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Gene Reports 30 (2023) 101719
Available online 2 December 2022
2452-0144/© 2022 Published by Elsevier Inc.
Assessment of allelic and genetic diversity, and population structure among
farmers' rice varieties using microsatellite markers and morphological traits
Pritesh Sundar Roy
a
,
1
, Shubhransu Nayak
b
,
1
, Soma Samanta
a
, Apurba Chhotaray
a
,
Soumya Mohanty
a
, Sudhiranjan Dhua
a
, Urmila Dhua
a
, Bhaskar Chandra Patra
a
,
Kapil Kumar Tiwari
c
, S.V. Amitha C.R. Mithra
d
, Rameswar Prasad Sah
a
, Lambodar Behera
a
,
*
,
Trilochan Mohapatra
e
a
ICAR-National Rice Research Institute, Cuttack 753006, Odisha, India
b
Odisha Biodiversity Board, RPRC, Bhubaneswar, Odisha, India
c
Bio Science Research Centre, SDAU, SK Nagar, Gujarat, India
d
ICAR-National Research Centre for Plant Biotechnology, New Delhi 110012, India
e
ICAR, Government of India, New Delhi, India
ARTICLE INFO
Edited by Dominic Voon
Keywords:
Allelic diversity
farmers' rice varieties
Genetic diversity
Microsatellite marker
Rice
Structure
ABSTRACT
Traditional rice varieties grown by the farmers serve as valuable genetic resources for future rice improvement.
These varieties are highly adapted to varied agro-ecological conditions. However, they are rapidly lost because of
the adoption of high-yielding varieties. The extent of allelic and genetic diversity present in the germplasm is a
prerequisite for the improvement of any crop and conservation strategies under adverse impacts of climate.
Farmers' rice varieties are usually poor yielders but are allelic treasurer for different traits, especially biotic and
abiotic stresses, grain qualities, early seedling vigor, input use efciency, etc. Therefore, the present study was
aimed for a detailed understanding of allelic and genetic diversity, and population structure of 607 farmers' rice
varieties using 36 uorescently labeled microsatellite markers and 53 morphological traits. A total of 363 alleles
was detected with an average of 10.33 alleles per locus and moderately high Nei's allelic/gene diversity (0.502)
was detected. Polymorphic information content ranged from 0.685 to 0.987 with an average of 0.901. 34 unique,
236 rare, 84 low-frequency and 44 high-frequency alleles were detected. 53 morphological traits harbored a total
of 195 variables with an average of 4.217 variables per trait. 50 out of 53 morphological traits showed poly-
morphism and highly signicant differences among varieties. High genetic diversity was observed among 607
farmers' rice varieties both at molecular (0.653) and phenotypic (0.656) levels. The dendrogram based on both
microsatellite markers and morphological traits grouped the 607 farmers' rice varieties into three major groups.
A moderate population structure was observed with two independent subpopulations SP1 and SP2, which have
membership percentages of 82.6 % and 17.4 %, xation index values of 0.19 and 0.194, respectively. The
AMOVA could explain 63 % of the total variation among varieties and 34 % within varieties. Our results showed
that the farmers' rice varieties of Odisha harbored higher levels of both allelic and genetic diversity. Hence, these
varieties would be useful for the identication of novel and elite alleles, and serve as a source of donors for the
development of climate-smart varieties with improved grain yield and qualities, and input use efciency, which
would be sustainable in changing climate scenario conditions and improve farmers' income.
Abbreviations: AMOVA, Analysis of Molecular Variance; CTAB, Cetyltrimethylammonium Bromide; FAO, Food and Agriculture Organization; F
IS
, Inbreeding
Coefcient; F
ST
, Fixation Index; FV, Farmers’ Variety; He, Expected Allelic/Gene Diversity; Ho, Expected Homozygosity; MCMC, Markov Chain Monte Carlo; Na,
Number of Alleles; Ne, Number of Effective Alleles; IPR, Intellectual Property Rights; PCR, Polymerase Chain Reaction; PCA, Principal Component Analysis; PCoA,
Principal Co-ordinate Analysis; PIC, Polymorphism Information Content; PPV&FRA, Protection of Plant Varieties and Farmers’Rights Authority; RGNMS, Rice Genic
Non-coding Microsatellite; Rs, Allelic Richness; SP, Subpopulation; SSR, Simple Sequence Repeat.
* Corresponding author.
E-mail addresses: lbehera.publi2018@gmail.com, lambodarjamujhadi@gmail.com (L. Behera).
1
Contributed equally to the work.
Contents lists available at ScienceDirect
Gene Reports
journal homepage: www.elsevier.com/locate/genrep
https://doi.org/10.1016/j.genrep.2022.101719
Received 13 July 2022; Received in revised form 15 November 2022; Accepted 27 November 2022
Gene Reports 30 (2023) 101719
3
diversity analyses. Details of the SSR loci used in the present study are
given in Supplementary Table S2. For multiplexing, primers were uo-
rescently labeled with four different dyes (FAM, VIC, NED, and PET) and
the PCR amplication was performed according to the procedure
described by Chen et al. (1997). The PCR amplications were performed
in a total reaction volume of 10
μ
l containing 20 ng genomic DNA, 2.0
picomoles of each forward and reverse primer, 1
μ
l of 10×buffer (0.1 M
Tris, pH 9, 0.5 M KCl, 15 mM MgCl
2
, 0.1 % gelatine), 200
μ
M each of
dNTPs and 0.3 U of Taq DNA polymerase. The PCR condition was an
initial denaturation at 94 ◦C for 5 min followed by 35 cycles of dena-
turation at 94 ◦C for 30 s, annealing (depending on the TM value of
primer) at 50◦-60 ◦C for 30 s, extension at 72 ◦C for 1 min and a nal
extension of 20 min at 72 ◦C. However, a reference blank was used in
individual PCR reaction to avoid any unambiguous amplication due to
contamination. The PCR products were mixed with the uorescent dyes
at 1:1:2:4 ration for FAM: VIC: NED: PET, respectively (Tiwari et al.,
2015). Further, 8.9
μ
l of Hi di formamide and 0.2
μ
l of an internal size
standard ROX500 (Applied Biosystems, USA) were mixed with 1.0
μ
l of
mixed microsatellite sample, the samples were denatured at 95 ◦C for 5
min and run in ABI3730xl 96 capillary automated fragment analyzer
system (ABI, Model 373xl) and the results were analyzed with GENE
MAPPER 4.1 (Applied Biosystems 2009).
2.3. Allele scoring
The size of each intense amplied fragment for all SSR loci was
determined by comparison with the size standard (100 bp DNA ladder)
and scored by incremental numbering from the lowest molecular weight
band to the progressively higher molecular weight bands to prepare the
genotype matrix.
2.4. Molecular data analysis
The amplied bands/alleles were scored as present (1) or absent (0)
for each genotype and primer combination. The data were entered into a
binary matrix and subsequently analyzed using different computer
software packages. The total number alleles, number of polymorphic
alleles, number of unique alleles, number of rare alleles, number of low
frequency alleles, number of high frequency alleles, number of multiple
alleles and polymorphism information content (PIC) were calculated to
assess the diversity of alleles of each marker locus. An allele that was
observed in >30 % of the 607 rice varieties was a high frequency/
abundant/common allele, while an allele having a frequency between 5
% and 30 % is called as a low frequency/intermediate allele. An allele
that was observed in <5 % was considered to be rare allele. The poly-
morphism information content (PIC) for each SSR marker locus was
calculated using the formula: PICi =1 −∑
n
j=1
(Pij)
2
, where n is the
number of marker alleles for marker i and P
ij
is the frequency of the j
th
allele of marker I (Anderson et al., 1993). Gene/allelic diversity pa-
rameters viz., number of alleles (Na), effective number of alleles (Ne),
expected homozygosity (Ho), expected heterozygosity (He) (Nei, 1973)
and Shannon Index (I) were evaluated using POPGENE V1.32 (htt
p://www.ualberta.ca/fyeh) with 1000 permutations. The allelic rich-
ness (Rs) for each SSR locus was measured using FSTAT 2.9.3 (Goudet,
2005). The Bayesian model-based clustering analysis of the varieties was
used for determining the optimal number of genetic clusters found
among rice varieties using the STRUCTURE software (Pritchard et al.,
2000) with 1,00,000 burn in periods and 1,00,000 Markov Chain Monte
Carlo (MCMC) replicates with ten independent runs (K) ranging from 1
to 10. The ΔK based on the change in the log probability of the data
between successive K values was estimated using the parameters
described by Evanno et al. (2005) using the software program Structure
Harvester v6.0 (Earl and von Holdt, 2012) and population clusters were
produced by the software Structure Plot developed by Ramasamy et al.
(2014) (http://btismysore.in/strplot). Moreover, varieties were further
grouped based on their collection by geographical location (28 districts)
and the genetic diversity parameters of the varieties within each district
were determined using POPGENE V1.32. The genetic variation within
Fig. 1. Sampling sites of farmers rice varieties from twenty-eight districts of Odisha, India and information on their distribution by agro-climatic zones.
P.S. Roy et al.
Gene Reports 30 (2023) 101719
16
weight, medium grain length, narrow to broad grain width, bear of
secondary branches in panicles, strong to cluster secondary branches,
medium to long panicle axis length, and medium to high number of
panicles per plant.
Presently, we are evaluating these farmers' varieties (FV) against
different biotic and abiotic stresses, grain qualities, and nutrient use
efciency. Recently, a research group from our institute evaluated these
600 farmers' rice varieties against brown planthopper using the standard
seed box screening method at the seedling stage. Based on the initial
screening result, a panel of 106 FV was selected for further evaluation
following different parameters of BPH resistance. These varieties were
genotyped with 87 gene-linked markers associated with 34 BPH resis-
tance genes. 18 varieties were identied as highly resistant (SES score
1), while 22 were moderately resistant (SES score 3). 10 markers were
found to be associated with BPH resistance genes. Bph6 and Bph30
exhibited strong resistance to BPH by having a signicant association
with different phenotypic parameters of BPH resistance (Anant et al.,
2021). Further, 600 farmers' rice varieties were tested for the presence of
endophytes. A total of 141 endophytes were identied. Some of them
enhanced the rice plant growth signicantly. Two endophytes were
found to be highly effective against the causal organisms of sheath blight
(Rhizoctonia solani) and seedling blight (Fusarium sp.). About 90–100 %
inhibition of growth of these pathogens was observed (data not sown).
These varieties are being presently used in different breeding and allele
mining programs to improve yield, grain quality, and climate resiliency,
which is required for sustainable production.
5. Future prospects
Genetic diversity is a prerequisite for breeding plants for desirable
traits. Traditional and extinct varieties have ancestry genes that spon-
taneously mutate throughout the course of long-term cultivation. These
mutations are random and cause a huge genetic variation both at the
morphological and molecular levels. In this study, the variation
observed in the farmers' varieties both at molecular and morphological
levels is obvious. This spontaneous variation or mutation has the
advantage of allowing the plant to endure the current climatic situation
for the long term for survival. Thus, only those mutated plants survived
which were coping with the climate vulnerability. As a result, these
plants serve as a repository for many unique genes needed for the
development of commercial cultivars with increased productivity and
climatic resilience. The presence of variation among the farmers' vari-
eties would serve as a source of donors for different traits contributing to
yield and survivability under stress. Because, states like Odisha have
diverse rice ecology including irrigated to rainfed food-prone ecology,
thus bear several kinds of genetic variation in germplasm collected over
the states. Besides, it reveals the nature of variation and farmers' varietal
dynamics cultivated over a region.
6. Conclusion
The present study provides a better understanding of allelic and
genetic diversity and population structure of 607 farmers' rice varieties.
Higher genetic diversity among 607 farmers' rice varieties was detected
both at molecular (0.653) and phenotypic (0.656) levels, revealing the
rich genetic diversity that exists within farmers' rice varieties. Moder-
ately high allelic/gene diversity (0.507) was detected. The number of
alleles varied from 3 to 27 alleles with an average of 10.2 per locus. 34
unique, 236 rare, 84 low-frequency, and 44 high-frequency alleles were
detected. Based on the Nei genetic distance, all the 607 varieties were
grouped into three major clusters, which were also validated by PCA
analysis both at molecular and phenotypic levels. Further, based on
population STRUCTURE analysis, we could differentiate the entire
collection into three subpopulations at K =3. These varieties are highly
differentiated, where 63 % of the total variation was because of differ-
ences among varieties. Hence, these varieties should be conserved and
exploited to harness different valuable genes/alleles available within
them. Since landraces are best adapted to different climatic conditions,
their characterization is of potential use for strengthening their con-
servation strategies and genetic enhancement, and use them as donors in
breeding programs for the development of climate-resilient rice varieties
with a higher yield to cope with climatic changing scenario conditions
and improve farmers' income.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
We thank Director, ICAR-National Rice Research Institute, Cuttack
for providing necessary facilities.
CRediT authorship contribution statement
The study was designed by TM, SRD, PSR and LB. SRD, UD and BCP
collected the farmers' rice varieties. SN, SS and SRD carried out
morphological trait characterization. SN, SS, AC, KKT, SVACRM carried
out genotyping work. PSR, SM, RPS and LB analyzed the data and per-
formed the statistical analysis. PSR, LB and TM have drafted the
manuscript. All authors read and approved the nal manuscript.
Funding source
The nancial supports provided by PPV and FRA, Ministry of Agri-
culture and Farmers Welfare, New Delhi, Government of India are
gratefully acknowledged. The funding agency has no role in the design
of the study and collection, analysis, interpretation of data and writing
manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.genrep.2022.101719.
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