Understanding the magnitude of the genetic diversity, population structure, and LD that exist in the crop germplasm are necessary precondition for achieving the genomic predictions of desired traits. This study investigated the genetic diversity and population structure of 185 bread wheat genotypes as a prerequisite for GWAS and genomic selection. The genotypes were evaluated under drought-stressed and well-watered conditions. Chromosome-wise genomic SNP distribution, MAF, observed heterozygosity, and the PIC were performed using GBS-based SNP markers. The relationship matrix was determined with the UPGMA module of TASSEL 5.0 to compile dendrograms. The population structure was determined with the help of the STRUCTURE 2.3.4 program. A total of 13,230 high-quality SNPs were identified and distributed across the three wheat genomes, of which 35.7% of the markers were located on the B sub-genome, 34.3% on the sub-genome A, and 30.0% on the D sub-genome. Based on the analysis of population structure, the genotypes were divided into six clusters. The mean MAF, PIC, and genetic diversity of the population were 0.24, 0.27, and 0.34, respectively. The average LD decay for the whole genome of significant marker pairs at r2 > 0.2 was 19.5 Mb for the A-genome, 23.2 Mb for the B-genome, and 24.3 Mb for the D genome. The percent membership of the genotypes to a specific cluster showed that cluster 6 had the largest membership with 22.1% of the population, whereas cluster 3 was the lowest with 12.5% of the population. The highest degree of genetic differentiation was detected in cluster 3 (Fst = 0.64), whereas the lowest was observed in cluster 2 (Fst = 0.26). The high genetic diversity identified among the clusters can be used to develop new bread wheat cultivars with desired traits. The moderate to high divergence detected among bread wheat genotypes within clusters suggested that the genotypes could be used further for GWAS.
Research Article
Genetic Diversity and Population Structure Analyses of Some Bread Wheat (Triticum aestivum L) Genotypes
https://doi.org/10.21203/rs.3.rs-4950235/v1
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bread wheat
genetic diversity
linkage disequilibrium
polymorphic information content
population structure
SNP markers
For around 40% of the world's population, bread wheat (Triticum aestivum L.), one of the most significant staple cereal food crops, provides dietary nutrients [1]. By 2021/2022, the predicted global wheat production in million metric tonnes (MMTs) was 778.6 from 222.12 million hectares [2], [3]. The world's population is expected to reach 9.7 billion people by 2050, with annual population growth expected to be 1.5% [4]. But, the average bread wheat yield growth rate of 0.6% per year is insufficient to guarantee worldwide food security [2], [5]. The wheat production and productivity program demands a 2.4% rise in yield per year to reach a goal of 60% by 2050 of the existing wheat production [6]. As the population continues to rise, the need for bread wheat is rising. Its genetic improvement is therefore urgently required to achieve maximum production, adaptability, and tolerance to drought stress and to maintain sustainable food security [7].
Wheat productivity and stability under drought stress may be enhanced by the production of drought-tolerant and high-yielding cultivars [8], [9]. However, the irregularity of drought conditions, coupled with the genetic complexity of drought tolerance and related response mechanisms, hinders wheat breeding for drought tolerance [9]. Bread wheat genotype selection strategies based on conventional breeding can be hastened through genomic approaches using genetic markers. Genetic markers are used as a direct measure of genetic diversity and are more reliable than indirect measures to increase the success of screening genotypes for drought tolerance [10]. To screen drought-tolerant genotypes, yield, and yield-related traits have been considered important traits, as these traits have a substantial relationship with grain yield (GY). Number of kernels spike− 1, thousand kernels weight, plant height, days to heading, and number of days to maturity were reported to have high significant associations with GY [11], [12].
Conventional breeding has played a vital role in the development of drought-tolerant and high-yielding cultivars for millennia; however, big data-linked software and advanced molecular marker technologies have been developed to help breeding programmes to investigate and isolate the confounding environment [13] effects for the period of selection [12]. The genetic gains of bread wheat might be enhanced by improving crop farming [14] and utilizing the genetic diversity of local germplasm sources [15], [16]. Genetic diversity dissection of drought tolerance of elite bread wheat genotypes is a prerequisite for plant breeding strategies, like domestication, inheritance, conservation, and evaluation of wheat germplasm [17], [18]. Limited germplasm genetic diversity, narrow genetic bases, reduction in farmland, and associated climate defects in the form of drought stresses, cause a continuous threat to world food security in developing countries [19], [20]. Many molecular markers linked with drought-tolerant genes were discovered throughout the bread wheat genome. The population structure and analysis of genetic diversity in bread wheat genotypes have been evaluated using molecular markers such as SNPs and DArTseq. There has been an increase in the use of single-nucleotide polymorphism (SNP) markers with a genome-wide distribution to find markers in linkage disequilibrium. Microarray-based DArTseq-derived SNPs are important in genome-wide association studies [21]. The prior studies had reported that the DArT and SNP markers have been used extensively in genetic studies of wheat to explain the genetic attributes of complex traits in bread wheat [21]–[23], and identify quantitative trait loci responsible for drought tolerance [24]. There are several SNP genotyping methods currently available, including diversity arrays technology (DArTseq), genotyping-by-sequencing (GBS), and restriction site-associated DNA sequencing [25], [26]. The SNP markers are well suited for genome-wide association studies to explore how genetic polymorphisms and population structure analyses relate to phenotypic variance [27], [28].
Understanding the magnitude of the genetic diversity, population structure, and linkage disequilibrium that exist in the crop germplasm are necessary preconditions for achieving the genetic make-up, composition, and genomic predictions of desired traits during selection. Genetic diversity analysis using the information on phenotypic traits is expensive, time-consuming, labour-intensive, and influenced by genotype and environment (G x E) interactions [29], [30]. The study of LD can be explained as the non-random relationship of alleles at different loci due to the genetic linkage [31]. The genetic diversity analysis in improved cultivars decreased due to domestication and sustainable selection for improvement [32]. However, Roncallo et al. [33] have found that a low LD decay in diversity from landraces to improved cultivars, although they observed an effect of breeding on the LD patterns and the allele’s frequency.
The investigation of population structure, linkage disequilibrium, and analysis of genetic diversity in the existing Ethiopian bread wheat germplasm is of great importance for supporting breeding efforts, genetic studies of important agronomic traits, and preservation of plant genetic resources. Most of the contemporary cultivars of Ethiopian bread wheat germplasm genetic diversity have not been characterized for drought tolerance using SNP markers.
Therefore, the study's main objective was to estimate the genetic diversity, degree of relatedness, and population structure of bread wheat genotypes using a 35K single nucleotide polymorphism genotyping array.
Genetic materials and phenotyping
A panel of 188 Ethiopian bread wheat genotypes was investigated for molecular genetic diversity, population structure, and analyses of linkage disequilibrium. The lists of genotypes are presented in Supplementary Table S1. The genotypes were phenotyped for eight key agronomic traits under two water regimes at field and greenhouse conditions. The phenotyped key agronomic traits considered for this research were: the number of days to heading (DH), number of days to maturity (DM), plant height (PH), spike length (SL), number of spikelets spike− 1 (SPS), numbers of kennels spike− 1 (NKPS), thousand kernel weight (TKW) and grain yield per ha− 1 (GY), as these traits are more considered for drought tolerance and have shown highly significant correlation with grain yield. The 188 genotypes of bread wheat were planted in a pot at a greenhouse to obtain good seeds. The seed samples were packaged and shipped to Diversity Array Technology (DArT) Pty Ltd, Canberra, Australia in a 96-well microtiter plate for whole genome sequencing on the DArTseq platform developed by [26]. The samples of 185 bread wheat were genotyped using 32,206 SilicoDArT markers assigned to 21 chromosomes. The sequenced data report for analysis was not received for genotypes Alidoro, Wane, and ETBW9378. After data imputation, 13,230 informative DArTseq-derived SNP markers and 185 genotypes were utilized. The selected SNP markers were filtered for minor allele frequency (MAF > 0.05) and maximum missing data per SNP < 30% before analyses and selected on 21 chromosomes of bread wheat and used to determine the genetic diversity, population structure, LD and MTAs for eight agronomic traits in the study.
Analyses of linkage disequilibrium and population structure
The population structure was studied using STRUCTURE v 2.3.4 software for the assessment of sub-populations and genetic relationships among the 185 genotypes [34]. The different genetic diversity parameters such as alleles per locus, polymorphic information content (PIC), marker index (MI), diversity index (DI), and heterozygosity were calculated using PowerMarkerV.3.25 [35]. The admixture and shared allele frequencies model was used to determine the number of clusters (K) in the range of 1 to 10. With no prior information on the origin of individual populations, the initial burn-in period for each run was set to 10,000 cycles, followed by 10,000 MCMC (Markov chain Monte Carlo) iterations. An online tool Structure Harvester version 0.6.93 was practiced to attain the peak of (K) for authentication of the STRUCTURE results [36] to identify a distinct peak of the curve in the change of likelihood (ΔK) at the true value of K. The log probability of the data [Ln (Pr Data)] was utilized to define the peak K value in STRUCTURE HARVESTER v.6.92 (34). The GAPIT program in R software was used to conduct linkage disequilibrium analysis following [37]. Out of the 13,230 polymorphic markers, trait-specific genome-wide markers with specific positions were used to determine linkage disequilibrium (LD). For each pair of loci, the squared allele frequency correlations (r2) at p-values < 0.001 were used to evaluate significant (LD). A heat map constructed using the LD Heatmap package served as a visual representation of the linkage disequilibrium [38] in R (R Core Team, 2022) based on pairwise r2 of the markers that were significantly associated with each of the traits by plotting the r2 values against the genetic distance in Mega base pairs (Mb).
SNP markers distribution among the three bread wheat genomes
A total of 13,230 significant SNP markers were identified in the A, B, and D genomes of the studied bread wheat population (Fig. 1A). The number of SNP markers identified was 4,718 for B-genome, 4, 538 for A-genome, and 3,974 for D-genome (Table 1 and Fig. 1A). The ratio of SNPs in the B to A genomes was 1.04, but it was 1.19 and 1.14 in the B to D and A to D genomes, respectively. The B genome has the highest amount of SNPs, followed by the A genome and the D genome. Among the chromosomes in the A genome, chromosome 3A had the highest number of SNPs (944), whereas chromosome 7A had the lowest (323) as indicated in Table 1 and Fig. 1B). In the B genome, the maximum (1,136) and the lowest (307) number of SNP markers were identified on chromosomes 3B and 4B, respectively. In the D genome, chromosome 3D had the highest number of SNPs (891) and chromosome 6D detected the lowest number of SNP markers (166), and this was also the lowest number of SNPs identified across the whole chromosomes (Table 1 and Fig. 1B). The number of SNP markers within 1 Mb window size and their distribution across 21 chromosomes of bread wheat sub-genomes A, B, and D, shows a higher density of SNPs on chromosomal arms than its centromere region (Fig. 1C).
| Chromosome Number | Genome | Total | ||
|---|---|---|---|---|
| A | B | D | ||
| 1 | 485 | 775 | 552 | 1,812 |
| 2 | 659 | 540 | 629 | 1,828 |
| 3 | 944 | 1,136 | 891 | 2,971 |
| 4 | 814 | 307 | 817 | 1,938 |
| 5 | 816 | 1,010 | 374 | 2,200 |
| 6 | 677 | 427 | 166 | 1,270 |
| 7 | 323 | 343 | 545 | 1,207 |
| Total | 4,718 | 4538 | 3974 | 13,230 |
Population genetic diversity
The genetic diversity of 185 bread wheat genotypes using 13,230 SNP markers showed that the minor allele frequency ranged from 0.0-0.10 (2126 SNPs) to 0.40–0.50 (2077 SNPs), with an average of 0.24. The gene diversity varied from 0.0-0.10 (73 SNPs) to 0.40–0.50 (5091 SNPs), with a mean of 0.34, while the PIC ranged from 0.0-0.10 (269 SNPs) to 0.30–0.40 (6164 SNPs), with mean of 0.27 (Fig. 2B and Table 2).
| SNP markers in the range | |||||
|---|---|---|---|---|---|
| Range | 0.00–0.10 | 0.10–0.20 | 0.20–0.30 | 0.30–0.40 | 0.40–0.50 |
| Gene diversity (He) | 73 | 2479 | 2622 | 2965 | 5091 |
| PIC | 269 | 2912 | 3885 | 6164 | |
| Minor allele frequency | 2126 | 3637 | 3041 | 2349 | 2077 |
| He = Expected heterozygosity | |||||
| PIC = Polymorphic information content | |||||
Analysis of population structure
A sharp top of Δk at k = 6 from k-value 1–10, resulting from the STRUCTURE HARVESTER, showed that the population evaluated was grouped into six genetic structures (Figs. 4 and 5). The Delta K displays the peak number of clustering in the main populations. The findings of STRUCTURE analysis in Table 3 and Fig. 5 showed that Cluster 1 contained twenty-three, Cluster 2 comprised forty-eight, Cluster 3 contained fifty-five, Cluster 4 only eight, Cluster 5 twenty-four, and Cluster 6 twenty-seven genotypes. Cluster 3 comprised of 55 genotypes and was the largest group followed by Cluster 2 which contained 48 genotypes. Understanding the relationship among different genotypes provides the basis for association mapping studies increased by the analysis of population structure [40]. The results of the structure harvester showed a sharp peak of delta K at K = 6 (Fig. 3) in the current study, based on markers with MAF > 0.05, indicating the presence of six clusters. All subsequent analyses and interpretations were done using this K value (K = 6) to show the genetic relationships and genotype selection. The percent membership expected heterozygosity (He) and fixation index (Fst) of all the 185 genotypes to a specific group was indicated in Table 3. Cluster 6 had the maximum membership with 22.1% of the population, whereas Cluster 3 had the minimum (12.5%). The degree of genetic diversity and average distance in each cluster suggested that the highest degree of genetic differentiation was detected in Cluster 3 (Fst = 0.6404), whereas the lowest value was observed in Cluster 2 (0.2642). The fixation index (Fst) value of 0.5223 was obtained for Cluster 1, 0.2642 for Cluster 2, 0.6404 for Cluster 3, 0.59 for Cluster 4, 0.4782 for Cluster 5 and 0.629 for Cluster 6, respectively. Similarly, the gene diversity expressed as expected heterozygosity (He) of genes among clusters varied from 0.1451 to 0.3059 with the mean fixation index (Fst) varying from 0.2642 to 0.6290 among clusters.
Table 3. Six genetic clusters, each with a list of bread wheat genotypes, membership percentage, predicted heterozygosity (He), and mean fixation index values (Fst).
The linkage disequilibrium (LD) patterns among the markers
The genome-wide LD analysis among 660,226 markers showed that 32.57% (215,053) of marker pairs had a highly significant (p < 0.001) LD (r2) with a mean r2 value of 0.34. The linkage disequilibrium (LD) across the bread wheat genome is presented in Table 4 and Table 5. The LD decay in r2 based on 13,230 SNP markers in three genomes of bread wheat genotypes was estimated, and the overall LD decay was relatively low to medium (r2 > 0.1) at a physical distance of 18.35 Mb (Fig. 6). For each chromosome, the average number of marker pairs in LD (r2 > 0.2) was estimated, and its distribution was assessed by taking the inter-marker distance into account. Consequently, r2 values over 0.2 were present in 18.3% of the significant marker pairings (Table 4). In the whole genome, only 0.25% of marker pairs were in complete intra-chromosomal linkage disequilibrium (LD) (r2 = 1). The 6A chromosome had the greatest marker pairs in complete linkage disequilibrium (LD) (r2 = 1), with 354, followed by the 3B (221) and 3D (126) chromosomes as presented in Tables 4 and 5. The 3D chromosome exhibited the maximum number of physically linked marker pairs (p < 0.001) (12,489), followed by 1B (12,268), 5B (11,295), and 6A (9,696) in LD (r2 > 0.2) (Table 4 and Table 5). The 6D chromosome showed extended high LD decay (71.38 Mb), whereas chromosome 6A showed the lowest (5.42 Mb) LD decay (Tables 5 and 6). The linkage disequilibrium (LD) by distance (r2 > 0.2) for physically linked marker pairs (p < 0.001) within chromosomes showed highest LD distance on chromosomes 6D (71.38 Mb), followed by 4B (56.75 Mb), 2A (28.62 Mb), 1A (25.56 Mb), 1B (25.43 Mb), 2B (24.79 Mb) and 4A (23.46 Mb) (Tables 4 and 5). The intra-chromosomal marker pairs in LD (r2) showed the highest in chromosome 6A (0.54), followed by 5B (0.54) and 1B (0.43), 6B (r2 = 0.39) and 3B (r2 = 0.38), considering the SNP pairs in LD (r2 > 0.2) as indicated in Tables 4 and 5. Moreover, the 6D had the lowest distribution of significant (p < 0.001) marker pairs (947) in LD (r2 > 0.2), while the maximum value was detected in the 3D chromosome (22,841) (Table 5).
The average r2 value at the bread wheat genome level was 0.11 for a total number of marker pairs. The linkage disequilibrium decay ranged from 18.25Mb (3D) to 83.37 Mb (6D) for the total number of marker-trait associations at the chromosome level of the bread wheat genome at r2 values 0.135 and 0.035, respectively as presented in Table 5. The results of this study showed that pairs having LD characteristics like a square of marker pair correlations r2, the number of significant marker pairs having LD at p < 0.001, the number of physically linked marker pairs (p < 0.001, r2 > 0.2) and LD decay were estimated for individual chromosomes and genomes indicated in Table 4. The linkage disequilibrium (r2) for the significant marker pairs ranged from 0.21 (6D) to 0.54 (5D, 6A) with an average of 0.33, while for physically linked marker pairs (p < 0.001, r2 > 0.2) the LD in r2 ranged from 0.43 for chromosome 4B to 0.71 for chromosome 5D with an average of 0.50. The physically linked marker pairs having LD at p < 0.001 and r2 > 0.2 had the lowest LD (r2 = 0.32) in the D genome (Table 5), while the highest value of LD (r2 = 0.35) in the B genome (Tables 5 and 6). Some marker pairs in linkage disequilibrium (LD) detected extended distances (Mb), but a noticeable and fast decline in LD was detected shorter distances (Mb). The average linkage disequilibrium (LD) decay for the whole genome of significant marker pairs at r2 > 0.2 was 19.49 Mb for the A genome, 23.21 for the B genome, and 24.25 Mb for the D genome, and at r2 = 1 was 16.13 for the A genome, 17.66 for B genome and 18.37 Mb for D genome. Moreover, the average LD decay for the whole bread wheat genome of total marker pairs was 32.90 Mb. It was observed that the LD decay was rapid in the A genome, followed by the B genome and D genome for significant marker pairs at r2 > 0.2 and complete LD (r2 = 1) as presented in Tables 4 and 5. The findings of the current investigation, the percentage of physically linked marker pairs having significant LD at p < 0.001 and r2 > 0.2 at A-genome ranged from 3.97% in chromosome 6A to 20.95% in chromosome 2A with an average value of 19.49%, at B-genome it ranged from 4.33% in chromosome 6B to 34.93% in chromosome 4B with an average value of 14.28% and at D-genome ranged from 5.50% (chromosome 5D) to 42.40% (chromosome 6D) with an average value of 14.28%.
Table 4. The total number of marker pairs, number of significant and physically linked marker pairs with corresponding physical distance (Mb), and average marker pairs LD (r2) values for each chromosome of bread wheat genome.
|
Chromosome |
Total number of marker pairs |
Number of significant marker pairs (p<0.001) |
Number of physically linked marker pairs |
||||||||
|
Markers |
Average r2 |
Dist. (Mb) |
Markers |
Average r2 |
Dist. (Mb) |
Number pairs (r2>0.2) |
Average (r2>0.2)) |
Dist. (Mb) |
Complete LD (r2 = 1) |
||
|
Markers |
Dist. (Mb) |
||||||||||
|
1A |
22975 |
0.095 |
32.41 |
6678 |
0.2880 |
23.21 |
3430 |
0.4492 |
25.56 |
15 |
13.62 |
|
1B |
38750 |
0.126 |
27.68 |
18445 |
0.4326 |
26.13 |
12268 |
0.5897 |
25.43 |
83 |
37.18 |
|
1D |
27600 |
0.079 |
35.87 |
6922 |
0.2569 |
32.20 |
2972 |
0.4466 |
22.92 |
13 |
17.84 |
|
2A |
32950 |
0.114 |
28.53 |
11268 |
0.3046 |
27.12 |
5968 |
0.4702 |
28.62 |
68 |
10.15 |
|
2B |
27000 |
0.108 |
34.32 |
8281 |
0.3149 |
28.03 |
3843 |
0.5460 |
24.79 |
25 |
36.17 |
|
2D |
31450 |
0.129 |
25.91 |
11635 |
0.3219 |
22.38 |
6548 |
0.4815 |
21.14 |
39 |
17.79 |
|
3A |
47200 |
0.019 |
20.32 |
12612 |
0.3003 |
14.57 |
6312 |
0.4868 |
13.37 |
97 |
19.40 |
|
3B |
44550 |
0.178 |
20.01 |
19634 |
0.3822 |
18.46 |
11295 |
0.5807 |
18.81 |
221 |
16.29 |
|
3D |
56800 |
0.135 |
18.25 |
22895 |
0.3089 |
13.98 |
12489 |
0.4685 |
12.52 |
126 |
30.32 |
|
4A |
40700 |
0.110 |
26.54 |
13428 |
0.3038 |
24.32 |
7541 |
0.4475 |
23.46 |
35 |
19.77 |
|
4B |
15350 |
0.071 |
59.70 |
3610 |
0.2569 |
56.32 |
1602 |
0.4261 |
56.75 |
10 |
6.29 |
|
4D |
40850 |
0.130 |
22.36 |
14577 |
0.3380 |
17.44 |
8108 |
0.5107 |
13.09 |
113 |
32.04 |
|
5A |
40800 |
0.116 |
23.10 |
14203 |
0.3038 |
22.21 |
7805 |
0.4561 |
22.39 |
64 |
8.99 |
|
5B |
50500 |
0.120 |
19.22 |
18675 |
0.2947 |
12.13 |
9569 |
0.4655 |
9.37 |
74 |
12.89 |
|
5D |
18700 |
0.140 |
35.62 |
4516 |
0.5364 |
17.32 |
3211 |
0.7112 |
9.33 |
140 |
18.05 |
|
6A |
33850 |
0.200 |
25.18 |
12105 |
0.5390 |
86.52 |
9696 |
0.6438 |
5.42 |
354 |
27.08 |
|
6B |
21350 |
0.113 |
38.91 |
5682 |
0.3890 |
14.16 |
3829 |
0.5172 |
7.03 |
153 |
8.17 |
|
6D |
8300 |
0.035 |
83.37 |
954 |
0.2087 |
75.58 |
286 |
0.4422 |
71.38 |
5 |
6.18 |
|
7A |
16150 |
0.042 |
44.94 |
1741 |
0.2935 |
26.29 |
789 |
0.5106 |
17.58 |
9 |
13.93 |
|
7B |
17150 |
0.064 |
37.66 |
3344 |
0.2797 |
27.60 |
1485 |
0.4855 |
20.28 |
9 |
6.64 |
|
7D |
27250 |
0.049 |
30.94 |
3848 |
0.2720 |
27.29 |
1800 |
0.4544 |
19.40 |
11 |
6.39 |
| Chromosome number | Number of marker pairs (p < 0.001) | Number of marker pairs (p < 0.001) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Number of marker pairs (r2 > 0.2) | Dist. (Mb) (r2 > 0.2) | Number of marker pairs (r2 = 1) | Dist. (Mb) (r2 = 1) | |||||||||
| A | B | D | A | B | D | A | B | D | A | B | D | |
| 1 | 3430 | 12268 | 2972 | 25.56 | 25.43 | 22.92 | 15 | 83 | 13 | 13.62 | 37.18 | 17.84 |
| 2 | 5968 | 3843 | 6548 | 28.62 | 24.79 | 21.14 | 68 | 25 | 39 | 10.15 | 36.17 | 17.79 |
| 3 | 6312 | 11295 | 12489 | 13.37 | 18.81 | 12.52 | 97 | 221 | 126 | 19.40 | 16.29 | 30.32 |
| 4 | 7541 | 1602 | 8108 | 23.46 | 56.75 | 13.09 | 35 | 10 | 113 | 19.77 | 6.29 | 32.04 |
| 5 | 7805 | 9569 | 3211 | 22.39 | 9.37 | 9.33 | 64 | 74 | 140 | 8.99 | 12.89 | 18.05 |
| 6 | 9696 | 3829 | 286 | 5.42 | 7.03 | 71.38 | 354 | 153 | 5 | 27.08 | 8.17 | 6.18 |
| 7 | 789 | 1485 | 1800 | 17.58 | 20.28 | 19.40 | 9 | 9 | 11 | 13.93 | 6.64 | 6.39 |
Dist = genetic distance, r2 = linkage disequilibrium correlation coefficient and LD = linkage disequilibrium
Distribution of SNP markers on the different genomes of bread wheat
In the present study, the 13,230 high-quality SNP markers by silico DArTseq protocol were used for genotyping 185 bread wheat genotypes and the D-genome identified the lowest number of SNP markers, which is in agreement with previous findings of [41], whereas the B genome contained the highest number of polymorphic markers. In bread wheat evolutionary history, the D-genome is the youngest one among the three genomes [42]. The B- genome and chromosome 2B contain the most SNPs, whereas the D-genome and 4D have the lowest markers [43]. It is expected that A and B genomes experienced sequence polymorphism that was caused by gene duplication and subsequent accumulation of mutations [44]. This could have led to the A and B genomes having more sequence diversity than the D genome [42], [44], [45]. Moreover, the maximum SNP markers were situated on chromosomes A and B than chromosome D, as reported by [41], [46], [47] in common wheat. The previous study reported by [46] showed that chromosome 2B had the highest number of markers and chromosome 4D had the lowest number of musings using 35,143 SNP markers. However, the results of this study showed that chromosome 3B had the highest number of SNP markers and chromosome 6D had the lowest number. In addition, earlier studies described by [41] revealed that chromosome 4D had the lowest amount of SNP markers and chromosome 3B had the highest number of SNP markers.
Population Genetic Diversity
This study has revealed high genetic diversity among genotypes that can be used in Ethiopia to produce superior bread wheat cultivars with important agronomic traits. In the present research, the distribution of effective high-quality 13,230 SNP markers in the A, B, and D genomes were in agreement with those previously reported by [30], [48]. The highest SNPs were mapped to the B genome followed by A and D genomes [30], which directly supports our findings. The progenitor Triticum turgidum genome likely existed for 200,000 years before the addition of the D genome to become Triticum aestivum, which gave it more time to develop genetic diversity. As a result of gene flow between Triticum aestivum and Triticum turgidum (AABB), the A and B genomes have higher sequence diversity than the D genome [30], [43]. To conduct a genetic diversity and population genetics study, the genotypic information of the genotypes under study, which might help in the breeding of bread wheat in the future, was used. The PIC values are extremely helpful for genetic research in wheat breeding programs since they assess the genetic diversity among accessions [30]. The PIC values indicate the presence of informative markers in a group of samples that can be used to analyze genetic diversity [49]. According to Botstein et al.[50], the polymorphic information content is classified into three categories: when PIC > 0.5, indicates that the marker is highly polymorphic, 0.5 > PIC > 0.25, shows that the marker is a moderate polymorphic marker, and when the PIC < 0.25, indicates that the marker is a low polymorphic marker. Due to the bi-allelic nature of SNPs, which only allow for maximum PIC values of 0.5, the PIC value of SNP markers would be low to moderately polymorphic [30].
The PIC value varied from 0.10 (269 SNPs) to 0.375 (6164 SNPs) based on the high-quality effective SNP markers, with an average PIC value of 0.27 in our research, which is consistent with the PIC value (0.27) obtained for a panel of spring wheat genotypes that demonstrated a moderate degree of polymorphism [51]. The earlier study by [52] genotyped a pane of Croatian bread wheat using a collection of 1,229 DArT markers, and the populations had an average PIC value of 0.30, indicating that the accessions from Croatia displayed moderate polymorphism. The recent investigation by [41] used 24,767 SNPs to genotype Asian and European common wheat germplasm and obtained PIC values of 0.32, which confirmed that the genotypes contained moderate genetic diversity. The average PIC value (0.33) in winter wheat was reported for 1,395 high-quality SNP markers, which exhibited the genotypes showed moderate polymorphism [53]. Similarly, the wheat population genotyped using a 9 K SNP array has a PIC value of 0.24 [54].
Analysis of population structure with SNP markers
Characterizing the history of domestication and the genetic relatedness of bread wheat accessions requires knowledge of genetic diversity and population structure. Six clusters were identified as a consequence of population structure analysis in this study; cluster 1 had twenty-three genotypes, cluster 2 forty-eight, cluster 3 fifty-five, cluster 4 only eight, cluster 5 twenty-four, and cluster 6 twenty-seven. In Table 4, it was shown what percentage of all 185 genotypes belonged to each category. Cluster 6 had a membership of 22.1% of the population, whereas Cluster 3 had a membership of 12.5%. Cluster 3 exhibited the largest level of genetic divergence, according to the average distance between each cluster and the degree of genetic variation. When comparing population structures, the fixation index value larger than 0.15 might be regarded as significant [55]. The fixation index (Fst) for each cluster was evaluated according to the STRUCTURE results, which indicated that there was a considerable degree of divergence among clusters. The fixation index is a measure of population differentiation due to genetic structure. However, the high gene flow accounts for the low genetic variation among subpopulations [29]. The highest Fst value was detected in cluster 3, while the lowest Fst value was observed in cluster 2, indicating that there was substantial divergence within each cluster. The allele richness and abundance in a population affect the gene diversity (He) value [30]. It offers an estimate of the genetic distance and average heterozygosity among a population's genotypes [56]. In our study, the cluster 3 subpopulation had the highest (He) score, suggesting that it had the most genetic diversity among the cluster's subpopulations.
The patterns of linkage disequilibrium among the markers
The marker pairs' existence in linkage disequilibrium over extended distances and closely related pairs with no substantial linkage disequilibrium detected in the present study has been stated by different authors in bread wheat [23], [33], [57]. This indicated that LD be affected by reasons other than genetic or geographic distance, such as genetic admixtures. The genome's lowest marker pair distance shows that the LD decay typically happened at substantially shorter distances, which may be attributed to repeated backcrossing to a small number of elite breeding lines with limited genetic variety. Several markers were significantly (p < 0.001) associated with traits located on genetic regions ranging from 5.42 (6A) Mb to 71.38 Mb (6D) in complete linkage disequilibrium (r2 = 1), indicating the chance of constricted to high linkage. Understanding the relationship among different genotypes provides the basis for association mapping studies increased by the analysis of population structure [40]. Similar findings by Reimer et al. [58] found that some loci were extending between 25 Mb and 41 Mb with LD > 0.7. The extent of LD across the genome determines the number of SNPs needed for GWAS. The population's LD value will indicate evolutionary changes that aid with the more accurate mapping of agronomic traits and the joint evolution of the linked sets of genes. The higher the number of markers required for improved mapping resolution, the lower the LD across the genome [59]. The significant MTAs depend on the distribution and amount of LD in the genome [60]. In this study, LD (r2) decay was slow across the genome of bread wheat. The linkage disequilibrium decay exists at a genetic distance of 40.32 Mb, showing the maximum linkage disequilibrium level in bread wheat reference set across the genome. Bread wheat genotypes showing a very slow rate of decay and maximum linkage disequilibrium were detected in genotypes may be due to low effective recombination rates. When compared to cross-pollinated crops, self-pollinated crops were found to have a slower rate of LD decay [61]. The extent of 18.25–83.37 Mb linkage disequilibrium decay in the marker-trait link of the bread wheat genome was observed in this study. Because of the complexity, size, and quantity of markers in the genome of bread wheat, the degree of LD might vary [62].
The study of this research assessed the analysis of genetic diversity, population structure, and linkage disequilibrium patterns in 185 Ethiopian bread wheat genotypes using 13,230 silico DArTseq-SNPs distributed to each of 21 chromosomes. The samples of 185 bread wheat were genotyped using 32,206 SilicoDArT markers dispersed across 21 chromosomes, and then a total of 13,230 highly polymorphic SNPs distributed across the genomes of bread wheat genotypes were used after data imputation. From the total of 13,230 SNP markers, 4718 (35.7%) of the markers were situated on the B-genome, 4538 (34.30%) on the A-genome, and 3974 (30.0%) on the D-genome. The markers on the B genome and chromosome 3B were the highest polymorphic, while the markers on the D genome and 6D chromosome were the lowest polymorphic. A sharp peak of delta K (ΔK) at K = 6 from k-value 1–10, resulting from the STRUCTURE HARVESTER and based on marker MAF > 0.05, showed that the population evaluated was grouped into six separate genetic structures. Significant differences were observed among genotypes for gene diversity, PIC, and LD. The polymorphic information content varied from 0.1 to 0.4, the fixation index ranged from 0.26 to 0.63, and the gene diversity was within the range of 0.1 to 0.5. The average values for the LD decay and the LD (r2) at p-value (p 0.001) were 0.34 and 18.35 Mb, respectively. The in-depth knowledge of the genetic diversity and population structure of the Ethiopian bread wheat genotypes revealed by this study may be crucial for inspiring the development of new cultivars that have enhanced the desired characteristics. Knowledge of gene diversity, population structure, and LD is essential for future association mapping investigations of critical agronomic traits. For all groups, the mean r2 declined as genome distance increased, indicating a low likelihood of LD between distantly located SNP pairs. The genotypes of bread wheat might be employed further for genome-wide association studies due to the low heterozygosity found across genotypes within clusters and the modest divergence among the clusters.
GBS = Genotype-by-sequencing
GWAS = Genome-wide association studies
LD = Linkage disequilibrium
MAF = Minor allele frequency
Mb = Mega base pair
PIC = Polymorphic information content
SNP = Single Nucleotide Polymorphisms
UPGMA = Unweighted pair group method with arithmetic mean
Declaration of interests
☒ The authors declare that they have no known conflicting financial interests or personal relationships that could have influenced the work reported in this paper.
Author Contribution
BMS: Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Validation, Visualization, Writing – Original Draft Preparation, and Writing & Editing. AA: Project Administration, Supervision, Validation, and Writing – Review & Editing. ZT: Formal Analysis, Project Administration, Resources, and Software.
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No competing interests reported.