
Genetic Dissection of Grain Size Traits Through Genome-Wide Association Study Based on Genic Markers in Rice
Received date: 2021-08-26
Accepted date: 2022-01-26
Online published: 2022-07-04
Grain size plays a significant role in rice, starting from affecting yield to consumer preference, which is the driving force for deep investigation and improvement of grain size characters. Quantitative inheritance makes these traits complex to breed on account of several alleles contributing to the complete trait expression. We employed genome-wide association study in an association panel of 88 rice genotypes using 142 new candidate gene based SSR (cgSSR) markers, derived from yield-related candidate genes, with the efficient mixed-model association coupled mixed linear model for dissecting complete genetic control of grain size traits. A total of 10 significant associations were identified for four grain size-related characters (grain weight, grain length, grain width, and length-width ratio). Among the identified associations, seven marker trait associations explain more than 10% of the phenotypic variation, indicating major putative QTLs for respective traits. The allelic variations at genes OsBC1L4, SHO1 and OsD2 showed association between 1000-grain weight and grain width, 1000-grain weight and grain length, and grain width and length-width ratio, respectively. The cgSSR markers, associated with corresponding traits, can be utilized for direct allelic selection, while other significantly associated cgSSRs may be utilized for allelic accumulation in the breeding programs or grain size improvement. The new cgSSR markers associated with grain size related characters have a significant impact on practical plant breeding to increase the number of causative alleles for these traits through marker aided rice breeding programs.
Amrit Kumar Nayak, Anilkumar C, Sasmita Behera, Rameswar Prasad Sah, Gera Roopa Lavanya, Awadhesh Kumar, Lambodar Behera, Muhammed Azharudheen Tp . Genetic Dissection of Grain Size Traits Through Genome-Wide Association Study Based on Genic Markers in Rice[J]. Rice Science, 2022 , 29(5) : 462 -472 . DOI: 10.1016/j.rsci.2022.07.006
| [1] | Agrama H A, Eizenga G C, Yan W. 2007. Association mapping of yield and its components in rice cultivars. Mol Breed, 19(4): 341-356. |
| [2] | Alqudah A M, Sallam A, Stephen Baenziger P, Börner A. 2020. GWAS: Fast-forwarding gene identification and characterization in temperate cereals: Lessons from barley: A review. J Adv Res, 22: 119-135. |
| [3] | Alvarado G, Rodríguez F M, Pacheco A, Burgueño J, Crossa J, Vargas M, Pérez-Rodríguez P, Lopez-Cruz M A. 2020. META-R: A software to analyze data from multi-environment plant breeding trials. Crop J, 8(5): 745-756. |
| [4] | Anandan A, Mahender A, Sah R P, Haque S, Pradhan S K, Roy P S, Singh O N, Ali J. 2021. Genetic diversity and population structure among an assorted group of genotypes pertinent to reproductive stage drought stress in rice (Oryza sativa L.). Acta Sci Agric, 5(3): 77-89. |
| [5] | Atwell S, Huang Y S, Vilhjálmsson B J, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone A M, Hu T T, Jiang R, Muliyati N W, Zhang X, Amer M A, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker J R, Faure N, Kniskern J M, Jones J D, Michael T, Nemri A, Roux F, Salt D E, Tang C, Todesco M, Traw M B, Weigel D, Marjoram P, Borevitz J O, Bergelson J, Nordborg M. 2010. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature, 465: 627-631. |
| [6] | Azharudheen T P M, Nayak A K, Behera S, Anilkumar C, Marndi B C, Moharana D, Singh L K, Upadhyay S, Sah R P. 2022. Genome-wide association analysis for plant type characters and yield using cgSSR markers in rice (Oryza sativa L.). Euphytica, 218(6): 1-13. |
| [7] | Bai X F, Luo L J, Yan W H, Kovi M R, Zhan W, Xing Y Z. 2010. Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7. BMC Genet, 11: 16. |
| [8] | Chakraborti M, Anilkumar C, Verma R L, Abdul Fiyaz R, Reshmi Raj K R, Patra B C, Balakrishnan D, Sarkar S, Mondal N P, Kar M K, Meher J, Sundaram R M, Rao L S. 2021. Rice breeding in India: Eight decades of journey towards enhancing the genetic gain for yield, nutritional quality, and commodity value. Oryza, 58: 69-88. |
| [9] | Ching A, Caldwell K S, Jung M, Dolan M, Smith O S, Tingey S, Morgante M, Rafalski A J. 2002. SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet, 3: 19. |
| [10] | Cho Y G, Ishii T, Temnykh S, Chen X, Lipovich L, McCouch S R, Park W D, Ayres N, Cartinhour S. 2000. Diversity of microsatellites derived from genomic libraries and GenBank sequences in rice (Oryza sativa L.). Theor Appl Genet, 100(5): 713-722. |
| [11] | Collard B C Y, Vera Cruz C M, McNally K L, Virk P S, MacKill D J. 2008. Rice molecular breeding laboratories in the genomics era: Current status and future considerations. Int J Plant Genomics, 2008: 524847. |
| [12] | Dai X, You C, Chen G, Li X, Zhang Q, Wu C. 2011. OsBC1L4 encodes a COBRA-like protein that affects cellulose synthesis in rice. Plant Mol Biol, 75(4): 333-345. |
| [13] | Duan P G, Xu J S, Zeng D L, Zhang B L, Geng M F, Zhang G Z, Huang K, Huang L J, Xu R, Ge S, Qian Q, Li Y H. 2017. Natural variation in the promoter of GSE5 contributes to grain size diversity in rice. Mol Plant, 10(5): 685-694. |
| [14] | Earl D A, vonHoldt B M. 2012. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour, 4(2): 359-361. |
| [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] | Evans L T. 1972. Storage Capacity as a Limitation on Grain Yield in Rice Breeding. Manila, the Philippines: International Rice Research Institute. |
| [17] | Fu F F, Xue H W. 2010. Coexpression analysis identifies Rice Starch Regulator1, a rice AP2/EREBP family transcription factor, as a novel rice starch biosynthesis regulator. Plant Physiol, 154(2): 927-938. |
| [18] | Fu F H, Wang F, Huang W J, Peng H P, Wu Y Y, Huang D J. 1994. Genetic analysis on grain characters in hybrid rice. Acta Agron Sin, 20(1): 39-45. |
| [19] | Furukawa T, Maekawa M, Oki T, Suda I, Iida S, Shimada H, Takamure I, Kadowaki K I. 2007. The Rc and Rd genes are involved in proanthocyanidin synthesis in rice pericarp. Plant J, 49(1): 91-102. |
| [20] | Gao D W, Sun W Q, Wang D W, Dong H L, Zhang R, Yu S B. 2020. A xylan glucuronosyltransferase gene exhibits pleiotropic effects on cellular composition and leaf development in rice. Sci Rep, 10(1): 3726. |
| [21] | Garris A J, Tai T H, Coburn J, Kresovich S, McCouch S. 2005. Genetic structure and diversity in Oryza sativa L. Genetics, 169(3): 1631-1638. |
| [22] | Gobu R, Shiv A, Anilkumar C, Basavaraj P S, Harish D, Adhikari S, Vinita R, Umesh H, Sujatha M. 2020. Accelerated crop breeding towards development of climate resilient varieties. Climate change and Indian Agriculture: Challenges and Adaptation Strategies. In: Rao C S, Srinivas T, Rao R V S, Rao N S, Vinayagam S S, Krishnan P. Climate Change and Indian Agriculture: Challenges and Adaptation Strategies, ICAR-National Academy of Agricultural Research Management, Hyderabad, Telangana, India: 49-69. |
| [23] | Guo X, Elston R C. 1999. Linkage information content of polymorphic genetic markers. Hum Hered, 49(2): 112-118. |
| [24] | Hill Jr R R, Rosenberger J L. 1985. Methods for combining data from gemrplasm evaluation trials 1. Crop Sci, 25(3): 467-470. |
| [25] | Hu X X, Wang C, Fu Y P, Liu Q, Jiao X Z, Wang K J. 2016. Expanding the range of CRISPR/Cas9 genome editing in rice. Mol Plant, 9(6): 943-945. |
| [26] | Hu Z J, Lu S J, Wang M J, He H H, Sun L, Wang H R, Liu X H, Jiang L, Sun J L, Xin X Y, Kong W, Chu C C, Xue H W, Yang J S, Luo X J, Liu J X. 2018. A novel QTL qTGW3 encodes the GSK3/SHAGGY-like kinase OsGSK5/OsSK41 that interacts with OsARF4 to negatively regulate grain size and weight in rice. Mol Plant, 11(5): 736-749. |
| [27] | Huang R Y, Jiang L R, Zheng J S, Wang T S, Wang H C, Huang Y M, Hong Z L. 2013. Genetic bases of rice grain shape: So many genes, so little known. Trends Plant Sci, 18(4): 218-226. |
| [28] | Huang X H, Han B. 2014. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol, 65: 531-551. |
| [29] | Hussain K, Zhang Y X, Anley W, Riaz A, Abbas A, Rani M H, Wang H, Shen X H, Cao L Y, Cheng S H. 2020. Association mapping of quantitative trait loci for grain size in introgression line derived from Oryza rufipogon. Rice Sci, 27(3): 246-254. |
| [30] | Jan R, Khan M A, Asaf S, Lee I J, Kim K M. 2020. Overexpression of OsF3H modulates WBPH stress by alteration of phenylpropanoid pathway at a transcriptomic and metabolomic level in Oryza sativa. Sci Rep, 10(1): 14685. |
| [31] | Kaneko K, Inomata T, Masui T, Koshu T, Umezawa Y, Itoh K, Pozueta-Romero J, Mitsui T. 2014. Nucleotide pyrophosphatase/ phosphodiesterase 1 exerts a negative effect on starch accumulation and growth in rice seedlings under high temperature and CO2 concentration conditions. Plant Cell Physiol, 55(2): 320-332. |
| [32] | Kang H M, Zaitlen N A, Wade C M, Kirby A, Heckerman D, Daly M J, Eskin E. 2008. Efficient control of population structure in model organism association mapping. Genetics, 178(3): 1709-1723. |
| [33] | Kassambara A, Mundt F. 2017. Factoextra: Extract and visualize the results of multivariate data analyses. [2021-7-25]. https://mirrors.sjtug.sjtu.edu.cn/cran/web/packages/factoextra/index.html. |
| [34] | Katara J L, Parameswaran C, Devanna B N, Verma R L, Anil Kumar C, Patra B C, Samantaray S. 2021. Genomics assisted breeding: The need and current perspective for rice improvement in India. Oryza, 58: 61-68. |
| [35] | Korte A, Farlow A. 2013. The advantages and limitations of trait analysis with GWAS: A review. Plant Methods, 9: 29. |
| [36] | Lipka A E, Tian F, Wang Q S, Peiffer J, Li M, Bradbury P J, Gore M A, Buckler E S, Zhang Z W. 2012. GAPIT: Genome association and prediction integrated tool. Bioinformatics, 28(18): 2397-2399. |
| [37] | Liu K J, Muse S V. 2005. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics, 21(9): 2128-2129. |
| [38] | Lu H, Redus M A, Coburn J R, Rutger J N, McCouch S R, Tai T H. 2005. Population structure and breeding patterns of 145 US rice cultivars based on SSR marker analysis. Crop Sci, 45(1): 66-76. |
| [39] | Ma X S, Feng F J, Zhang Y, Elesawi I E, Xu K, Li T F, Mei H W, Liu H Y, Gao N N, Chen C L, Luo L J, Yu S W. 2019. A novel rice grain size gene OsSNB was identified by genome-wide association study in natural population. PLoS Genet, 15(5): e1008191. |
| [40] | Mather D E, Hyes P M, Chalmers K J, Eglinton J, Matus I, Richardson K, Von Zitzewitz J, Marquez-Cedillo L, Hearnden P, Pal N. 2004. Use of SSR marker data to study linkage disequilibrium and population structure in Hordeum vulgare: Prospects for association mapping in barley. In: Jaroslav S, Jarmila J. 9th International Barley Genetics Symposium. Brno, Czech Republic: International barley genetics symposium: 302-307. |
| [41] | Meng L J, Zhao X Q, Ponce K, Ye G Y, Leung H. 2016. QTL mapping for agronomic traits using multi-parent advanced generation inter-cross (MAGIC) populations derived from diverse elite indica rice lines. Field Crops Res, 189: 19-42. |
| [42] | Mohanty S. 2013. Trends in global rice consumption. Rice Today, 12: 44-45. |
| [43] | Molla K A, Debnath A B, Ganie S A, Mondal T K. 2015. Identification and analysis of novel salt responsive candidate gene based SSRs (cgSSRs) from rice (Oryza sativa L.). BMC Plant Biol, 15: 122. |
| [44] | Molla K A, Azharudheen T P M, Ray S, Sarkar S, Swain A, Chakraborti M, Vijayan J, Singh O N, Baig M J, Mukherjee A K. 2019. Novel biotic stress responsive candidate gene based SSR (cgSSR) markers from rice. Euphytica, 215(2): 17. |
| [45] | Nanjo Y, Oka H, Ikarashi N, Kaneko K, Kitajima A, Mitsui T, Muñoz F J, Rodríguez-López M, Baroja-Fernández E, Pozueta- Romero J. 2006. Rice plastidial N-glycosylated nucleotide pyrophosphatase/phosphodiesterase is transported from the ER- Golgi to the chloroplast through the secretory pathway. Plant Cell, 18(10): 2582-2592. |
| [46] | Norton G J, Travis A J, Douglas A, Fairley S, Alves E D, Ruang- Areerate P, Naredo M, Elizabeth B, McNally K L, Hossain M, Islam M. 2018. Genome wide association mapping of grain and straw biomass traits in the rice Bengal and Assam Aus panel (BAAP) grown under alternate wetting and drying and permanently flooded irrigation. Front Plant Sci, 9: 1223. |
| [47] | Pahlich E, Gerlitz C. 1980. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemistry, 19: 11-13. |
| [48] | Patra B C, Anilkumar C, Chakraborti M. 2020. Rice breeding in India: A journey from phenotype based pure-line selection to genomics assisted breeding. Agric Res J, 57(6): 816-825. |
| [49] | Piepho H P, Möhring J, Melchinger A E, Büchse A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161: 209-228. |
| [50] | Ponce K, Zhang Y, Guo L B, Leng Y J, Ye G Y. 2020. Genome- wide association study of grain size traits in indica rice multiparent advanced generation intercross (MAGIC) population. Front Plant Sci, 11: 395. |
| [51] | Pritchard J K, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics, 155(2): 945-959. |
| [52] | Qiu X J, Pang Y L, Yuan Z H, Xing D Y, Xu J L, Dingkuhn M, Li Z K, Ye G Y. 2015. Genome-wide association study of grain appearance and milling quality in a worldwide collection of indica rice germplasm. PLoS One, 10(12): e0145577. |
| [53] | R Core Team. 2021. R, A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. [2021-11-25]. |
| [54] | Rafalski J A. 2010. Association genetics in crop improvement. Curr Opin Plant Biol, 13(2): 174-180. |
| [55] | Rahman S N, Islam M S, Alam M S, Nasiruddin K M. 2007. Genetic polymorphism in rice (Oryza sativa L.) through RAPD analysis. Indian J Biotechnol, 6: 224-229. |
| [56] | Raju B R, Mohankumar M V, Sumanth K K, Rajanna M P, Udayakumar M, Prasad T G, Sheshshayee M S. 2016. Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping. Mol Breeding, 36(3): 35. |
| [57] | Sahu R K, Patnaik S S C, Sah R P. 2020. Quality seed production in rice. Odisha, India: ICAR-National Rice Research Institute: 58. |
| [58] | Sakamoto T, Ohnishi T, Fujioka S, Watanabe B, Mizutani M. 2012. Rice CYP90D2 and CYP90D3 catalyze C-23 hydroxylation of brassinosteroids in vitro. Plant Physiol Biochem, 58: 220-226. |
| [59] | Sanghamitra P, Sah R P, Bagchi T B, Sharma S G, Kumar A, Munda S, Sahu R K. 2018. Evaluation of variability and environmental stability of grain quality and agronomic parameters of pigmented rice (O. sativa L.). J Food Sci Technol, 55(3): 879-890. |
| [60] | Seo H, Kim S H, Lee B D, Lim J H, Lee S J, An G, Paek N C. 2020. The rice basic helix-loop-helix 79 (OsbHLH079) determines leaf angle and grain shape. Int J Mol Sci, 21(6): 2090. |
| [61] | Shapiro S S, Wilk M B. 1965. An analysis of variance test for normality (complete samples). Biometrika, 52: 591-611. |
| [62] | Song X W, Li P C, Zhai J X, Zhou M, Ma L J, Liu B, Jeong D H, Nakano M, Cao S Y, Liu C Y, Chu C C, Wang X J, Green P J, Meyers B C, Cao X F. 2012. Roles of DCL4 and DCL3b in rice phased small RNA biogenesis. Plant J, 69(3): 462-474. |
| [63] | Tan Y F, Xing Y Z, Li J X, Yu S B, Xu C G, Zhang Q F. 2000. Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid. Theor Appl Genet, 101: 823-829. |
| [64] | Upadhyaya G, Das A, Ray S. 2021. A rice R2R3-MYB (OsC1) transcriptional regulator improves oxidative stress tolerance by modulating anthocyanin biosynthesis. Physiol Plant, 173(4): 2334-2349. |
| [65] | VanRaden P M. 2008. Efficient methods to compute genomic predictions. J Dairy Sci, 91(11): 4414-4423. |
| [66] | Varshney R K, Graner A, Sorrells M E. 2005. Genic microsatellite markers in plants: Features and applications. Trends Biotechnol, 23(1): 48-55. |
| [67] | Vieira M L C, Santini L, Diniz A L, de Freitas Munhoz C. 2016. Microsatellite markers: What they mean and why they are so useful. Genet Mol Biol, 39(3): 312-328. |
| [68] | Wang C H, Yang Y L, Yuan X P, Xu Q, Feng Y, Yu H Y, Wang Y P, Wei X H. 2014. Genome-wide association study of blast resistance in indica rice. BMC Plant Biol, 14: 311. |
| [69] | Wang Y H, Zheng Y M, Cai Q H, Liao C J, Mao X H, Xie H G, Zhu Y S, Lian L, Luo X, Xie H A, Zhang J F. 2016. Population structure and association analysis of yield and grain quality traits in hybrid rice primal parental lines. Euphytica, 212(2): 261-273. |
| [70] | Wei T, Simko V. 2021. R package ‘corrplot’: Visualization of a Correlation Matrix. Version 0.88. https://github.com/taiyun/corrplot. |
| [71] | Wu J H, Feng F J, Lian X M, Teng X Y, Wei H B, Yu H H, Xie W B, Yan M, Fan P Q, Li Y, Ma X S, Liu H Y, Yu S B, Wang G W, Zhou F S, Luo L J, Mei H W. 2015. Genome-wide association study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice. BMC Plant Biol, 15: 218. |
| [72] | Xing Y Z, Zhang Q F. 2010. Genetic and molecular bases of rice yield. Annu Rev Plant Biol, 61: 421-442. |
| [73] | Xu J L, Xue Q Z, Luo L J, Li Z K. 2002. Genetic dissection of grain weight and its related traits in rice (Oryza sativa L.). Chin J Rice Sci, 16: 6-10. (in Chinese with English abstract) |
| [74] | Yu J M, Pressoir G, Briggs W H, Vroh Bi I, Yamasaki M, Doebley J F, McMullen M D, Gaut B S, Nielsen D M, Holland J B, Kresovich S, Buckler E S. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet, 38(2): 203-208. |
| [75] | Yu J P, Xiong H Y, Zhu X Y, Zhang H L, Li H H, Miao J L, Wang W S, Tang Z S, Zhang Z Y, Yao G X, Zhang Q, Pan Y H, Wang X, Rashid M A R, Li J J, Gao Y M, Li Z K, Yang W C, Fu X D, Li Z C. 2017. OsLG3 contributing to rice grain length and yield was mined by Ho-LAMap. BMC Biol, 15(1): 28. |
| [76] | Zhang D L, Zhang H L, Qi Y W, Wang M X, Sun J L, Ding L, Li Z C. 2013. Genetic structure and eco-geographical differentiation of cultivated Hsien rice (Oryza sativa L. subsp. indica) in China revealed by microsatellites. Chin Sci Bull, 58(3): 344-352. |
| [77] | Zhang P, Liu X D, Tong H H, Lu Y G, Li J Q. 2014. Association mapping for important agronomic traits in core collection of rice (Oryza sativa L.) with SSR markers. PLoS One, 9(10): e111508. |
| [78] | Zhao D S, Li Q F, Zhang C Q, Zhang C, Yang Q Q, Pan L X, Ren X Y, Lu J, Gu M H, Liu Q Q. 2018. GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality. Nat Commun, 9(1): 1240. |
| [79] | Zhou Q Y, An H, Zhang Y, Shen F C. 2000. Study on heredity of morphological characters of rice grain. J Southwest Agric Univ, 22(2): 102-104. |
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