INT J CURR SCI 2015, 15: E 42-50 RESEARCH ARTICLE
ISSN 2250-1770
Correlation of molecular marker allele size with physio-morphological and micronutrient (Zn, Fe) traits among rice genotypes Basanti Brara*, Rajinder K. Jaina and Sunita Jainb a
Department of Biotechnology and Molecular Biology, CCS Haryana Agricultural University, Hisar-125 004, India b
Department of Biochemistry, CCS Haryana Agricultural University, Hisar-125 004, India *Corresponding author:
[email protected]
Abstract More than half of the world’s population, especially women and children in the developing countries, suffer from micronutrient malnutrition or ‘hidden hunger’ resulting from the consumption of meagre bioavailability vitamins and minerals containing diet. Micronutrient malnutrition is the condition that develops when the body does not get the optimum amount of the vitamins, minerals and other micronutrients which are essential to maintain metabolic regulation and organ function. Rice (Oryza sativa L.), the world’s most important food crop that feeds over half of the global population, is a model plant species for genomic research. So increasing nutrition element contents of rice can enhance nutrition uptake of human. In the present study, allele size of microsattelite associated with agronomic traits, as well as mineral (iron and zinc) traits was investigated based on 48 molecular markers in fourteen rice genotypes (Basmati, japonica and indica). There was significant correlation for 37 (r = -0.534*-0.936**) of 432 pairs among these markers and agronomic traits, as well as mineral traits. There was a significant correlation for 13 (r = -0.575*-0.939**) of 96 pairs between allele size of molecular markers and mineral traits, 10 (r = -0.534*-0.708**) of 144 pairs between molecular markers and plant traits, 9 (r = -0.603* to -0.936**) of 96 pairs between molecular markers and panicle traits and 5 (r = -0.849* to -0.653**) of 96 pairs between molecular markers and grain traits. Out of 48 molecular markers, ten are significantly correlated and associated with micronutrient (iron and zinc) traits and two with aromatic trait. Some of the markers were also correlated with other agronomic traits. For example, three, five, two, seven, five, markers, respectively, are significantly correlated with plant height, panicle/plant, panicle length, grain/plant and yield/plant, respectively. Keywords: Agronomic traits, Iron, Zinc, Molecular markers, Rice Received: 14thJanuary 2015; Revised: 28thFebruary; Accepted: 15thMarch; © IJCS New Liberty Group 2015 Introduction
research was concentrated on increasing the grain yield
Rice is monocot plants Oryza sativa (Asian rice) or
and to improve the resistance to environmental stresses,
Oryza glaberrima (African rice). As a cereal grain, it is
pests and pathogens (Borlaug 1998; 2000), but little or
the most widely consumed staple food for a large part of
no attention was given towards the enhancement of its
the world's human population, especially in Asia. It is the
nutritional quality. In 1966, the International Rice
grain with the second-highest worldwide production,
Research Institute (IRRI) released the first high yielding
after corn, according to data for 2010, Smith (1998).
rice variety ‘IR8’. In the subsequent decade, a small
Over the past few decades till 1990, most of the breeding
number of such high yielding varieties almost completely
Basanti Brar et al., 2015
replaced the thousands of the traditional rice landraces
genes in the genomes Lawson and Zhang (2006). There
previously cultivated by the farmers. This resulted in the
are 16 genes were cloned by Chinese scientists and 33
immense ‘genetic erosion’ and loss of biodiversity.
genes fine-mapped ones, Jiang et al. (2007). Ghd7,
Despite the loss, a lot of germplasm still exists and being
encoding a CCT domain protein, isolated from an elite
maintained by the international such as International Rice
rice hybrid, has major role on an array of traits in rice,
Research Institute (IRRI, The Philippines) and national
including number of grains/panicle and plant height, Xue
research institutions in various countries. Most of this
et al. (2008). Micronutrient malnutrition is a term used to
germplasm is yet to be tested for the nutritional quality
refer to diseases caused by a dietary deficiency of
traits. Phenotypic traits as well as mineral traits are the
vitamins or minerals. More than 2 billion people in the
most important and least understood traits in rice.
world
today
may
be
affected
by
micronutrient
Genetic variability of the simple sequence repeats
malnutrition. Anaemia and iron deficiency affect more
(SSRs) markers is important for understanding the
than 2 billion people in virtually all countries, WHO
phenotypic traits, including fingerprinting genotypes,
(2000). Those most affected are women and pre-school-
analyzing genetic diversity, determining variety identity,
age children (as many as 50 percent of whom may be
marker-assisted breeding, phylogenetic analysis, and
anaemic), but anaemia is also seen in older children and
map-based cloning of genes reported (Shen et al., 2004),
men also.
as well as key component of any breeding program for
Anaemia in infants and children is associated with
broadening the gene pool of rice. Microsatellites are
retarded physical growth, reduced resistance to infections
related with various genes that control agronomic traits.
and slow development of learning abilities. In adults, it
For example, the allele of RM5 marker on chromosome 1
causes fatigue, reduced work capacity and may cause
in Oryza rufipogon was associated with an 18% increase
reproductive impairment. Fe deficiency is one of the
in grain yield per plant as described by Xiao et al. (1996),
most prevalent micronutrient deficiencies, affecting an
and RM5303 was associated with low silicon rice (Lsi1)
estimated two billion people (Stoltzfus et al., 1998) and
gene according to Ma et al. (2006). Recent research has
causing 0.8 million deaths annually worldwide (WHO,
shown that SSRs have many important roles in
2002). Fe deficiency is ranked sixth among the risk
development, gene regulation and evolution (Lawson and
factors for death and disability in developing countries
Zhang, 2006). Microsatellites markers within genes can
with high mortality rates (WHO, 2002). Zinc deficiency
be subjected to stronger selective pressure than other
is the result of inadequate dietary intake of zinc, disease
genomic regions because of their functional importance
states that promote zinc losses, or physiological states
(Li et al., 2004). Functional markers are derived from
that require increased zinc. Populations that consume
polymorphic sites with gene involved in phenotypic trait
primarily plant based diets that are low in bio-available
variation (Andersen and Lubberstedt, 2003).
zinc often have zinc deficiencies (Sandstead, 1991;
The distribution of SSRs appears highly non
Solomons, 2001). Zinc deficiency may cause a decrease
random and varies a great deal in different regions of the
in appetite which can degenerate into anorexia, Suzuki et
www.currentsciencejournal.info
Basanti Brar et al., 2015
al. (2011). Most of the staple foods including rice
(grain/panicle, thousand grain weight) and panicle traits
provide diets of low nutritional quality including iron,
(panicle length and grains /panicle) were examined at
zinc.
appropriate stages during plant growth. Micronutrient malnutrition causes several diseases;
Fe and Zn estimation
the affected people are more prone to infection to other
In a 100 ml flask, 1 gm oven dried ground seed
diseases resulting in further deterioration in quality of
sample taken in 100 ml flask. To this, 25 ml of diacid
life. Biofortification-the breeding of staple plants/ foods
5:1 ratio (nitric acid: perchloric acid) was added. After
products with high bioavailable micronutrient content.
overnight incubation, next day samples were digested on
Possibility of evolving micronutrient dense crops using
hot plate. When clear white solution is formed, the final
plant breeding and/or biotechnological (marker assisted
volume was made with double distilled water and filtered
selection, transformation, etc.) strategies exists within the
through Whatman filter paper No 1. Reading was note
genomes of staple food crops. A lot of variability does
down with atomic absorption spectrophotometer.
exist for micronutrient (Fe, Zn, Vitaimin A, etc) content
Microsatellites marker analysis
and bioavailability in many crops including rice. In addition,
protocols
have
been
developed
Microsatellite marker based DNA fingerprint
for
database was generated for 14 rice varieties including
microsatellite marker analysis in rice. Literature about
mineral-rich and high-yielding rice varieties using 48
the correlation between microsatellite allele size and Fe
SSR primers, covering all 12 chromosomes. The
and Zn traits is very scanty, that’s why the present
genomic DNA with a modified 1% CTAB method,
research was undertaken to study the correlation between
Doyle and Doyle (1990). The PCR amplifying procedure
microsatellite molecular weight and mineral (Fe and zn)
was that of Panaud et al. (1996) with slight modification
traits as well as physio-morpological traits.
and subsequently run on 4% denatured polyacrylamide
Materials and Methods
gel electrophoresis for allelic profiling study.
Plant material
Statistical analysis
A total of 14 rice genotypes were collected from
The mean of three replications for each mineral
CCS HAU, Rice Research Station, Kaul, Kaithal. The
element
genotypes were raised during kharif season of 2008-09 at
Correlation as determined by applying Pearson’s method,
CCS HAU, Rice Research Station, Kaul (Kaithal) which
Pearson correlation coefficient was measured between
falls under semi-tropical regions of North India. These
the molecular weight of 48 SSR markers and two mineral
rice genotypes were reported for mineral variation and
elements as well as seven measured phenotypic traits of
genetic diversity (Brar et al., 2011, 2014).
14 rice genotypes. All the Statistical analysis was
Physio-morphological traits
performed using OPSTAT software.
From every line, six plants were randomly selected
concentration
in
rice
were
calculated.
Results and Discussion
and observations were recorded on the plant traits (plant
Various mineral as well as physio-morphological
height, tillers per plant, and panicles per plant), grain trait
traits were significantly associated with the allele size
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Basanti Brar et al., 2015
(molecular weight) of microsattelite markers (Table 1).
locus. The overall size of amplified PCR products ranged
There was very high polymorphism distributed over 12
from 73-585 bp (Table 2). The molecular size difference
chromosomes.
showed
between the smallest and largest allele at a SSR locus
polymorphism at all 48 SSR loci. A total of 258 alleles
varied from 3 (RM10772) to 328 (BAD2). The
were detected at 48 SSR loci (Table 2). The number of
polymorphism information content (PIC), which is an
alleles per locus ranged from 2 (RM34, RM248,
indicative level of polymorphism, varied from 0.245
RM6641, RM10772 and BADEX 7-5) to 9 (RM21,
(RM256) to 0.862 (RM21) with an average of 0.701 per
RM144 and RM447) with an average of 5.14 alleles per
locus.
The
fourteen
varieties
Table 1. Allele size of microsatellites in rice genotypes and its linking traits
Marker
No. of allele
Repeat motif
RM 1
6
(GA)26
Plant height
RM 17
6
(GA)21
Effective no. of tillers/ plant
RM-23
6
(GA)15
Zinc content
RM-144
9
(ATT)11
Plant height and yield/plant
RM-152
7
(GGC)10
Plant height, panicle/plant, grain/panicle, yield/plant, Fe and Zn content
(CT)17
panicle/plant, grain/panicle, yield/plant, Fe and Zn content
(CT)25
panicle/plant, grain/panicle, yield/plant, Fe and Zn content
RM-201 5 RM-205 4
linking traits of microsatellites
RM-234
5
(GA)25
Fe content
RM-235
6
(CT)24
Effective no. of tillers/ plant
RM-242
6
(CT)26
Panicle length
RM-256
6
(CT)21
grain/panicle
RM 300
5
(GTT)14
Zn content
RM 339
6
(CTT)8CCT(CTT)5
RM 447
4
(CTT)8
grain/panicle and Zn content
RM 585
5
(TC)45
Zn content
RM 6641
2
(GTA)14
Zn content
BAD2
3
--
panicle/plant, grain/panicle, yield/plant
Panicle length, effective no. of tillers /plant and grain/panicle
Association (correlation) of molecular marker allele size
Allele size of molecular markers and mineral (iron and
with physio-morphological traits and micronutrient (Zn,
zinc) contents
Fe) traits
Correlation analysis indicated that there was a
There was significant correlation association for 37
significantly associated only in 13 (r = -0.575*-0.939**)
(r =-0.534*-0.936**) of 432 pairs among these markers
of 96 pair traits between the allele size of molecular
and agronomic traits, as well as mineral traits (Table 2).
markers and mineral traits (Table 2). Iron contents in 14
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Basanti Brar et al., 2015
rice genotypes displayed significant correlation with
RM447 and BAD2. In present study, out of 48 molecular
specific alleles at RM152, RM201, RM205, RM234,
markers, ten (RM23, RM144, RN152, RM201, RM205,
RM447, RM 585 and RM6641 loci. Zinc content was
RM234, RM300, RM447, RM585, RM6641) are
significantly correlated with the specific alleles at RM23,
significantly correlated with micronutrient (iron and zinc)
RM144, RM152, RM205 and RM300.
traits and two (BAD2 and BADEX 7-5) associated with
Allele size of molecular markers and plant traits
aromatic trait. Some of the markers were also correlated
Correlation analysis indicated that there was a
with other agronomic traits. For example, three (RM1,
significant correlation for 10 (r = -0.534*-0.708**) of
RM144, RM152), five (RM152, RM201, RM205,
144 pair traits between the allele size of molecular
RM270, RM339), two (RM242, BAD2), seven (RM152,
markers and plant traits (Table 2). Plant height displayed
RM201, RM205, RM256, RM339, RM447, BAD2), five
significant correlation with allele size of RM1, RM144
(RM144, RM152, RM201, RM205, RM339) markers,
and RM152. Panicles per plant had significant associated
respectively, are significantly associated with plant
with the specific alleles amplified at RM152, RM201,
height, panicle/plant, panicle length, grain/plant and
RM205, RM270 and RM339. Effective numbers of tillers
yield/plant.
displayed significant correlation with allele size of
Size of alleles for SSR markers associated with mineral
RM17, RM235 and BAD2.
(Fe and Zinc) contents
Allele size of molecular markers and grain traits
The overall size of PCR products amplified using 48
Correlation analysis indicated that there was a
markers for 14 rice genotypes ranged from 73-120 bp
significant correlation only in 5 (r = -0.849* to -0.653**)
(RM30) to 232-238 bp (RM137). Mineral elements
of 96 pair traits between the allele size of molecular
played an important role in forming genetic diversity
markers and grain traits (Table 2). Grain yield per plant
(Zeng et al., 2006). Correlation analysis indicated that
displayed significant correlation with allele size of
there was a significant correlation only in 13 (r = -
RM144
RM205
0.575*-0.939**) of 96 pair traits between the allele size
(-0.787**) and RM339 (-0.849**), but 1000 grain weight
of molecular markers and mineral traits such as iron and
had no correlation with any of the 48 molecular markers.
zinc. Out of 48 markers five (RM23, RM144, RM152,
Allele size of molecular markers and panicle traits
RM205 and RM300) were significantly correlated with
Correlation analysis indicated that there was a significant
zinc content and seven (RM152, RM201, RM205,
correlation only in 9 (r = -0.603* to -0.936**) of 96 pair
RM234, RM447, RM 585 and RM6641) loci was
traits between the allele size of molecular markers and
significantly associated with the iron content, among
panicle traits (Table 2). Panicle length displayed
which RM 234 associated with days to heading, days to
significant correlation with allele size of molecular
maturity and maximum root length reported by Thomson
markers RM242 (0.552*) and BAD2(-0.603**). Grain
et al. (2003), Miyata et al. (2007). RM 234 also
per panicles had significantly associated with the specific
associated with P content, penicle traits as well as grain
alleles at RM144, RM201, RM205, RM256, RM339,
length, Zeng et al. (2009).
(-0.829**),
RM152
(-0.835**),
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Table 2. Correlation of molecular marker allele size with physo-morphological and micronutrient (Zn and Fe) traits
RM1 RM17 RM21 RM23 RM30 RM34 RM53 RM81 RM137 RM144 RM152 RM162 RM201 RM205 RM228 RM234 RM235 RM237 RM240 RM242 RM248 RM256 RM257 RM270 RM284 RM296 RM-300 RM301 RM310 RM315 RM320 RM324 RM333 RM335 RM339 RM400 RM440 RM447 RM528 RM547 RM585 RM1089 RM3331 RM3412 RM6641 RM10772 BAD2 BADEX75
Molecular marker allele Size Range (bp) 82-125 150-184 130-164 120-146 73-120 160-165 178-192 118-130 232-238 160-280 108-159 217-255 144-173 123-156 108-153 124-156 85-134 128-142 95-145 193-225 74-102 107-137 122-182 105-115 128-142 100-130 100-130 140-153 80-123 133-155 174-270 133-180 146-250 104-135 115-176 186-245 120-210 110-170 245-278 226-256 174-250 170-235 130-160 210-250 134-135 392-395 95-103 257-585
Plant heights
Panicle/ plant
Panicle length
Effective no. of tillers /plant
Grain/ panicle
Yield /plant
1000 grain weight
Fe content
Zn content
-0.534* 0.226 -0.26 -0.369 -0.201 -0.057 0.115 -0.315 0.234 -0.560* -0.613* 0.304 -0.513 -0.399 -0.204 0.018 0.134 -0.332 0.014 0.082 -0.325 -0.524 0.351 -0.026 0.396 -0.398 0.453 -0.078 0.062 0.217 -0.031 -0.024 -0.255 0.077 -0.197 0.513 -0.07 -0.336 0.244 0.234 -0.195 -0.348 -0.222 -0.173 0.439 -0.232 -0.145
-0.448 0.465 0.053 -0.31 -0.264 -0.198 0.067 0.021 0.262 -0.43 -0.798** 0.27 -0.807** -0.737** -0.258 0.046 0.264 -0.019 -0.098 -0.067 -0.107 -0.525 0.323 -0.552* 0.133 -0.129 0.233 -0.17 -0.088 0.293 -0.077 -0.049 -0.145 0.058 -0.676** 0.324 -0.431 -0.392 0.217 0.16 0.166 -0.406 -0.374 0.002 0.164 -0.345 -0.295
0.023 0.495 0.316 -0.387 -0.238 -0.077 0.327 0.291 0.197 -0.089 0.376 0.14 0.378 0.382 0.235 -0t.077 0.418 -0.235 0.267 0.551* 0.084 0.054 0.03 0.078 0.191 -0.179 -0.013 0.32 0.042 0.016 0.23 0.396 -0.515 0.422 0.385 0.481 0.358 0.047 -0.117 0.439 -0.199 0.1 -0.314 0.078 0.216 -0.15 -0.603*
-0.249 0.708** 0.209 0.014 -0.19 -0.199 0.201 0.26 0.428 0.04 -0.396 0.233 -0.403 -0.367 0.097 0.249 0.598* -0.054 0.212 0.297 0.2 -0.09 0.172 -0.429 0.14 -0.084 0.219 0.231 -0.17 0.13 -0.006 0.279 -0.12 -0.185 -0.128 0.197 -0.422 -0.364 0.118 0.138 0.05 -0.33 -0.337 -0.015 0.164 -0.312 -0.668**
0.436 -0.16 0.479 0.002 0.226 0.141 0.106 0.479 -0.234 0.365 0.803** -0.241 0.807** 0.721** 0.266 -0.07 -0.275 0.078 0.008 0.118 0.21 0.646* -0.276 0.3 -0.189 0.213 -0.361 0.126 0.006 -0.315 0.223 0.03 -0.16 0.245 0.673** -0.055 0.528 0.753** -0.382 0.027 -0.095 0.383 0.179 0.166 -0.336 0.402 0.936**
-0.291 -0.256 -0.364 -0.118 0.186 -0.19 0.161 -0.315 0.32 -0.653* -0.829** 0.233 -0.835** -0.787** -0.143 0.11 0.068 -0.014 -0.015 0.085 -0.51 0.081 0.385 -0.187 0.214 -0.088 0.29 0.023 -0.254 0.053 0.193 -0.121 -0.232 -0.03 -0.849** -0.128 -0.248 0.149 0.055 -0.054 -0.343 -0.336 0.219 -0.193 0.108 0.119 -0.203
-0.139 -0.082 -0.217 -0.208 0.129 -0.156 0.162 -0.256 0.307 -0.166 0.181 0.198 0.178 0.142 -0.063 0.058 0.115 -0.144 0.179 0.167 -0.517 0.176 0.266 -0.17 0.09 -0.201 0.145 0.011 -0.358 0.047 0.338 0.025 -0.281 0.078 0.219 0.053 -0.216 0.078 0.023 -0.069 -0.288 -0.427 0.05 -0.042 0.034 -0.041 0.425
0.475 -0.069 -0.068 -0.072 -0.386 0.426 -0.424 -0.137 -0.331 0.345 0.653* 0.09 0.678** 0.615* -0.21 -0.613* -0.479 -0.228 -0.228 -0.3 0.148 -0.261 -0.04 0.37 -0.495 -0.224 -0.465 -0.264 -0.318 0.384 -0.257 0.019 -0.251 0.497 0.413 0.239 0.173 -0.668** -0.214 -0.335 0.939** 0.209 -0.235 0.424 -0.713** -0.468 -0.202
-0.45 0.006 -0.275 0.589* -0.184 0.136 0.43 -0.254 0.311 -0.575* -0.609* 0.406 -0.609* -0.599* -0.398 0.194 0.161 -0.342 -0.455 -0.338 0.106 -0.046 0.319 0.175 0.34 -0.441 0.559* 0.072 0.005 0.159 -0.058 0.156 -0.142 -0.313 -0.274 0.272 -0.374 -0.1 0.488 0.504 -0.117 -0.172 -0.128 -0.238 0.201 0.029 -0.217
-0.478
-0.009
0.136
0.02
0.442
-0.361
-0.313
-0.212
-0.427
* Significant at ≤0.05; **Significant at ≤0.01
Allele size of SSR markers associated with phenotypic
deficiency and plant height (Thomson et al., 2003). In the
traits
present study, we identify seven markers (RM144, Ten (r = -0.534*-0.708**) of 144 pair traits
RM152,RM201, RM 205, RM339, RM447 and BAD2)
between the allele size and plant traits, such as some
with pleiotropic effects, that have associated with more
markers associated with plant height (RM1, RM144 and
than one traits as well as markers that were identified to
RM152) and tillers per plant displayed a significant
be epistatic, indicating that population wide analysis
correlation with the allele size (molecular weight) of
served as an effective tool in deciphering marker–trait
three molecular markers (RM17, RM235 and BAD2),
associations.
respectively. Panicles per plant displayed a significant
Vanniarajan et al. (2012). In general, allele size of
correlation with the allele size (molecular weight) of
microsatellites is more associated with plant traits, grain
RM152, RM201, RM205, RM270 and RM339. In the
and panicle traits than mineral element content in rice.
previous study, RM235 associated with panicles per
The data on combined mineral (Fe & Zn) as well as
plant (Thomson et al. 2003). K content was significantly
phenotypic traits and allele size of microsatellites within
correlated with the allele size of RM235, RM253,
genes may provide strong evidence on the allele size of
RM225, RM5, RM81A, and RM244, indicating that K
SSRs associated with mineral (Fe & Zn) content and the
content was associated with yield traits, Zeng et al.
SSRs on phenotypic traits, especially in mineral and
(2005). Grain traits were significantly associated with
grain traits. As to further seek for the relationship
RM235, Zeng et al. (2009). Panicles per plant displayed a
between the molecular weight of microsatellites and
significant correlation with the molecular weight of
physio-morphological as well as mineral traits in rice
RM242. Grain per panicles had associated with the
based on markers associated.
molecular sizes of RM144, RM201, RM205, RM256,
Acknowledgement
These
results
were
supported
by
RM339, RM447 and BAD2. Out of these RM256 on
The financial support and infrastructure provided
chromosome 8 was found associated significantly with
by Department of Molecular Biology and Biotechnology,
weight of 100 grains, Vanniarajan et al. (2012).Previous
Chaudhary Charan Singh University, Hisar is highly
studies showed that RM5 was associated with grains per
acknowledged.
panicle; especially the yield increasing genes increased
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