SSR marker based molecular diversity among advanced breeding lines of Katarni rice (Oryza sativa. L.) and validation of phenotypic selection through functional markers

doi:10.21203/rs.3.rs-3405529/v1

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SSR marker based molecular diversity among advanced breeding lines of Katarni rice (Oryza sativa. L.) and validation of phenotypic selection through functional markers

https://doi.org/10.21203/rs.3.rs-3405529/v1

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Like Basmati, Katarni is a traditional aromatic rice cultivar grown primarily in its geographical indicated areas i.e. Bhagalpur, Banka and Munger districts of Bihar, India. However, known for exquisite grain quality characteristics, it suffers from lodging problem, and is very late maturing leading tolow productivity, constant decrease in acreage and farmer preference. Introgression of the semi-dwarfing gene (sd1) in Katarni was performed through marker assisted backcross breeding and advanced to obtain the homogeneous population. The derived lines were analyzed for the presence of genes for semi-dwarfism (sd1), aroma (badh2), flowering date (Hd3a) and kernel length (GS3) using functional markers. Improved lines of Katarni rice were found positive for sd1 gene in PCR and most of the entries simultaneously contained genes for short grain type, thus implicating the applicability of these markers in marker-assisted backcross breeding. Twenty three advanced breeding lines of Katarni were phenotypically evaluated for target traits viz., plant height, days to 50% flowering, grain length, grain width and leaf aroma through sensory test alongwith their parental checks. Molecular profiling of the entries was carried out to develop DNA fingerprint using 81 SSR markers which were found to be highly informative and capable of distinguishing between genotypes. The PIC value was highest for marker RM15189 and RM182. The genetic distance detected through UPGMA clustering indicated five distinct groups, and maximum degree of similarity was observed between lines namely, ASGL11 and ASGL12 followed by ASGL9 and ASGL11. Except Hd3A, the molecular profiling through functional markers were also in congruity with the morphological data.

Allelic diversity

Aromatic rice

DNA fingerprinting

Functional markers

Marker assisted breeding

PIC value

A major section of the world's population relies mostly on rice (Oryza sativa L.) as a source of sustenance. According to India's Annual Report 2020–21 (Anonymous 2022), rice has been grown on 43.79 million ha, producing 116.42 million t and yielding 2659 kg per hectare. On the other hand, rice is cultivated in the state of Bihar over an area of 3.30 million ha, producing 8.09 million ton with an average productivity of 2447 kg per hectare (Anonymous 2022). India is a leading producer and exporter of Basmati rice, which is renowned for its aroma, cooking and grain type. Aromatic rice comprised both Basmati and non-Basmati grain types, are highly priced due to their grain texture, specific pleasant aroma and other special qualities. The most common, ceremonial, and best-quality scented rice in Bihar is Katarni Rice, a traditional non-basmati variety. It is renowned for its flavor, palatability, and ability to produce Chura (beaten rice). It flowers between the end of October and the beginning of November, maturing in December. Its height ranges from 140 to 160 cm (Smriti et al. 2016). However, Katarni has a low yield of 25 to 30 Q/ha susceptible to lodging due to its tall stature (140–160 cm), vulnerable to pests and diseases and late maturing (155–160 days). The tall and week culm results in lodging at maturity and creates problems in mechanical harvesting. Semi-dwarf rice varieties can evade the damage due to their strong culm and short height (Berry et al. 2002). Due to photoperiod sensitivity, its flowering in Katarni is delayed considerably under long-day treatments (Kumar et al., 2018a) making it unfit for traditional rice-wheat cropping system.

The semi-dwarfing gene sd1 is one of the most crucial genes used in rice breeding and played a vital role in first green revolution during 1960s. Due of its recessive nature, the culm of sd1 gene containing plant is shorter and more resistant to lodging. The dominant allele of the sd1 gene, encodes gibberellin 20 oxidase-2 (GA20ox-2) and a deletion or substitution mutation in this gene renders the protein inactive. The bioactive growth hormone GA3, which regulate plant height, is therefore lacking in the plants (Spiemeyer et al. 2002). Increased levels of 2-acetyl-1-pyrroline (2AP) have been linked to the aroma of fragrant rice (Buttery et al. 1983; Lorieux et al. 1996; Widjaja et al. 1996; Yoshihashi 2002). The betaine aldehyde dehydrogenase (badh2) gene, which is a single recessive gene on chromosome 8, regulates the aroma in rice (Bradbury et al., 2005; Peng et al., 2018). As a result of badh2 enzyme activity, loss caused by inhibition, deletion, or mutation of the fgr gene, levels of 2-AP precursors rise and 2-AP accumulates to produce fragrance in aromatic rice (Shan et al. 2013; Chakraborty et al. 2016; van Quoc et al. 2023). In addition, the time of flowering or the heading date (HD) is thought to be an essential factor not only for the quantity but also for the quality of rice grain production in general and photosensitive rice varieties like the Katarni, in particular (Fan et al. 2005). According to Tamaki et al. (2007), the Hd3a locus has a significant role in flowering time and has been roughly mapped on chromosome 6 (Monna et al., 2002). Grain kernel length (KL) is an important agronomic and grain quality trait for breeding of rice (Oryza sativa L.), influencing seed weight and ultimately the crop yield (Luo et al. 2004). A QTL named GS3, located in the peri-centromeric region of chromosome 3 is reported to be responsible for 80–90% of the variation in kernel length. Later, the locus was also resolved as a minor QTL for grain width and thickness (Fan et al. 2006). It is crucial to generate lines with shorter plant height, early flowering, with desired quality traits through marker assisted backcross breeding (MABC). Microsatellites (SSRs) are essential tool for identifying the genetic diversity among rice accessions (Sajib et al., 2012; Ma et al., 2011). These markers are widely used in genetic diversity analysis, the creation of molecular maps, and gene mapping (Ma et al. 2011) as they are highly informative, primarily monolocus, codominant, easily analyzed, and cost-effective (Gracia et al. 2004). They are also capable of detecting high levels of allelic diversity. Incorporating semi-dwarf trait into rice has been effectively accomplished through a number of molecular breeding strategies without affecting fragrance (Rajpurohit et al., 2011).

Katarni rice was tagged with geographical indication (GI) in 2018 for its unique cooking qualities which is linked to its confined GI territory. For enhancing area beyond GI area under this covered rice genetic resource, yield barrier and late maturity problem has to be addressed without compromising the unique grain qualities, a marker-assisted Backcross breeding (MAB) program was started at Bihar Agricultural University by crossing Katarni with semi-dwarf rice types namely, Rajendra Sweta, BPT5204, and MTU7029 in order to shorten the height and maturity time. Further, through marker-assisted forward and backcross breeding approach, the generation was advanced (Vaibhav et al. 2019; Suvidha et al. 2020) to obtain the homogeneous population. It is necessary to characterize the advanced Katarni derived lines for distinctness both at molecular and morphological level with respect to parental source lines and confirming the presence of target genes. Functional markers for the target traits along with genome wide SSR markers were used in the present study to evaluate advanced breeding lines of Katarni and to assess the genetic diversity.

Plant materials

The experimental population used in this study was obtained by crossing tall and aromatic Katarni rice (150–155 days maturity) with three semi-dwarf varieties Rajendra Sweta (135 − 130 days maturity), BPT5204 (130–135 days maturity) and MTU7029 with an objective to obtain early maturing lines of Katarni with reduced plant height retaining its aroma. Sobhini and CR Sugandha Dhan were also included in the trial along with the parental checks.

The experiment was conducted during Kharif season 2022 at Rice Section, Bihar Agricultural University, Sabour, Bhagalpur. Twenty three Katarni derived lines along with six checks (Table 1) were grown in a replicated trial with three replications. The genotypes were grouped under early, medium and late maturity on the basis of days of flowering in their previous generation and denoted as ASGE, ASGM and ASGL, respectively. Observations were recorded for key quantitative traits viz., Plant height (cm) (PH), Days to 50% flowering (DFF), Grain length (mm) (GL), Grain breadth (mm) (GW).

Table 1

List of rice genotypes used under study
S. No.	Entry Name	Pedigree	Generation
1.	ASGE-2	Katarni x R. Sweta	BC₂F₇
2.	ASGE-3	Katarni x MTU-7029	F₉
3.	ASGE-6	Katarni x R.Sweta	F₉
4.	ASGE-7	Katarni x R.Sweta	BC₃F₆
5.	ASGM-3	Katarni x R.Sweta	F₉
6.	ASGM-4	Katarni x R.Sweta	F₉
7.	ASGM-7	Katarni x R.Sweta	F₉
8.	ASGM-11	Katarni x R.Sweta	BC₃F₆
9.	ASGM-12	Katarni x R.Sweta	BC₃F₆
10.	ASGM-21	Katarni x R.Sweta	BC₃F₆
11.	ASGM-24	Katarni x R.Sweta	BC₃F₆
12.	ASGL-2	Katarni x BPT-5204	BC₂F₅
13.	ASGL-4	Katarni x R.Sweta	BC₁F₈
14.	ASGL-5	Katarni x R.Sweta	BC₃F₆
15.	ASGL-6	Katarni x R.Sweta	BC₃F₆
16.	ASGL-9	Katarni x R.Sweta	BC₃F₆
17.	ASGL-11	Katarni x R.Sweta	BC₃F₆
18.	ASGL-12	Katarni x R.Sweta	BC₃F₆
19.	ASGL-22	Katarni x BPT-5204	BC₂F₅
20.	ASGL-25	Katarni x BPT-5204	BC₂F₅
21.	ASGL-27	Katarni x R.Sweta	BC₃F₆
22.	BC-166	Katarni x R.Sweta	BC₃F₆
23.	BC-169	Katarni x R.Sweta	BC₃F₆
24.	KATARNI	CHECKS
25.	RAJENDRA SWETA
26.	BPT-5204
27.	MTU-7029
28.	SOBHINI
29.	CR SUGANDHADHAN

Sensory analysis test for aroma

The sensory test of rice aroma was performed by using 1.7% KOH solution (Sood and Siddiq 1978) by incubating one gram of fresh leaf sample in a test tube with 10 ml solution of 1.7% KOH. After 30 minutes of incubation, the aroma was evaluated and scored at 0 to 3 (0 – No aroma, 1 – Slight aroma, 2 – Moderate aroma, 3 – Strong aroma) scale by smelling the treated leaf samples through a panel of 5 persons.

Genomic DNA extraction and molecular marker analysis

Genomic DNA was extracted from the leaf of a 21-day-old plant using a rapid DNA isolation technique Kumar et al. (2018b). The genotypes under study were characterized using a total of 81 SSR markers. Additionally, four gene-specific markers- sd1, badh2, Hd3a, and GS3 - for semi-dwarfism, fragrance, heading date, and kernel length, respectively, were utilized to select the early maturing, semi-dwarf, and desired quality fragrant genotypes (Table 2 and Table 3). The sequences of forward and reverse SSR markers were obtained from the Gramene markers database (https://www.gramene.org/archive). The PCR amplification was carried out in a gradient thermal cycler (Applied Biosystems model VERITI) utilizing SSR primers and gene-specific primers. Ten µl of PCR reaction volume contained 2 µl of genomic DNA, 0.5 µl of 10 µMforward and reverse primers, 2.5 µl of GeneDirex/SRL PCR mix, and 4.5 µl of MilliQ water. For SSR markers, the PCR protocol was set up so that the template DNA was first denatured at 94°C for 4 minutes, then there were 35 cycles of denaturation for 30 seconds at 94°C, annealing for 40 seconds at 55°C, extension for 40 seconds at 72°C, and final extension for 10 minutes at 72°C.The annealing temperature for gene-specific primers was set at 58°C while maintaining the other PCR conditions. In a SIGMA-SVI gel electrophoresis device, the amplified products were separated at 2% agarose gel in 1X sodium borate (SB) conductive media. The gel was photographed using the gel documentation system (UVTECH, Bangalore GeNei, India).

SSR data analysis

By comparing the migration distance of amplified fragments in relation to the molecular weight of known size markers, the size of the most intensively amplified fragments was calculated. The SSR bands were carefully assessed for each genotype's presence (1) or absence (0) after the gel electrophoresis of the amplified products during and scoring of the images obtained from gel documentation system. Only easily reproducible and distinct bands were taken into account. An online marker efficiency calculator (iMEC) was used to compute the average number of alleles, heterozygosity index (H), and polymorphism information content (PIC) based on a binary matrix (Amiryousefi et al. 2018).

The number of positive matches (a), total sample size (n), and number of negative matches (d) were used to determine Jaccard's similarity coefficient (GS) equal to (GS) = a/ (n-d), using pairwise combinations of genotypes (Jaccard 1908). The unweighted pair group method (UPGMA; Sneath and Sokal 1973) was used to perform cluster analysis on this matrix, and the SAHN (sequential, agglomerative, hierarchical, and nested clustering) module of the NTSYSpc program was used to create the dendrogram. The Numerical Taxonomy and Multivariate Analysis system, Version 2.1 (NTSYSpc; Rohlf 1997) was used to calculate the dissimilarity matrix, genetic distances, and cluster analysis.

Table 2

List of functional markers, sequence and their location in the genome
S.No	Primer name	Target Trait		Sequence	Chr. No.	Reference
1.	sd1	semi-dwarfing	sd1-F sd1-R	F-CACGCACGGGTTCTTCCAGGTG R-AGGAGAATAGGAGATGGTTTACC	1	Spiemeyer et al 2002
2.	badh2	Aroma	External Sense Primer (ESP)	TTGTTTGGAGCTTGCTGATG	8	Bradbury et al. 2005
			Internal Fragrant Antisense Primer (IFAP)	CATAGGAGCAGCTGAAATATATACC
			Internal Non-fragrant Sense Primer (INSP)	CTGGTAAAAAGATTATGGCTTCA
			External Antisense Primer (EAP)	AGTGCTTTACAAAGTCCCGC
6.	Hd3a	Heading days	Hd3a-F Hd3a-R	F- TATCAGCTGGGCATATATAGTGTC R- CACGTAGTACCTGTCCTTAAAC	6	Nayyeripasand et al. 2013
7.	GS3	Kernel length	EFP	AGGCTAAACACATGCCCATCTC	3	Ramkumar et al. 2010
			ERP	CCCAACGTTCAGAAATTAAATGTGCTG
			IRSP	AACAGCAGGCTGGCTTACTCTCTG
			IFLP	ACGCTGCCTCCAGATGCTGA

Tabel 3. List of SSR markers, linkage group, number of alleles and their PIC value used under genetic diversity analysis

S.No.	Primer name	Chr. No.	No. of alleles	H	PIC value	S.No.	Primer name	Chr. No.	No. of alleles	H	PIC value
1.	RM5	1	4	0.37	0.36	2.	RM237	1	5	0.32	0.38
3.	RM246	1	5	0.43	0.34	4.	RM297	1	3	0.42	0.34
5.	RM431	1	4	0.48	0.32	6.	RM3825	1	3	0.40	0.35
7.	RM138	2	3	0.44	0.33	8.	RM208	2	2	0.38	0.36
9.	RM250	2	3	0.33	0.38	10.	RM263	2	3	0.43	0.34
11.	RM324	2	4	0.27	0.39	12.	RM530	2	3	0.44	0.33
13.	RM6843	2	2	0.33	0.38	14.	RM6318	2	4	0.39	0.35
15.	RM7	3	3	0.46	0.33	16.	RM60	3	2	0.31	0.38
17.	RM156	3	4	0.42	0.34	18.	RM231	3	2	0.38	0.36
19.	RM251	3	5	0.43	0.34	20.	RM571	3	4	0.38	0.36
21.	RM1256	3	5	0.48	0.32	22.	RM15189	3	2	0.00	0.43
23.	RM5474	3	2	0.50	0.31	24.	RM127	4	5	0.43	0.34
25.	RM252	4	5	0.40	0.35	26.	RM559	4	3	0.44	0.33
27.	RM164	5	8	0.41	0.35	28.	RM538	5	3	0.44	0.33
29.	RM5575	5	4	0.38	0.36	30.	RM3	6	4	0.43	0.34
31.	RM111	6	2	0.50	0.31	32.	RM190	6	4	0.38	0.36
33.	RM204	6	2	0.50	0.31	34.	RM276	6	5	0.32	0.38
35.	RM340	6	3	0.43	0.34	36.	RM469	6	3	0.44	0.33
37.	RM527	6	3	0.50	0.31	38.	RM557	6	3	0.44	0.33
39.	RM20285	6	4	0.38	0.36	40.	RM3414	6	4	0.42	0.34
41.	RM18	7	3	0.46	0.33	42.	RM125	7	4	0.36	0.37
43.	RM182	7	1	0.00	0.43	44.	RM234	7	4	0.50	0.31
45.	RM478	7	2	0.41	0.34	46.	RM505	7	2	0.49	0.31
47.	RM5793	7	4	0.38	0.36	48.	RM25	8	3	0.44	0.33
49.	RM210	8	5	0.39	0.36	50.	RM223	8	3	0.49	0.31
51.	RM264	8	4	0.39	0.35	52.	RM339	8	5	0.35	0.37
53.	RM407	8	5	0.42	0.34	54.	RM447	8	4	0.39	0.35
55.	RM3155	8	5	0.32	0.38	56.	RM3395	8	4	0.39	0.35
57.	RM5493	8	4	0.38	0.36	58.	RM22866	8	4	0.38	0.36
59.	RM22899	8	4	0.38	0.36	60.	RM23161	8	3	0.44	0.33
61.	RM23595	8	3	0.44	0.33	62.	RM205	9	6	0.41	0.34
63.	RM215	9	1	0.43	0.34	64.	RM257	9	4	0.38	0.36
65.	RM147	10	2	0.50	0.31	66.	RM222	10	5	0.37	0.36
67.	RM239	10	3	0.42	0.34	68.	RM4	11	2	0.50	0.31
69.	RM590	10	4	0.38	0.36	70.	RM144	11	9	0.50	0.31
71.	RM21	11	4	0.38	0.36	72.	RM206	11	6	0.31	0.38
73.	RM167	11	2	0.50	0.31	74.	RM224	11	2	0.33	0.38
75.	RM287	11	4	0.45	0.33	76.	RM552	11	3	0.43	0.34
77.	RM26076	11	4	0.37	0.36	78.	RM19	12	6	0.38	0.36
79.	RM277	12	1	0.40	0.35	80.	RM463	12	1	0.45	0.33
81.	RM7003	12	2	0.50	0.31

Morphological and biochemical observations

Morphological data on twenty nine genotypes of Katarni has been given in Table 4. Plant height ranged from 82.33 cm to 135.93 cm with a mean value of 104.7 cm. Katarni had the maximum plant height (135.93 cm), while minimum height was observed for ASGL22 (82.33 cm), which is much below the height of recurrent parent i.e. Katarni. Most of the lines were having a short to medium range of plant height. The days to 50% flowering ranged from 107 days to 141 days with a mean value of 127 days indicating the variation in maturity period of the selected lines with a maximum maturity period exhibited by ASGL22, while the early line ASGE2 exhibit a maturity period of 107 days. Aromatic rice generally has thin and long slender grains (Singh et al. 2000), and grain length and width determines the grain shape that is preference character to consumers with aroma. Grain length of the genotypes under study ranged from 4.09 mm (CR Sugandha Dhan) to 7.53 mm (BC-166). Most of the lines were found in the range of 5 mm − 6 mm, comparable to the length of recurrent parent. Grain width ranged from 1.47 mm (ASGE6) to 2.34 mm (MTU-7029).

Table 4

Variation for morphological traits in twenty nine rice germplasm accessions
S.No.	Genotypes	PH (cm)	DFF	GL (mm)	GW (mm)
1.	ASGE-2	111.47	107.00	5.90	1.84
2.	ASGE-3	97.47	112.00	5.96	1.86
3.	ASGE-6	111.00	113.00	5.45	1.47
4.	ASGE-7	97.20	132.00	5.59	1.80
5.	ASGM-3	105.13	118.00	5.16	1.87
6.	ASGM-4	88.47	132.00	5.01	1.81
7.	ASGM-7	110.53	123.00	5.96	2.06
8.	ASGM-11	100.67	124.00	5.34	2.03
9.	ASGM-12	108.07	130.00	6.01	1.80
10.	ASGM-21	112.93	135.00	5.81	1.78
11.	ASGM-24	106.00	135.00	5.81	1.81
12.	ASGL-2	104.40	140.00	5.98	1.79
13.	ASGL-4	88.40	128.00	5.20	1.89
14.	ASGL-5	107.53	127.00	5.59	1.74
15.	ASGL-6	106.53	135.00	5.86	1.73
16.	ASGL-9	100.00	140.00	5.54	1.63
17.	ASGL-11	113.67	141.00	5.59	1.73
18.	ASGL-12	107.80	141.00	5.42	1.87
19.	ASGL-22	82.33	141.00	5.51	1.76
20.	ASGL-25	100.67	140.00	5.99	1.91
21.	ASGL-27	105.60	141.00	5.25	1.76
22.	BC-166	120.33	108.00	7.53	1.73
23.	BC-169	87.47	131.00	5.14	1.73
24.	KATARNI	135.93	140.00	5.53	1.79
25.	RAJENDRA SWETA	101.67	117.00	5.42	1.66
26.	BPT-5204	96.33	129.00	5.43	1.84
27.	MTU-7029	107.47	121.00	5.62	2.34
28.	SOBHINI	122.80	114.00	4.55	1.86
29.	CR SUGANDHADHAN	98.34	109.00	4.09	2.05
	Mean	104.70	128.00	5.56	1.83
	Maximum	135.93	141.00	7.53	2.34
	Minimum	82.33	107.00	4.09	1.47
	CV	3.24	2.78	3.97	5.10
	CD	5.57	5.81	0.36	0.15

In rice, the aroma gene expresses in both leaf and grains and generally detected either through heating or reaction with a 1.7% solution of KOH. In the leaf sensory test, the genotypes were found to give strong (sensory score 3) to moderate (sensory score 2) type aroma. Maximum score of 3 was found for Katarni (strongly aromatic). None of the genotypes were found non-fragrant. Although, the sensory method of detection of aroma is most convenient and crude method (Lorieux et al. 1996; Garland et al. 2000), the ability to distinguish between mildly aromatic and non-aromatic samples is limited. The chances of error by any analyst due to saturation of nostril sensation cannot be ruled out; hence using molecular marker for detecting aroma trait could be an unbiased approach which was also used in the present investigation.

Allelic Diversity Analysis using gene specific markers

To confirm the introgression of semi-dwarf gene, the Katarni derived lines were genotyped with gene specific marker for semi-dwarfism i.e. sd1 as reported by Speilmeyer et al. 2002. On PCR screening, it was observed that all the entries exhibited amplification of approximately 350 bp including semi-dwarf parental checks Rajendra Sweta, MTU7029 and BPT5204, while Katarni and Shobhini which were tall in plant height exhibiting an amplification of 731 bp (Fig. 4).The cases of gene specific marker for fragrance, i.e. badh2, a four set of primers were used (Bradbury et al. 2005) in such a way that it amplified a common band of approximately 580 bp in all genotypes, a band of 355 bp in homozygous non-fragrant and a band of 257 bp in individuals with homozygous fragrant genotype. In the third case both bands of sizes 355 bp and 257 bp were present indicating an individual in heterozygous state (Fig. 1). PCR screening of the lines with badh2 marker, three lines of early, five lines of medium and nine lines of late maturity duration amplified a 257 bp product of fragrance including Katarni and Shobhini, three lines one of early and two of medium amplified product size of both 257 and 355 and are heterozygous non-fragrant including CR Sugandha Dhan, while the rest of the lines were found non-fragrant including Rajendra Sweta, MTU7029 and BPT5204 (Fig. 5). Hd3a is a major QTL that controls flowering date in rice. It amplifies two allelic forms A and B with the approximate sizes of 110 and 170 bp, respectively (Nayyeripasand et al. 2013), and the cultivars carrying allele A flowers significantly later than cultivars carrying allele B. In the present study, most of the genotypes were found of early maturing types i.e. having an amplification product of 170 bp. Four genotypes were of late maturing types, namely, ASGE3, ASGM11, ASGL22 and CR Sugandha Dhan, and one genotype, viz., ASGL4 was found heterozygous (Fig. 6). In the marker analysis of lines with GS3 that controls kernel length or grain size in rice, out of 4 primers; two primer pairs were multiplexed in a single PCR reaction to get a co-dominant type of amplification product. The external primers amplified a region of 365 bp as a common fragment in both long and short grain types, while the allele specific primers have to amplify region of either 147 bp (C allele) in short grain types or 262 bp (A allele) in long grain types (Ramkumar et al. 2010) (Fig. 2). By using this gene specific marker, two lines were found heterozygous namely, ASGE2 and BC166, while the rest of the lines amplified a product size of 147 bp i.e short grain type (C allele) (Fig. 7). The molecular amplification pattern with GS3 gene specific primer was in congruity with the grain length data of 29 genotypes used in this study.

SSR Data Analysis

Polymorphism and marker efficiency

A total of 289 alleles were detected at the loci of 81 microsatellite markers across 29 accessions of rice. The informativeness of the markers on different parameters has been shown in Table 3. Allele frequency of the markers ranged from 1 (RM182, RM215, RM277 and RM463) to 9 (RM144) with an average of 3.56 alleles per locus. Figure 8 shows a representative gel image of PCR amplification by primer RM252. PIC value refers to the value of a marker for detecting polymorphism within a population, depending on the number of detectable alleles and the distribution of their frequency; thus, it provides an estimate of the discriminating power of the marker (Nagy et al. 2012). In the present study, it ranged from 0.31 (RM5474, RM111, RM204, RM527, RM234, RM505, RM223, RM147, RM4, RM144, RM167 and RM7003) to 0.43 (RM15189 and RM182) with an average of 0.35. Expected heterozygosity (H) of a marker is the probability of heterozygosity of an individual for the locus in a population. It differed among the markers and ranged from 0.00 (RM15189 &RM182) to 0.50 (RM5474, RM111, RM204, RM527, RM234, RM147, RM4, RM144, RM167 and RM7003).

Jaccard’s similarity coefficient and Dendrogram analysis

A similarity coefficient matrix based on alleles generated through 81 SSR markers was generated to study the level of similarity/relatedness among Katarni derived lines. The Jaccard’s similarity coefficient varied from 0.09 to 0.91 indicating great variations among the studied genotypes. Highest similarity coefficient value of 0.91 was found between the Line No. 17 (ASGL11) and 18 (ASGL12), followed by 0.72 between Line No. 16 (ASGL9) and Line No. 17 (ASGL11), while least similarity coefficient value of 0.09 was found between the genotype CR Sugandha Dhan and Line No. 18 (ASGL12), followed by 0.10 between CR Sugandha Dhan and Line No. 17 (ASGL11).

Analysis of genetic similarity values among the rice genotypes used led to the construction of dendrogram which is presented in Fig. 9. Cluster Analysis was performed by using UPGMA based on similarity co-efficient values, and the 29 rice genotypes were grouped into five main clusters i.e. Cluster I, Cluster II, Cluster III, Cluster IV and Cluster V, with approximately 0.35 similarity coefficient distance as the threshold value for clustering. Cluster I and II included four genotypes each, and all the early duration lines were clustered into Cluster I. Cluster III included maximum number of eleven entries and all the medium duration lines were clustered into Cluster III. Cluster IV and Cluster V included five genotypes each and all the checks excluding Katarni were clustered into Cluster V.

Molecular marker-assisted selection (MAS) is an approach that has been successfully used in several crops to overcome the problems associated with dependency on the accuracy of phenotypic selection in conventional plant breeding. The advancement of sequencing technology has enabled the availability of complete rice map-based sequence to facilitate the gene identification and function through molecular markers. The gene specific markers based on single nucleotide changes (SNPs) or deletion in the wild type genes are able to describe the functional polymorphism between the genotypes. In the present study, the functional molecular markers both for sd1 and badh2 genes were utilized which helped to select the semi-dwarf and fragrant plants not only in early generations, but also at early growth stage of the plant. There are many successful examples in the rice breeding programme, where MAS has been used for the improvement of agronomic traits as well as quality traits. Raina et al (2019) and Srivastava et al (2019) performed the marker assisted selection in traditional fragrant cultivar Ranbir Basmati and Kalanamak, respectively, using sd1 and badh2 gene specific markers and found similar results. Riaz et al. (2023) develop high yielding basmati advance lines with good quality attributes through a marker-assisted selection. Similar experiment was performed by Kumar et al. (2022) in Katarni rice to validate the presence of semi-dwarfism and fragrance by using gene specific markers for the respective trait. Rice is a short-day plant and log day conditions delays flowering and there is a complicated network of genes for controlling flowering date in rice. There are two major pathways controlling flowering time one is H1-Hd3a dependent and other is Ehd1-Hd3a dependent (Liu et al. 2021). The incongruity of data of days to 50% flowering data with the PCR amplification pattern using Hd3a gene specific primer in present study might be due to involvement of many different genes responsible for flowering regulators that might modulate the expression of Hd1 and Edd1 gene in present study. Carsono et al. (2021) assessed the performance of F₂ progenies with different gene specific markers for semi-dwarfism, fragrance, heading days and compare the result with the morphological data. Sarhadi et al. (2009), Jewel et al. (2011), Golam et al. (2011) found consistent findings while working with aromatic lines. Sensory analysis test detected the strength of aroma and as per the analysis Katarni was found to be the most aromatic. The result obtained was in accordance with the result found out by Nguyen and Truong 2016 and Kumar et al. 2018a.

Genetic diversity assists in the development of genotype that is suitable and adaptable to rapid climate change through the introduction of foreign genes (Jasim Aljumaili et al, 2018). SSRs are considered to be appropriate for assessment of genetic diversity, fingerprinting for varietal identification and assessment of seed purity because of their ability to detect large numbers of discrete alleles accurately and efficiently (Sajib et al, 2012; Nachimuthu et al, 2015; Ganie et al, 2016; Jasim Aljumaili et al, 2018). In the present investigation, genetic diversity among twenty Katarni derived lines along with 6 checks was determined using 81 microsatellite markers. Allele frequency of the markers ranged from 1 to 9 with an average of 3.56 alleles per locus. The number of alleles per locus detected in the present study is comparable to earlier reports (Anisuzzaman et al. 2022, and Suvidha et al. 2020) used 30 SSR markers to assess diversity among 14 rice genotypes, where 107 polymorphic alleles were detected with an average of 3.56 alleles per locus. Similarly, Singh et al (2016) identified 63 alleles with an average of 2.75 alleles per locus. PIC value ranged from 0.31 to 0.43 with an average of 0.35, which is comparable to three previous estimates of microsatellite analysis in rice viz., 0.38 to 0.65 with an average of 0.56 (Tripathi et al. 2020), 0.032 to 0.588 with a mean PIC of 0.366 (Rashmi et al. 2017). In a similar study by Jasim Aljumaili et al. (2018), reported a total of 131 alleles, and the number of alleles per locus ranged from 2 to 7 with an average of 4.09. The expected heterozygosity differed among the markers and it ranged from 0.01 to 1.13 with an average of 0.60. Based on the molecular profiling of genotypes with different SSR markers sufficient amount of genetic diversity was observed among the parents and their advanced breeding lines. Cluster analysis based on similarity coefficients placed 29 rice genotypes into 5 major groups with a similarity coefficient of around 0.35. Similar results were obtained by Suvi et al. (2020); Rahman et al. (2012), and Singh et al. (2015). It was also observed that genotype of different maturity duration falls under separate clusters.

The present study can be concluded by the effectiveness of gene specific and SSR markers in establishing the similarity and categorizing the breeding lines into separate groups. Thus, the generated information from these markers can be used in future breeding programme.

In the present study, the improved lines were found shorter in plant height and contains gene for aroma. On screening of the lines with GS3 gene specific marker for kernel length, most of the lines were found to have short grain size, which was supported by the result obtained by phenotypic evaluation of the lines. In conjunction with the completion of the rice genome sequencing project, QTL analysis has become able to contribute to our understanding of natural variations in rice, however, such QTL information has not been fully exploited in rice breeding programs due largely to the nature of QTL analysis. Since it is based on statistical analysis, its reliability differs from one QTL analysis to another; in addition, information concerning the interactions with other genes and the effect of environmental conditions is hardly available. Hence, this investigation considers only the major genes that are directly involved to validate the expression of the concerned traits. The PIC value indicates that all these markers were highly informative and capable of distinguishing between genotypes. A total of 289 alleles were detected at the loci of 81 microsatellite markers with an average of 3.56 alleles per locus. The PIC values ranged from 0.31 to 0.43 and the highest PIC value of 0.43 was determined for the marker RM15189 and RM182 which were proven to be the best appropriate markers for discriminating among rice genotypes. The genetic divergence analysis divided 29 rice genotypes into five clusters with cluster III having the most cultivars (11) and clusters I and II having the least (4). Based on dendrogram the maximum degree of similarity was observed between genotype ASGL11 and ASGL12 followed by ASGL9 and ASGL11. The maximum value of the dissimilarity coefficient was discovered between CR Sugandha Dhan and ASGL12 (0.91), followed by CR Sugandha Dhan and ASGL11 (0.90), whereas lowest value was seen between ASGL11 and ASGL12 (0.09) showing highly diverse genotypes. The markers used here were found to of high value as they can effectively developed the DNA Fingerprint of the advanced breeding lines of Katarni and characterized the genotypes at molecular level. The information generated can further be used for constructing database for breeding programs, and isolation of a novel semi-dwarf, early maturing and aromatic lines. The promising lines having exquisite quality parameters similar to Katarni differing only in plant height and maturity can help farmers in terms of productivity and income generation.

Acknowledgements

The authors acknowledge assistance in terms of research grants provided by Bihar Agricultural University, Sabour and Science and Engineering Research Board, Department of Science and Technology, Government of India. This article bears BAU Communication number 1470/230902.

Funding

This work was supported by Science and Engineering Board, Department of Science and Technology, Govt. of India by grant no. SB/YS/LS-81/2013 and has also received research support from Bihar Agricultural University, Sabour

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Conflict of interest:

The authors declare no conflict of interest

Data availability

The molecular data generated or analyzed during this study are included in the paper.

Authors contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rabiya Parveen and Mankesh Kumar. The first draft of the manuscript was written by Mankesh Kumar. Zafar, Satyendra and Tushar commented on previous versions of the manuscript. Final draft was checked by S P Singh and P K Singh. All authors read and approved the final manuscript.

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No competing interests reported.

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SSR marker based molecular diversity among advanced breeding lines of Katarni rice (Oryza sativa. L.) and validation of phenotypic selection through functional markers

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Abstract

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INTRODUCTION

Materials and methods

Plant materials

Sensory analysis test for aroma

Genomic DNA extraction and molecular marker analysis

SSR data analysis

Results

Morphological and biochemical observations

Allelic Diversity Analysis using gene specific markers

SSR Data Analysis

Polymorphism and marker efficiency

Jaccard’s similarity coefficient and Dendrogram analysis

Discussion

Conclusions

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References

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