 0.937 0.045 0.044 0.052 0.066 0.062 0.020
I6 0.067 0.094 0.078 0.091 0.027 … 0.066 0.08 0.088 0.928 0.93 0.934 0.071 0.079 0.069 0.074 0.066 0.046
K1 0.923 0.923 0.923 0.923 0.923 0.923 … 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923
K2 0.953 0.935 0.939 0.952 0.940 0.923 0.026 … 0.954 0.903 0.889 0.889 0.947 0.94 0.93 0.921 0.918 0.944
120

K3 0.959 0.920 0.915 0.939 0.949 0.916 0.073 0.047 … 0.903 0.896 0.907 0.954 0.962 0.919 0.903 0.928 0.941
K4 0.082 0.093 0.097 0.136 0.074 0.075 0.096 0.102 0.101 … 0.978 0.942 0.122 0.114 0.088 0.077 0.079 0.081
K5 0.074 0.081 0.091 0.125 0.072 0.073 0.113 0.118 0.11 0.022 … 0.967 0.104 0.114 0.073 0.063 0.071 0.085
K6 0.092 0.106 0.098 0.119 0.065 0.068 0.109 0.118 0.098 0.059 0.034 … 0.105 0.111 0.084 0.1 0.083 0.076
L1 0.048 0.065 0.086 0.033 0.956 0.931 0.069 0.055 0.047 0.885 0.901 0.901 … 0.958 0.936 0.93 0.941 0.958
L2 0.043 0.089 0.085 0.066 0.956 0.924 0.079 0.062 0.039 0.892 0.892 0.895 0.043 … 0.942 0.922 0.92 0.943
L3 0.034 0.048 0.048 0.074 0.950 0.934 0.074 0.073 0.085 0.915 0.929 0.92 0.066 0.06 … 0.987 0.965 0.943

Genome wide association study in Quercus brantii.

L4 0.045 0.035 0.047 0.084 0.936 0.929 0.08 0.082 0.102 0.926 0.939 0.905 0.073 0.081 0.013 … 0.976 0.932
L5 0.061 0.056 0.057 0.077 0.940 0.936 0.085 0.085 0.075 0.924 0.931 0.921 0.061 0.083 0.035 0.025 … 0.933
L6 0.050 0.068 0.087 0.060 0.980 0.955 0.056 0.058 0.060 0.922 0.919 0.927 0.043 0.058 0.058 0.070 0.069 …
Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

Table 5 The average genetic distances within and between the studied regions.
Region Average distance within populations Average distance between populations
Kermanshah Ilam Lorestan
Kermanshah 0.082 -
Ilam 0.065 0.078 -
Lorestan 0.056 0.080 0.059 -
Table 6. Analysis of molecular variance for 18 natural stands of Persian oak.
Variance Percentage Variance Percentage Variance Percentage Variance Percentage Variance Percentage
22 22 22 22 22
78 78 78 78 78
100 100 100 100 100
Table 7 Amount of molecular variance observed within 18 oak
stands.
Stand df SS MS Variance
K1 9 138.4 15.38 97.0
Figure 7. Q-Plot of evaluated genotypes for pattern of allele K2 9 176.1 19.57
frequencies across 15 multi-locus ISSR markers. K3 9 165.1 18.34
K4 9 123.1 13.68
UPGMA algorithm on Nei’s pair-wise distance ma-
trix (15) of genotypes and also the stands showed the K5 9 129.9 14.43
presence of five separate groups (potentially subpopu- K6 9 140.4 15.60
lations). The obtained dendrogram is representing these I1 9 190.0 21.11 117.3
five groups. Analysis of genetic structure was conducted I2 9 172.4 19.16
to demonstrate these findings based on the frequency of
I3 9 181.3 20.14
alleles across all genotypes (whole population).
The pattern of genetic structure has been assessed in I4 9 181.6 20.18
the set of sampled trees. The maximum value of Delta K I5 9 165.4 18.38
Genotypes as the number of distinct subpopulations in I6 9 164.8 18.31
oak forests in central Zagros was five (Figure 6). These L1 9 175.9 19.54 113.7
subpopulations are presented in the presented Q-plot L2 9 173.6 19.29
(Figure 7) which obtained of pattern of allele frequency
L3 9 182.2 20.24
distributed across the genotypes. The Q-matrix as co-
factor used in latter association analyses. L4 9 174.5 19.39
All sampled trees were evaluated for age (Diame- L5 9 152.1 16.90
ter at Breast Height, DBH), growth type (High tree or L6 9 165.4 18.38
Coppice form), dieback rate and also geographic coor-
dinates of tree place. This information is presented at Although all these loci were confirmed in MLM model
table 8 and also used as phenotypic data in association but just 10 of produced alleles were significantly asso-
analysis process. ciated to evaluated trait. All details of this association
General linear model (GLM) using genotyping analysis have been presented in table 9.
and phenotyping data sets along with Q-matrix (gene- Association of trunk shape of stem in Persian oak
tic structure of evaluated stands) carried out to detect investigated as another important trait. All high trees
linked markers to dieback trait and growth type of oak have one of cylindrical non-fork shape or non-cylindri-
trees. Since the GLM procedure has some false posi- cal forked (old coppice like) shape. None forked and
tive results, the obtained results reconsidered by mixed forked trees scored by 1 and 0, respectively. Associa-
linear model (MLM) procedure. All association results tion of trait with genotyping data of trees carried out
with at least 95 percent of confidence (p-value<0.05) again using both GLM and MLM models. Eight of mar-
have been reported for tree tolerance to dieback (Table kers have amplified fragments linked to genetic factors
9) and tree growing type (Table 10). It is necessary to which control growth type in this oak species. In these
mention that calculation of K- matrix (Kinship data marker loci there were 11 alleles (PCR fragments with
derived from general similarity in genetic background different size) which determine 43 percent of growth
arising from shared kinship) was the pre-request of run- type trait by GLM model. All these eight loci and their
ning MLM association analysis (supplementary data). linked alleles were confirmed in MLM model. Details
The result of association study for dieback trait about these significantly associate markers and their
shows that seven of markers linked to dieback tolerance. association has been come in table 10. Partial r2 for all
In these marker loci there were 14 alleles (PCR frag- associate markers as the contribution or determination
ments with different size) which determine 55 percent coefficient of them has been presented.
of dieback trait by GLM model. R2 value in tables 9 &
10 is an estimate for the impact of recognized genomic Discussion
fragments (QTLs) on the studied traits. This technique
(ISSR) is just confirmed genetic control of these traits. The objectives of present study were analyzing the
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Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

Table 8. Sampling from 18 stands of Persian oak with known affected by dieback phenomena in central Zagros area, Iran (K=Kermanshah, I=
Ilam, L=Lorestan).
none- DBH Dieback% Elevation(m)
Sample forked
Stand Longitude (N) forked
Size Trees% Range Mean Range Mean Range Mean
Latitude (E) Trees%
34° 08´ 1215-
Kalkosh (K1) ‎10‎ 77.78 22.22 5-70 20.67 0-75 31.67 1233
46 10 1255
34 14 1094-
Imamieh (K2) ‎10‎ 50 50 8-20 14.2 0-30 16.5 1102.2
46 02 1108
34 06 1304-
Avalviar (K3) ‎10‎ 40 60 11-50 28.5 0-20 10 1314.6
46 03 1326
34 13 1586-
Charzebar(K4) ‎10‎ 100 0 4-15 7.2 0-40 15.5 1598.1
46 40 1617
34 21 1386-
Srokhalizheh(K5) ‎10‎ 80 20 10-38 19.4 10-40 17.5 1408.2
46 06 1423
34 06 1378-
Aliabad(K6) ‎10‎ 70 30 8-20 13.7 0-20 5.3 1387.5
46 27 1395
33 39 917-
Bwineh (L1) ‎10‎ 70 30 10-55 34.2 0-20 9.5 946.3
47 07 968
33 30 1277-
Komeil malmir(L2) ‎10‎ 90 10 5-30 18.6 0-50 23 1299.2
47 31 1310
33 31 1286-
Benarkooh(L3) ‎10‎ 90 10 5-25 15 0-30 12.5 1291.7
47 52 1294
33 26 1203-
Shoorab(L4) ‎10‎ 80 20 10-30 17.2 15-70 41.5 1208.8
48 11 1214
33 20 903-
Chameshahran(L5) ‎10‎ 60 40 5-50 23.1 10-80 44 929
47 55 953
33 40 943-
Dargonbad(L6) ‎10‎ 90 10 10-20 14.4 5-40 19 945.5
47 05 950
34 44 1318-
Sarab (I1) ‎10‎ 54.55 45.45 18-90 43.64 0-10 6.82 1352.3
46 21 1364
34 42 11 4 9 -
Tajarian (I2) ‎10‎ 0 100 50-90 76.1 0-40 20 1151.6
46 19 1156
33 42 1256-
Kalilali (I3) ‎10‎ 50 50 25-40 33.8 5-30 14 1281.6
46 33 1302
Mohammad-Gholi 33 48 11 8 3 -
‎10‎ 90 10 10-45 23.1 5-60 26.5 1190.6
(I4) 46 30 1197
33 05 1008-
Dinarkooh(I5) ‎10‎ 100 0 5-20 15 0-40 22.5 1021.6
47 20 1041
33 16 947-
Haranmar(I6) ‎10‎ 80 20 15-40 27.4 20-40 30 954.4
47 07 960

Table 9 The results of association analysis for tree tolerance to dieback using GLM and MLM procedures.
GLM Model MLM Model
Marker Allele Fragment Size Partial r2
(Genotype + Q + Phenotype) (Genotype + K+ Q + Phenotype)
ISSR008 4 550 0.000 0.01130 0.0736
ISSR016 9 750 0.001 0.02880 0.0608
12 900 0.009 - 0.0357
17 1400 0.019 0.01440 0.0288
2 500 0.041 0.02540 0.0217
ISSR017
3 600 0.017 - 0.0295
8 800 0.000 0.01730 0.0622
ISSR155
9 900 0.001 0.01960 0.052
2 400 0.021 0.03200 0.0276
ISSR165 3 450 0.020 0.02620 0.028
9 800 0.021 - 0.0278
1 350 0.034 0.03790 0.0234
UBC810
5 600 0.005 0.02780 0.0402
UBC842 2 250 0.006 - 0.0393
Determination Coefficient (r2) 0.5506 0.4183
GLM: General linear model. Q: Population structure data or Inferred ancestry of individuals. MLM: Mixed linear model. K: Kinship data derived
from general similarity in genetic background arising from shared kinship.

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Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

Table 10 The results of association analysis for growing type trait using MLM and GLM procedures.
GLM Model MLM Model
Marker Allele Fragment Size Partial r2
(Genotype + Q + Phenotype) (Genotype + K+ Q + Phenotype)
ISSR 008 4 550 0.013 0.02350 0.0335
1 300 0.022 0.02340 0.0284
ISSR 016
8 700 0.026 0.03150 0.0269
ISSR 017 2 500 0.001 0.00170 0.0632
4 700 0.000 0.00048 0.0831
ISSR 165 9 800 0.021 0.03540 0.0288
1 350 0.017 0.02630 0.0305
UBC 810
7 700 0.006 0.01780 0.0401
UBC 836 6 600 0.020 0.02410 0.0295
UBC 895 6 650 0.044 0.04680 0.022
UBC 841 1 250 0.005 0.00860 0.0422
Determination Coefficient (r2) 0.43 0.43
GLM: General linear model. Q: Population structure data or Inferred ancestry of individuals. MLM: Mixed linear model. K: Kinship data derived
from general similarity in genetic background arising from shared kinship.

population structure and identification of molecular counted for in analysis of genetic association between
markers associated with the level of tolerance to oak phenotyping and genotyping data to avoid false positive
dieback phenomena and also the growth type of Persian (spurious) associations. Good sampling and by using
oak. Site specific DNA markers have not been deve- appropriate algorithms to detect groupings in a popu-
loped on this species and polymorphic ISSR markers lation can accounting for these issues in an association
recognized in our work can be used for marker assistant Genetic analysis (Figure 7; 13, 21, 23, 27).
selection in Persian oak and potentially other oak spe- Mixed linear model (MLM) methods have proven
cies. useful in controlling for population structure and relate-
Based on the genetic variation measures, the level of dness within genome-wide association studies (26, 27).
genetic diversity in Q. brantii is low in the natural popu- Here, the validation of detected markers in General
lations of this forest tree in central Zagros area. Same Linear Model implemented by calculating the Kinship
situation, (He=0.15-0.22) for this spices in northern matrice of genotypes and running the mixed linear mo-
regions of Zagros oak forest has been reported (21). del. There were a noticeable number of markers linked
Also, other Quercus species have low levels of gene to both evaluated traits (Table 9 and 10). Seven ISSR
diversity in a report from Denmark (22) on Q. rubor markers are informative for dieback phenomena and
(He= 0.248) and Q. Petraea (He= 0.258). In contrary eight ISSR markers for growth form of oak tree. Re-
to these reports, a high level of molecular heterogeneity gards to the determination coefficient of these markers,
has been measured for Q. rubra and Q. ellipsoidalis in some share markers associate to both traits indicating
Peninsila, Mishigan (Mean He= 0.73). The significance a molecular correlation (indirect relationship) between
of variance between populations (stands) in AMOVA rates of dieback with the growth type of oak tree. As
means populations are subdivided in some way. So, here the most affected stands have dominantly coppice form
we are faced with a structured population. that is a surprising result. Because all Persian oak cop-
As genetic diversity is a key factor in survival and pice stands are old growing and probably they are root
adoptability of a species to adapt to changing environ- sprouts from previously declined genotypes.
ments, this low rate of genetic variation in natural stands The results of the present study showed that geno-
of Persian oak displays vulnerability of this oak forests typing data which obtained from low throughput DNA
to the current climate change that Zagros regions have marker systems like Inter simple sequence repeats can
during recent decades (3). The mountainous topo-geo- generate reliable polymorphism patterns applicable for
graphy of these regions probably limits gene flow and investigation of genetic structure of diversity within
wide cross pollination. Another reason or homogeneity tree species such as Q. bantii that there is no previous
in oak forests probably is the limited acorn dispersal information about their genome sequences. Probably,
from maternal trees and a small amount of cloning by site specific markers like simple sequence repeats (SSR
root sprouts. Other researchers previously have reported and EST-SSR markers) are the next nearest option that
all these observations in Quercus genera (22-24). their transferability from other oak relatives should be
Large phenotypic variability was observed for the tested on Persian oak. The low levels of genetic diver-
dieback symptoms in studied genotypes indicates sui- sity detected in natural stands of Persian oak are ano-
tability of the genotypes for association study (Table 8). ther point must be considered in restoration of declined
Regard to the results of structure analysis, there were areas of these forests. The measured genetic distance
five different subpopulations among evaluated mate- of stands would be helpful for restoring heterogeneity
rials. In other words, the population consists of five to these natural forests. Attention population structure
genetically different subpopulations. It is necessary to and differentiation in evaluated genotypes and using
account for population genetic structure and also any association study by MLM model was another achie-
relatedness due to non-random mating must be ac- vement of present research. The significance of some of
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Abdol-Reza Shamari et al. Genome wide association study in Quercus brantii.

amplified fragments in some of ISSR markers revealed sequence repeat (ISSR) polymorphism and its application in plant
genetic control for tolerance to dieback in Q. brantii. breeding. Euphytica, 128(1): 9–17.
Noticeable contribution of associated markers to inves- 12. Neale DB (2007) Genomics to tree breeding and forest health.
tigated traits can be useful for marker assisted selection Curr Opin Genetics Dev. 2017; 17:539–544.
(MAS) and pre-screening of Persian oak seedlings in 13. Awais Khan K and Korban SK. Association mapping in forest
early growth stages. trees and fruit crops. J Exp Bot. 2012; 63(11): 4045-4060.
14. Minamikawa MF, Nonaka K, Kaminuma E, Kanegae HK, Onogi
Conflict of interest A, Goto S, et al. Genome-wide association study and genomic pre-
The authors declare that they have no conflict of inte- diction in citrus: Potential of genomics-assisted breeding for fruit
rest. quality traits. Nat Sci Rep. 2017; 7: 4721.
Author’s contribution 15. Doyle jj, Doyle jl. A rapid DNA isolation procedure for small
The authors declare that they all have made an impor- quantities of fresh leaf tissue. Phytochemical Bulletin. 1987; 19: 11-
tant scientific contribution to the study and have assisted 15.
with the drafting or revising of the manuscript. 16. Aga E, Bekele E, Bryngelsson T. Inter-simple sequence repeat
(ISSR) variation in forest coffee trees (Coffea arabica L.) popula-
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