Investigating Genetic Diversity and Population Structure in Rice Breeding from Association Mapping of 116 Accessions Using 64 Polymorphic SSR Markers

Author:

Singh Alok1ORCID,Kumar Devendra2ORCID,Gemmati Donato34ORCID,Ellur Ranjith5ORCID,Singh Ashutosh6ORCID,Tisato Veronica34ORCID,Dwivedi Devendra1,Singh Sanjay5ORCID,Kumar Kishor7ORCID,Khan Nawaz1,Singh Ajay8ORCID

Affiliation:

1. Department of Plant Molecular Biology and Genetic Engineering, Acharya Narendra Deva University of Agriculture & Technology, Ayodhya 224229, India

2. Department of Plant Biotechnology, College of Biotechnology, Uttar Pradesh Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go-Anusandhan Sansthan (DUVASU), Mathura 281001, India

3. Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy

4. Centre Haemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy

5. Division of Genetics, ICAR- Indian Agricultural Research Institute, New Delhi 110012, India

6. Department of Biotechnology & Crop Improvement, Rani Laxmi Bai Central Agricultural University, Jhansi 284003, India

7. Center for Advanced Materials (CAM), Department of Mechanical and Industrial Engineering, Qatar University, Doha P.O. Box 2713, Qatar

8. German Federal Institute for Risk Assesment (BfR), 10589 Berlin, Germany

Abstract

Genetic variability in rice breeding programs plays a very crucial role. It provides an outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For a greater understanding of population structure, the genetic relationship among different rice lines is indispensable prior to the setting of a correlation among dynamic alleles and traits. In the present investigation, the genetic diversity and population structure of 116 rice accessions were studied to understand genetic relatedness and diversity among them using 64 polymorphic SSR markers. A genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model- and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% was likely due to differences within groups. This study suggested that population structure and genetic relatedness should be considered to establish marker–trait associations for association mapping when working with the core collection of germplasm lines.

Publisher

MDPI AG

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