Genetic diversity analysis of volunteer wheat based on microsatellite simple sequence repeats(SSR) markers

DOI: https://doi.org/10.21203/rs.3.rs-2307377/v1

Abstract

In order to study the genetic diversity and population structure within and between volunteer wheat and cultivated wheat(Triticum aestivum L.), 195 volunteer wheats and 29 cultivated wheats were analyzed based on 16 pairs of highly-polymorphic microsatellite simple sequence repeats (SSR) primers and a microchip capillary electrophoresis (MCE) detection system. A total of 110 polymorphic alleles were detected by capillary electrophoresis(CE) with each pair of primers identifying 2–15 alleles with an average of 6.875 alleles. The polymorphic information content (PIC) ranged from 0.1089 to 0.7843, with an average of 0.5613. Genetic diversity arguments from 224 samples showed that volunteer wheat was more varied than cultivated wheat. Based on the SSR information, the 224 samples were classified into seven groups, which corresponded to the volunteer wheats and cultivated wheats through principle component analysis (PCA). We propose that volunteer wheat and cultivated wheat have rather distant phylogenetic relationships. Hence, it is important for wheat breeding to study the genetic relationship between volunteer wheat and cultivated wheat.

Introduction

Wheat(Triticum aestivum L.) is one of the world’s foremost food crops, providing a staple food source for more than half the world’s human population (Röder et al., 1998). Volunteer wheat is an emerging form of wheat with weed characteristics that is widely distributed in all the major wheat-planting areas and the frequency of volunteer wheat is 50.3%-92.1% (Su et al., 2021a). It is a self-pollinating species with 42 chromosomes, the same as cultivated wheat(Fan et al., 2010), so volunteer wheat is also referred to as “wild wheat” or “semi-wheat”. The plant height of volunteer wheat was 30.0%~135.7% higher than that of cultivated wheat, the ear length was smaller than that of cultivated wheat, the number of grain number per spike was less than that of cultivated wheat, the thousand-grain weight was 22.6%~42.4% lower than that of cultivated wheat, and the stem diameter was significantly smaller than that of cultivated wheat(Su et al., 2021a). In addition, volunteer wheat is a species of wheat with aggressive tillering, rapid growth, and effective seed dispersal and could be widely used for modern wheat breeding (Fan et al., 2010). The endogenous abscisic acid content of the long-dormant volunteer wheat seeds was significantly higher than that of the short-dormant material, which enabled them to remain dormant in the field without germination and naturally over-summer, making it very easy to mix with cultivated wheat(Su et al., 2022).

The infestation of volunteer wheat has already caused serious damage to cultivated wheat by competing for water, fertilizer, sunlight and other resources, resulting in significant reductions in wheat yield (Shivrain et al., 2010; Su et al., 2021b). The photosynthetic characteristics, chlorophyll fluorescence parameters, pigment content and malondialdehyde content of volunteer wheat were higher than those of cultivated wheat(Sun et al., 2021). In addition, it is a particularly troublesome weed due to similarities to cultivated wheat, making it extraordinarily difficult to control with herbicides or other methods (Gealy et al., 2002). Furthermore, there are various types of volunteer wheats, with different morphologies and possibly different origins (Kane et al., 2007; Su et al., 2021a). However the moisture content, protein content, wet gluten content, water absorption, softening degree and extensibility of volunteer wheat were higher than those of cultivated wheat(Su et al., 2021b). Although there have been some studies of the genetic diversity of volunteer wheat, the genetic relationship of volunteer wheat still remains controversial. Consequently, it is necessary to clarify the genetic diversity of volunteer wheat in order to provide effective control and utilization.

Since the late 1980s, wheat breeders and geneticists have identified and used DNA molecular markers including sequence characterized amplified regions (SCAR), single nucleotide polymorphisms (SNPs), inter simple sequence repeats (ISSRs), and SSRs to improve wheat breeding and quality (Ali et al., 2019). Most reported studies have been carried out on the genetic diversity, genetic mapping, population structure and phylogenetic relationship of barley(Hordeum vulgare L.)(Wang et al., 2014; Liu et al., 2011; Lai et al., 2016; He et al., 2013), spring wheat (Wang et al., 2013), wheat (Zhao et al., 2009), and winter wheat (Li et al., 2014). SSR markers have been widely used to study genetic mapping, genetic diversity, resistance identification, marker-assisted selection and parental analysis (Ali et al., 2019) of food crops including wheat (Prasad et al., 2000), maize (Zea mays Linn. Sp.) (Wang et al., 2014), soybean [Glycine max (L.) Merr. ] (Zhang et al., 2014), potato (Solanum tuberosum L.) (Liao et al., 2014), and weedy rice (Oryza Satiua L.) (Shao et al., 2011; Li et al., 2018). SSR primer pairs are considered the most useful marker for plant breeding and plant genetics, because of their co-dominant, multi-allelic nature, and relative abundance with an excellent genome coverage (Ali et al., 2019; Aitken et al., 2005). Ivandic et al. (2002) used 33 SSR markers to analyze the genetic diversity of 39 wild barley genotypes from Israel, Turkey and Iran, showing that most of the materials could be classified by country. Fu et al. (2007) used SSR markers to analyze 47 wheat varieties in Sichuan, China and found that the genetic background of the selected materials was very similar.

At present, there are three methods used to detect SSR marker fragments: agarose gel electrophoresis (AGE), natured and denatured polyacrylamide gel electrophoresis (PAGE), and microchip capillary electrophoresis (MCE). MCE is a useful tool that is widely applied to the management and analysis of plant genetic resources (Piergiovanni and Angela, 2013). It is rather faster, more accurate and more efficient than the other two detection methods. MCE also has the advantages of only requiring small quantities of reagents and samples, so that microfluidic instrumentation can achieve efficient separation of molecular species (Minucci et al., 2014; Revermann, 2007).

Germplasm resources are the material basis of breeding, while accurate analysis and evaluation of germplasm resources is the premise of rational utilization of resources, thus it is necessary to fully understand the degree of genetic variation of breeding materials. There is little information on the genetic relationship and molecular identification of volunteer wheat, and thus a complete system and set of evaluation criteria have not been developed. To better understand the genetic relationship of these cultivated wheat and volunteer wheat strains, this study aimed to characterize the genetic diversity and population structure of 224 samples by MCE technology. It is hoped that it is of great significance to reveal the genetic diversity between the volunteer wheat and the cultivated wheat population as well as within the population, to understand its genetic background, and to further the comprehensive management strategy and utilization of gene resources of associated wheat.

Materials And Methods

Plant materials

Two hundred twenty-four samples were used in the study, consisting of 195 volunteer wheat seeds and 29 cultivated wheat seeds (Table 1). In May and June 2017, accompanying wheat seeds were collected from mature and unharvested wheat fields in Henan Province, Hebei Province, Shandong Province, Shanxi Province, Anhui Province and Jiangsu Province in China's Huang Huai Hai wheat-growing area. Each volunteer wheat variety came from the same volunteer wheat plant, the distance between different varieties was more than 10 km. The location of a wheat field associated with wheat seed collection was randomly selected using global positioning satellite (GPS) coordinates and a map developed by the ArcView geographic information system (GIS) software program. Twenty-nine cultivated wheat seeds constituting the main wheat varieties in the Yellow-Huaihai Wheat-Planting Area were obtained from the Henan Academy of Agricultural Science. In July 2017, volunteer wheat and cultivated wheat seeds were sown in pots containing nutrient medium and placed in a constant temperature incubator. Leaf samples were collected from a freshly-expanded young trifoliate leaf of emerged seedlings of each pot 15 days after planting, wiped off with deionized water, and kept at -80°C until DNA extraction.

Table 1

Details of the 224 samples from six provinces of China used in the study

Code Province Region/Variety

Code Province Region/Variety

W-1 Henan Xi County

W-2 Xi County

W-3 Xi County

W-4 Luoshan County

W-5 Luoshan County

W-6 Zhengyang County

W-7 Zhengyang County

W-8 Zhengyang County

W-9 Zhengyang County

W-10 Biyang County

W-11 Biyang County

W-12 Biyang County

W-13 Biyang County

W-14 Biyang County

W-15 Changge City

W-16 Changge City

W-17 Changge City

W-18 Changge City

W-19 Changge City

W-20 Yuzhou City

W-21 Yuzhou City

W-22 Yuzhou City

W-23 Yuzhou City

W-24 Yuzhou City

W-25 Jia County

W-26 Jia County

W-27 Jia County

W-28 Jia County

W-29 Jia County

W-30 Jia County

W-31 Ruzhou City

W-32 Ruzhou City

W-33 Ruzhou City

W-34 Wuyang County

W-35 Wuyang County

W-36 Wuyang County

W-37 Wuyang County

W-38 Wuyang County

W-39 Wuyang County

W-40 Ningling County

W-41 Ningling County

W-42 Ningling County

W-43 Xiayi County

W-44 Xiayi County

W-45 Xiayi County

W-46 Yongcheng City

W-47 Yongcheng City

W-48 Yongcheng City

W-49 Minquan County

W-50 Minquan County

W-51 Minquan County

W-52 Minquan County

W-53 Minquan County

W-54 Minquan County

W-55 Minquan County

W-56 Minquan County

W-57 Minquan County

W-58 Minquan County

W-59 Minquan County

W-60 Minquan County

W-61 Minquan County

W-62 Minquan County

W-63 Minquan County

W-64 Minquan County

W-65 Kaifeng County

W-66 Kaifeng County

W-67 Kaifeng County

W-68 Kaifeng County

W-69 Kaifeng County

W-70 Kaifeng County

W-71 Wuzhi County

W-72 Wuzhi County

W-73 Wuzhi County

W-74 Wuzhi County

W-75 Wuzhi County

W-76 Wuzhi County

W-77 Wuzhi County

W-78 Hui County

W-79 Hui County

W-80 Hui County

W-81 Hui County

W-82 Shangshui County

W-83 Shangshui County

W-84 Shangshui County

W-85 Huaiyang County

W-86 Huaiyang County

W-87 Huaiyang County

W-88 Taikang County

W-89 Taikang County

W-90 Taikang County

W-91 Taikang County

W-92 Taikang County

W-93 Taikang County

W-94 Taikang County

W-95 Zhoukou City

W-96 Zhoukou City

W-97 Zhoukou City

W-98 Mianchi County

W-99 Mianchi County

W-100 Mianchi County

W-101 Mianchi County

W-102 Mianchi County

W-103 Mianchi County

W-104 Mianchi County

W-105 Mianchi County

W-106 Mianchi County

W-107 Mianchi County

W-108 Mianchi County

W-109 Mianchi County

W-110 Mianchi County

W-111 Mianchi County

W-112 Mianchi County

W-113 Mianchi County

W-114 Mianchi County

W-115 Mianchi County

W-116 Mianchi County

W-117 Mianchi County

W-118 Mianchi County

W-119 Mianchi County

W-120 Mianchi County

W-121 Mianchi County

W-122 Mianchi County

W-123 Mianchi County

W-124 Mianchi County

W-125 Mianchi County

W-126 Jiyuan City

W-127 Jiyuan City

W-128 Jiyuan City

W-129 Jiyuan City

W-130 Jiyuan City

W-131 Xun County

W-132 Xun County

W-133 Jiangsu Xuzhou City

W-134 Xuzhou City

W-135 Xuzhou City

W-136 Anhui Suzhou City

W-137 Bozhou City

W-138 Bozhou City

W-139 Bozhou City

W-140 Bozhou City

W-141 Bozhou City

W-142 Shandong Jiyang County

W-143 Jiyang County

W-144 Jiyang County

W-145 Jiyang County

W-146 Jiyang County

W-147 Ningjin County

W-148 Ningjin County

W-149 Ningjin County

W-150 Ningjin County

W-151 Ningjin County

W-152 Guan County

W-153 Shanxi Xinjiang County

W-154 Xinjiang County

W-155 Hongtong County

W-156 Hongtong County

W-157 Hongtong County

W-158 Hongtong County

W-159 Hongtong County

W-160 Hongtong County

W-161 Hongtong County

W-162 Hongtong County

W-163 Hongtong County

W-164 Huozhou City

W-165 Huozhou City

W-166 Xiangfen County

W-167 Xiangfen County

W-168 Xiangfen County

W-169 Pingyao City

W-170 Hebei Shenzhou City

W-171 Shenzhou City

W-172 Shenzhou City

W-173 Shenzhou City

W-174 Guantao County

W-175 Guantao County

W-176 Guantao County

W-177 Guantao County

W-178 Guantao County

W-179 Guantao County

W-180 Guantao County

W-181 Guantao County

W-182 Guantao County

W-183 Guantao County

W-184 Handan County

W-185 Handan County

W-186 Handan County

W-187 Handan County

W-188 Feixiang County

W-189 Feixiang County

W-190 Wuyi County

W-191 Wuyi County

W-192 Jing County

W-193 Jing County

W-194 Jing County

W-195 Anxin County

A AK58

B Ganmai 8

C Cunmai 1

D Cunmai 8

E Zhoumai 16

F Zhoumai 18

G Zhoumai 22

H Zhoumai 26

I Zhoumai 27

J Kaimai 18

K Kaimai 21

L Yannong 21

M Yannong 24

N Yunong 416

O Zhengmai 366

P Zhengmai 379

Q Zhengmai 7698

R Zhengmai 9023

S Xiaoyan 6

T Fanmai 8

U Yangmai 15

V Yimai19

W Huaimai 20

X Neixiang 188

Y Tianmin 198

Z Bainong 207

a Xinong 979

b Qiule 2122

c Luo 4-168

DNA extraction

DNA was extracted from leaf tissues using the cetyl tri-methyl ammonium bromide (CTAB) method (Wang et al., 2005) with some modifications. DNA quality and concentrations of samples were measured using 1% AGE with samples diluted to a working concentration of 50 ng/µL, and stored at -20°C for further analysis.

SSR primers and PCR amplification

Fifty SSR primer pairs were synthesized based on the microsatellite molecular marker linkage map (Röder et al. 1998), and sixteen polymorphic SSR primer pairs were selected to analyze the 224 samples (Table 2). For amplification of a target gene, PCR-cycling amplification reactions were set up in 20 µL of reaction mix containing 10 µL of Premix Taq (TaKaRa Taq Version 2.0 plus dye), 50 ng genomic DNA, 1 µL of each primer, and 7 µL ddH2O. The following PCR conditions were used: initial denaturation at 94℃ for 5 min, 38 cycles of denaturation at 94℃ for 45 s, annealing at 54–62℃ for 45 s, extension at 72℃ for 1.5 min, followed by final extension at 72℃ for 7 min in a thermocycler.

Table 2

Primer sequences used for SSR analysis

NO.

Primer code

Forward (5'-3')

Reverse (5'-3')

SSR motif

Annealing temperature (℃)

1

Xgwm43-7B

CACCGACGGTTTCCCTAGAGT

GGTGAGTGCAAATGTCATGTG

(CA)22

54

2

Xgwm131-3B

AATCCCCACCGATTCTTCTC

AGTTCGTGGGTCTCTGATGG

(CT)22

56

3

Xgwm136-1A

GACAGCACCTTGCCCTTTG

CATCGGCAACATGCTCATC

(CT)58

56

4

Xgwm148-2B

TCACAGAGAGAGAGGGAGGG

ATGTGTACATGTTGCCTGCA

(GA)37imp

56

5

Xgwm179-5A

AAGTTGAGTTGATGCGGGAG

CCATGACCAGCATCCACTC

(GT)15

56

6

Xgwm182-5D

TGATGTAGTGAGCCCATAGGC

TTGCACACAGCCAAATAAGG

(CT)18

56

7

Xgwm 264-3B

GAGAAACATGCCGAACAACA

GCATGCATGAGAATAGGAACTG

(CA)9A(CA)24

54

8

Xgwm 284-3B

AATGAAAAAACACTTGCGTGG

GCACATTTTTCACTTTCGGG

(GA)17

58

9

Xgwm 285-3B

ATGACCCTTCTGCCAAACAC

ATCGACCGGGATCTAGCC

(GA)27

62

10

Xgwm350-7D

ACCTCATCCACATGTTCT

ACGGCATGGATAGGACGCCC

(GT)14

56

11

Xgwm368-4B

CCATTTCACCTAATGCCTGC

AATAAAACCATGAGCTCACTTGC

(AT)25

58

12

Xgwm369-3A

CTGCAGGCCATGATGATG

ACCGTGGGTGTTGTGAGC

(CT)11(T)2(CT)21

62

13

Xgwm456-3D

TCTGAACATTACACAACCCTGA

TGCTCTCTCTGAACCTGAAGC

(GA)21

62

14

Xgwm540-5B

TCTCGCTGTGAAATCCTATTT

CAGGCATGGATAGAGGGGC

(CT)3(C)2(CT)16

58

15

Xgwm547-3B

GTTGTCCCTATGAGAAGGAACG

TTCTGCTGCTGTTTTCATTTAC

(CA)12

62

16

Xgwm637-4A

AAAGAGGTCTGCCGCTAACA

TATACGGTTTTGTGAGGGGG

(CA)18

58

Marker scoring

The PCR products for MCE were analyzed using LabChip GX Touch software (www.perkinelmer.com) to reveal capillary electrophoregrams of PCR-amplified SSR-DNA fragments. Data were scored manually in a binary format into a data matrix file, with the presence of a band scored as “1”and its absence scored as “0”.

Data analysis

The allelic data matrix of “1” or “0” was used to perform genetic analysis using Powermarker 3.25 software (Tan et al., 2015), including the number of effective alleles (Ne), heterozygosity (He), Nei’s genetic diversity (h) and PIC (Tan et al., 2015). DNA amplification products detected by MCE were expressed in the form of peaks except for spectral bands, and the target peak value represented the size of the amplified fragment which could be read out directly (Figs. 1 and 2) (Wang et al., 2007). The size (bp) of amplified target fragments was saved in ‘text’ format. Unweighted pair-group method with arithmetic mean (UPGMA) trees were constructed in the imported dialog box of Mega 5 software (https://www.megasoftware.net/) by cluster analysis (Nei, 1973). Population structure was estimated using PCA with the EIGEN subprogram as implemented in NTSYS-pc version 2.10e (Ali et al., 2019).

Results

Total allele amplification of sixteen SSR markers

A total of 110 alleles were amplified from the DNA of the 224 samples of 195 volunteer wheats and 29 cultivated wheats with the 16 fluorescence-labeled SSR primer pairs and capillary CE detection system. The number of alleles detected by the MCE system varied from as few as two (Xgwm 369-3A) to as many as 15 (Xgwm 179-5A), with a mean of 6.875 per SSR primer pair. Three SSR primer pairs, namely Xgwm 264-3B, Xgwm 182-5D and Xgwm 179-5A, were highly polymorphic, each producing 10 to 15 alleles. Another seven SSR primer pairs, namely Xgwm350-7D, Xgwm 284-3B, Xgwm 285-3B, Xgwm 43-7B, Xgwm 136-1A, Xgwm 540-5B and Xgwm 148-2B, were moderately polymorphic, each producing six to nine alleles. The remaining six SSR primer pairs, namely Xgwm 456-3D, Xgwm 131-3B, Xgwm 368-4B, Xgwm 369-3A, Xgwm 547-3B and Xgwm 637-4A, were less polymorphic, producing fewer than six alleles each (Table 3).

Table 3

Amplified results of 224 samples with 16 SSR primer pairs.

Marker

Major. Allele. Frquency

Allele No

Gene Diversity

Heterozygosity

PIC

1

0.4509

7.0000

0.6778

0.0000

0.6256

2

0.5804

4.0000

0.5665

0.6161

0.5002

3

0.4423

7.0000

0.7139

0.9279

0.6740

4

0.3504

9.0000

0.7288

0.3125

0.6819

5

0.2768

15.0000

0.8090

0.7098

0.7843

6

0.4018

11.0000

0.7591

0.4018

0.7300

7

0.3348

10.0000

0.7844

0.5759

0.7562

8

0.4643

7.0000

0.7056

0.0357

0.6690

9

0.4241

7.0000

0.6348

0.3571

0.5655

10

0.3884

6.0000

0.7263

0.4688

0.6822

11

0.9397

4.0000

0.1140

0.0134

0.1089

12

0.6563

4.0000

0.5332

0.1786

0.5012

13

0.4375

2.0000

0.6043

0.0000

0.5198

14

0.7612

8.0000

0.4020

0.0580

0.3802

15

0.4412

4.0000

0.6496

0.6154

0.5779

16

0.8661

5.0000

0.2394

0.0625

0.2236

Mean

0.5135

6.8750

0.6030

0.3333

0.5613

Genetic variability

The PIC values of these primer pairs ranged from 0.1089 (Xgwm 368-4B) to 0.7843 (Xgwm 179-5A), with a mean of 0.5613 (Table 3). The genetic diversity index ranged from 0.114 (Xgwm 368-4B) to 0.809 (Xgwm 179-5A), with a mean of 0.603 (Table 3). The heterozygosity of SSR applied alleles ranged from 0 (Xgwm 43-7B, Xgwm 456-3D) to 0.9279 (Xgwm 136-1A), with an average of 0.3333 (Table 3), which showed a relatively low level of gene exchange among the populations sampled (Liu et al., 2018).

Principal coordinate analysis (PCA)

PCA data for all 224 samples are shown in Fig. 4. The PCA result showed a genetic structure of volunteer wheat and cultivated wheat, which was similar to the phylogenetic tree (Fig. 3). The amount of overall variance accounted for by the two-dimensional plot was 9.577% of Dim 1 and 7.736% of Dim 2. This is an acceptable fit, based on the data of the 224 samples which showed two distinct groups and supported the efficacy of the PCA approach and SSR alleles used in the analysis.

Cluster analysis

The dendrogram constructed by the UPGMA cluster analysis classified the 224 samples into seven groups (Fig. 3). Group I comprised 12 volunteer wheats collected from Henan (5 samples), Hebei (6) and Shanxi (1); Group II consisted of 29 wheats; Group III comprised 95 volunteer wheats collected from Henan (50), Hebei (19), Shanxi (16) and Shandong (10); Group IV contained five volunteer wheats collected from Henan (5); Group V comprised 21 volunteer wheats collected from Henan (20) and Jiangsu (1); Group VI contained 54 volunteer wheats collected from Henan (48) and Anhui (6), while Group VII contained eight volunteer wheats collected from Henan (6), Hebei (1) and Shandong (1). The cluster analysis results showed the presence of geographic differences in the genetic diversity of volunteer wheats. On the whole, volunteer wheats of Henan mainly clustered into Group III and IV, and Group IV was almost completely occupied by the volunteer wheats of Henan. In addition, the samples of Hebei, Shandong, and Shanxi mostly partitioned to Group III, while Jiangsu and Anhui only occurred in Group IV. The above results showed that: volunteer wheats from the same or near regions were clustered together, for instance, the cluster included WW-6, 7, 8, 9 in Group I and the cluster included WW-20, 21, 22, 28, 29, 30, 31, 32, 33 in Group IV; but volunteer wheats from the same region were not completely clustered into one type, for example among WW-15, 16, 17, 18, and 19, only WW-15 was classified into Group I, the others belonged to Group IV; even some experimental samples from different places were clustered together, the cluster in Group IV consisted of WW-42, 152, 5, 4, 54, 24, 170, and 48.

However, Group II consisted of all 29 cultivated wheats without any volunteer wheat. The cluster analysis result mainly agreed with the pedigree of 29 cultivated wheats. The varieties ‘ C ’ and ‘ I ’ with the common male parent ‘A’ and different female parents were divided into the same cluster but finally clustered into different taxa, other strains such as ‘ I ’ and ‘ Z ’, ‘ Y’ and ‘ Z ’ produced similar conclusions. A few varieties with close genetic relationships could not cluster together, such as ‘ E’ and ‘ Z ’, ‘ E’ and ‘ Q’.

Discussion

Morphological indexes such as plant height, stem diameter, tiller number, spike shape, thousand grain weight and yield, optical characteristics such as photosynthesis and fluorescence, and quality indexes such as grain protein content, wet gluten content, test weight, hardness and flour yield of volunteer wheat differed siginaficantly from those of cultivated wheat(Su et al., 2021a; Su et al., 2021b; Sun et al., 2021; ). In addition, the dormancy of volunteer wheat seeds with high endogenous abscisic acid content was strong, which enabled them to remain dormant for a long time in the field(Su et al., 2022). SSR markers are very useful for a variety of applications in plants, including population structure analysis, segregation analysis, marker-assisted selection, assessment of genetic relationships between individuals, population genetics and phylogenetic studies (Ahmad, 2013; Ali et al., 2019). In this study, the genetic diversity and population structure of 224 samples of volunteer wheat and cultivated wheat were analyzed by 16 SSR primer pairs. The 16 primer pairs primed the amplification of 110 polymorphic SSR alleles detectable by the CE platform. Every primer pair was able to amplify varying numbers of SSR alleles from all samples tested. Three SSR primer pairs, namely Xgwm 264-3B, Xgwm 182-5D and Xgwm 179-5A, were highly polymorphic, each producing 10 to 15 alleles, of which SSR primer pairs, namely Xgwm 182-5D and Xgwm 179-5A, produced more than ten alleles among the 224 accessions. Additionally, we found that more types of target alleles were present in volunteer wheat compared to cultivated wheat, indicating that volunteer wheat has more abundant genetic diversity than cultivated wheat. This may also be related to the low number of cultivated wheat varieties we chose. These findings are similar to those reported previously by Wang et al. (2015), who showed that volunteer wheat possibly originated from different genetic ancestry.

The results of cluster analysis of the 224 experimental samples showed that: (1) the 224 samples were divided into seven groups, with all volunteer wheats clustered into three groups and all cultivated wheats exclusively classified to the remaining four groups. These results indicated that the two populations–volunteer wheat and cultivated wheat–have significant genetic differences and a relatively distant genetic relationship. The highest number of effective alleles, number of observed alleles and polymorphism index were observed in samples of volunteer wheat. However, it was previously reported that volunteer wheat belongs to the wheat species, but shows some differences from cultivated wheat (Fan et al., 2010). (2) From the perspective of geographical distribution, experimental samples with the same or near region of origin were clustered together, but the experimental materials from the same region were not completely clustered into one type, and some samples from different places were clustered together. The main reason for this phenomenon may be the following five aspects: a. the complexity of breeding materials and the selection of breeding direction, resulting in the occurrence of the type with significant genetic differences; b. in the process of long-term adaptation to the environment, convergence of the materials led to the crossing of different characters (Wang et al., 2015); c. gene mutation; d. errors in analyzing data caused by the deviant target peak in the MCE; e. the SSR markers with insufficient representation (Yang et al., 2018). At present, the origin and evolution of volunteer wheat are closely related to human farming practices, and differences in environmental conditions and farming methods (returning straw) can influence the evolution of volunteer wheat.

In conclusion, this study revealed the presence of significant genetic differences between volunteer wheat and cultivated wheat, indicating that the two populations have a relatively distant relationship. Therefore, it is important for wheat breeding to study the genetic relationship between volunteer wheat and cultivated wheat, so as to provide richer genetic variation for breeding and to explore the favorable alleles.

Declarations

Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Wangcang Su, Hongle Xu, Lanlan Sun. The study was conceptualized by Renhai Wu and Chuantao Lu. All authors have read and agreed to the published version of the manuscript. 

Funding This work was supported by Distinguished Young Scholars from the Henan Academy of Agricultural Sciences (2022JQ04), Scientific and Technological Research and Development of Henan (222301420110) and Special Project on Research and Development of

Key Institutions in Henan Province (grant no. 2022TD08)

Confict of interest The authors have no relevant fnancial or non-fnancial interests to disclose.

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