Assessing and Broadening Genetic Diversity of Elymus sibiricus Germplasm for the Improvement of Seed Shattering

Abstract

Siberian wild rye (Elymus sibiricus L.) is an important native grass in the Qinghai-Tibet Plateau of China. It is difficult to grow for commercial seed production, since seed shattering causes yield losses during harvest. Assessing the genetic diversity and relationships among germplasm from its primary distribution area contributes to evaluating the potential for its utilization as a gene pool to improve the desired agronomic traits. In the study, 40 EST-SSR primers were used to assess the genetic diversity and population structure of 36 E. sibiricus accessions with variation of seed shattering. A total of 380 bands were generated, with an average of 9.5 bands per primer. The polymorphic information content (PIC) ranged from 0.23 to 0.50. The percentage of polymorphic bands (P) for the species was 87.11%, suggesting a high degree of genetic diversity. Based on population structure analysis, four groups were formed, similar to results of principal coordinate analysis (PCoA). The molecular variance analysis (AMOVA) revealed the majority of genetic variation occurred within geographical regions (83.40%). Two genotypes from Y1005 and ZhN06 were used to generate seven F₁ hybrids. The molecular and morphological diversity analysis of F₁ population revealed rich genetic variation and high level of seed shattering variation in F₁ population, resulting in significant improvement of the genetic base and desired agronomic traits.

1. Introduction

Elymus sibiricus L., commonly known as siberian wildrye, is a perennial, cold-season, self-pollinating, and allotetraploid grass with the StStHH genome constitution (2n = 28) [1]. Indigenous to northern Asia, E. sibiricus germplasm are especially rich and diverse in north China, where it is distributed primarily in Qinghai-Tibet Plateau, Inner Mongolia, Sichuan, ** more efficient conservation and breeding strategies.

The development of neutral molecular markers has made it fast, reliable and accurate to reveal the genetic diversity of germplasm. Compared with other molecular markers like inter simple sequence repeat (ISSR), sequence-related amplified polymorphism (SRAP), and start codon targeted (SCoT) etc, EST-SSRs are highly polymorphic, abundant and are accessible to research in laboratories via published primers sequences. What is more, EST-SSRs have a higher level of transferability across related species than genomic-SSRs because EST-SSRs originate from the transcribed regions in genomes and possess conserved sequences among homologous genes [5]. Along with the development of next-generation sequencing, transcriptome sequencing has also become an efficient method to identify large EST sequences and develop EST-SSR markers [10]. To date, EST-SSRs have been widely used for genetic diversity [11], genetic map** [12], and DNA fingerprinting [13].

The pattern of genetic variability of the available germplasm substantially affects the choice of breeding materials and the success of plant breeding programs. The objectives of the present study were to (i) compare the genetic diversity and relationship among E. sibiriucs accessions from North China; (ii) broaden the genetic diversity of E. sibiricus by crossing two genetically and morphologically diverse genotypes and assess genetic variation of the hybrid population.

2. Results

2.1. Seed Shattering Degree of 36 E. sibiricus Accessions

The BTS value among 36 E. sibiricus accessions varied from 31.86 gf (PI655140) to 92.34 gf (ZhN06), with an average of 53.28 gf. Sixteen accessions had a relatively low seed shattering degree with BTS more than the average value of seven accessions including four wild accessions (PI655140, PI595182, HZ02 and XH09) and three cultivars (Hongyuan, Chuancao2 and Tongde) had a relatively high seed shattering degree with the BTS value of less than 40 gf. The other 13 accessions had a moderate seed shattering degree (Figure 1).

2.2. Polymorphism of EST-SSR Markers and Genetic Relationships of 36 E. sibiricus Accessions

Furthermore, we analyzed the genetic diversity and variation of 36 E. sibiricus accessions with variation of seed shattering degree (

Table 1. E. sibiricus accessions used in the study.

Population	Code	Accession	Origin	Status
SC	1	Y1005	Sichuan, China	Wild
SC	2	SAU003	Kangding, Sichuan, China	Wild
SC	3	SAU133	Aba, Sichuan, China	Wild
SC	4	SAU139	Kangding, Sichuan, China	Wild
SC	5	SAU137	Aba, Sichuan, China	Wild
SC	6	SC02	Ruoergai, Sichuan, China	Wild
SC	7	SC03	Ruoergai, Sichuan, China	Wild
SC	8	Hongyuan	Hongyuan, Sichuan, China	Breeding line
SC	9	Chuancao2	Hongyuan, Sichuan, China	Cultivar
NM	10	PI 499453	Inner Mongolia, China	Wild
NM	11	PI 665507	Inner Mongolia ,China	Wild
NM	12	PI 499456	Inner Mongolia, China	Cultivated
NM	13	PI 610886	Inner Mongolia, China	Wild
XJ	14	PI 655140	**njiang, China	Wild
XJ	15	PI 595182	**njiang, China	Wild
XJ	16	PI 595180	**njiang, China	Wild
XJ	17	Y2003	**njiang, China	Wild
XJ	18	PI 619577	**njiang, China	Wild
XJ	19	PI 499462	**njiang, China	Wild
XJ	20	PI 499468	**njiang, China	Cultivated
GS	21	XH02	**ahe, Gansu, China	Wild
GS	22	XH03	**ahe, Gansu, China	Wild
GS	23	XH09	**ahe, Gansu, China	Wild
GS	24	LQ04	Luqu, Gansu, China	Wild
GS	25	LQ01	Luqu, Gansu, China	Wild
GS	26	HZ01	Hezuo, Gansu, China	Wild
GS	27	HZ02	Hezuo, Gansu, China	Wild
GS	28	ZhN01	Zhuoni, Gansu, China	Wild
GS	29	ZhN06	Zhuoni, Gansu, China	Wild
GS	30	MQ01	Maqu, Gansu, China	Wild
GS	31	LT01	Lintan, Gansu, China	Wild
GS	32	LT02	Lintan, Gansu, China	Wild
QH	33	Tongde	Qinghai, China	Cultivar
QH	34	Qingmu1	Qinghai, China	Cultivar
QH	35	PI 531669	Qinghai, China	Wild
QH	36	PI 504462	Qinghai, China	Wild

Note: SC, Sichuan, China; NM, Inner Mongolia, China; XJ, **njiang, China; GS, Gansu, China; QH, Qinghai, China.

). One hundred EST-SSR primers selected from Elymus, Pseudoroegneria and Leymus EST database, and 112 novel E. sibiricus EST-SSR markers developed by transcriptome sequencing were chosen to conduct the primers′ screening. Finally, 40 EST-SSR primers that successfully amplified clear and stable bands were selected to evaluate the genetic diversity of these 36 accessions (

Table 2. Results achieved by Elymus (Elw), Pseudoroegneria (Ps), Leymus (Lt) and E. sibiricus (ES) EST-SSR markers, all detected in 36 E. sibiricus accessions.

Primer ID	Forward Primer (5′-3′)	Reverse Primer (5′-3′)	T	M	TP	%P	PIC
Elw0300s019	TTCATCCATCCAATTCTAGCACAA	GAAGGAGAAGATGGAATCCTTGAA	8	0	8	100.00	0.49
Elw0669s043	CATCTCACGGCAAGTAAATGAACA	TGCGAGATGGGGTACAATTTTTAT	12	0	12	100.00	0.35
Elw1197s069	ATGGCCGTAACCCTTTACCTGTAT	TTTCAAAGCCTTTCCAAGTGAATC	7	1	6	85.71	0.48
Elw1420s081	GGATAGACCCATGAGCTGACTGAT	CTTTCTCCACAAGTTGAACACAAGA	11	0	11	100.00	0.35
Elw1468s087	TAGCAATAAGTTGCTGCTGCTGTT	CCACCTCTAAATTAATCACCACGAA	11	0	11	100.00	0.47
Elw1675s092	CAGTTAAAATGCTTGTCCAAATGC	CCATGATTGTTCTGTCAAGAAACG	10	1	9	90.00	0.48
Elw2676s146	AATTCGAAAGCTGTGGACTTGTCT	CAATTTTGCTCTCAAGAGAACCGT	10	1	9	90.00	0.48
Elw2698s152	CAAAGCATGTGTAGGCAGTCTTGT	TAACAATGATCAGTTGATCGGACC	9	0	9	100.00	0.50
Elw2807s159	CCCAAGAAGCAAAAGTGAAGTTGA	ATAATTGCTGTAAAACGGCAGGAA	11	0	11	100.00	0.50
Elw2808s160	TTTCATATCCGATACCCAGAAAGC	GGGCGACAAGGGTACTACTAACAA	4	0	4	100.00	0.45
Elw3264s184	TGGACTGCTTTGGGACATAATAGG	CTGAATCATAGCCACCCTGAAAAC	6	0	6	100.00	0.42
Elw3384s187	AGCTCCTGATAGAAAGAGCCATCA	GGCTGCTGGAACTGAAGACAGTA	16	0	16	100.00	0.36
Elw3492s190	TGTTGTTGTTCCAGTTCCAGTCTC	AAAAACAACCACACAAGGTTGTCA	9	2	7	77.78	0.43
Elw3995s226	CTCTAGGGTTTTGGGATTTTAGCC	GTTGTGGAGGTCGGAGAAGGT	7	0	7	100.00	0.49
Elw4419s261	AGGGTGACTTGTCTTTGGGTGTAA	AGTCAGATGAAGGATGGCTGAAAC	8	1	7	87.50	0.50
Elw5447s306	TCCTCAAACTCCTCCTCTCTTCG	GAGGTAAGTCTCGACATCCTCGAC	9	0	9	100.00	0.50
Elw5616s393	TAGTAGCGTGGCACTCCTCTTCTT	GGTACAAACCACCAAAGGTACTGC	22	0	22	100.00	0.42
Elw5627s404	AGATGAAGCTGGTAACCGAGACAG	ATTTCCTCTAATGGAAGCTCTGGC	17	0	17	100.00	0.46
Ps2283	GCCACAACAAGAGAAGACCTTGC	GACCTGCATGATGCTCTCGC	13	0	13	100.00	0.40
Ps261	CTCGAATCCAGCTGAACAATTTCT	AGTCGATCCTCACCTTCATCTCC	9	0	9	100.00	0.45
Ps3447	AGCTTTATGAAGATCGCCACTCAC	CTGCTGCTGCTACCGTTCTTATTT	13	0	13	100.00	0.50
Ps3577	CATCTTGCATATAGCTCCTTCGCT	CTCAAGAAACCCACAATCCAATTC	5	0	5	100.00	0.48
Ps938	TTGCTCCTATGGTTCCACGTAGTT	AAAGTGAAATTCTGCCATCAGAGC	13	1	12	92.31	0.49
Ltc0209	CAGGAACATGAACAGAAGCCTGTC	GTACTGGTCGAACCACCCAAAGT	12	1	11	91.67	0.50
Ltc0096	GCGCACTACCGCCTCTTAGTT	GTCCAGGTAGCACACCTCCG	8	2	6	75.00	0.49
ES-7	CCTCCTCCGTTACCATGTTG	CCCTGCTTTTCCCTCTCTG	4	3	1	25.00	0.27
ES-22	AAGATATCCTGATGCTGGACAAA	GATCAGATCAATAGCTTGAGCG	7	5	2	28.57	0.23
ES-23	CGTACTTGCGCCAGAAGTG	AGGTGTCCATCGAAGGGTC	14	2	12	85.71	0.45
ES-51	GAGCTGAGCTGAGAAGAAAACAG	CACAATCATCTCATCTTCCTTCC	14	0	14	100.00	0.44
ES-97	ACTGTGGGAGAAGGTGAGAGACT	CTTTCCTCCAGCTCATGGTG	8	4	4	50.00	0.49
ES-123	AGCATGAAGCTCGACTGTGAGT	GCGAGTACATCTCGTACTTCTGG	12	4	8	66.67	0.49
ES-125	GAGCATCGACAGATTATTCCTTG	CGAAGGAACCTCTGCAAGAC	5	3	2	40.00	0.23
ES-144	GGTAGTCGTTGACCCAGATGTC	CACATTGTAAACTGGTCCTCCTC	5	0	5	100.00	0.49
ES-231	TAGCTGGTCATGCCTAGGAGTAG	CCAGGTGTCAGGATATAGCAAAA	6	1	5	83.33	0.44
ES-236	TCGCATGCTTATAATCCTTTGAC	TGAGGTCTCTGTCAATACCAACA	6	3	3	50.00	0.35
ES-253	CATCTCTTCAAACTTGGATTGGT	GTGATCTATACCATTGGCCTCAA	12	4	8	66.67	0.46
ES-259	CTCCTCTACCTGTCTGCTGCTA	AGATCGTCGACTACGTCAAGAAG	11	0	11	100.00	0.47
ES-310	CGTAGCAATTCCATTCTATCCAG	TGGTGAGCTAGATTGACACTGAG	9	6	3	33.33	0.33
ES-347	CATGAAGATGATGCGTGTTTTAAT	CCGACTCCTAATTGAACTCGTAA	5	3	2	40.00	0.47
ES-405	AGAGAAAAGGAGATTCTCATCCC	GCTGCTCTGCATCCTACTCTATC	2	1	1	50.00	0.43
Mean			9.5	1.2	8.3	87.11	0.44
Total			380	49	331

T, total number of amplified bands; M, number of monomorphic bands; TP, total number of polymorphic bands; % P, percentage of polymorphism; PIC, polymorphic information content.

). The 40 primers generated 380 bands, 331 of which were polymorphic. The percentage of polymorphism (P) was 87.11%. The total bands (T) per primer ranged from 2 (ES-405) to 22 (Elw5616s393) with 9.5 bands per primer. Across the 36 accessions, the polymorphic information content (PIC) values ranged from 0.23 (ES-22 and ES-125) to 0.50 (Elw2698s152 and Elw2807s159, etc.) with an average of 0.44, suggesting a high level of polymorphism.

The population structure of the 36 accessions was investigated using the Hardy-Weinberg Equilibrium by using STRUCTURE V2.3.4 software. Based on maximum likelihood and delta K (ΔK) values, the number of optimum groups was four (Figure 2). Among them, 18 accessions from Sichuan, Inner Mongolia and ** low seed shattering cultivars in the future. Except for seed shattering, other traits such as flag leaf length and width also showed the positive heterosis. Our results confirmed that some morphological traits of E. sibiricus could be improved by means of hybridization. When compared with the phenotypic-based dendrogram, marker-based cluster revealed poor correlation with morphological characteristics. The phenotypic-based dendrogram using limited morphological data could be affected by environment factors. In comparison, a marker-based cluster is more efficient and allows genetic diversity analysis using any physiological stage or tissue, suggesting its potential in analyzing genetic diversity and relationship of E. sibiricus.

Based on our results, the genetic diversity of hybrid population is 59.92%. Furthermore, 8.44% and 1.95% of polymorphic bands were exclusively present in F₁ lines and parents, respectively. These gained and missed bands were considered as polyploidization-induced rearrangements within coding regions. Hybridization of more genomes with different sizes and compositions in a single nucleus followed by chromosome doubling can induce several types of genomic modifications and rearrangement in the hybrids [26,27]. These new rearranged bands might be associated with effects of heterosis and contribute to surprisingly low seed shattering in the hybrids. However, whether these novel bands were responsible for new genes associated with seed shattering or other important traits is still not clear. In the future, molecular markers combined with sequence data might provide new evidence.

4. Materials and Methods

4.1. Plant Materials

A total of 36 E. sibiricus accessions were used in the study, comprising wild collections, breeding lines, cultivars, and cultivated types (Table 1). Seeds of these accessions were obtained from National Plant Germplasm System (NPGS, USA), Lanzhou University, Sichuan Agricultural University and Sichuan Academy of Grassland Science. All accessions were grouped into five geographic regions: SC (Sichuan), NM (Inner Mongolia), XJ (**, of which 100 EST-SSR markers were previously developed from Elymus (Elw hereafter), Pseudoroegneria (Ps hereafter) and Leymus (Lt hereafter) EST database [29,30,31] and 112 novel E. sibiricus EST-SSR markers were developed by transcriptome sequencing [32]. The DNA samples of 5 accessions with different geographical origins were used for primer screening. Then 40 EST-SSR primers that successfully amplified and produced clear and stable bands of the expected size by PCR amplification were used in the final analysis (

Table 3. Genetic variability within five geographic regions of E. sibiricus.

POP	NPB	PPB (%)	I	H	Na
SC	204	73.12	0.2650	0.1739	1.5368
NM	137	56.85	0.1947	0.1301	1.3605
XJ	177	66.79	0.2368	0.1570	1.4658
GS	299	86.17	0.3594	0.2315	1.7868
QH	70	32.71	0.0946	0.0623	1.1842
Mean	177.4	63.13	0.2301	0.1510	1.4668

NPB, number of polymorphic band; PPB, percentage of polymorphic bands; I, Shannon information index of diversity; H, Nei′s genetic diversity; Na, observed number of alleles.

). The PCR amplification and SSR genoty** were carried out as described by **e et al. [2] and Zhou et al. [32]. Amplification fragments were then separated on 6% denatured polyacrylamide gels electrophoresis (PAGE). The resulting gel was stained by AgNO₃ solution, and photographed by a digital camera (D7000, Nikon, Tokyo, Japan).

4.3. Phenotypic Traits Measurement

The seeds of F₁ lines and their parents were germinated in plastic boxes with moistened blotter paper at room temperature. After germination seedlings were grown in a greenhouse under a 25/15 °C day/night temperature regimes until they were 8 weeks old. Then they were transplanted to field plots in the research farm, Yuzhong, Gansu, China (latitude 35°34′ N, longitude 103°34′ E, elevation 1720 m). Plants were spaced 0.5 m within rows and 1 m between rows. A total of 12 phenotypic traits, including seed shattering (SS), plant height (PH), leaf length (LL), leaf width (LW), flag leaf length (FLL), flag leaf width (FLW), culm diameter (CD), culm number (CN), tiller number (TN), panicle length (PL), awn length (AL) and 1000-seed weight (1000-SW) were measured using the methods described by Zhao et al. [8]. Seed shattering degree of E. sibiricus accessions was determined by measuring pedicel breaking tensile strength (BTS), which is inversely proportional to shattering degree. Thirty randomly chosen spikelets of each plant were examined at 28 days after heading, and their average BTS values were calculated. The heterosis of hybrids were estimated on mid-parent values and high-parent value using the following formula: mid-parent heterosis (%) = (F₁ − MP)/MP × 100%, higher-parent heterosis (%) = (F₁ − HP)/HP × 100 %, where F₁ is the mean of the hybrids, MP is the mean of parents, HP is the value of higher parent [33].

4.4. Data Analysis

The amplified bands were scored as present (1) or absent (0), and only reproducible bands were considered. The resulting present/absent data matrix was analyzed using POPGENE 32 Version 1.31 [34]. Number of polymorphic band (NPB), percentage polymorphic band (PPB), Shannon information index of diversity (I), Nei′s gene diversity (H), and observed number of alleles (Na) and polymorphic information content (PIC) were used to evaluate genetic diversity. PIC was calculated for each primer according to the formula: PIC = 1 − p² − q², where p is frequency of present band and q is frequency of absent band [35]. The Analysis of Molecular Variance (AMOVA) was used to partition the total EST-SSR variation into within populations and among populations [36]. The input files for POPGENE and AMOVA were prepared with the aid of DCFA1.1 program written by Zhang and Ge [37]. Population structure of the 36 E. sibiricus accessions was analyzed using STRUCTURE v2.3.4 software with the ′admixture mode′, burn-in period of 10,000 iterations and a run of 100,000 replications of Markov Chain Monte Carlo (MCMC) after burn in [38]. For each run, 10 independent runs of STRUCTURE were performed with the number of clusters (K) varying from 1 to 8. Mean L (K) and delta K (ΔK) were estimated using the method described by Evanno et al. [39], maximum likelihood and delta K (ΔK) values were used to determine the optimum number of groups. A principal coordinate analysis (PCoA) was constructed based on Jaccard′s genetic similarity matrix using DCENTER module in NTSYS (version 2.10) [40]. A dendrogram was constructed using the GenStat (version 17.1) and free tree + tree view (version 1.6.6 for Windows) software. The phenotypic data were analyzed using SPSS software (SPSS, version 22 for Windows, SPSS Inc., Chicago, IL, USA).

5. Conclusions

This study showed a high level of genetic diversity and a clear population structure of 36 E. sibiricus accessions from its primary distribution area in China. The finding that larger variation existed within geographical regions will provide a guideline for the collection and conservation of E. sibiricus germplasm. More genetic variation of the species can be captured when sampling a larger number of plants from special eco-geographical regions. Meanwhile, cross breeding is an effective way to obtain more genetic and phenotypic variation. F₁ lines of E. sibiricus exhibited a higher genetic variation in the major agronomic traits. In addition, some F₁ lines showed obvious heterosis over parents, especially in seed shattering performance. These hybrids could be used as important genetic resources for genetic improvement of E. sibiricus in future breeding improvement programs.