traits among rice genotypes - International Journal of Current Science

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


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



(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



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


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


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**),


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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