SSR marker-based genetic diversity and marker-trait association analysis in aromatic rice (Oryza sativa) landraces

MAUMITA BURMAN; SUNIL KUMAR NAIR; ABHINAV SAO; DEEPAK GAURAHA; DEEPAK SHARMA

doi:10.56093/ijas.v93i4.133177

Authors

MAUMITA BURMAN Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492 012, India
SUNIL KUMAR NAIR Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492 012, India
ABHINAV SAO Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492 012, India
DEEPAK GAURAHA Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492 012, India
DEEPAK SHARMA Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492 012, India

https://doi.org/10.56093/ijas.v93i4.133177

Keywords:

Aromatic rice, Genetic diversity, Landraces, Molecular markers, Variability

Abstract

The present study on SSR marker-based genetic diversity and marker-trait association analysis in aromatic rice (Oryza sativa L.) landraces was carried out at R. H. Richharia Research Laboratory, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh during 2020. A total of 25 PCR-based simple sequence repeats (SSR) markers were evaluated in a set of 90 aromatic rice landraces along with 6 checks which includes 1 non-aromatic and 5 aromatic check varieties. Phenotypic data for marker-trait association analysis were taken for 24 yield attributing traits. Polymorphic Information Content (PIC) value ranged from 0.52 (RM316) to 0.79 (RM553) with a mean of
0.69 which reveals that all the markers used in this study were highly informative and useful for diversity analysis of a wide range of genotypes. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Rho’s similarity based cluster analysis revealed that all 5 aromatic check varieties falls in one cluster while the 1 non-aromatic check variety (Mahamaya) forms a separate cluster. Mixed linear model (MLM) was applied to perform genome-wide association mapping where 41 significant marker-trait associations were observed for 24 yield attributing traits. The potential markers identified in the study may provide new opportunities for rice breeder to improve yield and its attributing traits through marker assisted selection approach.

Downloads

Download data is not yet available.

References

Aljumaili S J, Rafii M Y, Latif M A, Sakimin S Z, Arolu I W and Miah G. 2016. Genetic diversity of aromatic rice germplasm revealed by SSR markers. BioMed Research International 3: 1–11.

Bradbury P J, Zhang Z, Kroon D E, Casstevens T M, Ramdoss Y and Buckler E S. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23: 2633–35. DOI: https://doi.org/10.1093/bioinformatics/btm308

Doyle J J and Doyle J L. 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin 19: 11–15.

Earl D A and HoldtVon B M. 2012. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the evanno method. Conservation Genetics Resources 4(2): 359–61. DOI: https://doi.org/10.1007/s12686-011-9548-7

Evanno G, Regnaut S and Goudet J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 2611–20. DOI: https://doi.org/10.1111/j.1365-294X.2005.02553.x

Hammer Q, Harper D A T and Ryan P D. 2001. PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica 4: 9.

Liu K and Muse S V. 2005. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics 21: 2128–29. DOI: https://doi.org/10.1093/bioinformatics/bti282

Moran M D. 2003. Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos 100: 403–05. DOI: https://doi.org/10.1034/j.1600-0706.2003.12010.x

Pritchard J K, Stephens M and Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–59. DOI: https://doi.org/10.1093/genetics/155.2.945

Rani M G and Adilakshmi D. 2011. Genetic analysis of blast resistance in rice with simple sequence repeats (SSR). Journal of Crop Improvement 25: 232–38. DOI: https://doi.org/10.1080/15427528.2011.555834

Shamim M Z, Manzar H, Sharma V K and Kumar P. 2016. Microsatellite marker based characterization and divergence analysis among rice varieties. Indian Journal of Biotechnology 15: 182–89.

Surapaneni M, Balakrishnan D, Mesapogu S, Raju A K, Rao Y V and Neelamraju S. 2016. Genetic characterization and population structure of Indian rice cultivars and wild genotypes using core set markers. Biotechnology 6: 95–106. DOI: https://doi.org/10.1007/s13205-016-0409-7

Susiyanti S, Rusmana R, Maryani Y, Sjaifuddin S, Krisdianto N and Syabana M A. 2020. The physicochemical properties of several Indonesian rice varieties. Biotropia - The Southeast Asian Journal of Tropical Biology 27: 41–50.

Swamy B P M, Shamsudin N A A, Rahman S N A, Mauleon R, Ratnam W, Cruz M T S and Kumar A. 2017. Association mapping of yield and yield relatedtraits under reproductive stage drought stree in rice (Oryza sativa L.). Rice 10: 21–28. DOI: https://doi.org/10.1186/s12284-017-0161-6

Talukdar P R, Rathi S, Pathak K, Chetia S K and Sarma R N. 2017. Population structure and marker-trait association in indigenous aromatic rice. Rice Science 24: 145–54. DOI: https://doi.org/10.1016/j.rsci.2016.08.009