=Paper= {{Paper |id=Vol-2534/86_short_paper |storemode=property |title=Genetic Markers Combination Calculation in Wood Samples Identification |pdfUrl=https://ceur-ws.org/Vol-2534/86_short_paper.pdf |volume=Vol-2534 |authors=Alexey S.Pyataev,Alexey A. Ibe,Elena A. Shilkina }} ==Genetic Markers Combination Calculation in Wood Samples Identification== https://ceur-ws.org/Vol-2534/86_short_paper.pdf

Genetic Markers Combination Calculation
in Wood Samples Identification
Alexey S.Pyataev1,2, Alexey A. Ibe2, Elena A. Shilkina2
1
Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av 31, Krasnoyarsk,
Russia, 660037
2
Branch of FBI «Russian Centre of Forest Health» – «Centre of Forest Health of Krasnoyarsk Krai», Akademgorodok
50A building 2, Krasnoyarsk, Russia, 660036

Abstract. This paper proposes a method for determining the microsatellite markers
combination used to study the genetic structure of a woody plant population using the
example of Pinus sylvestris. The developed method optimizes genetic analyzes
conduction in the tasks of the forest genetic resources state monitoring.

Keywords: genetic structure, microsatellite markers, Pinus sylvestris.

1 Introduction
Over the past decade due to the negative economic and environmental consequences of illegal logging increasing
attention has been paid to the origin of timber products in the world [1,2]. Statistics of imports and exports conducted
by WWF experts in 2008 showed that a significant amount of illegally harvested wood enters the European and
Chinese markets from Russia and Eastern Europe. In the Russian Federation only in 2008–2016 period were recorded
197,228 cases of illegal logging, total damage amounted to 104.5 billion rubles, reimbursed - 2.83 billion rubles.
(2.7% of the amount of damage assessed). Illegal forests use has been identified in almost all regions of the Russian
Federation [3]. In order to prevent these offenses, law enforcement authority officers should be able to conclusively
identify the true origin of the wood transported [4].
One of the promising areas of evidence of the timber trade legality is the use of molecular-genetic methods of
analysis. These methods are based on the using of genetic markers - microsatellites - varying regions (loci) in nuclear
DNA and DNA of organelles (mitochondria and plastids) consisting of tandemly repeated nucleotide sequences.
These markers are characterized by a high level of polymorphism and are often found in the genome [5].
The molecular-genetic method compared with the traditional denrochronological identification avoids timely
collection of data such as tree age, diameter, height, thickness. The insufficient number or lack of clear annual rings
due to wood decay severely limits the use denrochronological identification of wood samples [6].
However, nowadays there are no methods that allow to obtain minimal combinations of molecular primers that
give the lowest probability of a coincidence of related multi-focused genotypes. Thus, the task of developing a
method for determining the optimal sequence of microsatellite markers used to study the genetic structure of a woody
plant population using the example of Scots pine is an urgent one.

2 The microsatellite markers combination determination method
Wood samples of pine (Pinus sylvestris L.) taken from plantations growing near the Balakhta in the Krasnoyarsk
Krai served as a reference sample. The selection cotained 29 samples, which is representative and accepted in the
analysis of nuclear codominated nuclear markers. In molecular genetic studies, the average number of samples in the
selection less than 30 individuals per cenopopulation [7-8]. Basic methodology for the DNA study relies on the
following stages, i.e., DNA extraction (based on the lysis of cell walls), DNA amplification via polymerase chain
reaction (PCR) method with specific primers for nuclear microsatellite or organelle alleles, genotyping of the PCR
products in automated sequencer, and finally comparison of the DNA profiles obtained for all samples. The wood was
thoroughly crushed, homogenized, and the DNA was isolated by the CTAB method [9]. The method is based on the
cells breakdown under the cetyltrimethylammonium bromide (CTAB) effect, the removal of proteins using
chloroform and the precipitation of DNA with isopropanol. PCR was performed with the use of a commercial kit of
reagents "ScreenMix" (JSC "Eurogen", Russia). Amplification was carried out in the thermal cycle T100 Thermal
Cycler (BioRad). The amplification products were separated by electrophoresis on 6% polyacrylamide gel using Tris-
Borate-EDTA electrode buffer and stained with ethidium bromide. PCR products were visualized by UV using gel
documentation system (Figure 1). Data analysis was performed using Vilber Lourmat Bio Capt V. 12.5.0.0 software.

Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
As a result of preliminary work to identify the most polymorphic and stably amplifying loci, 10 microsatellite
markers were selected. Table 1 presents the characteristics of recommended nuclear microsatellite loci for Scots pine
[10–13].

Figure 1. The electrophoregrams of nuclear microsatellite loci lw_isotig04306 and PtTx3116 of Pinus sylvestris
L. The numerals 191, 197, 134, 137, 161 on lw_isotig04306 and PtTx3116 electrophoregrams represent the alleles of
amplified DNA fragments. M, molecular-weight size marker

Table 1. Nuclear microsatellite locus characteristics which are selected for operation with Scots pine samples.

Amplicon Temperature, Alleles
Num Locus Мотив Source
size, bp °C count

1 psy119 (GCT)7 315-324 55 3 [10]

2 psy157 (ACC)7 187-202 55 6 [10]

3 PtTx3116 (TTG)7(TTG)5 122-226 55 8-10 [11]

4 PtTx3107 (CAT)14 150-182 55-45↓1 5-6 [11]

5 PtTx4001 (CA)15 201-224 60-50↓1 4-7 [11]

6 lw_isotig21953 (ATGGG)7 208 60 7 [12]

7 PtTx4011 (CA)20 230-284 60-50↓1 21 [11]

8 SPAC11.4 (AT)5(GT)19 130-170 65-55↓1 38 [13]

9 lw_isotig04306 (TCC)7 196 55 3 [12]

10 lw_isotig27940 (TGGA)5 231 55 3 [12]

Currently, the processing of the results of genetic analysis is carried out using proprietary software GenAlEx [14],
a free add-in for MS Excel. The results obtained using the GenAlEx program are shown in Table 2. The smallest
chance of a coincidence of multilocus genotypes (2.2E-08) is achieved only with 10 genetic markers combination:
psy119, psy157, PtTx3116, PtTx3107, PtTx4001, lw_isotig21953, PtTx4011, SPAC11.4, lw_isotig04306,
lw_isotig27940. This value indicates a low probability of random genotypes coincidence [15]. The order of genetic
markers is presented in table 1 and in figure 2.

Table 2. The species identity probability with the same multilocus genotype at increased the genetic markers
combination.

Locus combinations Identity probability
1 1
1+2 0,76
1+2+3 0,059
1+2+3+4 6,3 ∙ 10−3
1+2+3+4+5 1,3 ∙ 10−3
1+2+3+4+5+6 6,6 ∙ 10−5
1+2+3+4+5+6+7 2,3 ∙ 10−5
1+2+3+4+5+6+7+8 1,5 ∙ 10−6
1+2+3+4+5+6+7+8+9 2,3 ∙ 10−7
1+2+3+4+5+6+7+8+9+10 2,2 ∙ 10−8

Genetic analysis conduction of the identity of wood samples determination, using all 10 markers, is very laborious
and expensive. To reduce these costs is proposed a method for determining the optimal minimum sequence of
markers to eliminate false positive sample identification. The purpose of the method proposed is to find a number and
sequence of markers that will give the minimum probability of false-positive sample identification.
The method proposed in this paper is based on the analysis of the alleles pairs occurrence of each locus among
the samples of a deliberately unique selection. As a reference selection used samples of Scots pine, taken from natural
plantation, growing near the Balakhta in the Krasnoyarsk Krai. The reference selection contains 29 numbered unique
samples and initially tested with 10 markers.

1.2E+00

1.0E+00
Identity probability

8.0E-01

6.0E-01

4.0E-01

2.0E-01

0.0E+00

Locus combinations

Figure 2. The species identity probability with the same multilocus genotype at increase in the combination of
genetic markers.

Let us denote
𝑀 = {𝑀𝑖 , 𝑖 = 1. .10},
as a set of action markers results. Each marker for each sample gives us a pair of values:
𝑀𝑖 = {𝑚𝑗 = (𝑎𝑗 , 𝑏𝑗 ): 𝑗 = 1. .29, 𝑎𝑗 ∈ 𝑁, 𝑏𝑗 ∈ 𝑁}.
Table 3 shows the molecular weight values of the selection alleles pairs, measured in “bp” (base pair in English-
language literature), or in n.p. – nucleotide pairs in the Russian-language literature.

Table 3. Allele pairs inside locus by samples.

lw_isotig lw_isotig
№ обр. lw_isotig04306 PtTx3107 … SPAC 11.4
21953 27940
1 258/258 187/187 235/255 159/165 … 146/156
2 223/263 187/193 247/247 153/159 … 138/142
3 203/223 187/187 247/247 159/165 … 152/152
4 248/258 184/187 247/247 180/180 … 142/152
5 258/258 187/193 247/247 159/165 … 146/146
6 203/203 175/187 235/235 162/165 … 142/146
… … … … … … …
24 248/263 184/187 235/235 153/153 … 138/146
25 248/248 178/187 255/255 159/168 … 146/160
26 223/243 193/193 239/247 159/159 … 138/160
27 248/253 187/193 239/255 165/165 … 138/142
28 248/253 187/187 239/255 165/165 … 138/146
29 203/203 193/193 255/263 162/165 … 138/138

Selection sample IDs are grouped by the value pairs shown in Table 3 for each marker:
𝐺𝑖 = {𝑔𝑘 (𝑖) = {𝑚𝑛 }: 𝑘 ∈ 𝑁, 𝑘 < 30, 𝑛 ∈ 𝑁, 𝑛 < 30}.
Sample grouping results by allele values on the example of the lw_isotig04306 marker is shown in Table 4.

Table 4. Grouped samples by the values of the lw_isotig04306 marker alleles.

lw__isotig04306 ids
175/187 {6,19}
178/187 {18,25}
181/187 {16,17,22}
184/184 {7,10,13}
184/187 {4,11,12,20,24}
184/193 {15}
187/187 {1,3,8,21,23,28}
187/193 {2,5,9,14,27}
193/193 {26,29}

The next stage of the method is to rank the sets of identifiers 𝐺𝑖 grouped in pairs from the sets 𝑀𝑖 by the number
of unique pairs, i.e. in priority 𝐺𝑖 , in which the maximum number |𝑔𝑘 (𝑖)| = 1.
Then there is an iterative process of intersection of sets 𝐺𝑖 . Each set of grouped genotypes intersects with other
sets of grouped genotypes. The first step of the iteration is a pairwise intersection of the sets 𝐺𝑖 . The results of the
intersection of only those sets, which are subsets of a capacity at least two:
𝑅 = {𝑅𝑙 = 𝑔𝑚 (𝑖) ∩ 𝑔𝑛 (𝑖): |𝑅𝑙 | > 1; 𝑙, 𝑚, 𝑛, 𝑖, 𝑗 ∈ 𝑁; 𝑚, 𝑛 < 30; 𝑖, 𝑗 ≤ 10}.
Then 𝑅𝑙 are ranked in cardinality ascending. At the next iterations, 𝑅 intersects with the remaining 𝐺𝑖 :
𝑅 = 𝑅 ∩ 𝐺𝑖 .
The process continues until the intersection becomes an empty set. Thus, by analyzing a test selectionin a similar
way, it is possible to obtain an optimal sequence of markers that uniquely identifies samples of the test species.
For Scots pine samples, taken in natural plantation growing growing near the Balakhta in the Krasnoyarsk Krai,
the optimal minimal combination of genetic markers was the sequence lw_isotig21953, SPAC11.4, PtTx3107.
To verify the method, a control group of Scots pine wood from the same plantation was selected and
analyzedTable 5 presents the results of grouping samples of the control group according to the values of the
lw_isotig04306 marker alleles.

Table 5. Control group grouped samples by the values of the lw_isotig04306 marker alleles.

lw_isotig04306 ids
175/187 {15,16}
178/187 {24,23}
181/187 {14,13,22}
184/184 {9,19,21}
184/187 {29,10,11,12,20}
184/193 {28}
187/187 {26,27,5,6,8,18}
187/193 {25,3,4,7,17}
193/193 {1,2}

Sample analysis of the control group showed the effectiveness of the selected markers sequence. In the case of
false positive sample identification, the control group is additionally analyzed with the lw_isotig27940 marker.
The analysis results of the control group of samples were checked by the GenAlEx program with the indicated
sequence of markers. The species identity probability with the same multilocus genotype at increase in the
combination of genetic markers is given in Table 6 and in Figure 3.

Table 6. The species identity probability with the same multilocus genotype at increase in the combination of
genetic markers.

Locus combinations Identity probability
6 5,2 ∙ 10−2
6+8 3,4 ∙ 10−3
6+8+4 3,6 ∙ 10−4
6+8+4+10 3,5 ∙ 10−5

The order of genetic markers is presented in table 1.

6.0E-02

5.0E-02
Identity probability

4.0E-02

3.0E-02

2.0E-02

1.0E-02

0.0E+00
1 1+2 1+2+3 1+2+3+4
Locus combinations

Figure 3. The species identity probability with the same multilocus genotype at increase in the combination of
genetic markers

Comparing the results of tables 2 and 6 shows that using the sorted marker sequence decrease the probability of a
random coincidence of multilocus genotypes. To achieve an acceptable result, the use of only three markers was
sufficient.

3 Conclusion
To determine the identity of Scots pine samples , taken in natural plantation growing near the Balakhta of the
Krasnoyarsk Krai, the sequence of genetic markers {lw_isotig21953, SPAC11.4, PtTx3107} was the minimum
optimal combination. The effectiveness of the selected sequence of markers tested on the control group. The proposed
method of the optimal sequence microsatellite markers selection can significantly reduce labor, time and material
costs in the wood samples identity determination. In further studies, it is planned to use this algorithm for the marker
selection in relation to other sets to similar genetic analysis.

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