Mapping of Qtl for Anaerobic Germination Using the Donor Ac39416a in the Genetic Background of Swarna Sub-1 (Oryza Sativa L.)

doi:10.21203/rs.3.rs-574662/v1

Download PDF

Research Article

Mapping of Qtl for Anaerobic Germination Using the Donor Ac39416a in the Genetic Background of Swarna Sub-1 (Oryza Sativa L.)

https://doi.org/10.21203/rs.3.rs-574662/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Anaerobic germination is an important trait in particular for cultivation under direct seeding method in kharif season, as well as during nursery rising for transplant rice, as sometimes unexpected rains immediately after sowing will drastically reduce the plant population.

Methods and Results: In the present investigation phenotypic screening for Anaerobic germination (AG) was carried out using 188 F_2:3population of Swarna Sub1/AC39416A at RARS (APRRI), Maruteru. The mean anaerobic germination per cent recorded after the two weeks of submergence ranged from 0% to 95% with overall mean of 47.51% whereas, for three weeks of submergence, the mean anaerobic germination per cent recorded between 0 and 95%, with overall mean of 37.66%. 134 (19.42%) out of 687 Simple Sequence Repeats (SSR) markers surveyed were polymorphic between the parents. Linkage analysis was done with 83 SSR markers showing polymorphism clearly using the integrated software called QTL IciMapping software version 4.1.0. The length of linkage map constructed across whole genome was 3600.8 cM and identified seven QTLs viz., qAG2, qAG3, qAG7-1, qAG7-2, qAG9, qAG10 and qAG12. All these seven QTLs explained phenotypic variance of about 37.47% collectively for AG trait, with their individual contributions ranging from 3.5% to 8.67% of phenotypic variation and LOD scores of 2.6 to 5.86. The LOD score and phenotypic variance is 5.86 and 8.67% respectively for qAG10 a novel QTL identified in the present study using ICIM method.

Conclusion: QTL “qAG12-1” identified in this study may be considered for introgression into popular elite rice varieties otherwise susceptible for anaerobic germination after fine mapping studies.

Molecular Biology

Anaerobic Germination

Screening

Simple Sequence Repeats (Microsatellites)

Genotyping

QTL mapping

Rice production is influenced by many of the biotic and abiotic stresses throughout the world, where abiotic stresses alone contribute to nearly 50% of the total losses in the yield. Under coastal irrigated ecosystem major abiotic stresses viz., floods, cyclones (causes lodging of the crop) and salinity resulting in decline in the productivity of rice. Severe and unexpected heavy rains leaves no time to leach excess water in to ground which leads to flooding, a major climate change challenge severely affecting productivity and often place a major limitation on the cultivation. Three types of floods viz., submergence during germination (germination under anaerobic conditions), flash floods (complete submergence up to 2 weeks) and stagnant flooding (30-50cm water depth) are the most prevailing types of floods in coastal Andhra Pradesh (Reddy et al., 2015).

Flooding during seed germination might be a consequence of unevenly levelled fields or early and unforeseen rains, or even, when rice fields are purposely flooded after sowing to combat weeds. Among all abiotic stress, tolerance to flooding during the process of seed germination i.e anaerobic germination (AG) is the rarest phenomenon (Zhang et al., 2018). Rice is the only chief cereal that exhibits a degree of tolerance to anaerobic conditions during germination, which is limited to coleoptiles emergence and partial growth, but not adequate to triumph over the stress (Miro et al., 2017). Semi-aquatic nature of rice made it to survive few days of submergence and broad genetic diversity among the rice landraces and traditional varieties has enabled its cultivation in different agro ecological zones and water regimes. Although rice could tolerate flooding, its germination is limited to coleoptiles elongation as in susceptible genotypes; however root and primary leaf fall short to develop normally (Kumar et al., 2018). Tolerance of rice crop to flooding stress through enhanced germination and early growth of the seedlings is a prerequisite for successful cultivation in regions where floods is a recurrent problem.

Anaerobic germination is characterized by rapid elongation of coleoptile under submergence, with concomitant delay in development of radical (Kretzschmar et al., 2015). A series of biochemical properties such as, changes in the enzymatic activities of α-amylase, peroxidase and alcohol dehydrogenase influences anaerobic stress tolerance in rice. Positive influence of α-amylase in improving the germination of the seed is by converting starch into sugars (Perata et al., 1993). The tolerance mechanism that enables rice to germinate in the absence of O₂ is based mainly on the fact that rice seeds are able to degrade their starchy reserves under anoxia also (Magneschi and Perata. (2009) and Nghi et al., 2019). The ability of the rice coleoptile to elongate under anoxia represents an unveiled enigma, whereas the mechanisms and genes involved in adaptation of rice flora to submergence have newly been discovered.

Identification of the molecular markers linked to QTLs or genes controlling tolerance to submergence during germination would assist selection for this character, which have low heritability (Angaji et al., 2010). Screening of markers for polymorphism between the parents forms the basis for tagging of the desired gene, fine mapping of the gene in the rice chromosome and in the subsequent Marker Assisted Selection (MAS) programmes (Reddy et al., 2018). Of the various types of DNA markers, PCR-based markers called simple sequence repeats (SSRs) or microsatellites are used widely due to their high degree of polymorphism, technically simple method of finding and are cost efficient (Gonzaga et al., 2015). The quantitative trait locus (QTL) analysis and other molecular methods are employed in order to find the genetic locus that underlies the trait of interest. If a genetic locus has been discovered and characterized has major effect on the trait, it can be transferred subsequently into modern high-yielding cultivars, but are stress-sensitive using marker-assisted breeding technology to achieve stress-tolerant cultivars efficiently (Mustroph, 2018). QTL mapping for AG in rice has begun to identify the promising loci that promote increased germinability under flooding in experiments of Jiang et al., 2004; Jiang et al., 2006; Angaji et al., 2010; Septiningsih et al., 2013b and Baltazar et al., 2014. QTLs reported previously for anaerobic germination in rice were listed in (Table. 4).

Breeding for increased anaerobic germination or flooding tolerance during germination has been attempted previously by many workers, but the progress is little due to the limitation of donors with AG i.e genetic diversity, limited knowledge on the genetics and complex mechanisms of tolerance and methods used for screening or measurement of tolerance (Jiang et al., 2004). Keeping in vision the importance of anaerobic germination the present investigation was planned and executed using the parents Swarna Sub1 and AC39416A for generating F_2:3 mapping population in lieu of identification of QTLs responsible for AG.

Plant material and Mapping population:

The experimental plant material consisting of 188 F_2:3 mapping population was developed by crossing Swarna Sub1 and AC39416A at RARS (formerly APRRI), Maruteru, West Godavari district of Andhra Pradesh. Swarna sub1 is a variety developed by IRRI, Philippines by introgression of sub1gene into a mega rice variety Swarna. It has submergence tolerance during vegetative stage for 7–10 days, but lacks tolerance to submergence during germination. Several donors were identified for anaerobic germination tolerance across the world. In RARS, Maruteru the cultures, AC39416A and AC39397 were identified as good donors for the anaerobic germination trait. Hence, in the present study the parents Swarna sub1 and AC39416A were used for generating mapping population. The 188 F_2:3 lines along with their corresponding contrast parents screened phenotypically and genotypically to develop reliable data in an attempt to unravel the tolerance of submergence during germination.

Screening for tolerance to anaerobic conditions during germination:

Phenotypic Screening of 188 F_2:3 population of Swarna Sub1 / AC39416A along with parents was conducted at RARS, Maruteru, West Godavari district of Andhra Pradesh, located at an altitude of 5m above MSL, 81.44⁰E longitude and 26.38⁰N latitude, using complete randomized design with two replications in concrete tank as per Septiningsih et al. (2013b). Initially anaerobic stress is created and then level of their tolerance was recorded. From each line about 25 healthy seeds were soaked for a period of about 24 hours and incubated in dark or closed chamber yet again for 24 hours so that seeds will start germinating. Then pre-germinated seeds were sown in pro-trays which are filled with well puddled soil in such a way that the sprouted portion facing upwards or top end. And finally the pro-trays were arranged randomly inside the concrete tank and submergence is imposed by filling water up to 15 cm above the trays. Constant depth of water is maintained throughout the submergence treatment i.e two weeks and three weeks separate experiments. After submergence treatment for two weeks and three weeks pro-trays were kept outside of the concrete tank for about one week of de-submergence treatment during which watered daily, finally record the data from survived lines.

Genotyping of mapping population:

Fresh, young leaf samples were collected from all 188 F_2:3lines and parents Swarna Sub1 and AC39416A at tillering stage (45 DAS) during early hours of a day and stored at -20ºC. Genomic DNA isolation was done using the modified Cetyl Tri Methyl Ammonium Bromide (CTAB) method Zheng et al. (1995). Parental polymorphism survey was conducted using a total of 687 SSR (Microsatellite) markers. The SSR primer sequences and other information like physical position, annealing temperature, expected PCR product size and sequences were obtained from Gramene markers database (http://www.gramene.org.in). Genotyping of entire population was done using the polymorphic SSR markers. 7.5 µl of master mix was added to each well of PCR plate having 2.5 µl of template DNA to make the final volume to 10 µl per cell. Then PCR plate was kept in a thermal cycler for the reaction to take place.PCR amplified products i.e DNA samples (10 µl) were loaded into wells of 3 %agarose gels and run for 2 hours at constant mode with 110 volts where, DNA molecules with in an agarose gel matrix were subjected to steady electric field; it will migrate through the gel towards the positive electrode, anode since DNA has a strong negative charge at neutral P^H. The pores in the gel separate the linear fragments of DNA according to their size. The DNA fragments were then visualized under UV-trans-illuminator as bands and documented using gel documentation system (SYNGENE Gene flash U.K.).

Linkage map construction:

Linkage between the markers and QTL was detected by a statistical test called the Logarithm of Odds (LOD) score method. Integrated software called QTL IciMapping software version 4.1.1 (Wang et al., 2016) was used for linkage analysis using 83 polymorphic SSR markers for which map positions were taken from http://www.gramene.org. Grouping of all the 83 markers across 12 linkage groups (chromosomes) was done based on anchor information, for removal of unanchored markers and select anchor order, for ordering the markers for fitting on the best positions. And then outputting is done after which map show tool is selected to draw linkage map of SSRs. Linkage maps were constructed using linkage map construction tool in biparental populations (MAP) of ICIM software following Kosambi mapping function.

QTL analysis:

QTL IciMapping software version 4.1.1 is integrated software for linkage analysis and genetic mapping in biparental populations. QTL analysis was performed with 83 SSR markers that are polymorphic between the contrasting parents to study the association of genotypic and phenotypic data of the entire population screened using integrated software QTL IciMapping V.4.1 software (Wang et al., 2016). QTLs were detected by Single Marker Analysis (SMA) and Inclusive Composite Interval Mapping for the additive QTL (ICIM-ADD) methods in the present study.

Phenotypic screening of mapping population:

Early generation biparental mapping population consisting of 188 F_2:3 lines developed with contrasting parents Swarna Sub1 / AC39416A was screened for AG. The number of seedlings survived after submergence treatment was counted and expressed as anaerobic germination percent. The mean anaerobic germination per cent recorded after the two weeks of submergence among the population of 188 lines ranged from 0–95% with overall mean of 47.51% whereas mean AG per cent of the contrasting parents SwarnaSub1 and AC39416A was 40% and 85% respectively indicating significant differences for the trait. For three weeks of submergence treatment, the mean anaerobic germination per cent recorded between 0 and 95%, with overall mean of 37.66% and the mean AG per cent of the two contrasting parents was 27% and 75.6%, respectively, indicating that there was wider variation in the mapping population for anaerobic germination. (Fig. 1)

Plant survival rate was calculated by counting the seedlings survived after one week of de-submergence. The average survival rate of Swarna Sub1 and AC39416A was 35% and 80% respectively after two weeks of submergence, whereas for F_2:3 mapping population it ranged from 0 to 95% with overall mean of 36.74%. The plant survival rate after three weeks of submergence for Swarna Sub1 was 17.6% whereas 72% for the donor AC39416A. The average survival rate of population was 15.5% which indicated clear cut variation and following normal distribution. (Fig. 2)

SSR marker based linkage map construction:

A total of 687 SSR markers covering all the 12 chromosomes of rice were used for parental polymorphism survey, of which 134 (19.42%) SSR markers were found to be polymorphic. But, only 83 (12.08%) SSR markers which have shown clear distinct polymorphic bands are further used for generating genotypic data for construction of the linkage map and QTL analysis. The level of polymorphism was ranged from 7.50–27.59% with an average of 13.25% for all the chromosomes. (Table 1). Linkage map provides information about the number of markers on each chromosome, marker order, name and position on the chromosome. We used integrated software called QTL IciMapping software version 4.1.1 (Wang et al., 2016) for linkage map construction using 83 SSR markers for which map positions were taken from http://www.gramene.org. The whole genome length of linkage map constructed using 83 SSR markers was 3600.8 cM. (Fig. 3) The map length of each chromosome varied with number of markers used in each linkage group. The map lengths of all linkage groups are 219.09, 384.8, 482.98, 224.4, 254.08, 220.23, 357.68, 296.02, 127.63, 220.24, 280.71, 532.94 cM respectively.

Table 1

Chromosome wise list of markers screened, number of polymorphic markers along with per cent of polymorphism.
Chromosome Number	Number of Markers			Polymorphism (%)
Chromosome Number	Surveyed	Polymorphic	Anchored	Polymorphism (%)
1	37	10	7	18.92
2	29	9	8	27.59
3	64	14	11	17.19
4	54	12	6	11.11
5	80	10	6	7.50
6	61	16	5	8.20
7	62	12	8	12.90
8	68	8	7	10.29
9	64	8	5	7.81
10	57	13	6	10.53
11	49	11	5	12.24
12	62	11	9	14.52
Total	687	134	83	13.25

QTL mapping analysis:

QTL analysis was performed with 83 SSRs. In Single Marker Analysis, six markers viz., RM 15554, RM 401, RM 5711, RM 21700, RM 28073 and RM 1584 were found to be linked with anaerobic germination trait which are located on chromosome 3, 4, 7, 7, 12 and 12 respectively. All these QTLs individually accounted for a total of 4.24–10.02% phenotypic variance (R²). Highest phenotypic variance (10.02%) was recorded on chromosome 12 by qAG12-1 with peak marker RM 1584. (Table 2)

Table 2

Peak Markers linked to anaerobic germination identified in F_2:3 population of Swarna Sub1/AC39416A using Single Marker Analysis.
S.No	QTL	Chromosome	Peak Marker	LOD	PVE(%)	Add	Dom
1	qAG-3	3	RM15554	2.97	4.24	-3.40	12.52
2	qAG-4 (novel QTL)	4	RM401	3.31	4.72	0.24	-16.04
3	qAG-7-1	7	RM5711	3.86	5.46	-1.36	14.48
4	qAG-7-2	7	RM21700	3.88	5.49	2.17	-15.29
5	qAG-12-1	12	RM1584	7.39	10.02	-7.03	-18.73
6	qAG-12-2	12	RM28073	4.29	6.03	6.23	13.47

A total of seven putative QTLs viz., qAG2, qAG3, qAG7-1, qAG7-2, qAG9, qAG10 and qAG12 were identified and mapped using Icimapping (ICIM) method with manual input threshold LOD score of 2.5. (Table 3) Out of 7 QTLs found, 2 QTLs were located on chromosome 7, 1 QTL on each of the chromosomes 2, 3, 9, 10 and 12. All these 7 QTLs explained phenotypic variance of about 37.47% collectively for AG trait, with their individual contributions ranging from 2.99–8.67% of phenotypic variation and LOD scores of 2.65 to 5.86. The phenotypic variance explained by qAG2, was highest (8.66%) followed by qAG10 (8.60%) which were mapped on chromosome 2 and 10 respectively. Whereas, the highest LOD score (5.86) was shown by qAG10and qAG-7-2.

Table 3

QTLs for tolerance to submergence during germination identified in F_2:3 population of Swarna Sub1/AC39416A in Inclusive Composite Interval Mapping (ICIM) method.
S.No	QTL	Chromosome	Flanking Markers	LOD	PVE (%)	Add	Dom
1	qAG-2	2	RM263 - RM6933	2.73	8.60	-15.01	13.94
2	qAG-3	3	RM15554 - RM15561	2.65	5.15	7.44	23.93
3	qAG-7-1	7	RM6697 - RM5711	5.05	3.53	-0.64	15.91
4	qAG-7-2	7	RM418 - RM21700	5.86	3.62	1.08	-16.27
5	qAG-9	9	RM23958 - RM1553	2.90	4.91	-12.95	2.20
6	qAG-10 (novelQTL)	10	RM25735 - RM591	5.86	8.67	-2.39	23.76
7	qAG-12	12	RM28759 - RM1584	5.47	2.99	-4.52	-14.20

Table 4

List of QTLs reported by earlier workers for anaerobic germination in rice (*Oryza sativa* L.).
S. No.	Parents	QTLS	Chromosome	Flanking Markers	Reference
1	IR64/ Kharsu 80A F2:3	qAG3	3	id3002377- id3004190	Baltazar et al. (2019)
		qAG7.1	7	id7000519- id7002260
		qAG7.2	7	id7002427- id7003359
		qAG7.3	7	id7003853- id7004429
2	Tai Nguyen/ Anda F2:3	qAG1a	1	43902 -48,214	kim and Reinke et al. (2018)
		qAG1b	1	id1006871- 327,392
		qAG8	8	id8001299- 8,107,849
		qAG11	11	id11003544- 1,194,923
3	IR64-AG1,	qAG9-2	9	RM 24161	Sartaj et al. (2016)
	Ciherang-Sub1AG1
	and Bg 358,
	BC₁F₁
4	48 rice genotypes	qAG2	2	RM 341	Reddy et al. (2015)
4	48 rice genotypes	qAG11	11	RM 206	Reddy et al. (2015)
5	IR64/Nanhi F2:3	qAG2.1	2	id2001831-id2003094	Baltazar et al. (2014)
		qAG2.2	2	id2006621-id2007526
		qAG3	3	id3007932-id3010875
		qAG7	7	wd7000465-id7002784
		qAG11	11	id11009201-id11010245
6	IR42/Ma-Zhan Red F2:3	qAG2	2	RM263–RM5378	Septiningsih et al. (2013b)
		qAG5	5	RM5361
		qAG6	6	RM204–RM402
		qAG7.1	7	RM3583–RM21427
		qAG7.2	7	RM7338–RM346
		qAG7.3	7	RM21803–RM234
		qAG9	9	RM553–RM3808
		qAG12	12	RM313–RM28766
7	IR64/Khao Hlan BC2F2 population	qAG1-1	1	RM582-RM10713	Angaji et al. (2010)
		qAG1-2	1	RM11125-RM104
		qAG2-1	2	RM327-RM6318
		qAG3-1	3	RM7097-RM520
		qAG7-1	7	RID12i-RM5606
		qAG7-2	7	RM21868-RM172
		qAG8-1	8	RM210-RM149
		qAG9-1	9	RM8303-RM5526
		qAG9-2	9	RM3769-RM105

In this study F_2:3 mapping population of Swarna Sub1/AC39416A was used for mapping QTLs for anaerobic germination. Phenotypic screening experiment revealed significant differences among the population for the traits studied. Among 188 F_2:3 population of Swarna Sub1/AC39416A studied 12 lines for two weeks treatment and five lines for three weeks treatment have shown AG percent on par with the donor parent AC39416A. Barik et al., (2019) reported similar trend of variation in anaerobic germination per cent. Doley et al., (2018) noticed that survival per cent was correlated positively with coleoptiles elongation which helps in obtaining oxygen from surroundings. Greater variability in germplasm lines screened for anaerobic germination was also described by Umarani et al., (2018) which is in accordance with the present results. Similar pattern of variation in survival per cent of population was described by Septiningsih et al., (2013b) in the F_2:3 population of IR 64 / Ma-Zhan Red and Baltazar et al. (2014) in F_2:3 population of IR 64 / Nanhi during screening for tolerance to anaerobic conditions during germination.

In general, rice seeds contain the complete set of enzymes needed for the degradation and use of starch for the growth and maintenance of the growing embryo; however, the activities of these enzymes are affected by anaerobic conditions due to the low availability of oxygen (Ismail et al., 2012). Some of these enzymes, especially alcohol dehydrogenase 1 (ADH1), rice alpha amylase (RAmy3D), and sucrose synthase, are more active in anoxia-tolerant rice genotypes under low-oxygen conditions during germination but are inhibited in sensitive genotypes, RAmy3D encoding starch-degrading enzymes, up-regulated during germination under anaerobic conditions. This increased gene expression under anaerobic conditions leads to higher amylase activity for starch hydrolysis, which in turn enhances the activity of ADH1, a key enzyme involved in alcohol fermentation that is crucial for rice seed germination under anaerobic conditions. Upon germination, ethylene produced by the growing embryo may further promote cell expansion and starch hydrolysis, along with reduced abscisic acid (ABA) biosynthesis and increased gibberellic acid (GA) biosynthesis (Rauf et al., 2019). Hence, tolerance of anaerobic conditions during germination is an essential trait for direct-seeded rice cultivation in both rainfed and irrigated ecosystems (Septiningsih et al., 2013b).

Polymorphism is a measure of genetic diversity and varies with the parental combinations. The contrasting parents Swarna Sub1 and AC39416A selected for development of mapping population were initially surveyed for polymorphism using SSR markers to identify polymorphic markers between them. Only 134 (19.42%) SSR markers were found to be polymorphic among 687 SSRs screened. But, only 83 SSR markers shown clear distinct polymorphic bands are further used for generating genotypic data for construction of the linkage map and QTL analysis. The polymorphism percentage of markers on chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 was 18.92, 27.59, 17.19, 11.11, 7.50, 8.20, 12.90, 10.29, 7.81, 10.53, 12.24 and 14.52 per cent respectively. Among the 12 chromosomes surveyed, chromosome 3 recorded the maximum number of polymorphic markers (eleven) followed by chromosome 12 (nine) and chromosome 2 and 7 (eight). The polymorphism percentage was reported to be highest in chromosome 2 (27.59%) and lowest in chromosome 9 (7.58%). Earlier studies of parental polymorphism using SSR markers in rice done by Jiang et al. (2006), Angaji (2008), Angaji et al. (2010), Septiningsih et al. (2013b) and Waghmare et al. (2018) revealed that 121(32%), 170 (27.8%), 192 (28%), 115 (10.5%), 118 (11.1%) and 41 (20.82%) primers were polymorphic from a total of 197, 1066, 1074, 680, 610 and 653 SSR’s surveyed. The extent of polymorphism recorded in the present investigation 19.42% is comparable with earlier reports. Integrated software QTL IciMapping V.4.1 software (Wang et al., 2016) was used for linkage map construction.The whole genome length of linkage map constructed using 83 SSR markers was 3600.8 cM. Lal et al. (2018) Performed linkage mapping with 60 SSR primers using QTL ICIM software version 4.0 Software. Whereas Pramudyawardani et al. (2018) used QTL ICIMapping V3.2 software for construction of linkage map using 97 SNP and 7 SSR markers.

QTL analysis in the present research using the software was performed using two mapping methods namely Single Marker Analysis (SMA) and Inclusive Composite Interval Mapping for the additive QTL (ICIM-ADD). Single marker analysis (SMA) revealed that six markers were found to be linked with anaerobic germination in the F_2:3 population of Swarna Sub1 / AC39416A. LOD score of 2.96 and phenotypic variance of 4.24% has been recorded with RM 15554 on chromosome 3, whereas RM 401, RM 5711, RM 21700, RM 28073 and RM 1584 have varying LOD scores 3.31, 3.85, 3.88, 4.28 and 7.39 and phenotypic variance of 4.71%, 5.45%, 5.48%, 6.03% and 10.01% respectively. A total of seven QTLs were identified and mapped using inclusive composite interval mapping (ICIM-ADD) method for anaerobic germination. qAG2 was found to be flanked between RM 263 and RM 6933 on chromosome 2 with LOD score of 2.73 and phenotypic variance of 8.60%. qAG3 identified on chromosome 3 was flanked between RM 15554 and RM 15561 and explaining 5.15% of phenotypic variation with LOD score of 2.65. The QTLs qAG7-1, qAG7-2 on chromosome 7 were flanked between RM 6697 and RM 5711, RM 418 and RM 21700, have LOD scores of 5.05, 5.85 and phenotypic variation of 3.52% and 3.62% respectively. On chromosome 9, QTL qAG9 was identified flanking between RM 23958 and RM 1553 with 2.89 and 4.90% of LOD score and phenotypic variation respectively. Whereas qAG10 on chromosome 10 and qAG12 on chromosome 12 were flanked between RM 25735 and RM 591, RM 28759 and RM 1584 with 5.86, 5.47 and 8.67%, 2.99% of LOD scores and phenotypic variation respectively.

Among the QTLs identified for AG in the present investigation viz qAG2, qAG3, qAG4, qAG7-1, qAG7-2, qAG9, qAG10, qAG12-1 and qAG12-2 using SMA and ICIM methods, qAG2, qAG3, qAG7-1, qAG7-2, qAG9, qAG12-1 and qAG12-2 were also reported in earlier studies. The novel QTLs identified in the present study are qAG4 with LOD score of 3.31 and phenotypic variance of 4.72% and qAG10 with LOD score of 5.86 and phenotypic variance of 8.67%. In both the SMA and ICIM methods the QTLs viz qAG3, qAG7-1, qAG7-2 and qAG12 are commonly identified. Among the QTLs identified the QTL qAG12-1, has shown highest LOD score (7.39) and phenotypic variance (10.02%) and considered as major QTL for AG in the F_2:3 population of Swarna Sub1/AC39416A.

Similar results of QTL analysis using QTL cartographer was reported by Angaji (2008) where qAG2 located on chromosome 2, with LOD score of 4.44 and phenotypic variation of 14.5%. QTL qAG12 on chromosome 12, with LOD of 5.71 and phenotypic variation of 29.24% by IM method was also found to linked with peak marker RM 28759 in the present investigation. QTLs reported for tolerance of flooding conditions during germination on chromosome 2, 3, 7 and 9, with highest LOD and phenotypic variation of 15.32 and 20.59 respectively has noted on chromosome 9 for QTL qAG9-2 by Angaji et al. (2010) are in line with identified QTLs of present investigation. Similar QTLs were identified by Septiningsih et al. (2013b) on chromosome 2, 7 and 12, for submergence tolerance during germination. They reported that the QTL qAG2 has peak marker RM 263 with 3.7 and 9.3% of LOD value and phenotypicvariance respectively, whereas in the present investigation it has recorded 2.73 and 8.60% of LOD score value and phenotypic variance respectively. Baltazar et al., (2014) also identified similar QTLs, qAG2-2 on chromosome 2 having LOD value of 2.43 and phenotypic variation of 9.79% and qAG7 on chromosome 7 with LOD score and phenotypic variation of 13.93 and 14.06% respectively. The QTL, qAG3 flanked between RM 15554 and RM 15561 in the donor parent AC39416A was also identified in RILs developed with same donor in the earlier studies conducted at RARS, Maruteru during 2017-18 (Annual report, RARS, Maruteru, 2017) Similar QTLs on chromosome 3 and 7 were also identified and mapped by Baltazar et al. (2019) governing tolerance to submergence during germination.

In conclusion, the QTLs identified in the study majorly qAG12-1 may be considered for introgression into popular elite rice varieties otherwise susceptible for anaerobic germination after characterization of the mechanism underlying anaerobic germination and fine mapping.

QTLs: Quantitative Trait Loci

SSR: Simple Sequence Repeats

AG: Anaerobic Germination

QTL Icim: QTL Inclusive Composite Interval Mapping

Author Contribution Statement: Dr. N. Chamundeswari, planned the experiment and contributed to development of mapping population. Dr. N. Chamundeswari and Dr. N. Veronica helped in conducting the experiment. Dr. T. Haritha and Dr. Reddy Yamini helped in writing and reviewing manuscript. All authors read and approved the manuscript.

Funding: I acknowledge ANGRAU, Lam, Guntur, for providing financial support for successful completion of research project.

Acknowledgements:

The technical assistance and support of Dr. Narne Chamundeswari, Senior Scientist (Genetics and Plant Breeding) is gratefully acknowledged for successful completion of research project. I also acknowledge Agricultural College, Bapatla and RARS (formerly APRRI), Maruteru, ANGRAU, for providing facilities to conduct the research.

Compliance with ethical standards

Conflict of interest: The authors declared that there is no potential conflict of interest.

Research is not involving Human Participants and/or Animals

Informed consent: Nil

Angaji SA (2008) Mapping qtls for submergence tolerance during germination in rice. African Journal of Biotechnology 7(15):2551–2558
Angaji SA, Septiningsih EM, Mackill DJ, Ismail AM (2010) Qtls associated with tolerance of flooding during germination in rice (Oryza sativa L). Euphytica 172:159–168
Annual report, 2017-18. Regional Agricultural Research Station, Godavari Zone, Maruteru, West Godavari, A.P
Baltazar MD, Ignacio JCI, Thomson MJ, Ismail AM, Septiningsih EM (2014) QTL mapping for tolerance of anaerobic germination from IR64 and the aus landrace Nanhi using SNP genotyping. Euphytica 197:251–260
Baltazar MD, Ignacio JCI, Thomson MJ, Ismail AM, Mendioro MS, Septiningsih EM (2019) QTL mapping for tolerance to anaerobic germination in rice from IR64 and the aus landrace Kharsu 80A. Breed Sci 18159:1–7
Barik J, Kumar V, Lenka SK, Panda D (2019) Genetic potentiality of lowland indigenous indica rice (Oryza sativa L.) landraces to anaerobic germination potential. Indian Journal of Plant Physiology. 1–13
Doley D, Barua M, Sarma D, Barua PK (2018) Screening and enhancement of anaerobic germination of rice genotypes by pre-sowing seed treatments. Curr Sci 115(6):1185–1190
Gonzaga ZJ, Aslam K, Septiningsih EM, Collard BC (2015) Evaluation of SSR and SNP markers for molecular breeding in rice. Plant Breeding Biotechnology 3(2):139–152
Gramene marker database http://archive.gramene.org/markers.
Ismail AM, Johnson DE, Ella ES, Vergara GV, Baltazar AM (2012) Adaptation to flooding during emergence and seedling growth in rice and weeds, and implications for crop establishment. AoB Plants. 2012: 1–18
Jiang L, Hou MY, Wang CM, Wan JM (2004) Quantitative trait loci and epistatic analysis of seed anoxia germinability in rice (Oryza sativa). Rice Sci 11(5–6):238–244
Jiang L, Liu S, Hou M, Tang J, Chen L, Zhai H, Wan J (2006) Analysis of qtls for seed low temperature germinability and anoxia germinability in rice (Oryza sativa L.). Field Crops Research 98(1):68–75
Kretzschmar T, Pelayo MAF, Trijatmiko KR, Gabunada LFM, Alam R, Jimenez R, Mendioro MS, Slamet-Loedin IH, Sreenivasulu N, Bailey-Serres J, Ismail AM, Mackill DJ, Septiningsih EM (2015) A trehalose-6-phosphatephosphatase enhances anaerobic germination tolerance in rice. Nature Plants 1:1–5
Kumar D, Das PK, Sarmah BK (2018) Reference gene validation for normalization of RT-q PCR assay associated with germination and survival of rice under hypoxic condition. Journal of Applied Genetics 59:419–430
Lal D, Ravikumar RL (2018) Development of genetic linkage map and identification of QTLs for agronomic traits in chickpea (Cicer arietinum L.). International Journal ofCurrentMicrobiology Applied Sciences 7(6):66–77
Magneschi L, Perata P (2009) Rice germination and seedling growth in the absence of oxygen. Annals of Botany 103:181–196
McCouch SR (2008) CGSNL (Committee on Gene Symbolization, Nomenclature and Linkage, Rice Genetics Cooperative). Gene nomenclature system for rice. Rice 1(1):72–84
Miro B, Longkumer T, Entila FD, Kohli A, Ismail AM (2017) Rice seed germination underwater: Morpho-physiological responses and the bases of differential expression of alcoholic fermentation enzymes. Frontiers in plant science 8:1–17
Mustroph A (2018) Improving flooding tolerance of crop plants. Agronomy 8(9):1–25
Nghi KN, Tondelli A, Vale G, Tagliani A, Mare C, Perata P, Pucciariello C (2019) Dissection of coleoptile elongation in japonica rice under submergence through integrated genome wide association mapping and transcriptional analyses. Plant Cell Environ 42(6):1832–1846
Perata P, Geshi N, Yamaguchi J, Akazawa I (1993) Effect of anoxia on the induction of -amylase in cereal seeds. Planta 191:402–408
Pramudyawardani EF, Aswidinnoor H, Purwoko BS, Suwarno WB, Islam MR, Verdeprado H, Collard BC (2018) Genetic Analysis and QTL Mapping for Agronomic and Yield-Related Traits in Ciherang-Sub1 Rice Backcross Populations. Plant Breeding Biotechnology 6(3):177–192
Rauf M, Choi YM, Lee S, Lee MC, Oh S (2019) Evaluation of anaerobic germinability in various rice subpopulations: identifying genotypes suitable for direct-seeded rice cultivation. Euphytica 215(2):1–15
Reddy AV, Girija Rani M, Satyanarayana PV, Suryanarayana Y, Chamundeswari N, Ravikumar BNVSR, Ramana RPV, Vishnuvardhan KM (2015) Physiological and molecular response of rice genotypes for different types of flooding. Current Biotica 8(4):345–350
Reddy BS, Rani MG (2018) Evaluation of Near Isogenic Lines (NILs) of rice for major abiotic stresses of coastal areas. Electronic Journal of Plant Breeding 9(3):808–814
Septiningsih EM, Ignacio JCI, Sendon PMD, Sanchez DL, Ismail AM, Mackill DJ (2013b) QTL mapping and confirmation for tolerance of anaerobic conditions during germination derived from the rice landrace Ma-Zhan Red. Theoretical and Applied Genetics. 126: 1357–1366
Umarani E, Hemalatha V, Bhadana VP, Senguttuvel P, Subbarao LV, Neeraja CN, Ravi P, Yugandhar, Kota S (2018) Screening of rice (Oryza sativa L.) genotypes for anaerobic germination. International Journal of Pure Applied Bioscience 6(5):1318–1325
Waghmare SG, Sindhumole P, Shylaja MR, Mathew D, Francies RM, Abida PS, Sajini S (2018) Research Article Analysis of simple sequence repeat (SSR) polymorphism between N22 and Uma rice varieties for marker assisted selection. Electronic Journal of Plant Breeding 9(2):511–517
Wang J, Meng L, Li H, Zhang L (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal 3(3):269–283
Zhang J, Rahman ARB (2018) Preferential geographic distribution pattern of abiotic stress tolerant rice. Rice 11(1):1–16
Zheng K, Subudhi PK, Domingo J, Magantay G, Huang N (1995) Rapid DNA isolation for marker assisted selection in rice breeding. Rice Genetics NewsLetter 12:255–258

Download PDF

Version 1

posted

You are reading this latest preprint version

Mapping of Qtl for Anaerobic Germination Using the Donor Ac39416a in the Genetic Background of Swarna Sub-1 (Oryza Sativa L.)

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Plant material and Mapping population:

Screening for tolerance to anaerobic conditions during germination:

Genotyping of mapping population:

Linkage map construction:

QTL analysis:

Results

SSR marker based linkage map construction:

QTL mapping analysis:

Discussion

Abbreviations

Declarations

References

Status:

Version 1