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Article

Population Genetic Analysis of a Bread Wheat Panel from Northern and Huang-Huai Agro-Ecological Regions in China

by
Xiaojie Jin
1,2,†,
Huimin Gao
3,†,
Qian Liu
2,
Yun Zhao
2,
Wenchuang He
4,
Guijuan Wang
5,
Yu Zhou
6,
Zheng Song
1,
Xiaobin Zhao
1,
Xifeng Ren
5,6,
Yanchun Peng
1,* and
Yingjun Zhang
2,*
1
Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Hubei Hongshan Laboratory, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
2
Laboratory of Crop Genetics and Breeding of Hebei, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
3
Institute of Cash Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
4
Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
5
Hubei Provincial Seed Administration Bureau, Wuhan 430070, China
6
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(9), 2408; https://doi.org/10.3390/agronomy13092408
Submission received: 14 August 2023 / Revised: 13 September 2023 / Accepted: 16 September 2023 / Published: 18 September 2023
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Bread wheat (Triticum aestivum L.) is one of the most extensively cultivated cereal crops around the world. Here, we investigated the population structure and genetic diversity of a panel mainly originated from two wheat agro-ecological regions (northern winter wheat region, NW; and the Huang-Huai River Valley’s facultative wheat region, HH) in China based on a 15K SNP array. Population genetic analysis revealed that the optimal population number (K) was three, and the three groups were roughly related to ecological regions, including NW (mainly Hebei), HH1 (Henan-Shaanxi), and HH2 (Shandong). Within HH, HH1 had a higher nucleotide diversity (π = 0.31167), minor allele frequency (MAF = 0.2663), polymorphism information content (PIC = 0.2668), and expected heterozygosity (Hexp = 0.3346) than HH2. Furthermore, our results demonstrated that genetic diversity decreases with the advancement of wheat breeding. Finally, inference of ancestry informative markers indicated that the genomes of the three pure groups from the three provinces (Hebei, Henan, and Shandong) of the two regions have genomic regions with different mosaic patterns derived from the two landrace groups. These findings may facilitate the development of wheat breeding strategies to target novel desired alleles in the future.

1. Introduction

Bread wheat (Triticum aestivum L.) is one of the most widely cultivated cereal crops in the world [1,2,3]. There has been increasing paleobotanical, archaeological, and genomic evidence revealing that wheat is an exotic crop to China that was probably expanded to China approximately 4500 years ago along the ancient Silk Road [2,4,5,6,7,8]. Since then, it gradually became one of the staple crops in China [9,10,11]. Bread wheat was likely introduced to northwest China first and then spread to east China and gradually to south and southwest China [12,13]. For thousands of years, numerous artificial selections have been carried out for the domestication of landraces to cultivated varieties, with the aim to improve the yield and quality of grains and resistance to stresses [14,15,16,17,18]. After being introduced into China, bread wheat gradually differentiated into various ecotypes for adaptation to the different ecological environment in China.
It is necessary to study the population structure and genetic diversity of bread wheat in east Asia, particularly in China [3,19,20,21]. Since bread wheat was expanded into east Asia from its geographical origin, the southwestern coastal area of the Caspian Sea [1,6,22,23,24,25,26,27], it had little chance to hybridize with its tetraploid ancestors [10]. Some studies have indicated that Chinese wheat landraces and worldwide hexaploid wheat have similar differences in nucleotide diversity among the A, B, and D genomes [1,19,21]. Consistent with wild emmer wheat [28], genetically related bread wheat landraces have close geographical origins, and the population is generally distributed along certain geographical lines [10]. However, this phenomenon has rarely been reported in Chinese modern wheat breeding lines.
This study aims to explore the population structure and genetic diversity of a panel consisting of some Chinese modern wheat breeding lines mainly originating from the northern winter wheat region and the Huang-Huai River Valley’s facultative wheat region. The panel was composed of 302 bread wheat accessions, which can reflect about 70 years of wheat breeding processes in some provinces of China, particularly in Hebei, Henan, and Shandong. The results may clarify whether genetically related Chinese modern wheat breeding lines have close geographical origins, and whether the genetic diversity decreases or increases along with the advancement of breeding. Finally, some landraces were used to explore the genomic regions contributing to differences in genomes among different geographical populations. During domestication, obtainment or loss of genetic variation of populations was associated with adaptability to adversity [29] and trait transformation such as grain yield, quality, and micronutrient content change [30]. Studying the population structure and genetic diversity is a foundation for the genetic mapping of a phenotype. Therefore, the findings are expected to facilitate the development of breeding strategies to target novel elite alleles of the desired phenotype in the future.

2. Materials and Methods

2.1. Study Regions

Due to differences in temperature, precipitation, and altitude across different geographical regions, wheat shows great differences in ecological adaptability in China. According to He et al. [31], China can be divided into ten agro-ecological regions for wheat: the northern winter wheat region, Huang-Huai River Valley’s facultative wheat region, the middle and low Yangtze Valley autumn-sown spring wheat region, the southern autumn-sown spring wheat region, the southwestern autumn-sown spring wheat region, the northeastern spring wheat region, the northern spring wheat region, the northwestern spring wheat region, the Qinghai-Tibetan plateau spring-winter wheat region, and the Xinjiang winter-spring wheat region (Figure 1). Among them, the northern winter wheat region (NW; mainly including Hebei, Shanxi, Beijing, and Tianjin) and the Huang-Huai River Valley’s facultative wheat region (HH; mainly including Henan, Shandong, Shaanxi, North Anhui, and Jiangsu) are the most important wheat-producing regions (Figure 1), totally accounting for 52% of the total harvested area and 60–70% of the total wheat production in China [31]. Therefore, it is necessary to study the population structure and genetic diversity of wheat varieties in these two regions.

2.2. Plant Materials

The studied panel was composed of 302 bread wheat accessions, comprising twelve landraces and 290 Chinese cultivars (lines) (Table S1). Most of the 290 accessions originated from NW and HH (Table S1), and these accessions covered fourteen provinces/municipalities of China, including Hebei (84), Henan (61), Shandong (59), Shanxi (19), Beijing (15), Jiangsu (13), Shaanxi (11), Hubei (8), Anhui (6), Sichuan (4), Gansu (3), Heilongjiang (2), Ningxia (3), and Tianjin (2) (Table S1). These accessions were approved from 1954 to 2021, which therefore can reflect about 70 years of wheat breeding processes in northern China, particularly for NW and HH (Table S1).

2.3. DNA Extraction and Genotyping by a Wheat 15K SNP Array

The genomic DNA of each bread wheat accession was extracted from the third fully unfolded leaf using the DNA quick Plant System by Tiangen Biotech (Beijing, China) Co., Ltd., (www.tiangen.com; accessed on 8 May 2022) according to the manufacturer’s instructions. After extraction and dilution, the DNA at a concentration of 50 ng uL−1 was used for genotyping with a 15K SNP array from China Golden Marker Co., Ltd., (Beijing, China). After nucleic acid amplification, labeling, and chip hybridizing, the raw data were obtained. In order to obtain qualified genotype data of each sample, we conducted both sample and marker quality control according to the workflow presented in Figure S1. The SNPs with minor allele frequency (MAF) < 0.05 and missing data >20% were removed. After filtering, a ped file and a map file including 13,705 SNPs were obtained from the total 15,020 SNPs in the bread wheat panel, including 13,196 markers with known physical positions in the reference genome of Chinese Spring V1.0 [32] and 509 markers with unknown physical positions (Figure 2A). The 509 SNPs in unknown physical positions were reserved for subsequent analysis because they were just mapped to scaffolds but not in proper regions on the 21 chromosomes of Chinese Spring V1.0 when the reference genome was assembled.

2.4. Population Structure Analysis

A maximum likelihood tree was constructed for the 302 bread wheat accessions by using the software iqtree v.1.6.1.2 [33]. Briefly, the ped and map files were converted into a vcf file by using Plink version 1.90b6.18 [34], and the generated vcf file was converted into a fasta file by using the python script vcf2phylip.py (https://github.com/edgardomortiz/vcf2phylip/releases accessed on 10 May 2022). The fasta file was run by using iqtree v.1.6.1.2 with the parameter “-m MFP --alrt 1000 -b 1000”. The generated maximum likelihood tree was visualized by using the online tool iTOL (http://itol.embl.de/ accessed on 15 July 2022). Before inferring the genetic ancestry of each accession, Plink version 1.90b6.18 was used to convert the ped and map files into the structure_in format as the input file of STRUCTURE v.2.3.4 [35]. The software STRUCTURE v.2.3.4 was employed to infer the genetic ancestry of each accession with a predetermined number of clusters (K) from 2 to 7. Each K value was run 10 times with the main parameters provided in Supplementary Materials. The Markov chain in each analysis was set to 100,000 burn-in steps and 200,000 further steps were used for parameter value estimation. The online software STRUCTURE HARVESTER [36] and CLUMPP_Linux64.1.1.2 [37] were used to process the STRUCTURE output and evaluate the most probable K value by examining the delta K. Principal component analysis (PCA) was performed to evaluate the genetic relationship for accessions by using the software Plink version 1.90b6.18.

2.5. Genetic Diversity Evaluation

The fasta file with 13,705 SNP markers was divided into three fasta files according to the three geographical populations (NW, HH1, and HH2) (Table S1). We estimated the nucleotide diversity (π) per base within populations with the software Dnasp5.0 [38] by using the corresponding fasta files as input files, respectively. The software Plink version 1.90b6.18 was employed to calculate the minor allele frequency (MAF) at each locus of each population by using the ped and map files as input files with the parameter “--freq --noweb --missing --withing pop.cov”, where “pop.cov” is the grouped file of samples from the three geographical populations. At each population, the polymorphism information content (PIC) and the expected heterozygosity (Hexp) at each locus were calculated by using the following formulas reported by Zhou et al. [39]:
PIC = 1 − p2 − q2 − 2p2q2
Hexp = 1 − p2 − q2,
where p and q are the frequencies of the two alleles at a locus within the population according to Guo and Elston [40], respectively. After calculating PIC and Hexp at the 13,705 loci of each population, we averaged the PIC and Hexp as the population’s PIC and Hexp, respectively.

2.6. Inference of Ancestry Informative Markers

The analysis was carried out in four steps. First, one gene pool for each of the five bread wheat groups (Hebei, Henan, Shandong, Landrace1, and Landrace2) was constructed based on the previously reported method [39,41,42]. Briefly, if a locus had two or more variants, the minority variants were treated as errors in a group. If variants in a locus had the same frequency, they were selected randomly. After the five gene pools were constructed, we selected those unique loci (i.e., loci with different genotypes in the two landrace groups) in group Landrace1 and Landrace2. Then, in the three pure cultivar groups, we traced their ancestry informative genotypes derived from the two landrace groups at each unique locus. Subsequently, SNPEFF was employed to annotate the shared SNPs between the landrace and cultivar groups [43]. The final results were visualized using the Circlize package (version 0.4.15) in R [44].

3. Results

3.1. Distribution of SNP Markers across the Wheat Genome

A total of 302 bread wheat accessions mainly from NW and HH were selected for genome-wide genotyping (Table S1). After genotyping and filtering, we obtained 13,705 high-quality SNPs across the 21 chromosomes (13,196) of wheat and the chromosome Unkown (chrUN) (509) (Figure 2A). The number of SNPs from chromosome 1A to 7A was 607, 906, 594, 766, 690, 463, and 735 (Figure 2A), respectively. In the B and D sub-genomes, we found 670, 735, 997, 589, 763, 768, and 640 SNPs from 1B to 7B, and 337, 596, 505, 248, 518, 404, and 665 SNPs from 1D to 7D, respectively (Figure 2A). The highest proportion (39.12%) of SNPs was mapped in the B sub-genome, followed by the A sub-genome (36.08%) and the D sub-genome (24.80%) (Figure 2A). Although SNPs were unevenly distributed among sub-genomes, they were evenly distributed on each chromosome (Figure 2B).

3.2. Phylogenetic Relationship in the Bread Wheat Panel

The 13,705 loci generated by genotyping were used for population genetic analysis. A phylogenetic tree and population structure are presented in Figure 3. Three major populations (A, B, and C) could be clearly observed from the phylogenetic tree. In addition, some accessions were located in some intermediate positions among the three major groups, which were considered admixture types (Figure 3A). Population structure analysis was performed to estimate the individual ancestry and admixture proportions of each accession when assuming the existence of certain populations. By using the simulating population number (K) from 2 to 7, K = 3 was found to be the closest to the real clustering state of the accessions for the appearance of the highest delta K value (Figure 3B). Moreover, the 302 bread wheat accessions were split into three groups, which was consistent with the results of the phylogenetic tree (Figure 3C). We assigned each accession to its corresponding population (A, B, and C) when its ancestral coefficient was greater than 0.5 at K = 3. The accessions with ancestral coefficients lower than 0.5 in each group were assigned to the admixture group. Then, the geographical distribution of accessions of each of the populations A, B, and C was examined, respectively. As a result, the distribution of accessions in the three populations was roughly in conformity with the three geographical origins, including NW (A, most samples from Hebei), HH1 (B, most samples from Henan-Shaanxi), and HH2 (C, most samples from Shandong) (Table S1).
According to the first and second eigenvectors of PCA, all accessions could be divided into three groups (Figure 4A), which were consistent with populations A, B, and C in the phylogenetic tree and population structure analysis results. Hence, this result further verified the phylogenetic relationships among the accessions obtained from the phylogenetic tree and population structure analysis (Figure 3A,C). Those accessions assigned to the admixture group in the phylogenetic tree (Figure 3A) and the population structure analysis (Figure 3C) were also found to be located in intermediate positions of the three groups (Figure 4A). Based on the 99% confidence intervals of the location of accessions, we found a lower genetic diversity for the C group than for the A and B groups (Figure 4A). When coloring all accessions according to their geographic distributions, we found that the A, B, and C groups were roughly represented by most studied accessions from NW (mainly derived from Heibei), HH1 (mainly derived from Henan-Shaanxi), and HH2 (mainly derived from Shandong), respectively (Figure 4B). These results suggested that modern wheat breeding lines in northern China have obvious genetic differentiation due to environmental differences between or within agro-ecological regions.
Obvious evolutionary divergence could be observed within the studied landraces in the PCA scatter plot when these accessions were colored (Figure 4C). Three landraces (Chinese Spring, Baihulutou, and Mazhamai) were separated from the other nine accessions (Figure 4C), and they had a higher ancestral coefficient of the NW group (Figure 4D). However, most of the landraces showed a mixed state (Figure 4D), i.e., the ancestral coefficients of NW, HH1, and HH2 were lower than 0.5. These results suggested that the landraces of bread wheat might have a higher genetic heterozygosity than the bred varieties.

3.3. Genetic Diversity within the Three Geographical Groups

Since the above population structure analysis and PCA results indicated that the ancestral components of these accessions are related to their geographical distribution (Figure 3C and Figure 4B), it is necessary to separately evaluate the genetic diversity of accessions from NW, HH1, and HH2. Four indices (π, MAF, PIC, and Hexp) were used to evaluate the genetic diversity within the three groups. The results revealed that within the HH agro-ecological regions, HH1 had higher nucleotide diversity (π = 0.31167), minor allele frequency (MAF = 0.2663), polymorphism information content (PIC = 0.2668), and expected heterozygosity (Hexp = 0.3346) than HH2 (Table 1). NW had very close genetic diversity indices to HH1 with π = 0.31058, MAF = 0.2597, PIC = 0.2675 and Hexp = 0.3360.
Moreover, according to their breeding years, the 292 bread wheat cultivars were divided into three groups: before 2000, 2000–2009, and after 2009. The four genetic diversity indices (π, MAF, PIC, and Hexp) were also calculated within the three groups, respectively. Although the number of samples varied greatly among different groups, it could be clearly observed that the group with breeding years closer to the present had lower genetic diversity as indicated by the lower π and MAF values (Table 1).

3.4. Genomes of the Three Geographical Groups Showed Differently Patterned Mosaics of Different Landrace Groups

The population structure analysis results indicated that the optimal population number K was 3, and the three groups were related to three geographical origins (NW, HH1, and HH2) (Figure 3C and Figure 4B; Table S1). Furthermore, PCA could divide the landrace accessions into two groups: Landrace1 (including Mazhamai, Baihulutou, and Chinese Spring) and Landrace2 (including Laofulin 10, Shuangfengshou, Baiqimai, Songhuajiang 1, Hengdalihong, Bawangbian, Shijiazhuang 75, Hongheshang, and Xiaobaimang) (Figure 4C). Hence, it would be interesting to determine which genomic regions of the three geographical groups can be traced back to the two landrace groups. Based on the results of K = 3 in the population structure inference, we selected individuals that originated from Hebei (representing the NW group), Henan (representing the HH1 group), and Shandong (representing the HH2 group) with ≥80% corresponding ancestry coefficients to be added into the pure Hebei, Henan, and Shandong groups, respectively. In total, 55 accessions met the selection criteria (Table 2). We selected the ancestry-informative marker inference method to infer the genetic effect of landraces on cultivars by constructing gene pools for the five bread wheat groups (three geographical groups and two landrace groups) based on major allele frequency differentials in each locus (Figure 5). The following analysis would be focused on the 13,705 SNP loci. As a result, in 21 chromosomes, the majority of the loci of the Landrace1 group had higher major allele frequencies than those in the Landrace2 group, suggesting that the Landrace1 group was more homogeneous while the Landrace2 group was more heterozygous (Figure 5a–c). In 13,705 loci, we found 6057 loci showing different genotypes in two landrace groups, suggesting possibly great genetic differentiation between the two landrace populations. In addition, among these 6057 loci, 2353 loci shared the same genotypes between the Landrace1 group and the pure Hebei group, while only 1720 or 1423 loci shared the same genotype between the Landrace1 group and the pure Henan group or the pure Shandong group (Table 3). In addition, the Landrace2 group shared more loci of the same genotype with the pure Henan (4337) and Shandong (4634) groups but fewer loci with the pure Hebei group (3704) (Table 3). These results indicated that the Landrace1 group might have a higher genetic connection to Hebei bread wheat, while the Landrace2 group might have greater impacts on the genomic composition of bread wheat from Henan and Shandong. Furthermore, in these loci shared between the landrace and cultivar groups, 48, 63, 36, 75, 37, and 74 loci were annotated as missense variants in Landrace1-Hebei, Landrace2-Hebei, Landrace1-Henan, Landrace2-Henan, Landrace1-Shandong, and Landrace2-Shandong, respectively.
Out of 6057 loci, 5844 loci on 21 chromosomes were used to detect which genomic region of the three geographical groups can be traced back to the two landrace groups. As a result, genomes of the three geographical groups showed differently patterned mosaics coming from the two landrace groups (Figure 5e–i). For example, the Landrace1 group contributed more loci with the same genotype to the pure Hebei group in chromosomes 1B, 3B, 5B, 5D, and 7A (Figure 5h), while Landrace2 group contributed more loci with the same genotype to the pure Henan group in chromosomes 1B, 2D, 4D, and 5D (Figure 5h) and the pure Shandong group in chromosomes 1A and 3A (Figure 5i). These results also indicated that the two landrace groups had inconsistent contribution to the distribution of genome on 21 chromosomes in the three geographical populations.

4. Discussion

4.1. Genetically Related Chinese Modern Wheat Breeding Lines Have Close Geographical Origins

Domesticated crops will be confronted with acute adaptive challenges when transferred from their domestication centers to new latitudes [45]. Wheat has spread to a wide range of climates, where the environment can affect genetic selection to result in genetic variations [18]. Based on both population structure analysis and PCA, our population genetics analysis results demonstrated that most genetically related Chinese wheat modern breeding lines tend to have close geographical origins (Figure 3C and Figure 4B), which is similar to the findings in a previous study of bread wheat landraces [8,10]. This phenomenon may be a result of the difference in the adaptability of bread wheat to different latitudes, temperature, and precipitation. However, the PCA scatter plot (Figure 4B) shows that some wheat accessions from the three populations (NW, HH1, and HH2) did not show great genetic distance from each other and even exhibited highly similar genetic components. One possible explanation is that these three regions are geographically close with a narrow span of latitude, and it is relatively easy to improve the yield performance through the introduction and hybridization of cross-regional wheat varieties. This will lead to similar genetic components in some accessions from different regions. Actually, a previous study has indicated that the genetic distance of wheat landraces between the Tibetan plateau and northwestern China is not very great, possibly because the two zones are geographically close [10].
Environmental factors, including altitude, frost-free days, annual sunlight, temperature, precipitation, and soil type, may have driven the regional/local genetic divergence and altered the genetic diversity of landrace wheat, wild emmer wheat, and wild barley [10,39,46,47]. Artificial selection has been confirmed to be a major force shaping the population structure of wheat germplasm [3,14,48,49]. In modern wheat breeding lines, the genetic architecture of agronomic traits is shaped by the interaction between long-term artificial directional selection and early population genetic structure [50,51]. Studying the population structure and genetic diversity is critical for the genetic mapping of traits such as grain yield, quality, and micronutrient content [30]. A hypothesis was proposed that compared with the A and B sub-genomes, only a small Aegilops tauschii population genetically contributed the D sub-genome of hexaploid wheat, thereby causing lower nucleotide diversity and divergence frequency in the D sub-genome [1,6,10,27]. Besides the initial population size, artificial selection can also greatly shape the genetic diversity of the population. Our results clearly demonstrated that the genetic diversity (π and MAF) decreases with the advancement of wheat breeding (Table 1), possibly because during the breeding process, the effective population size of the parents is declining, and breeders only focus on a few traits for directed selection. However, the very small intervals in the groups ‘2000–2009′ and ‘after 2009′ might cause a bias in the genetic diversity assessment of the two groups because many parents of varieties within the groups might be closely related to each other. The decline of genetic diversity often results in the deletion of genes for some key traits in modern breeding lines. Therefore, it is necessary to integrate some landraces and early breeding lines carrying excellent traits for the breeding of modern wheat lines in the future [52].

4.2. Modern Wheat Breeding Lines Have Mosaic Genomic Regions Derived from Different Landrace Groups

Although very few landraces are used as parents for hybridization in modern wheat breeding programs due to their undesirable plant height and yield, it does not mean that landraces have no genetic contribution to modern breeding lines. At present, research on the population genomics of wheat landraces has been mainly focused on genetic differentiation with modern breeding lines and searching for artificially selected genomic regions [2,10,14,53,54]. For instance, a study has indicated that about 6.7% of the wheat genome falls within the selective sweeps between landraces and cultivars [55]. The allelic erosion and a diversity bottleneck can be inferred through comparative genomics between landraces and modern breeding lines [2]. Just like the findings in maize and soybean [56,57], some artificially selected genomic regions contain the functional genes or loci that regulate known phenotypes for disease resistance, vernalization, quality, adaptability, and yield-related traits [2,55,58]. However, there have been few reports about the genomic relation between landraces and modern breeding lines.
According to the inference of ancestry informative markers, 2353 out of the 6057 loci shared the same genotypes in the Landrace1 group and the pure Hebei group, while only 1720 and 1423 loci shared the same genotype between the Landrace1 group and the pure Henan group and between the Landrace1 group and the pure Shandong group, respectively (Table 3). These results suggest that the Landrace1 group contributes more to the genetic composition of modern breeding lines from Hebei province. The Landrace2 group shared more loci with the same genotype with the pure Henan (4337) and Shandong (4634) groups but fewer loci with the pure Hebei group (3704) (Table 3), indicating that the Landrace2 group contributes more to the genetic composition of modern breeding lines from Henan and Shandong. Those missenses which were stop gained and stop retained variants shared by landrace-cultivar groups might show evolutionary benefits in the process of wheat domestication, while other variants such as intergenic region SNPs might result from the rearrangement of repetitive elements (Table S2). Furthermore, genomes of the three geographical groups showed differently patterned mosaics of the genomes derived from the two landrace groups (Figure 5e–i). In barley, Dai et al. [42] indicated that wild barley of the Fertile Crescent contributes more genomic regions to the genome of cultivated barley in chromosomes 1H, 2H, and 3H, while wild barley of Tibet contributes more genomic regions in chromosomes 4H, 5H, 6H, and 7H. In addition, Zhou et al. [39] reported that wild barley of Tibet genetically contributes more to cultivated barley of China than wild barley from the Fertile Crescent. Wheat landraces are highly valuable genetic and breeding resources. A better understanding of their relationship with modern breeding lines can help develop strategies to target novel elite alleles for future wheat breeding [2].

4.3. Conclusions and Prospects

Northern winter wheat region (NW) and the Huang-Huai River Valley’s facultative wheat region (HH) are the most important wheat producing regions in China. Here, population genetic analysis of 302 bread wheat accessions revealed three main groups that are largely associated with geographical regions, including NW (mainly Hebei), HH1 (Henan-Shaanxi), and HH2 (Shandong). Genetic diversity decreases with the advancement of wheat breeding. Inference of ancestry informative markers indicated that modern wheat breeding lines exhibit mosaic genomic regions derived from different landrace groups. These findings deepen our understanding of the genomic changes driven by both human and ecological factors during domestication and may facilitate the development of breeding strategies to target novel elite alleles in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13092408/s1, Figure S1: A workflow for filtering raw data of the 15K SNP array; Table S1: List of 302 wheat accessions of the panel; Table S2: Summary of the annotated SNPs of the landrace-cultivar groups that share common genotypes.

Author Contributions

Y.Z. (Yingjun Zhang), X.R. and Y.P. designed the experiment. X.J., H.G., Q.L., G.W., Z.S., Y.Z. (Yun Zhao) and X.Z. did most of the experiments. X.J., H.G., Y.Z. (Yu Zhou), and W.H. conducted the data analysis. X.J. and H.G. drafted the manuscript. Y.Z. (Yingjun Zhang), X.R. and Y.P. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (U22A6009), the Hebei Modern Agricultural Industrial Technology System (HBCT2023010201), the HAAFS Science and Technology Innovation Special Project (2022KJCXZX-LYS-1, 2022KJCXZX-LYS-20), the Project for Hebei Scientific and Technological Innovation Team of Modern Wheat Seed Industry (21326318D), the Science and Technology Program of Hebei (C2022301004), the earmarked fund for China Agriculture Research System (CARS-5).

Data Availability Statement

The names of the accession number(s) can be found in the article/Supplementary Material.

Acknowledgments

We thank Zuoxiong Liu from the Foreign Language School of Huazhong Agricultural University for polishing and editing of the English language of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cheng, H.; Liu, J.; Wen, J.; Nie, X.; Xu, L.; Chen, N.; Li, Z.; Wang, Q.; Zheng, Z.; Li, M.; et al. Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol. 2019, 20, 136. [Google Scholar] [CrossRef] [PubMed]
  2. Liu, J.; Rasheed, A.; He, Z.; Imtiaz, M.; Arif, A.; Mahmood, T.; Ghafoor, A.; Siddiqui, S.U.; Ilyas, M.K.; Wen, W.; et al. Genome-wide variation patterns between landraces and cultivars uncover divergent selection during modern wheat breeding. Theor. Appl. Genet. 2019, 132, 2509–2523. [Google Scholar] [CrossRef] [PubMed]
  3. Hao, C.; Jiao, C.; Hou, J.; Li, T.; Liu, H.; Wang, Y.; Zheng, J.; Liu, H.; Bi, Z.; Xu, F.; et al. Resequencing of 145 cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol. Plant 2020, 13, 1733–1751. [Google Scholar] [CrossRef]
  4. Zhang, Y.Z. The ancient crops in Xinjiang. Agric. Archaeol. 1983, 3, 122–126. [Google Scholar]
  5. Li, S.C.; Wang, H. Reconsideration of carbonization wheat found in Donghuishan Site. Collect. Stud. Archaeol. 2013, 10, 399–405. [Google Scholar]
  6. Wang, J.R.; Luo, M.C.; Chen, Z.X.; You, F.M.; Wei, Y.M.; Zheng, Y.L.; Dvorak, J. Aegilops tauschii single nucleotide polymorphisms shed light on the origins of wheat D-genome genetic diversity and pinpoint the geographic origin of hexaploid wheat. New Phytol. 2013, 198, 925–937. [Google Scholar] [CrossRef] [PubMed]
  7. Long, T.W.; Leipe, C.; Jin, G.; Wagner, M.; Guo, R.; Schroder, O.; Tarasov, P.E. The early history of wheat in China from 14C dating and Bayesian chronological modelling. Nat. Plants 2018, 4, 272–279. [Google Scholar] [CrossRef] [PubMed]
  8. Wang, Z.; Hao, C.; Zha, J.; Li, C.; Jiao, C.; Xi, W.; Hou, J.; Li, T.; Liu, H.; Zhang, X. Genomic footprints of wheat evolution in China reflected by a Wheat660K SNP array. Crop J. 2020, 9, 29–41. [Google Scholar] [CrossRef]
  9. Shewry, P.R.; Hey, S.J. The contribution of wheat to human diet and health. Food Energy Secur. 2015, 4, 178–202. [Google Scholar] [CrossRef] [PubMed]
  10. Zhou, Y.; Cheng, Z.; Chen, M.; Chen, J.; Zhu, T.; Wang, R.; Liu, Y.; Qi, P.; Chen, G.; Jiang, Q.; et al. Uncovering the dispersion history, adaptive evolution and selection of wheat in China. Plant Biotechnol. J. 2018, 16, 280–291. [Google Scholar] [CrossRef]
  11. Guo, W.; Xin, M.; Wang, Z.; Yao, Y.; Hu, Z.; Song, W.; Yu, K.; Chen, Y.; Wang, X.; Guan, P.; et al. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat. Commun. 2020, 11, 1–12. [Google Scholar] [CrossRef]
  12. Zhuang, Q.S. Chinese Wheat Improvement and Pedigree Analysis; Agricultural Publisher of China: Beijing, China, 2002; ISBN 7-109-07944-9. [Google Scholar]
  13. Zeng, X.S. On the expansion of wheat in ancient China. J. Chin. Dietary Cul. 2005, 1, 99–133. [Google Scholar]
  14. Cavanagh, C.R.; Chao, S.; Wang, S.; Huang, B.E.; Stephen, S.; Kiani, S.; Forrest, K.; Saintenac, C.; Brown-Guedira, G.L.; Akhunova, A.; et al. Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc. Natl. Acad. Sci. USA 2013, 110, 8057–8062. [Google Scholar] [CrossRef] [PubMed]
  15. Lopes, M.S.; El-Basyoni, I.; Baenziger, P.S.; Singh, S.; Royo, C.; Ozbek, K.; Aktas, H.; Ozer, E.; Ozdemir, F.; Manickavelu, A.; et al. Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change. J. Exp. Bot. 2015, 66, 3477–3486. [Google Scholar] [CrossRef] [PubMed]
  16. He, F.; Pasam, R.; Shi, F.; Kant, S.; Keeble-Gagnere, G.; Kay, P.; Forrest, K.; Fritz, A.; Hucl, P.; Wiebe, K.; et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. Nat. Genet. 2019, 51, 896–904. [Google Scholar] [CrossRef]
  17. Pont, C.; Leroy, T.; Seidel, M.; Tondelli, A.; Duchemin, W.; Armisen, D.; Lang, D.; Bustos-Korts, D.; Goue, N.; Balfourier, F.; et al. Tracing the ancestry of modern bread wheats. Nat. Genet. 2019, 51, 905–911. [Google Scholar] [CrossRef] [PubMed]
  18. Walkowiak, S.; Gao, L.; Monat, C.; Haberer, G.; Kassa, M.T.; Brinton, J.; Ramirez-Gonzalez, R.H.; Kolodziej, M.C.; Delorean, E.; Thambugala, D.; et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 2020, 588, 277–283. [Google Scholar] [CrossRef] [PubMed]
  19. Akhunov, E.D.; Akhunova, A.R.; Anderson, O.D.; Anderson, J.A.; Blake, N.; Clegg, M.T.; Coleman-Derr, D.; Conley, E.J.; Crossman, C.C.; Deal, K.R.; et al. Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes. BMC Genom. 2010, 11, 1–22. [Google Scholar] [CrossRef]
  20. Wang, S.; Wong, D.; Forrest, K.; Allen, A.; Chao, S.; Huang, B.E.; Maccaferri, M.; Salvi, S.; Milner, S.G.; Cattivelli, L.; et al. Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J. 2014, 12, 787–796. [Google Scholar] [CrossRef] [PubMed]
  21. Jordan, K.W.; Wang, S.; Lun, Y.; Gardiner, L.J.; MacLachlan, R.; Hucl, P.; Wiebe, K.; Wong, D.; Forrest, K.; IWGSC; et al. A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes. Genome Bio. 2015, 16, 1–18. [Google Scholar] [CrossRef]
  22. Kihara, H. Discovery of the DD-analyser, one of the ancestors of Triticum vulgare. Agric. Hortic. 1944, 19, 889–890. [Google Scholar]
  23. Dvorak, J.; Luo, M.C.; Yang, Z.L.; Zhang, H.B. The structure of the Aegilops tauschii genepool and the evolution of hexaploid wheat. Theor. Appl. Genet. 1998, 97, 657–670. [Google Scholar] [CrossRef]
  24. Kilian, B.; Ozkan, H.; Deusch, O.; Effgen, S.; Brandolini, A.; Kohl, J.; Martin, W.; Salamini, F. Independent wheat B and G genome origins in outcrossing Aegilops progenitor haplotypes. Mol. Biol. Evol. 2006, 24, 217–227. [Google Scholar] [CrossRef] [PubMed]
  25. Tanno, K.I.; Willcox, G. How fast was wild wheat domesticated? Science 2006, 311, 1886. [Google Scholar] [CrossRef] [PubMed]
  26. Marcussen, T.; Sandve, S.; Heier, L.; Spannagl, M.; Peeifer, M.; IWGSC; Jakobsen, K.S.; Wulff, B.B.H.; Steuernagel, B.; Mayer, K.X.; et al. Ancient hybridizations among the ancestral genomes of bread wheat. Science 2014, 345, 1250092. [Google Scholar] [CrossRef] [PubMed]
  27. International Wheat Genome Sequencing Consortium (IWGSC); Mayer, K.F.X.; Rogers, J.; Dolezel, J.; Pozniak, C.; Eversole, K.; Feuillet, C.; Gill, B.; Friebe, B.; Lukaszewski, A.J.; et al. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 2014, 345, 1251788. [Google Scholar] [CrossRef] [PubMed]
  28. Nevo, E.; Noy-Meir, I.; Beiles, A.; Krugman, T.; Agami, M. Natural selection of allozyme polymorphisms: Micro-geographical spatial and temporal ecological differentiations in wild emmer wheat. Isr. J. Bot. 1991, 40, 419–449. [Google Scholar]
  29. Salarpour, M.; Abdolshahi, R.; Pakniyat, H.; Heidari, B.; Aminizadeh, S. Mapping quantitative trait loci for drought tolerance/susceptibility indices and estimation of breeding values of doubled haploid lines in wheat (Triticum aestivum). Crop Pasture Sci. 2021, 72, 500–513. [Google Scholar] [CrossRef]
  30. Shariatipour, N.; Heidari, B.; Tahmasebi, A.; Richards, C. Comparative genomic analysis of quantitative trait loci associated with micronutrient contents, grain quality, and agronomic traits in Wheat (Triticum aestivum L.). Front. Plant Sci. 2021, 12, 709817. [Google Scholar] [CrossRef] [PubMed]
  31. He, Z.; Rajaram, S.; Xin, Z. A History of Wheat Breeding in China; CIMMYT: Texcoco, Mexico, 2001; ISBN 970-648-079-X. [Google Scholar]
  32. International Wheat Genome Sequencing Consortium (IWGSC); Appels, R.; Eversole, K.; Stein, N.; Feuillet, C.; Keller, B.; Rogers, J.; Pozniak, C.J.; Choulet, F.; Distelfeld, A.; et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 2018, 361, eaar7191. [Google Scholar] [CrossRef] [PubMed]
  33. Nguyen, L.T.; Schmidt, H.A.; von Haeseler, A.; Minh, B.Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef]
  34. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.W.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed]
  35. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef] [PubMed]
  36. Earl, D.A.; vonHoldt, B.M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  37. Jakobsson, M.; Rosenberg, N.J. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 2007, 23, 1801–1806. [Google Scholar] [CrossRef] [PubMed]
  38. Librado, P.; Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009, 25, 1451–1452. [Google Scholar] [CrossRef] [PubMed]
  39. Zhou, Y.; Lu, G.; Sun, G.L.; Sun, D.K.; Ren, X.F. Transcriptome and metabolite insights into domestication process of cultivated barley in China. Plants 2022, 11, 209. [Google Scholar] [CrossRef] [PubMed]
  40. Guo, X.; Elston, R. Linkage information content of polymorphic genetic markers. Human Hered. 1999, 49, 112–118. [Google Scholar] [CrossRef] [PubMed]
  41. Rubin, C.J.; Zody, M.C.; Eriksson, J.; Meadows, J.R.S.; Sherwood, E.; Webster, M.T.; Jiang, L.; Ingman, M.; Sharpe, T.; Ka, S.; et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature 2010, 464, 587–591. [Google Scholar] [CrossRef]
  42. Dai, F.; Chen, Z.H.; Wang, X.; Li, Z.; Jin, G.; Wu, D.; Cai, S.; Wang, N.; Wu, F.; Nevo, E.; et al. Transcriptome profiling reveals mosaic genomic origins of modern cultivated barley. Proc. Natl. Acad. Sci. USA 2014, 111, 13403–13408. [Google Scholar] [CrossRef] [PubMed]
  43. Cingolani, P.; Platts, A.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef] [PubMed]
  44. Gu, Z.; Gu, L.; Eils, R.; Schlesner, M.; Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 2014, 30, 2811–2812. [Google Scholar] [CrossRef] [PubMed]
  45. Allaby, R.G.; Kistler, L.; Gutaker, R.M.; Ware, R.; Kitchen, J.L.; Smith, O.; Clarke, A.C. Archaeogenomic insights into the adaptation of plants to the human environment: Pushing plant-hominin co-evolution back to the Pliocene. J. Human Evol. 2015, 79, 150–157. [Google Scholar] [CrossRef] [PubMed]
  46. Ren, J.; Chen, L.; Sun, D.; You, F.M.; Wang, J.; Peng, Y.; Nevo, E.; Beiles, A.; Sun, D.; Luo, M.C.; et al. SNP revealed genetic diversity in wild emmer wheat correlates with ecological factors. BMC Evol. Boil. 2013, 13, 1–15. [Google Scholar] [CrossRef] [PubMed]
  47. Li, K.; Ren, X.F.; Song, X.; Li, X.; Zhou, Y.; Harlev, E.; Sun, D.; Nevo, E. Incipient sympatric speciation in wild barley caused by geological-edaphic divergence. Life Sci. Alliance 2020, 3, 1–12. [Google Scholar] [CrossRef] [PubMed]
  48. Bonman, J.M.; Babiker, E.M.; Cuesta-Marcos, A.; Esvelt-Klos, K.; Brown-Guedira, G.; Chao, S.; See, D.; Chen, J.; Akhunov, E.; Zhang, J.; et al. Genetic diversity among wheat accessions from the USDA National Small Grains Collection. Crop Sci. 2015, 55, 1243–1253. [Google Scholar] [CrossRef]
  49. Hao, C.; Wang, Y.; Chao, S.; Li, T.; Liu, H.; Wang, L.; Zhang, X. The iSelect 9 K SNP analysis revealed polyploidization induced revolutionary changes and intense human selection causing strong haplotype blocks in wheat. Sci. Rep. 2017, 7, 1–13. [Google Scholar] [CrossRef] [PubMed]
  50. Walsh, B. Population- and quantitative-genetic models of selection limits. In Plant Breeding Reviews; Janick, J., Ed.; Wiley: Hoboken, NJ, USA, 2003; Volume 24, pp. 177–225. ISBN 9780471353164. [Google Scholar]
  51. Hamblin, M.T.; Buckler, E.S.; Jannink, J.L. Population genetics of genomics-based crop improvement methods. Trends Genet. 2011, 27, 98–106. [Google Scholar] [CrossRef]
  52. Yu, Z.; Peng, Y.; Islam, M.; She, M.; Lu, M.; Lafiandra, D.; Roy, N.; Juhasz, A.; Yan, G.; Ma, W. Molecular characterization and phylogenetic analysis of active y-type high molecular weight glutenin subunit genes at Glu-A1 locus in wheat. J. Cereal Sci. 2019, 86, 9–14. [Google Scholar] [CrossRef]
  53. Guo, X.; Gao, A.; Liu, W.; Yang, X.; Li, X.; Li, L. Evaluation of genetic diversity, population structure, and linkage disequilibrium among elite Chinese wheat (Triticum aestivum L.) cultivars. Aust. J. Crop Sci. 2011, 5, 1167–1172. [Google Scholar] [CrossRef]
  54. Hao, C.; Wang, L.; Ge, H.; Dong, Y.; Zhang, X. Genetic diversity and linkage disequilibrium in Chinese bread wheat (Triticum aestivum L.) revealed by SSR markers. PLoS ONE 2011, 6, e17279. [Google Scholar] [CrossRef] [PubMed]
  55. Li, A.; Hao, C.; Wang, Z.; Geng, S.; Jia, M.; Wang, F.; Han, X.; Kong, X.; Yin, L.; Tao, S.; et al. Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. Mol. Plant 2022, 15, 504–519. [Google Scholar] [CrossRef] [PubMed]
  56. Van Heerwaarden, J.; Huford, M.B.; Ross-Ibarra, J. Historical genomics of North American maize. Proc. Natl. Acad. Sci. USA 2012, 109, 12420–12425. [Google Scholar] [CrossRef] [PubMed]
  57. Han, Y.; Zhao, X.; Liu, D.; Li, Y.; Lightfoot, D.A.; Yang, Z.; Zhao, L.; Zhou, G.; Wang, Z.; Huang, L.; et al. Domestication footprints anchor genomic regions of agronomic importance in soybeans. New Phytol. 2016, 209, 871–884. [Google Scholar] [CrossRef] [PubMed]
  58. Li, J.; Wan, H.S.; Yang, W.Y. Synthetic hexaploid wheat enhances variation and adaptive evolution of bread wheat in breeding processes. J. Syst. Evol. 2014, 52, 735–742. [Google Scholar] [CrossRef]
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Figure 1. Division of wheat agro-ecological regions in China. The northern winter wheat region and the Huang-Huai River Valley’s facultative wheat region are colored orange and green, respectively. The original map was drawn according to He et al. [31] using software Arcgis 10.2 and Adobe Illustrator CS6.
Figure 1. Division of wheat agro-ecological regions in China. The northern winter wheat region and the Huang-Huai River Valley’s facultative wheat region are colored orange and green, respectively. The original map was drawn according to He et al. [31] using software Arcgis 10.2 and Adobe Illustrator CS6.
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Figure 2. Distribution of 13,705 SNPs in the genome of wheat. (A) The number of SNPs for A, B, and D sub-genomes. (B) The number of SNPs within 1 Mb window size in the 21 chromosomes.
Figure 2. Distribution of 13,705 SNPs in the genome of wheat. (A) The number of SNPs for A, B, and D sub-genomes. (B) The number of SNPs within 1 Mb window size in the 21 chromosomes.
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Figure 3. Phylogenetic tree and population structure of 302 bread wheat accessions. (A) The phylogenetic tree of the 302 bread wheat accessions. (B) The delta K values when K was 2 to 7. (C) The population structure plots of the 302 bread wheat accessions when K was 2 to 4.
Figure 3. Phylogenetic tree and population structure of 302 bread wheat accessions. (A) The phylogenetic tree of the 302 bread wheat accessions. (B) The delta K values when K was 2 to 7. (C) The population structure plots of the 302 bread wheat accessions when K was 2 to 4.
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Figure 4. Principal component analysis (PCA) of 302 bread wheat accessions. The (A–C) panels show the same PCA colored according to different factors. (A) Positions of the four assigned groups (A, B, C, and Admixture) according to the results at K = 3 (Figure 3C). The small solid circles represent wheat accessions, and the large hollow circles represent the 99% confidence interval of the location of the accessions in the four groups. (B) All accessions were colored according to their geographic distributions. (C) Twelve landraces were colored in the PCA scatter plot. (D) Population structure of twelve landraces at K = 3. Three red pentagrams represent three materials that belong to the same category.
Figure 4. Principal component analysis (PCA) of 302 bread wheat accessions. The (A–C) panels show the same PCA colored according to different factors. (A) Positions of the four assigned groups (A, B, C, and Admixture) according to the results at K = 3 (Figure 3C). The small solid circles represent wheat accessions, and the large hollow circles represent the 99% confidence interval of the location of the accessions in the four groups. (B) All accessions were colored according to their geographic distributions. (C) Twelve landraces were colored in the PCA scatter plot. (D) Population structure of twelve landraces at K = 3. Three red pentagrams represent three materials that belong to the same category.
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Figure 5. Graphical genotypes of ancestry informative markers in the three geographical groups. The circos plot was composed of nine circles (a–i). (a) Genome coordinates of 21 chromosomes. (b) Major allele frequencies of the Landrace1 group (including landrace accessions Chinese Spring, Baihulutou, and Mazhamai). (c) Major allele frequencies of the Landrace2 group (including landrace accessions Laofulin 10, Shuangfengshou, Baiqimai, Songhuajiang 1, Hengdalihong, Bawangbian, Shijiazhuang 75, Hongheshang, and Xiaobaimang). (d) A total of 5844 loci with different genotypes for the two gene pools of the landrace groups. (e,f) Genotypes of the Landrace1 group (e) and Landrace2 group (f) in the 5844 loci were colored green and yellow, respectively. (g,i) In the 5844 loci, genotypes of the pure Hebei group (g), the pure Henan group (h), and the pure Shandong group (i) were colored according to the genotypes of the Landrace1 group and the Landrace2 group.
Figure 5. Graphical genotypes of ancestry informative markers in the three geographical groups. The circos plot was composed of nine circles (a–i). (a) Genome coordinates of 21 chromosomes. (b) Major allele frequencies of the Landrace1 group (including landrace accessions Chinese Spring, Baihulutou, and Mazhamai). (c) Major allele frequencies of the Landrace2 group (including landrace accessions Laofulin 10, Shuangfengshou, Baiqimai, Songhuajiang 1, Hengdalihong, Bawangbian, Shijiazhuang 75, Hongheshang, and Xiaobaimang). (d) A total of 5844 loci with different genotypes for the two gene pools of the landrace groups. (e,f) Genotypes of the Landrace1 group (e) and Landrace2 group (f) in the 5844 loci were colored green and yellow, respectively. (g,i) In the 5844 loci, genotypes of the pure Hebei group (g), the pure Henan group (h), and the pure Shandong group (i) were colored according to the genotypes of the Landrace1 group and the Landrace2 group.
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Table 1. Estimate of genetic diversity per base pair for three groups.
Table 1. Estimate of genetic diversity per base pair for three groups.
GroupsnπMAFPICHexp
Region:
HH1720.311670.26630.26680.3346
NW1200.310580.25970.26750.3360
HH2590.278350.23530.23780.2940
Years:
Before 2000390.349950.28340.27280.3435
2000–2009980.330230.26470.27470.3458
After 20091550.285100.24890.26290.3291
Note: n: number of accessions; π, nucleotide diversity; MAF, minor allele frequency; PIC, polymorphism information content; Hexp, expected heterozygosity.
Table 2. List of the 67 wheat accessions for inference of ancestry informative markers.
Table 2. List of the 67 wheat accessions for inference of ancestry informative markers.
CodeAccessions NameGroupCodeAccessions NameGroup
1Chinese SpringLandrace135Yingman 208Shandong
2BaihulutouLandrace136Shannong k32561Shandong
3MazhamaiLandrace137Shannong 27Shandong
4Laofulin 10Landrace238Taishan 5366Shandong
5ShuangfengshouLandrace239Shannong 2149Shandong
6BaiqimaiLandrace240Lumai 14Shandong
7Songhuajiang 1Landrace241Daimai 2251Shandong
8HengdalihongLandrace242Jimai 60Shandong
9BawangbianLandrace243Keyuan 026Shandong
10Shijiazhuang 75Landrace244Luyan 213Shandong
11HongheshangLandrace245Yimai 1Shandong
12XiaobaimangLandrace246Zimai 28Shandong
13Nongda 399Hebei47Yimai 2Shandong
14Jifeng 717Hebei48Yangguang 503Shandong
15Jinhe 9123Hebei49Yangguang 10Shandong
16Shimai 22Hebei50Yannong 836Shandong
17Jimai 817Hebei51Jingyang 670Shandong
18Jimai 26Hebei52Wennong 5Shandong
19Shimai 28Hebei53Aikang 58Henan
20ShiH09-7075Hebei54Zhoumai 35Henan
21Shi 4185Hebei55Zhengyumai 9989Henan
22Shimai 12Hebei56Cunmai 8Henan
23Shimai 14Hebei57Dunfeng 801Henan
24Heng 11-6021Hebei58Xinmai 28Henan
25Jimai 161Hebei59Zhengmai 9023Henan
26Kenong 1006Hebei60Yimai 6Henan
27Jimai 120Hebei61Zhoumai 16Henan
28Kenong 8162Hebei62Xun 2016Henan
29Kenong 1002Hebei63Fengyuan 2017Henan
30Qingnong 9Shandong64Xianmai 10Henan
31Qingmai 6Shandong65Cunmai 12Henan
32Liangxing 99Shandong66Fengdecunmai 10Henan
33Jimai 22Shandong67Zhoumai 18Henan
34Shannong 24Shandong
Table 3. Number of SNPs sharing the same genotype between three geographic populations and two landrace groups.
Table 3. Number of SNPs sharing the same genotype between three geographic populations and two landrace groups.
GroupsHeibeiHenanShandong
Landrace1235317201423
Landrace2370443374634
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Jin, X.; Gao, H.; Liu, Q.; Zhao, Y.; He, W.; Wang, G.; Zhou, Y.; Song, Z.; Zhao, X.; Ren, X.; et al. Population Genetic Analysis of a Bread Wheat Panel from Northern and Huang-Huai Agro-Ecological Regions in China. Agronomy 2023, 13, 2408. https://doi.org/10.3390/agronomy13092408

AMA Style

Jin X, Gao H, Liu Q, Zhao Y, He W, Wang G, Zhou Y, Song Z, Zhao X, Ren X, et al. Population Genetic Analysis of a Bread Wheat Panel from Northern and Huang-Huai Agro-Ecological Regions in China. Agronomy. 2023; 13(9):2408. https://doi.org/10.3390/agronomy13092408

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Jin, Xiaojie, Huimin Gao, Qian Liu, Yun Zhao, Wenchuang He, Guijuan Wang, Yu Zhou, Zheng Song, Xiaobin Zhao, Xifeng Ren, and et al. 2023. "Population Genetic Analysis of a Bread Wheat Panel from Northern and Huang-Huai Agro-Ecological Regions in China" Agronomy 13, no. 9: 2408. https://doi.org/10.3390/agronomy13092408

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