Population structure of rice (Oryza sativa) landraces from high altitude area of Indian Himalayas

Annals of Applied Biology ISSN 0003-4746

R E S E A R C H A R T I C L E

Population structure of rice (Oryza sativa) landraces from highaltitude area of Indian Himalayas

A. Pandey1, I.S. Bisht2 & K.V. Bhat2

1 ICAR RC for NEH Region, Umiam, Meghalaya, India

2 National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, India

KeywordsIndian Himalayas; landrace; Oryza sativa L.;

population structure; rice.

CorrespondenceI.S. Bisht, National Bureau of Plant Genetic

Resources, Pusa Campus, New Delhi 110 012,

India.

Email:

[email protected];

[email protected]

Received: 22 September 2010; revised version

accepted: 19 September 2011.

doi:10.1111/j.1744-7348.2011.00516.x

Abstract

This study examined the genetic diversity in 20 rice landrace populations fromparts of traditional farming areas of the Indian Himalayas using 11 mappedsimple sequence repeats (SSR) loci. Twenty-four individuals sampled fromeach of the 20 landraces (480 individuals), which were collected from farmersfrom Northwest to Northeast Himalaya, showed that all landraces showedwithin population variation and none were homogeneous. The number ofpolymorphic loci in a landrace population ranged from 5 to 11. A total of 71alleles were recorded of which 58 were common and 13 were rare. Of the 71alleles, 46 were common to both Northwest and Northeast regions, whereas 9were unique to the former and 16 were unique to the latter. The mean numberof alleles per locus was 6.45 and for landrace populations from Northwest andNortheast regions were 5.0 and 5.64, respectively. Population differentiation,as shown by a high FST value (0.61), was greater for Northeast populations. Theunweighted pair group method with arithmetic mean (UPGMA) dendrogramclassified the populations into three major clusters: cluster I comprised sevenpopulations from the Northwest region, cluster II comprised seven populationsfrom the Northeast region and cluster III comprised populations from bothregions. Investigating the population genetic structure can help monitor changein diversity over time and space, and also help devise a rational plan formanagement of crop landraces on-farm under farmer management.

Introduction

Rice (Oryza sativa L.) is an important crop of the Indiansubcontinent and the staple food for its large population.This crop is cultivated in almost all the Indian states undera remarkably wide range of agroclimatic conditions andecological situations. Although semi-dwarf rice varietiesnow occupy most of the cultivated area in India, yetancestral varieties can still be found in almost allupland marginal areas. Considerable landrace diversityexists in the Indian Himalayas (Hore, 2005; Bisht et al.,2007; Rana et al., 2009). Selection made by variousethnic groups inhabiting different altitudes and climaticconditions in the Himalayan region, practicing differentforms of cultivation and diverse culture and traditions hascontributed to the diversity of the rice crop in this region

(Pandey et al., 2011). The Himalayan region of India thus

represents a valuable centre of genetic diversity of rice

crop.

Population structure of rice landraces under farmer

management has been described in several earlier studies

(Pusadee et al., 2009; Zhang et al., 2009; Kumar et al.,

2010a,b; Sreejayan et al., 2011). These studies provide

valuable information on periodic monitoring of diversity

loss over time and space, as also their judicious

conservation under ex situ and in situ (on-farm) settings.

Over the whole Kumaon region of Northwest (NW)

Himalaya, and particularly the Bageshwar district of

Uttarakhand state, rice is an important cereal grown since

time immemorial. Despite efforts to spread improved

varieties, local landraces are still largely grown in the

16 Ann Appl Biol 160 (2012) 16–24 © 2011 NBPGR (ICAR), New DelhiAnnals of Applied Biology © 2011 Association of Applied Biologists

A. Pandey et al. Population structure of rice landraces

region because of their wider adaptability in nicheenvironments. More than 80% of the cropped area inUttarakhand Himalaya is rainfed (Bisht et al., 2007). InNortheast (NE) Himalayan region, rice is also the majorstaple and upland rice is a typical example of rainfedrice. Upland areas of this region are characterised by anaerobic soil in which no attempt is normally made toimpound the water (Hore, 2005). In such heterogeneousand highland growing environments rice landraces aremainly grown, as varieties developed by centralisedbreeding programmes are often not well adapted tothese marginal areas and their microclimates. Such ricelandraces have played a very important role in the localfood security and sustainable development of agriculture,in addition to their values as genetic resources for ricegenetic improvement.

The on-farm managed populations from NW Himalayastudied here were collected from specific niche envi-ronments spanning about 2000 km2 of the Uttarakhandstate; the altitudes ranging from 1500 to 2200 m abovesea level. In the NE Himalayas, the landrace populationswere collected from Arunachal Pradesh and parts of theadjoining Assam state. In these mountainous regions, noformal in situ (on-farm) programmes were initiated forpromoting the conservation of the traditional landraces.For making a rational plan to conserve rice landraces,it is necessary to assess the level of genetic diversitypresent within and among the landrace populations.Assessment of genetic diversity can provide informationon the genetic distinctiveness of farmer-named varieties

and genetic similarity/distinctness among populations.The present research problem used the sequence-taggedmicrosatellite (STMS) marker system to investigate theinter- and intrapopulation diversity among rice landracepopulations from temperate, higher elevation areas ofIndian Himalayas with a view to assess the potentials andconsequences of on-farm management of rice landracesin traditional farming.

Materials and methods

The experimental materials comprised 20 rice landracepopulations, both locally common and rare types, fromhigh altitude areas of the Indian Himalayas (Table 1).Of these, 10 populations were sampled from partsof Uttarakhand state in NW Himalaya (group I) and10 landrace populations were collected from parts ofArunachal Pradesh and Assam in NE Himalaya (groupII; Fig. S1). The populations were collected from areaswhere these landraces were grown in more farms inlarge numbers and in above average field sizes (commonwidespread types) together with some rare landracesgrown in small fields from specific locations (rare localisedtypes). Proper sampling strategies were followed forcollecting these on-farm managed populations during2006 and 2007 cropping seasons.

Sample preparation

Twenty-four individual plants were sampled for eachlandrace. The seeds were selected randomly from the

Table 1 Passport information of rice landrace populations

Accession No. Collector No. Landrace Village District State

IC-556525 NMB 2825 Chhoti (rare) Suding Bageshwar UttarakhandIC-556528 NMB 2828 Jumariya (rare) Loharkhet Bageshwar UttarakhandIC-556531 NMB 2831 Rwat Hati (rare) Leeti Bageshwar UttarakhandIC-556532 NMB 2832 Jangai (rare) Leeti Bageshwar UttarakhandIC-556533 NMB 2833 Ghesu (common) Leeti Bageshwar UttarakhandIC-556544 NMB 2844 Madguri (common) Kunera Bageshwar UttarakhandIC-556555 NMB 2855 Dallu (rare) Bargaon Bageshwar UttarakhandIC-556557 NMB 2857 Taknoi (rare) Bargaon Bageshwar UttarakhandIC-556559 NMB 2859 Bakua (rare) Kuri Bageshwar UttarakhandIC-556560 NMB 2860 Ratan (rare) Udiyari Bageshwar UttarakhandIC-558249 NKGA 14 Kunkuni Joha (common) Barudaloni Sonitpur AssamIC-558259 NKGA 24 Pakhori Bora (common) Saguli Kota Lakhimpur AssamIC-558275 NKGA 40 Kola Bora (common) Joradhra Lakhimpur AssamIC-558293 NKGA 58 Dheku (common) Rani East Siang Arunachal PradeshIC-558298 NKGA 63 Chota Bora (rare) Rani East Siang Arunachal PradeshIC-558304 NKGA 69 Kebang (common) Ronttung East Siang Arunachal PradeshIC-558306 NKGA 71 Kebang White (rare) Ronttung East Siang Arunachal PradeshIC-558308 NKGA 73 Motum Arik (rare) Kebang East Siang Arunachal PradeshIC-558313 NKGA 78 Dheku (common) Pangin East Siang Arunachal PradeshIC-558330 NKGA 95 Bali (common) Gauri West Siang Arunachal Pradesh

Ann Appl Biol 160 (2012) 16–24 © 2011 NBPGR (ICAR), New Delhi 17Annals of Applied Biology © 2011 Association of Applied Biologists

Population structure of rice landraces A. Pandey et al.

sample and planted in the field for collecting leafsamples. Young actively growing leaves of 30-day-oldplants were collected and used for DNA extraction.Leaves were harvested and immediately stored at−80◦C until total genomic DNA was extracted byusing a modified cetyltrimethylammonium bromide(CTAB) method (Saghai-Maroof et al., 1984). The DNAquantification was performed by using Nanodrop™ 1000Spectrophotometer (Thermo Scientific, Wilmington, DE,USA). These estimates were confirmed by staining DNAwith ethidium bromide after electrophoresis in 0.8%agarose gel at 100 V for 1 h in Tris acetate EDTA (TAE)buffer (0.04 M Tris acetate, 0.001 M ethylenediaminetetra acetic acid (EDTA), pH 8.0) using known DNAconcentration standards.

Sequence-tagged microsatellite genotyping

The STMS markers as detailed in Zhang et al. (2004) wereused for genotypic analysis. Eleven primer pairs were cho-sen for STMS analysis from each rice chromosome, exceptchromosome 1, where no polymorphism was detected byscreening with three additional chromosome 1-specificmarkers. PCRs were carried out in a Bioer XP cycler (BioerTechnology Co. Ltd, Hangzhou, P.R. China) using PCRmicroplates (Axygen Inc., California, USA). Each 25-μLreaction mixture contained 1× reaction buffer, 25 mMMgCl2, 1U Taq DNA polymerase (GeNei™ BangaloreGenei, Bangalore, India), 200 μM each of four deoxynu-cleotides (dNTPs), 0.25 μM of primer and approximately40 ng of template DNA. The Touch Down PCR amplifi-cation conditions were as follows: initial extended step ofdenaturation at 94◦C for 6 min followed by 30 cyclesof denaturation at 94◦C for 1 min, primer annealingat respective annealing temperature for 1 min, primerextension at 72◦C for 1 min and primer elongation at72◦C for 6 min. The annealing temperature in the firstcycle was 10◦C higher than the annealing temperatureand was subsequently reduced in each cycle by 1◦C forthe next 10 cycles, and then continued at the primerannealing temperature for the remaining 20 cycles. ThePCR products were subjected to electrophoresis in aQIAxcel capillary electrophoresis system (Qiagen, Hilden,Germany). The system includes a device, cartridge, Bio-Calculator Analysis software and computer, and it appliescapillary electrophoresis and fluorescence technology.

Data analysis

Electrophoretic patterns were analysed by using theBioCalculator software. Allele calling was based on thesize of largest peak; as capillary separations of most STMSamplicons consist of a cluster of peaks in electrogram,

only the most conspicuous was used and the others wereconsidered as artefacts and rejected. The polymorphisminformation content (PIC) value for STMS locus wascalculated by using Allele procedure of SAS 9.2 software(SAS Institute Inc., Cary, NC, USA).

Genetic diversity parameters of the two groups wereanalysed using PopGene version 1.32 software (Yehet al., 2000). Principal co-ordinate analysis using soft-ware package XLSTAT (http://www.xlstat.com/) and thesquare root correction for negative eigenvalues was alsoused to examine genotypic relationships. Finally, thedata were subjected for analysis of molecular variation(AMOVA) using Arlequin version 3.1 software (Excoffieret al., 2005). Population structure by AMOVA is based onan analysis of variance of gene frequencies, taking intoaccount the number of mutational differences betweenmolecular haplotypes. Fixation indices (Weir & Cocker-ham, 1984) and population pairwise FST (pairwise esti-mates of the correlation of alleles between populations)values were also computed by using the above software.

Results

Eleven STMS primers were used for detailed geneticdiversity analysis of rice landrace populations. Twenty-four individual plant DNA extraction per population wereused for STMS analysis with 11 markers and a total of 71alleles were detected with an average of 6.45 alleles perlocus (Table 2). The representative amplification profiles(RM19) are shown in Fig. S2.

The effective number of alleles and expected heterozy-gosity per population ranged from 1.17 (Motum Arik)to 2.26 (Chota Bora) and from 0.11 (Dheku) to 0.51(Chota Bora), respectively. The genetic diversity in termsof Shannon’s information index was highest (0.90) forChota Bora and lowest (0.18) for Dheku (Rani), bothfrom the NE region (group II). Genotypic differencesbetween the individuals were detected for each of thelandrace populations by the STMS loci with the numberof polymorphic loci within a population ranging from5 to 11. Three populations (Jumariya, Kola Bora andDheku) exhibited the lowest level of polymorphism(P = 45.45%), whereas seven populations (Chhoti, RwatHati, Dallu, Ratan, Kunkuni Joha, Chota Bora andKebang) exhibited the highest level of polymorphism(P = 100%). The total number of alleles per landracepopulation ranged from 16 in Kola Bora to 37 in KunkuniJoha and Chota Bora (Table S1).

The UPGMA dendrogram (Fig. 1) based on Nei’sgenetic distances (Table S2) grouped the populations intothree major clusters. Cluster I comprised seven landracepopulations from NW Himalaya, whereas seven lan-draces from NE Himalaya were present in cluster II. The



Table 2 The chromosome location (CL), repeat motif, number of alleles per locus (n), polymorphism information content (PIC) and allele size range for

all 11 sequence-tagged microsatellite (STMS) loci

Allele Size (bp)

Locus CL Repeat Motif n PIC Range Difference

RM263 2 (GA)34 4 0.406 184–201 17RM218 3 (GA)24 6 0.600 110–147 37RM241 4 (GA)31 7 0.743 108–151 43RM164 5 (GA)16TT(GT)4GAG 4 0.391 245–298 53RM217 6 (GA)20 7 0.806 117–158 41RM11 7 (GA)17 5 0.729 118–143 25RM25 8 (GA)18 6 0.621 128–157 29RM242 9 (GA)26 6 0.730 192–233 41RM216 10 (GA)18 7 0.709 121–189 68RM224 11 (GA)13 9 0.796 117–160 43RM19 12 (ATC)10 10 0.755 160–281 121Total – 71 – – –Mean – 6.45 0.662 – –

Figure 1 Dendrogram of 20 rice landraces based on Nei’s (1972) genetic

distance: unweighted pair group method with arithmetic mean (UPGMA;

modified from NEIGHBOR procedure of PHYLIP version 3.5).

remaining six landraces, three from each region, formedcluster III. Cluster II primarily contains glutinous ricevarieties of the NE region, locally known as bora or birain,which are tall and photoperiod sensitive and growntogether with sali rice (winter rice). Kunkuni Joha fromAssam, a scented (aromatic) rice variety locally calledJoha in the NE region, was grouped in cluster III. Baliand Motum Arik from Arunachal Pradesh were grouped

in cluster III. Bali is the most important landrace in Siangdistrict of Arunachal Pradesh, which serves as a typicalexample of adaptation through phenotypic plasticity andtolerance to multiple diseases. Two of the three landracesfrom a single village (Leeti) from the NW region werepresent in cluster III, while the third one was grouped incluster I.

In the principal coordinate analysis (PCoA), the firstthree co-ordinates explained 65.92% of the total varia-tion, with 39.88% explained by the first co-ordinate and15.44% by the second co-ordinate. The first PCoA sepa-rates cluster III from the rest with an exception of landraceDallu (cluster I) and the second one separates cluster Ifrom cluster II. In cluster III, the second PCoA separatesKunkuni Joha and Rwat Hati from the rest (Fig. 2).The grouping obtained through PCoA confirmed thatobtained by UPGMA cluster analysis.

Of the 71 alleles detected in this study, 46 (65%) alleleswere present in both group I (populations from NWHimalaya) and group II (populations from NE Himalaya).Nine alleles (3 rare with frequency less than 0.05) werepresent in group I but not in group II, whereas 16 alleles(7 rare) were present in group II but not in group I. Threealleles were rare in both groups, five alleles were rare ingroup I but not in group II and nine alleles were rare ingroup II but not in group I. Nine alleles (two rare) wereunique to any individual landrace population. Populationdifferentiation, as shown by FST values, was relativelyhigh in group II when compared with group I (Table 3).

Analysis of molecular variance showed that themaximum percentage of variation present was amongpopulations within groups (57.18%) followed by 31.60%within populations and 11.22% among groups. Thevariation present among the landraces was greater than



Figure 2 PCoA scatter plot of the first and second principal co-ordinates of 20 rice landrace populations.

the variation present within the landraces. Pairwiseestimates of FST, computed by distance method usingAMOVA approach, among the 20 rice landraces rangedfrom 0.02 to 0.92 (Table 4). IC-558259 (Pakhori Bora)from NE Himalaya showed high genetic differentiationwith all other populations with an average FST of 0.694and a range of 0.89–0.45. IC-556557 (Taknoi) fromNW Himalaya had the lowest average differentiation(FST = 0.368).

Discussion

The STMS analysis of rice landrace populations inthis study indicated enough polymorphism to fullydifferentiate the inter- and intrapopulation diversity andconfirmed the value of microsatellite loci for geneticdiversity studies of rice landraces found in earlier studies(Kholi et al., 2004; Bajracharya et al., 2006; Kobayashiet al., 2006; Barry et al., 2007; Steele et al., 2009; Kumaret al., 2010a,b). The presence of alleles, unique to a specificlandrace population, indicates a unique genetic variabilityat certain loci. This information is useful to identifyaccessions with exclusive genetic variability, whoseselection can increase the allele richness of gene banks.

The UPGMA dendrogram of the 20 landrace popu-lations (Fig. 1) showed three clusters. Clusters I and IIcomprised accessions representing populations from NWand NE Himalaya, respectively, and largely represented

locally common alleles. Cluster III comprised populationsfrom both regions and largely represented commonwidespread alleles based on Marshall & Brown (1975)criteria. The presence of populations from both distantlylocated regions in cluster III indicates the occurrence ofinformal seed exchange between the regions.

The rice landraces of the NW Himalayan regionwere more diverse than the NE Himalayan landraces,although the population differentiation was greater forNE Himalayan landraces. In this study, two landracepopulations of the same name ‘Dheku’ collected fromtwo different villages in the East Siang district (ArunachalPradesh) grouped together in UPGMA clustering, but thehigh pairwise FST value (0.72) between them indicatedthat both are genetically highly differentiated. Suchexceptionally high genetic differentiation and highlyhomozygous nature (FST = 0.7276; GST = 0.7202) hasalso been reported in Njavara medicinal rice landracegenotypes from traditional production areas in Kerala,India (Sreejayan et al., 2011). Such very high partitioning(FST = 68%) of total variability among individuals ofa single landrace has also been reported in the Thailandrace Bue Chomee (Pusadee et al., 2009). The geneticcohesiveness that prevents the same named populationfrom evolving an independent varietal type is, however,the result of stabilising selection performed by farmersthat maintains the status quo of gene frequencies (Peraleset al., 2003; Sreejayan et al., 2011).



Tab

le3

Gro

upw

ise

sum

mar

yof

gene

ticva

riat

ion

stat

istic

s,ex

pec

ted

hete

rozy

gosi

ty,F

STan

dge

neflo

wva

lue

for

all1

1lo

ciof

20la

ndra

cep

opul

atio

nsof

rice

Gro

upI(

NW

Him

alay

a)G

roup

II(N

EH

imal

aya)

Sum

mar

yof

Gen

etic

Var

iatio

nfo

rA

ll20

Land

race

s

Locu

sN

aN

eI

He

F ST

Nm

Na

Ne

IH

eF S

TN

mN

aN

eI

He

F ST

Nm

RM

263

21.

90.

670.

470.

640.

144

1.87

0.78

0.47

0.27

0.67

42.

080.

790.

520.

510.

24R

M21

86

3.29

1.42

0.7

0.3

0.58

52.

140.

990.

530.

610.

166

2.75

1.31

0.64

0.45

0.3

RM

241

64.

331.

540.

770.

350.

476

3.94

1.56

0.75

0.64

0.14

74.

441.

670.

780.

50.

25R

M16

44

1.8

0.74

0.45

0.79

0.07

41.

910.

830.

480.

760.

084

1.86

0.79

0.46

0.78

0.07

RM

217

54.

521.

560.

780.

50.

257

4.3

1.58

0.77

0.73

0.09

75.

761.

860.

830.

640.

14R

M11

43.

621.

330.

730.

720.

14

2.95

1.2

0.66

0.83

0.05

54.

31.

520.

770.

80.

06R

M25

52.

471.

120.

60.

290.

66

2.66

1.28

0.63

0.38

0.4

63.

091.

330.

680.

40.

37R

M24

25

2.98

1.18

0.67

0.55

0.2

63.

081.

260.

680.

590.

176

4.27

1.55

0.77

0.63

0.15

RM

216

42.

351.

080.

580.

340.

487

2.39

1.15

0.58

0.49

0.26

73.

981.

50.

750.

550.

2R

M22

46

4.19

1.6

0.76

0.44

0.31

74.

331.

560.

770.

570.

199

5.5

1.91

0.82

0.54

0.21

RM

198

4.25

1.67

0.77

0.41

0.36

63.

651.

380.

730.

730.

0910

4.11

1.65

0.76

0.57

0.19

Mea

n5

3.24

1.26

0.66

0.47

0.28

5.64

3.02

1.23

0.64

0.61

0.16

6.45

3.83

1.44

0.71

0.58

0.18

St.D

ev1.

551.

010.

340.

12–

–1.

210.

920.

280.

11–

–1.

861.

270.

370.

12–

–

He

,exp

ecte

dhe

tero

zygo

sity

;I,S

hann

on’s

info

rmat

ion

ind

ex;N

a,o

bse

rved

num

ber

ofal

lele

s;N

e,e

ffec

tive

num

ber

ofal

lele

s;N

m(g

ene

flow

estim

ated

from

F ST

)=0.

25(1

−F S

T)/F

.

It is important to understand how farmers characterisetheir crops. For instance, a farmer may identify anamed variety by its seed colour, seed shape, seed sizeand cooking quality. The farmer will then select forhigh-yielding plants within these categories to increaseyield potential of the landrace (Jarvis et al., 2000), butthe stabilising effect of the four key attributes aboveprevents a named landrace from evolving into fourindependent types within the same region. This studyindicates that considerable diversity in rice landracepopulations is maintained in traditional production. Inthe context of crop varietal diversity, it has been reportedthat considerable crop genetic diversity continues to bemaintained on-farm, in the form of traditional cropvarieties (Jarvis et al., 2008). Further, rice genetic diversityfrom the high altitude region of Nepal was predicted toincrease even when the number of varieties decreases.Genetic diversity has been reported to be maintainedeven when landraces are displaced by modern varieties.Using a model it was found that the partial replacementof landraces increased genetic diversity if the modernvarieties were adopted on up to 65% of the area. Onlyabove these levels did overall diversity decline (Steeleet al., 2009).

As the variation present among the landraces is greaterthan the variation present within the landraces, it isimportant to conserve as many landrace populations aspossible with optimum population size from these regionsfor conservation.

Assessment of landrace populations is important foruse of the germplasm. There is increasing recognitionthat the diverse needs of resource-poor farmers cannotbe addressed by the breeding of a restricted range ofhigh-yielding, high-input varieties. Yields of improvedvarieties in favourable conditions have reached a plateauin many countries or even subsequently declined. It hasbeen suggested that a range of varieties are needed tofulfil specific socio-economic as well as agroecologicalneeds in small farm systems and that breeding methodsneed to be reassessed to produce varieties useful to smallfarmers (Wood, 1993).

The population genetic parameters can therefore behelpful in measuring the average genetic diversity of afield/village/region, the differences in allele frequenciesamong different populations and the differences in thelevel of polymorphism among populations. Understand-ing the population genetic structure will also be helpful inmonitoring diversity loss over time and space, and also fordevising a rational conservation plan for management offarmer landraces on-farm. The variations due to environ-mental adaptations provide important donor germplasmfor crop improvement to users (Jarvis et al., 2000; Kumaret al., 2010a).



Tab

le4

Pai

rwis

eF S

Tva

lue

for

all2

0ri

cela

ndra

cep

opul

atio

ns

Pop

ulat

ion

12

34

56

78

910

1112

1314

1516

1718

1920

1IC

-556

525

–2

IC-5

5652

80.

89–

3IC

-556

531

0.69

0.41

–4

IC-5

5653

20.

900.

190.

53–

5IC

-556

533

0.00

0.78

0.58

0.74

–6

IC-5

5654

40.

430.

800.

650.

750.

00–

7IC

-556

555

0.76

0.68

0.47

0.66

0.48

0.46

–8

IC-5

5655

70.

160.

680.

470.

630.

000.

000.

22–

9IC

-556

559

0.80

0.71

0.64

0.70

0.47

0.43

0.20

0.21

–10

IC-5

5656

00.

430.

400.

260.

480.

340.

440.

220.

000.

31–

11IC

-558

249

0.82

0.31

0.38

0.37

0.61

0.69

0.47

0.50

0.62

0.56

–12

IC-5

5825

90.

800.

890.

820.

860.

630.

450.

660.

690.

740.

690.

83–

13IC

-558

275

0.87

0.92

0.82

0.89

0.79

0.64

0.68

0.68

0.87

0.76

0.84

0.58

–14

IC-5

5829

30.

870.

890.

830.

860.

660.

690.

710.

750.

760.

770.

830.

720.

63–

15IC

-558

298

0.69

0.67

0.62

0.67

0.48

0.56

0.33

0.51

0.50

0.70

0.45

0.64

0.51

0.41

–16

IC-5

5830

40.

730.

800.

770.

790.

500.

610.

630.

610.

690.

730.

740.

630.

140.

020.

35–

17IC

-558

306

0.45

0.56

0.64

0.64

0.15

0.45

0.47

0.13

0.52

0.41

0.63

0.73

0.65

0.59

0.51

0.47

–18

IC-5

5830

80.

800.

650.

570.

530.

470.

660.

670.

540.

750.

680.

630.

840.

860.

840.

660.

770.

70–

19IC

-558

313

0.74

0.90

0.81

0.86

0.18

0.62

0.69

0.57

0.75

0.76

0.85

0.83

0.84

0.72

0.75

0.67

0.60

0.83

–20

IC-5

5833

00.

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Acknowledgements

The authors are thankful to the Director, National Bureauof Plant Genetic Resources (NBPGR), New Delhi, India,for providing the facilities for the study. The scholarshipprovided to the senior author (A. P.) by the Council forScientific and Industrial Research (CSIR), India, for PhDdegree is gratefully acknowledged.

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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Figure S1 Map of Uttarakhand and Arunachal Pradesh(including Sonitpur and Lakhimpur districts of Assam)depicting areas of rice landrace collection (denoted by ‘*’).

Figure S2 RM19 [sequence-tagged microsatellite(STMS) marker] profile generated by using QIAxcelanalyser.

Table S1 Summary diversity of 20 rice landracepopulations based on sequence-tagged microsatellite(STMS) markers.

Table S2 Nei’s original measures of genetic identity(above diagonal) and genetic distance (below diagonal)for all 20 rice landraces.

Please note: Wiley-Blackwell are not responsible forthe content or functionality of any supporting materialssupplied by the authors. Any queries (other than missingmaterial) should be directed to the corresponding authorfor the article.


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Population structure of rice (Oryza sativa) landraces from high altitude area of Indian Himalayas