Revealing Genetic Relationship and Prospecting of Novel Donors Among Upland Rice Genotypes Using qDTY-Linked SSR Markers

doi:10.1016/j.rsci.2018.10.001

Abstract

Abstract:

A total of 17 simple sequence repeat (SSR) markers linked to QTLs (qDTYs) governing grain yield under reproductive stage (RS) drought stress were used to assess the genetic relationship and prospecting new donors for qDTYs among 32 popular upland rice genotypes. These SSR markers generated a total of 36 alleles with an average allele count of 2.1 per locus. Polymorphic information content value of the markers ranged from 0.376 to 0.662 with an average value of 0.484. The expected heterozyogosity ranged from 0.381 to 0.632. STRUCTURE analysis divided the 32 genotypes into three sub-populations. Subsequent phenotyping revealed that all the tolerant genotypes were grouped into one sub-population, whereas the moderately tolerant and susceptible genotypes were grouped into separate sub-populations. Phylogenetic tree constructed by the unweighted neighbour-joining method also divided the genotypes into three clusters. The grouping pattern of genotypes into the clusters was similar to that into the STRUCTURE analysis, on the basis of drought tolerance level. The average value of genetic dissimilarity coefficient among the genotypes was observed to be 0.486. Furthermore, by combining genotyping data with phenotyping data, 16 new donors for 6 qDTYs were identified.

Key words: drought screening, genetic relationship, population structure, qDTY donor, upland rice, grain yield

Mukherjee Mitadru, Padhy Barada, Srinivasan Bharathkumar, Mahadani Pradosh, Yasin Baksh Sk, Donde Ravindra, Nath Singh Onkar, Behera Lambodar, Swain Padmini, Kumar Dash Sushanta. Revealing Genetic Relationship and Prospecting of Novel Donors Among Upland Rice Genotypes Using qDTY-Linked SSR Markers[J]. Rice Science, 2018, 25(6): 308-319.

Figures/Tables 10

References 54

[1]	Adhya T K, Singh O N, Swain P, Ghosh A.2008. Rice in Eastern India: Causes for low productivity and available options.J Rice Res, 2: 1-5.
[2]	Agricultural Statistics at a Glance.2014. Government of India Ministry of Agriculture Department of Agriculture & Cooperation Directorate of Economics & Statistics: 63.
[3]	Ahmad R, Quadir S, Ahmad N, Shah K H.2003. Yield potential and stability of nine wheat varieties under water stress conditions. Int J Agric Biol, 5(1): 7-9.
[4]	Ali M L, McClung A M, Jia M H, Kimball J A, McCouch S R, Georgia C E.2011. A rice diversity panel evaluated for genetic and agro-morphological diversity between subpopulations and its geographic distribution.Crop Sci, 51(5): 2021-2035.
[5]	Anderson J A, Churchill G A, Autrique J E, Tanksley S D, Sorrels M E.1993. Optimizing parental selection for genetic maps.Genome, 36(1): 181-186.
[6]	Barik Madhusmita, Dash S K, Padhi S, Swain P.2017. Effect of drought on morpho-physiological, yield and yield traits of chromosome segment substitution lines (CSSLs) derived from wild species of rice.Oryza, 54(1): 65-72.
[7]	Bernier J, Kumar A, Ramaiah V, Spaner D, Atlin G.2007. A large-effect QTL forgrain yield under reproductive-stage drought stress in upland rice.Crop Sci, 47(2): 507-516.
[8]	Boonjung H, Fukai S.1996. Effects of soil water deficit at different growth stage on rice growth and yield under upland conditions: 2. Phenology, biomass production and yield.Field Crops Res, 48(1): 47-55.
[9]	Botstein D, White R L, Skolnick M, Davis R W.1980. Construction of a genetic linkage map in man using restriction fragment length polymorphism.Am J Hum Genet, 32(3): 314-331.
[10]	Chakhonkaen S, Pitnjam K, Saisuk W, Ukoskit K, Muangprom A.2012. Genetic structure of Thai rice and rice accessions obtained from the international rice research institute.Rice, 5: 19.
[11]	Courtois B, Frouin J, Greco R, Bruschi G, Droc G, Hamelin C, Ruiz M, Clément G, Evrard J C, van Coppenole S, Katsantonis D, Oliveira M, Negrao S, Matos C, Cavigiolo S, Lupotto E, Piffanelli P, Ahmadi N.2012. Genetic diversity and population structure in a European collection of rice.Crop Sci, 52(4): 1663-1675.
[12]	Das B, Sengupta S, Parida S K, Roy B, Ghosh M, Prasad M, Ghose T K.2013. Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India.BMC Genet, 14: 71-84.
[13]	Dixit S, Swamy B P M, Vikram P, Ahmed H U, Sta Cruz M T, Amante M, Atri D, Leung H, Kumar A.2012a. Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities.Theor Appl Genet, 125(1): 155-169.
[14]	Dixit S, Swamy B P M, Vikram P, Bernier J, Sta Cruz M T, Amante M, Atri D, Kumar A.2012b. Increased drought tolerance and wider adaptability ofqDTY12.1 conferred by its interaction with qDTY2.3 and qDTY3.2. Mol Breeding, 30: 1767-1779.
[15]	Evanno G, Regnaut S, Goudet J.2005. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study.Mol Ecol, 14(8): 2611-2620.
[16]	Fischer R A, Maurer R.1978. Drought resistance in spring wheat cultivars: I. Grain yield responses in spring wheat.Aust J Agric Res, 29(5): 892-912.
[17]	Gao L Z.2005. Microsatellite diversity and population genetic structure of an endangered wild rice,Oryza officinalis(Poaceae) from China. Mol Ecol, 14(14): 4287-4297.
[18]	Ghimire K H, Quiatchon-Baeza L A, Vikram P, Swamy B P M, Dixit S, Ahmed H U, Hernandez J E, Borromeo T H, Kumar A.2012. Identification and mapping of QTL (qDTY1.1) with a consistent effect on grain yield under drought. Field Crops Res, 131: 88-96.
[19]	IRRI. 2017. Trends in global rice consumption. .
[20]	Kumar A, Verulkar S B, Dixit S, Chauhan B, Bernier J, Venuprasad R, Zhao D, Shrivastava M N.2009. Yield and yield-attributing traits of rice (Oryza sativa L.) under lowland drought and suitability of early vigor as a selection criterion. Field Crops Res, 114(1): 99-107.
[21]	Mishra K K, Vikram P, Yadaw R B, Swamy B P M, Dixit S, Sta Cruz M T S, Maturan P, Marker S, Kumar A.2013. qDTY12.1: A locus with consistent effect on grain yield under drought in rice. BMC Genet, 14: 12-21.
[22]	Nagaraju J, Kathirvel M, Kumar R R, Siddiq E A, Hasnain S E.2002. Genetic analysis of traditional and evolved Basmati and non- Basmati rice varieties by using fluorescence-based ISSR-PCR and SSR markers.Proc Natl Acad Sci USA, 99: 5836-5841.
[23]	Nei M.1972. Genetic distance between populations.Am Nat, 106: 283-292.
[24]	Pachauri V, Taneja N, Vikram P, Singh N K, Singh S.2013. Molecular and morphological characterization of India farmers rice varieties.Aust J Crop Sci, 7(7): 923-932.
[25]	Peakall R, Smouse P E.2006. GENALEX 6: Genetic analysis in Excel: Population genetic software for teaching and research.Mol Ecol Notes, 6: 288-295.
[26]	Peakall R, Smouse P E.2012. GenAlEx 6.5: Genetic analysis in Excel: Population genetic software for teaching and research: An update.Bioinformatics, 28: 2537-2539.
[27]	Perrier X, Flori A, Bonnot F.2003. Data analysis methods. In: Hamon P, Seguin M, Perrier X, Glaszmann J C. Genetic diversity of cultivated tropical plants. Montpellier France: Enfield(Science Publishers): 43-76.
[28]	Perrier X, Jacquemoud-Collet J P. 2006. DARwin software Jacquemoud-Collet J P. 2006. DARwin software [<date-in-citation content-type="access-date">2017/10/15</date-in-citation>].
[29]	Pradhan S K, Barik S R, Sahoo A, Mohapatra S, Nayak D K, Mahender A, Meher J, Anandan A, Pandit E.2016. Population structure, genetic diversity and molecular marker-trait association analysis for high temperature stress tolerance in rice.PLoS One, 11: e0160027.
[30]	Pritchard J K, Stephens M, Donnelly P.2000. Inference of population structure using multilocus genotype data.Genetics, 155(2): 945-959.
[31]	Rai M.2003. Genetic diversity in rice production: Past contribution and the potential of utilization for sustainable rice production. In: Tran D V, Duffy R. Sustainable Rice Production for Food Security: Proceedings of the 20th session of the International Rice Commission. Bangkok, United Nations: Food and Agricultural Organization.
[32]	Ramadan E A, Elmoghazy A M, El-Mowafi H F.2015. Molecular markers based genetic diversity analysis for drought tolerance in rice (Oryza sativa L.) using SSR markers. Int J Sci Res Agric Sci, 2: 137-146.
[33]	Ricepedia. 2017. The online authority on rice: Rice productivity. .
[34]	Roy S, Bannerjee A, Mawkhlieng B, Misra A K, Pattanayak A, Harish G D, Singh S K, Ngachan S V, Bansal K C.2015. Genetic diversity and population structure in aromatic and quality rice (Oryza sativa L.) landraces from North Eastern India. PLoS One, 10: e0129607.
[35]	Salem K F M, Sallam A.2016. Analysis of population structure and genetic diversity of Egyptian and exotic rice (Oryza sativa L.) genotypes. CR Biolog, 339(1): 1-9.
[36]	Shamsudin N A A, Swamy B P M, Ratnam W, Sta Cruz M T, Raman A, Kumar A.2016. Marker assisted pyramiding of drought QTLs into a popular Malaysian rice cultivar MR219.BMC Genet, 17: 30-43.
[37]	Shodhganga. 2017. Abstract. [<date-in-citation content-type="access-date">2017/11/17</date-in-citation>].
[38]	Siddiq E A.2000. Bridging the rice yield gap in India. In: Papademetriou M K, Dent F J, Herath E M. Bridging the Rice Yield Gap in Asia and Pacific Regions . Bangkok,United Nations: Food and Agricultural Organization: 84-111.
[39]	Sika K C, Kefela T, Sagbadja H A, Ahoton L, Saidou A, Baba- Moussa L, Baptiste L J, Kotconi S O, Gachomo E W.2015. A simple and efficient genomic DNA extraction protocol for large scale genetic analyses of plant biological systems.Plant Gene, 1: 43-45.
[40]	Singh N, Choudhury D R, Singh A K, Kumar S, Srinivasan K, Tyagi R K, Singh N K, Singh R.2013. Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties.PLoS One, 8: e84136.
[41]	Singh N, Choudhury D R, Tiwari G, Singh A K, Kumar S, Srinivasan K, Tyagi R K, Sharma A D, Singh N K, Singh R.2016. Genetic diversity trend in Indian rice varieties: An analysis using SSR markers.BMC Genet, 17: 127-139.
[42]	Swamy B P M, Ahmed H U, Henry A, Mauleon R, Dixit S, Vikram P, Tilatto R, Verulkar S B, Perraju P, Mandal N P, Variar M, Robin S, Chandrababu R, Singh O N, Dwivedi J L, Das S P, Mishra K K, Yadaw R B, Aditya T L, Karmakar B, Satoh K, Moumeni A, Kikuchi S, Leung H, Kumar A.2013. Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought.PLoS One, 8: e62795.
[43]	Swamy B P M, Shamsudin N A A, Rahman S N A, Mauleon R, RatnamW, Sta Cruz M T, Kumar A.2017. Association mapping of yield and yield-related traits under reproductive stage drought stress in rice (Oryza sativa L.). Rice, 10: 21-33.
[44]	Tarang A, Gashti A B.2016. The power of microsatellite markers and AFLPs in revealing the genetic diversity of Hashemi aromatic rice from Iran.J Int Agric, 15(6): 1186-1197.
[45]	Venuprasad R, Lafitte H R, Atlin G N.2007. Response to direct selection for grain yield under drought stress in rice.Crop Sci, 47: 285-293.
[46]	Venuprasad R, Sta Cruz M T, Amante M, Magbanua R, Kumar A, Atlin G A.2008. Response to two cycles of divergent selection for grain yield under drought stress in four rice breeding populations.Field Crops Res, 107(3): 232-244.
[47]	Verulkar S B, Mandal N P, Dwivedi J L, Singh B N, Sinha P K, Mahato R N, Dongre P, Singh O N, Bose L K, Swain P, Robin S, Chandrababu R, Senthil S, Jain A, Shashidhar H E, Hittalmani S, Vera Cruz C, Paris T, Raman A, Haefele S, Serraj R, Atlin G, Kumar A.2010. Breeding resilient and productive genotypes adapted to drought-prone rainfed ecosystem of India.Field Crops Res, 117: 197-208.
[48]	Vikram P, Swamy B P M, Dixit S, Singh R, Singh B P, Miro B, Kohli A, Henry A, Singh N K, Kumar A.2015. Drought susceptibility of modern rice varieties: An effect of linkage of drought tolerance with undesirable traits.Sci Rep, 5: e14799.
[49]	Vikram P, Swamy B P M, Dixit S, Ahmed H U, Sta Cruz M T, Singh A K, Kumar A.2011. qDTY1.1, major QTL for rice GY under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet, 12: 89-104.
[50]	Yadav S, Singh A, Singh M R, Goel N, Vinod K K, Mohapatra T, Singh A K.2013. Assessment of genetic diversity in Indian rice germplasm (Oryza sativa L.): Use of random versus trait linked microsatellite markers. J Genet, 92(3): 545-557.
[51]	Yadaw R B, Dixit S, Raman A, Mishra K K, Vikram P, Swamy B P M, Sta Cruz M T, Maturan P T, Pandey M, Kumar A.2013. A QTL for high grain yield under lowland drought in the background of popular rice variety Sabitri from Nepal.Field Crops Res, 144: 281-287.
[52]	Yeh F C, Boyle T J B.1997. Population genetic analysis of co-dominant and dominant markers and quantitative traits.Belg J Bot, 129: 157-163.
[53]	Yu S B, Xu W J, Vijayakumar C H M, Ali J, Fu B Y, Xu J L, Jiang Y Z, Marghirang R, Domingo J, Aquino C, Virmani S S, Li Z K.2003. Molecular diversity and multilocus organization of the parental lines used in the International Rice Molecular Breeding Program.Theor Appl Genet, 108(1): 131-140.
[54]	Zhang T, Ni X L, Jiang K F, Deng H F, He Q, Yang Q H, Yang L, Wan X Q, Cao Y J, Zheng J K.2010. Relation between heterosis and parental genetic distance based on molecular markers for functional genes related to yield traits in rice.Rice Sci, 17(4): 288-295.

Genotype	Type	Origin	Genotype	Type	Origin
Way Rarem ^a	indica	Indonesia	Browngora	indica	India
Vandana ^b	indica	India	Annada	indica	India
Sahabhagi Dhan ^c	indica	India/the Philippines	Anjali	indica	India
Hazaridhan	indica	India	Vanaprava	indica	India
Kalyani-II	indica	India	Blackgora	indica	India
NDR1045	indica	India	Heera	indica	India
Satyabhama	indica	India	Pathara	indica	India
IR20	indica	the Philippines	Poornima	indica	India
Sadabahar	indica	India	Sidhant	indica	India
Dular	indica	India	IR64	indica	the Philippines
Annapurna	indica	India	Azucena	japonica	the Philippines
Kalinga-III	indica	India	Curinga	japonica	Brazil
HND15	indica	India	CR2702	indica	India
Khandagiri	indica	India	Mahulata	indica	India
N22 ^d	indica	India	CR143-2-2	indica	India
Selumpikit	indica	India	CRMAS2620-1	indica	India

Genotype	Type	Origin	Genotype	Type	Origin
Way Rarem ^a	indica	Indonesia	Browngora	indica	India
Vandana ^b	indica	India	Annada	indica	India
Sahabhagi Dhan ^c	indica	India/the Philippines	Anjali	indica	India
Hazaridhan	indica	India	Vanaprava	indica	India
Kalyani-II	indica	India	Blackgora	indica	India
NDR1045	indica	India	Heera	indica	India
Satyabhama	indica	India	Pathara	indica	India
IR20	indica	the Philippines	Poornima	indica	India
Sadabahar	indica	India	Sidhant	indica	India
Dular	indica	India	IR64	indica	the Philippines
Annapurna	indica	India	Azucena	japonica	the Philippines
Kalinga-III	indica	India	Curinga	japonica	Brazil
HND15	indica	India	CR2702	indica	India
Khandagiri	indica	India	Mahulata	indica	India
N22 ^d	indica	India	CR143-2-2	indica	India
Selumpikit	indica	India	CRMAS2620-1	indica	India

Marker	Repeat motif	Chr	qDTY	Product length (bp)	Na	Ne	TAF	He	PIC	Reference
RM431	(AG)16	1	qDTY1.1	175-200	2	1.932	0.406	0.49	0.484	Vikram et al, 2011; Ghimire et al, 2012
RM11943	(GA)11	1	qDTY1.1	75-90	2	1.6	0.25	0.381	0.376	Vikram et al, 2011; Ghimire et al, 2012
RM12091	(AG)31	1	qDTY1.1	135-150	2	1.932	0.594	0.49	0.484	Vikram et al, 2011
OSR17	(AATT)5	2	qDTY2.2	260-280	2	1.969	0.438	0.5	0.492	Dixit et al, 2012a
RM555	(AG)11	2	qDTY2.2	220-230	2	1.678	0.719	0.41	0.405	Dixit et al, 2012a
RM236	(CT)18	2	qDTY2.2	260-270	2	1.992	0.469	0.506	0.498	Shamsudin et al, 2016
RM573	(GA)11	2	qDTY2.3	200-215	2	1.822	0.656	0.458	0.452	Dixit et al, 2012b
RM1367	(AG)27	2	qDTY2.3	250-275	2	1.822	0.344	0.458	0.452	Dixit et al, 2012b
RM517	(CT)15	3	qDTY3.2	250-275	2	1.882	0.375	0.476	0.468	Yadaw et al, 2013
RM523	(TC)14	3	qDTY3.2	190-210	2	1.932	0.406	0.49	0.484	Dixit et al, 2012b
RM7332	(ACAT)11	3	qDTY3.2	220-235	2	1.969	0.438	0.5	0.492	Dixit et al, 2012b
RM3	(GA)2GG(GA)25	6	qDTY6.2	100-150	3	1.784	0.281	0.446	0.662	Vikram et al, 2015
RM541	(TC)16	6	qDTY6.2	210-235	3	2.652	0.5	0.632	0.626	Dixit et al, 2012b
RM5371	(TC)13	6	qDTY6.2	115-125	2	1.932	0.406	0.49	0.484	Dixit et al, 2012b
RM28166	(CT)12	12	qDTY12.1	190-210	2	1.678	0.281	0.41	0.405	Mishra et al, 2013
RM28199	(ATAG)5	12	qDTY12.1	175-190	2	1.932	0.406	0.49	0.484	Mishra et al, 2013
RM511	(GAC)7	12	qDTY12.1	150-160	2	1.969	0.563	0.5	0.492	Mishra et al, 2013

Marker	Repeat motif	Chr	qDTY	Product length (bp)	Na	Ne	TAF	He	PIC	Reference
RM431	(AG)16	1	qDTY1.1	175-200	2	1.932	0.406	0.49	0.484	Vikram et al, 2011; Ghimire et al, 2012
RM11943	(GA)11	1	qDTY1.1	75-90	2	1.6	0.25	0.381	0.376	Vikram et al, 2011; Ghimire et al, 2012
RM12091	(AG)31	1	qDTY1.1	135-150	2	1.932	0.594	0.49	0.484	Vikram et al, 2011
OSR17	(AATT)5	2	qDTY2.2	260-280	2	1.969	0.438	0.5	0.492	Dixit et al, 2012a
RM555	(AG)11	2	qDTY2.2	220-230	2	1.678	0.719	0.41	0.405	Dixit et al, 2012a
RM236	(CT)18	2	qDTY2.2	260-270	2	1.992	0.469	0.506	0.498	Shamsudin et al, 2016
RM573	(GA)11	2	qDTY2.3	200-215	2	1.822	0.656	0.458	0.452	Dixit et al, 2012b
RM1367	(AG)27	2	qDTY2.3	250-275	2	1.822	0.344	0.458	0.452	Dixit et al, 2012b
RM517	(CT)15	3	qDTY3.2	250-275	2	1.882	0.375	0.476	0.468	Yadaw et al, 2013
RM523	(TC)14	3	qDTY3.2	190-210	2	1.932	0.406	0.49	0.484	Dixit et al, 2012b
RM7332	(ACAT)11	3	qDTY3.2	220-235	2	1.969	0.438	0.5	0.492	Dixit et al, 2012b
RM3	(GA)2GG(GA)25	6	qDTY6.2	100-150	3	1.784	0.281	0.446	0.662	Vikram et al, 2015
RM541	(TC)16	6	qDTY6.2	210-235	3	2.652	0.5	0.632	0.626	Dixit et al, 2012b
RM5371	(TC)13	6	qDTY6.2	115-125	2	1.932	0.406	0.49	0.484	Dixit et al, 2012b
RM28166	(CT)12	12	qDTY12.1	190-210	2	1.678	0.281	0.41	0.405	Mishra et al, 2013
RM28199	(ATAG)5	12	qDTY12.1	175-190	2	1.932	0.406	0.49	0.484	Mishra et al, 2013
RM511	(GAC)7	12	qDTY12.1	150-160	2	1.969	0.563	0.5	0.492	Mishra et al, 2013

Genotype	Q1	Q2	Q3	Sub-population	Genotype	Q1	Q2	Q3	Sub-population
Vandana	0.965	0.019	0.016	SP1	Sidhant	0.014	0.863	0.123	SP2
Mahulata	0.957	0.016	0.026	SP1	IR20	0.071	0.785	0.144	SP2 admix
CR143-2-2	0.946	0.033	0.022	SP1	Hazaridhan	0.228	0.653	0.119	SP2 admix
CR2702	0.938	0.051	0.011	SP1	IR64	0.018	0.568	0.414	SP2 admix
N22	0.935	0.014	0.051	SP1	Anjali	0.01	0.504	0.485	SP2 admix
Selumpikit	0.923	0.029	0.048	SP1	Annapurna	0.012	0.017	0.971	SP3
Azucena	0.913	0.029	0.058	SP1	Heera	0.027	0.027	0.946	SP3
Pathara	0.874	0.08	0.046	SP1	Sadabahar	0.037	0.022	0.941	SP3
Sahabhagi Dhan	0.802	0.186	0.011	SP1	Vanaprava	0.036	0.023	0.941	SP3
Curinga	0.783	0.123	0.094	SP1 admix	Kalyani-II	0.021	0.058	0.921	SP3
NDR1045	0.671	0.306	0.023	SP1 admix	Kalinga-III	0.061	0.021	0.917	SP3
CRMAS2620-1	0.021	0.957	0.021	SP2	Satyabhama	0.056	0.034	0.91	SP3
Poornima	0.074	0.911	0.015	SP2	HND15	0.104	0.035	0.861	SP3
Annada	0.057	0.872	0.072	SP2	Dular	0.028	0.123	0.849	SP3
Way Rarem	0.028	0.871	0.101	SP2	Khandagiri	0.022	0.442	0.536	SP3 admix
Blackgora	0.022	0.866	0.113	SP2	Browngora	0.029	0.484	0.487	SP3 admix