Molecular Characterization and Heterotic Groupingof Traditional Assam Rice (Oryza sativa L.)Praveen Kumar  ( bkpraveen13@gmail.com )

Assam Agricultural UniversityDebojit Sarma 

Assam Agricultural UniversityLaishram Monalisha Devi 

Assam Agricultural UniversityDibosh Bordoloi 

Assam Agricultural UniversityP. K. Barua 

Assam Agricultural UniversityBodeddula Jayashankar reddy 

Assam Agricultural University

Research Article

Keywords: Genetic distance, heterotic grouping, speci�c combining ability, heterosis, SSR marker

Posted Date: April 11th, 2022

DOI: https://doi.org/10.21203/rs.3.rs-1520975/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

Molecular Characterization and Heterotic Grouping of Traditional Assam Rice (Oryza

sativa L.)

Praveen Kumar1, Debojit Sarma1*, Laishram Monalisha Devi1, Dibosh Bordoloi1, P.K. Barua1,

and Bodeddula Jayashankar reddy1 1Department of Plant Breeding and Genetics, Assam Agricultural University, Jorhat-785013,

Assam, India;

*Corresponding Author’s email: bkpraveen13@gmail.com

ABSTRACT

Parents of heterotic hybrids belong to different heterotic groups with high genetic divergence.

Classification of traditional Assam rice germplasm in different pools would maximize the

heterosis and ensure food security for over 35 million people. In the present investigation, 53

polymorphic markers detected 133 alleles across 60 upland rice genotypes of Assam, with an

average of 2.5 alleles per marker. RM293 having the highest PIC value of 0.655, was the most

appropriate marker for discriminating among genotypes. The genetic divergence using the

Unweighted Neighbour-Joining (UNJ) method grouped the 60 genotypes into three major

clusters. The eleven most divergent genotypes were subject to diallel analysis following Model

1 Method II of Griffing (1956) for combining ability and heterosis estimates. Lack of

correlation between heterosis and genetic distance could be attributable to using a subset of

markers not linked to yield or concerned. In genetic distance based heterotic grouping, the

intra-group hybrids registered a higher frequency of crosses in concurrence with high values

of grain yield per plant, specific combining ability effect, mid-parent heterosis, better-parent

heterosis and standard heterosis than those of inter-group hybrids. An extensive choice of

parents possessing attractive traits constellation would lead to increased yield of the hybrids

with much better complementation and thus, needs emphasis together with a substantial

hereditary distance for augmentation of yield heterosis.

Keywords:Genetic distance, heterotic grouping, specific combining ability, heterosis, SSR

marker

INTRODUCTION

Rice, being the third most important crop in the world after wheat and maize,

feeds more than one half of the world population. Above 90% of rice is consumed by the

Asians. The alarming rate of population growth demands a viable and practical approach such

as hybrid rice for feeding the ever-increasing population of India as well as the world. Hybrid

rice has immense potential to transform rice cultivation in India with the development of stable,

better adapted and commercially accessible hybrid rice for enhancing rice productivity,

increasing farm income and reducing land required for intensive rice production, which allow

for reallocation to other agricultural and non-agricultural uses [1].

Plant breeders use many strategies for estimation of the hereditary variation in

the germplasm through the assessment of morphological/phenotypic, biochemical or protein

variants and DNA/RNA polymorphisms. Both phenotypic and biochemical variants are not the

best means of portraying the hereditary variation because of the environmental influences

imposing the numerical and phenotypic restriction. On the other hand, DNA-based molecular

markers are persistent, repeatable, steady, vigorous and profoundly dependable [2,3,4] Among

the accessible DNA markers, simple sequence repeat (SSR) is suitable because of their multi-

allelic nature, high reproducibility, codominant nature, plenitude and genome-wide coverage.

Various SSR markers have been developed and mapped in rice [5,6], which differ in the level

of polymorphism relying upon their area in the coding or noncoding portions, nature of their

recurrent themes and the genome-wide bounty.

Clustering of the available germplasm into groups is a prerequisite to identify

heterotic pools in any crop. Grouping can be accomplished through precise phenotyping and

genotyping of germplasm. Classifying germplasm, based on the phenotypic evaluation of test

crosses alone, is difficult due to the involvement of a large germplasm pool in any breeding

programme [7] To overcome this difficulty, [8] suggested clustering of the genotypes using

molecular markers into genetically similar groups, making crosses among the genotypes

representing different subgroups, their field evaluation, and finally identifying the heterotic

pools based on the per se performance, combining ability and heterosis. An alternative strategy

to increase the efficiency of the breeding programme capitalizes on the correct prediction of

heterosis through an accurate initial selection of optimal parental combinations to possibly cut

off the cost of trial evaluation and combination testing. Several earlier studies in different crops

suggested the use of molecular markers for the prediction of heterosis based on genetic

diversity of parental lines in Oryza sativa, Triticum aestivum, Brassica napus, Glycine max,

Helianthus annuus and Zea mays [9-17]

As defined by [18] a heterotic group is "a group of related or unrelated genotypes

from the same or different populations, which display similar combining ability and heterotic

response when crossed with genotypes from other genetically distinct germplasm groups".

Parents of heterotic hybrids usually derived from different heterotic groups with high genetic

divergence. [19] suggested a grouping of germplasm in divergent pools to maximize the

expected heterosis. The term heterotic pattern refers to a specific pair of two heterotic groups,

which expresses high heterosis and consequently high hybrid performance in their cross.

Identification and determination of heterotic groups and patterns are fundamentally essential

for breeding hybrid crops as indicated in several studies on maize, rye, sunflower, sorghum,

triticale and rice[18,20-30] Heterotic groups for hybrid crops could be determined by marker-

based groups as studied in maize [31-34] Heterotic groups of a crop can form based on

morphological differences, germplasm origins, pedigree information, and combining ability.

However, these traditional evaluation methods are time-consuming and usually not practical to

the breeders because of the large number of hybrid combinations required and tremendous

fieldworks. Information regarding the heterotic groups and patterns helps the plant breeders to

make use of their germplasm more efficiently and consistently through the exploitation of

complementary lines for maximizing the grain yield in a hybrid breeding programme. Breeders

may use heterotic group information for categorizing the genetic diversity and directing the

introgression of traits and formation of new heterotic groups. The existence of heterotic groups

attributes to the possibility that populations of divergent backgrounds might have distinctive

allelic diversity that could have originated from founder effects, genetic drift, or accumulation

of unique diversity by mutations or selection [35] Interallelic interaction (overdominance) or

repulsion phase linkage among the loci showing dominance (pseudo-overdominance) could

explain the observation of significantly better heterosis following a cross between genetically

divergent populations[36]. A lot of experimental studies support the concept of heterotic

grouping [37-39, 11,40-42] and verify that the inter-group hybrids significantly outyielded

intra-group hybrids. Furthermore, extensive germplasm exchange among breeding

programmes lacks detailed information on the pedigree and genetic background, as well as

effects of environment and GxE interaction, that makes parental assessment more

multifaceted[43]. So the molecular markers have provided with an efficient and effective way

to study the genetic diversity [44-46].

Several studies confirmed a non-significant correlation of genetic distance with

heterosis in chichpea, rice, alfalfa and maize [47-51]. So, the genetic distance may not be as

such reliable for rice hybrid breeding programme and predicting the heterosis at the DNA level.

Due to inadequate information on the association of functional molecular markers and yield,

the evidence derived from molecular markers at this time is limited to the use of assigning

parents into germplasm group or heterotic groups, and to provide a general guideline of

avoiding heterotic groups from blending during parent breeding. Contrary to the above, the

findings of [52,38] suggested that molecular markers might be useful for assigning parents to

heterotic groups.[53] underlined the benefits of a genome-based establishment of heterotic

patterns in rice as a requirement for a sustainable long-term success of hybrid rice breeding.

The information on SCA effects for heterotic grouping could be useful instead of molecular-

based heterotic grouping based on combining ability as reported by earlier workers [54-55,56-

58].

Rice is the primary household cereal crop of North East India for food and

nutritional security. It occupies 4.58 million hectares which are 75% of the total cultivated area

of the region [59] North-East India is the secondary centre of origin of rice and a hotspot of

rice genetic resources in the world. The rice landraces are grown in diverse ecosystems

spreading across the high altitudes of Sikkim and Arunachal Pradesh, the food-prone areas of

Assam, and rainfed, irrigated, upland, steep terraces and deepwater, Jhum and tilla land

ecologies of the region. Of the various classes of rice cultivated in Assam, upland rice cultivars

of North East India are grown during March-April and harvested in June-July known

agronomically as "aus/ahu" rice [60]. The photoperiod insensitive ahu rice landraces are

maintained by farmers since time immemorial and are endowed with enormous genetic

variability and valuable genes for various abiotic stress tolerances as they are not subject to any

selective breeding during their long history of cultivation. Recent genome sequence identifies

the aus rice as a distinct sub-population derived from both indica and japonica ecotypes

of Oryza sativa [61]. The aus cultivars are early maturing, photoperiod insensitive and

drought-tolerant [62]. However, the frequency of restorers in the aus group of rice is low [63].

The presence of broad diversity in morpho-agronomic as well as stress tolerance in the early

maturing photo-insensitive upland rice landraces of Assam offer scope for identification of

restorers/maintainers for the currently available WA CMS lines of early to medium duration.

Therefore, we used a group of 55 short duration (105 -125 days) rice genotypes mostly

comprising indigenous upland rice cultivars of Assam along with five maintainer lines of WA

CMS for SSR based diversity analysis. Diverse parents were identified representing the SSR

based clusters and crossed in a diallel fashion without reciprocals. The resultant hybrids were

evaluated to estimate heterosis and combining abilities. Heterotic groups were assigned based

on SSR diversity as well as yield-SCA.

MATERIALS AND METHODS

Experimental materials

The present study used a collection of 60 rice genotypes consisting of 39

indigenous upland cultivars of Assam, 16 improved varieties and breeding lines, and five

maintainers of wild abortive cytoplasmic male sterile (WA-CMS) lines (SupplementaryTable

1). All the molecular work, including DNA extraction, PCR and gel electrophoresis, were

performed in the marker laboratory of the Department of Agricultural Biotechnology, Assam

Agricultural University, Jorhat.

Table 1: List of the Ahu rice genotypes of Assam used in the investigation

Gen No. Name Origin Pedigree

1 Grem Dhan Assam Land race

2 Suryamukhi Assam Land race

3 Lal Aus Assam Land race

4 Sada Kara Assam Land race

5 Bau Murali Assam Land race

6 Kutuktara Assam Land race

7 Basmoti Assam Land race

8 Las Kach Assam Land race

9 Rash Kadam Assam Land race

10 Basantbahar Assam Land race

11 Kasalath Assam Land race

12 Rangai Assam Land race

13 Haru Begunigutia Assam Land race

14 Saiamara Assam Land race

15 Bor Begunigutia Assam Land race

16 Bor Mekohi Dhan Assam Land race

17 Joria Assam Land race

18 Lewly Assam Land race

19 Basmoti Red Assam Land race

20 Mentetoi Assam Land race

21 Sayjihari Assam Land race

22 Mayamoti Assam Land race

23 Nagina 22 Odisha Selection from Rajbhog

24 Guni Assam Land race

25 Meghi Assam Land race

26 Ikhojoi Assam Land race

27 Dehangi Assam Maibee R/CRM49//Maibee

28 Dimrou Assam Land race

29 Luit Assam Heera/Annada

30 Kmj 13A-6-1-2 Assam Mahsuri/Luit

31 Maizobiron Assam Land race

32 Dikhow Assam Heera/Annada

33 Chilarai Assam IR 24/CR 44-118-1

34 Kapilee Assam Heera/Annada

35 Local Ahu 2 Assam Landrace

36 Gopinath Assam Pusa 2-21/IR 36

37 Pyajihari Assam Land race

38 Kmj 13A-1-12-3 Assam Mahsuri/Luit

39 Kmj 13A-1-3-6 Assam Mahsuri/Luit

40 Koijapuri Assam Land race

41 Aus Joria Assam Land race

42 Koimurali Assam Land race

43 IR 36 IRRI IR 1561-228-1-2/IR1737//CR 94-13

44 Teraboli Assam Land race

45 Lachit Assam CRM 13-3241/Kalinga 2

46 Bali Ghungoor Assam Land race

47 Disang Assam Heera/Annada

48 Krishna E Assam Pure line selection in Krishna

49 Saiamura Assam Land race

50 Rangoli Assam Land race

51 Krishna Odisha GEB 24/TN-1

52 Kola Ahu Assam Land race

53 Ranga Ahu Assam Land race

54 Kmj 14S-4-3-4 Assam Mahsuri/Malbhog

55 Local Ahu 1 Assam Land race

56 IR 58025B IRRI Maintainer line

57 IR 68888B IRRI Maintainer line

58 IR 68897B IRRI Maintainer line

59 IR 79156B IRRI Maintainer line

60 IR 80555B IRRI Maintainer line

DNA extraction and PCR analysis

Total genomic DNA from each of the 60 rice genotypes was extracted from 5 g

leaves of 21 days old seedlings following the[64] protocol with minor modifications. Leaf

samples were cut into small bits with the help of sterile scissors and placed in a mortar, liquid

nitrogen poured into it, and ground the samples by using a pestle. The powdered leaf sample

was taken in a 2 ml Eppendorf tube, and 600 µL of extraction buffer was added. The Eppendorf

tubes with the samples were then put in a water bath running at 60°C for half an hour. 600 µL

of 24:1 Chloroform: Isoamyl alcohol was poured into each Eppendorf tube, mixed well and

centrifuged at 10,000 rpm for 10 minutes. The supernatant was taken out into fresh tubes, to

which a double volume of 100 per cent chilled ethanol was added and then kept at 4oC

overnight or -20oC for one hour. The samples were centrifuged at 10,000 rpm for 10 minutes,

and the supernatant was discarded. The pellet was washed with 70% ethanol by centrifuging at

10,000 rpm for 3 minutes, discarding the alcohol; the pellet was air-dried entirely. Finally, the

pellet was dissolved in TE buffer (50 µL) and stored at 4°C. Subsequently, 3 µL of RNase was

added to remove the RNA contamination and kept at 37°C for 30 minutes. 5 µL RNase blocking

agent was added, mixed well and centrifuged at 10,000 rpm. The clear solution was transferred

to a fresh microfuge tube. The quality of DNA was verified using 0.8% agarose gel.

Agarose gel was prepared by melting 0.8 g of Agarose in a total volume of 100

ml 1X TBE by heating and allowing them to cool. Ethidium Bromide (DNA intercalating

agent) was added when the temperature reached 55-50°C. The mixture was poured into a

levelled pre-set casting tray fitted clean comb and allowed to solidify. Then the comb and gel

casting assembly were detached from the casting tray and placed in the electrophoresis unit

with wells towards the cathode and submerged with 0.5X TBE to a depth of about 1 cm. 1 μL of DNA sample dissolved in TE was pipetted onto a parafilm and mixed well with 3 μL of 6X loading dye by pipetting up and down several times. The gel was run at 50 V (5 V/cm) for 1-

1.5 hours, and bands were visualized and documented using a gel documentation system. The

DNA sample with the strong band under the UV with minimum shearing was considered as

good quality DNA for the present study. The isolated DNA was also quantified, and its purity

was estimated with the help of Nanodrop 1000 (Thermo Scientific). The absorbance of

genomic DNA was measured at 260 nm to determine the concentration in the solution. The

absorbance at 280 nm was also taken to see the extent of protein contamination in the extracted

DNA.

Eighty-three SSR markers of dinucleotide repeats were used for the genotyping,

the sequences obtained from [65]. The amplification conditions were based on the[66]

procedure. PCR primers were diluted in 1:9 dilutions in distilled water for PCR amplification.

10 µL each forward and reverse primer were mixed thoroughly with 180 µL of distilled water

for PCR amplification for 60 samples. About 1 mL of diluted template DNA (20 ng/mL) of

each line was dispensed in the bottom of 96 well PCR plates (OXYGEN-MAKE). The reaction

mixture contained 1.0 μL Taq buffer, 20 pM each forward and reverse primer, 2.5 mM dNTPs mixture, 0.5 U Taq DNA polymerase, 1 mM MgCl2, 1 μL (10 ng/μL) template DNA and sterile distilled water as required. Different cocktails were prepared in Eppendorf tubes as described.

About 9 mL of the cocktail was added to each tube to make the final volume 10 mL. The PCR

was set as initial denaturation at 94oC (5 min.), followed by denaturation at 94oC (1 min.),

annealing at Tmax of 55 ± 2oC (1 min.), extension at 72oC (1 min.), final extension at 72oC (5

min.) and held sufficiently. Steps 2, 3 and 4 were programmed to run for 35 cycles.

For separating PCR products with SSR marker, 3.5% Agarose gel was used.

Agarose gel of 3.5% strength was prepared by melting 14 g of Agarose in a total volume of

400 ml 1X TBE by heating the oven and adding 14 μL of Ethidium bromide (10 mg/ml). The solution was cooled down to 50°C. The solution was poured into the gel-casting tray. After

solidification, the comb was removed and then mounted on a gel tank containing 1000ml of

1X TBE. To each PCR tube containing the amplified PCR products, 2 µl of 6X loading buffer

was added. 10 µL of each PCR product was loaded onto the gel wells. The gel was run at 90-

100 volts till Bromophenol Blue reached the end of the gel. The gel photograph was digitally

documented in Gel Documentation System (Alpha Innotech, USA).

Molecular data analysis

Genetic distance (GD) between each pair of parents was measured as Cavalli-

Sforza and Edwards chord distance[67], using PowerMarker version 3.25 [68]. Based on

Cavalli-Sforza and Edwards chord distances, a dendrogram was constructed illustrating the

genetic relationship among the rice genotypes using the Unweighted Neighbour-Joining (UNJ)

method as proposed by [69], which uses a criterion of weighted average, in DARwin 6 [70].

The number of alleles per locus, major allele frequency, gene diversity, heterozygosity and

polymorphism information content (PIC) values was calculated using PowerMarker version

3.25 [68].

Cavalli-Sforza chord distance (GD)

Cavalli-Sforza and Edwardschord distance between two populations

represented on the surface of a multidimensional hypersphere using allele frequencies at the jth

locus is given by

𝐷𝐶𝐻 = 2𝜋𝑚 ∑ √2 (1 − ∑ 𝑝𝑖𝑗𝑞𝑖𝑗𝑎𝑗𝑖=1 )𝑚

𝑗=1

Let 𝑝𝑖𝑗and 𝑞𝑖𝑗be the frequencies of ith allele at the jth locus in populations X and Y, respectively

and 𝑎𝑗 is the number of alleles at the jth locus, and m is the number of loci examined.The binary

data were used to generate a dissimilarity matrix and genetic diversity using chord distance in

PowerMarker version 3.25[68].

Gene diversity (He)

Gene diversity (Liu and Muse 2005) is defined as follows: 𝐻𝑒 = 1 − ∑ 𝑝𝑖2𝑚𝐼=1

Where, m is the number of alleles in a gene locus and p is the ith allele frequency.

Polymorphic information content (PIC)

The polymorphic information content (PIC) for each locus (Botstein et al. 1980)

is defined as follows: 𝑃𝐼𝐶𝑖 = 1 − ∑ 𝑝𝑖2𝑚𝑗=1 − ∑ ∑ 2𝑝𝑖2𝑝𝑗2𝑚

𝑗=𝑖+1𝑚−1𝑖=1

Where, pi and pj represent the frequencies of the ith and the jth alleles, respectively and m is the

number of alleles in a locus.

Evaluation of diallel crosses

Field experiments were conducted in the Instructional-cum-Research Farm of

Assam Agricultural University, Jorhat. The latitude, longitude and altitude of Jorhat are

26°44´N, 94°l0´E and 9l m above mean sea level, respectively. The soils of the experimental

site belong to the order Inceptisols with sandy loam texture and pH 4.8. The status of organic

C (124 μg g-1), available N (298 kg ha-1) and P (21 kg ha-1) was medium, and available K (102

kg ha-1) was low. The growing situation was shallow land with maximum water depth of 30

cm during peak monsoon.

Eleven parents from diverse SSR based clusterswere selected for crossing in a

diallel fashion without reciprocals. Crosses were made during Sali 2017, and early ahu 2018

and the resultant hybrids evaluated during Sali 2018. A randomized complete block design

(RCBD) was followed with two replications under two nitrogen (N) doses – @ 40 kg per ha

recommended for the transplanted ahu rice and @ 60 kg per ha for high yielding varieties

including hybrid rice. Phosphorus (P2O5) and Potash (K2O) were applied @ 20 kg per

ha.Twenty-one days old seedlings were transplanted in the main field on August 26, 2018.

Single seedling was planted per hill in single row plot of 2 m long spaced 20 cm apart. The

intra-row spacing was 20 cm. A border row was planted on both sides of each block with the

parent variety to eliminate the border effect. At physiological maturity, three random

competitive plants were harvested and oven dried at 70oC until constant weight. Filled grains

from the sampled plants were separated and the average weight was reported as grain yield in

g per plant at 12% moisture content.

Combining ability analysis

Combining ability analysis was done following Model 1 Method II of [71]. The

mathematical model for the combining ability analysis is 𝑋𝑖𝑗 = 𝜇 + 𝑔𝑖 + 𝑔𝑗 + 𝑠𝑖𝑗 + 𝑎𝑙 + (𝑔𝑎)𝑖𝑙 + (𝑔𝑎)𝑗𝑙 + (𝑠𝑎)𝑖𝑗𝑙 + (1 𝑏⁄ ) ∑ ∑ 𝑒𝑖𝑗𝑘𝑙𝑙𝑘

(i = j =1 ... p; k = 1 … b; l=1…a), where μ = the population mean; gi = the general combining

ability (GCA) effect of the ith parent; gj = the general combining ability effect of the jth

parent; sij = the specific combining ability (SCA) effect of the cross between ith and jth parents

such that sij = sji; (ga)il is the interaction of GCA effect of ith parent and lth environment, (ga)jl

is the interaction of GCA effect of jth parent and lth environment, (sa)ijl is the interaction of SCA

effect of ijth hybrid and lth environment, eijkl = the environmental effect associated with ijk th observation.

Heterosis Analysis

The estimates of heterosis (H) were calculated as percentage increase or

decrease of F1s over the mid parent (𝑀𝑃̅̅̅̅̅), better parent (𝐵𝑃̅̅ ̅̅ ) and standard parent (𝑆𝑃̅̅̅̅ ) values

following the method of [72].The heterosis was tested by least significant difference at 5% and

1% level of significance for error degree of freedom asper [73].

Heterotic grouping of the parental genotypes based on yield-SCA

The relationship between mid-parent heterosis and specific combining ability

effects is as follows: 𝑌𝑖𝑖 = 𝜇 + 2𝑔𝑖 + 𝑠𝑖𝑖 𝑌𝑗𝑗 = 𝜇 + 2𝑔𝑗 + 𝑠𝑗𝑗 𝑌𝑖𝑗 = 𝜇 + 𝑔𝑗 + 𝑔𝑗 + 𝑠𝑖𝑗

Where, 𝑌𝑖𝑖 or 𝑌𝑗𝑗 = the mean of any parent population, 𝑌𝑖𝑗 = the mean yield of the F1 hybrid

between ith and jth parent, μ is the general mean, gi and gj are the general combining ability

effects (GCA) for the ith and jth parents, and sii, sjj and sij are the specific combining ability

effects for ithand jthparents, and ijth cross (Gardner 1967). The mid-parent heterosis or hij

(Falconer & Mackay 1996) is given as: ℎ𝑖𝑗 = 𝑌𝑖𝑗 − 12 (𝑌𝑖𝑖 + 𝑌𝑗𝑗) = 𝑠𝑖𝑗 − 12 (𝑠𝑖𝑖 + 𝑠𝑗𝑗).

The 11 x 11 matrices with the specific combining ability data estimated from 2

environments for the 55 crosses were used as a distance matrix for cluster analysis using

unweighted Neighbour-joining (UNJ) method as proposed by Gascuel (1997) in DARwin 6

(Perrier and Jacquemoud-Collet 2006). The SCA values from each variety with itself (sii or sjj)

excluded from the analysis. Cluster analysis identified three heterotic groups. The 55 SCA

values were arranged in decreasing order and divided into 3 classes (1 to 18; 19–36; and 37–55). In each class, the number of crosses within and between heterotic groups was derived. A

Chi-square test determines whether the hypothesis of a random distribution of crosses among

the classes is accepted or rejected. A significant Chi-square indicates a higher frequency of

crosses between heterotic groups in the ranks with higher values of SCA. The mid-parent

heterosis data (MPH) instead of SCA data followed the same procedure.A method to compare

the increase in mid-parent heterosis (MPH) between heterotic groups to that within heterotic

group values was proposed by[8], which is defined as follows: ∆𝑀𝑃𝐻𝑖𝑗 = [100 × 𝑀𝑃𝐻𝑖𝑗12 (𝑀𝑃𝐻𝑖𝑖 + 𝑀𝑃𝐻𝑗𝑗)] − 100. ∆𝑀𝑃𝐻𝑖𝑗 is the increase in mid-parent heterosis between heterotic groups i and j to that within

heterotic group values (𝑀𝑃𝐻𝑖𝑖 𝑎𝑛𝑑 𝑀𝑃𝐻𝑗𝑗).

The possible existence of two heterotic groups instead of three was tested by

calculating the means of SCA within and between heterotic groups for the following

combinations: (a) (1+2)*3, (b) (1+ 3)*2 and (c) (2+3)*1. None of these combinations gave

higher mean SCA between groups and lower mean SCA within groups than those obtained

when we consider the three heterotic groups and, therefore, the hypothesis of having two

heterotic groups instead of three get discarded.

RESULT AND DISCUSSION

Polymorphism of SSR markers

Eighty-three SSR markers (Supplementary Table 2) distributed throughout the

12 chromosomes were used to assess the extent of molecular diversity across the 60

rice genotypes, and 53 were polymorphic, and the rest were monomorphic. DNA bands were

scored for DNA fingerprinting analysis with the molecular data generated using 53 SSR

markers. Supplementary Fig. 1 shows representative gel pictures of the polymorphic markers

RM 293, RM 447, RM 429, RM 337, RM 245, RM 152 and RM 216. The

major allele frequency, number of alleles per locus, gene diversity, heterozygosity and PIC

values (Table 1) were calculated for each SSR marker using Power Marker v 3.25 (Liu and

Muse 2005). All the genotypes scored the allelic weight of the detected SSR bands throughout

all the 60 genotypes. DARwin 6 used an unweighted neighbour-joining method to construct

the dendrogram showing the distance-based interrelationship among the genotypes. For the

phylogenetic tree, the genetic distances were chord distances (Supplementary Table 3)

calculated in PowerMarker v 3.25 (Liu and Muse 2005).

Supplementary Table 2: List of SSR markers used in the present study

S

No

.

SSR

marker

Chr.

No. Forward primer Reverse primer

Anneal.

Temp. (oC)

1 RM361

4 1

GTATCAGTTAGCCC

CCGAGC

GAAGGAAGCAGAAG

CAGGTG 55

2 RM1 1 GCGAAAACACAATG

CAAAAA

GCGTTGGTTGGACC

TGAC 55

3 RM24 1 GAAGTGTGATCACT

GTAACC

TACAGTGGACGGCG

AAGTCG 55

4 RM318 2 GTACGGAAAACATG

GTAGGAAG

TCGAGGGAAGGATC

TGGTC 55

5 RM530 2 GCACTGACCACGAC

TGTTTG

ACCGTAACCCGGAT

CTATCC 55

6 RM29 2 CAGGGACCCACCTG

TCATAC

AACGTTGGTCATAT

CGGTGG 55

7 RM573 2 CCAGCCTTTGCTCCA

AGTAC

TCTTCTTCCCTGGAC

CACAC 55

8 RM438 2 CTTATCCCCCCGTCT

CTCTC

CTCTCTGCCACCGAT

CCTAC 55

9 RM124

60 2

TGGCACTACAGTGA

CAACAAACC

AGGGACTTTATCCA

AAGGACACG 55

10 RM125

69 2

GCTCATCATCATCAT

CGCAGTGG

ATCCATGTGGCAGA

CACACTTGC 55

11 RM279 2 GCGGGAGAGGGATC

TCCT

GGCTAGGAGTTAAC

CTCGCG 55

12 RM423 2 AACGCCTCATCTAC

CAATGG

ATACGTGAACCCGG

TCAATC 55

13 RM555 2 TTGGATCAGCCAAA

GGAGAC

CAGCATTGTGGCAT

GGATAC 55

14 RM520 3 AGGAGCAAGAAAAG

TTCCCC

GCCAATGTGTGACG

CAATAG 55

15 RM60 3 AGTCCCATGTTCCAC

TTCCG

ATGGCTACTGCCTGT

ACTAC 55

16 RM545 3 CAATGGCAGAGACC

CAAAAG

CTGGCATGTAACGA

CAGTGG 55

17 RM103

8 3

TGGTTCGATTCGGAT

TTC

AAGCTATTCACAAG

CAGCTC 55

18 RM293 3 TCGTTGGGAGGTAT

GGTACC

CTTTATCTGATCCTT

GGGAAGG 55

19 RM135

2 3

ACGAGTTGTACTCT

GGTTGC

TCTCGGTTTTTATCT

TGCTG 55

20 RM156

69 3

GTGGGTTGGGTGGT

GTTGTTCG

ACGCCATCAGGAAC

TCCATCTGC 55

21 RM347 3 CACCTCAAACTTTTA

ACCGCAC

TCCGGCAAGGGATA

CGGCGG 55

22 RM252 4 TTCGCTGACGTGAT

AGGTTG

ATGACTTGATCCCG

AGAACG 55

23 RM261 4 CTACTTCTCCCCTTG

TGTCG

TGTACCATCGCCAA

ATCTCC 55

24 RM273 4 GAAGCCGTCGTGAA

GTTACC

GTTTCCTACCTGATC

GCGAC 55

25 RM374

2 4

CTCTTCATCCCCCAA

GCC

GAGAAGAAGAACAG

AGCTGCG 55

26 RM127 4 GTGGGATAGCTGCG

TCGCGTCG

AGGCCAGGGTGTTG

GCATGCTG 55

27 RM335 4 CAAGTTTACGGCAG

CTAGGC

GAGTGGAGCACAAG

GAAAGG 55

28 RM164 5 TCTTGCCCGTCACTG

CAGATATCC

GCAGCCCTAATGCT

ACAATTCTTC 55

29 RM249 5 GGCGTAAAGGTTTT

GCATGT

ATGATGCCATGAAG

GTCAGC 55

30 RM440 5 CATGCAACAACGTC

ACCTTC

ATGGTTGGTAGGCA

CCAAAG 55

31 RM536

1 5

GCACGTGACTCCAT

CATCTC

ATGCAGATGATAGC

CCAAGG 50

32 RM178 5 TCGCGTGAAAGATA

AGCGGCGC

GATCACCGTTCCCTC

CGCCTGC 67

33 RM334 5 GTTCAGTGTTCAGTG

CCACC

GACTTTGATCTTTGG

TGGACG 55

34 RM413 5 GGCGATTCTTGGAT

GAAGAG

TCCCCACCAATCTTG

TCTTC 55

35 RM459 5 AGTTTGAAGTTTGTC

TTGAA

AGTTACCAAAAGTT

TAATCG 55

36 RM204 6 GTGACTGACTTGGT

CATAGGG

GCTAGCCATGCTCTC

GTACC 55

37 RM50 6 ACTGTACCGGTCGA

AGACG

AAATTCCACGTCAG

CCTCC 55

38 RM253 6 TCCTTCAAGAGTGC

AAAACC

GCATTGTCATGTCG

AAGCC 55

39 RM217 6 GCAGCAAGAGCAAG

AAATCC

GTTCCTGCCGTACCA

GCAG 55

40 RM30 6 AAACAACGACGTCC

CTGATC

GTGCCTCCGTGGTTA

TGAAC 55

41 RM11 7 TCTCCTCTTCCCCCG

ATC

ATAGCGGGCGAGGC

TTAG 55

42 RM429 7 TCCCTCCAGCAATGT

CTTTC

CCTTCATCTTGCTTT

CCACC 55

43 RM534

4 7

GCACATCTTGTGATC

GGATTAACG

CTCACGGACGAAGT

CAAGTTTGG 55

44 RM125 7 TACCTCCTAGCTTTA

CTTAT

ACTGATCTCTATCTC

ATTGT 55

45 RM336 7 CTTACAGAGAAACG

GCATCG

GCTGGTTTGTTTCAG

GTTCG 55

46 RM44 8 ACGGGCAATCCGAA

CAACC

TCGGGAAAACCTAC

CCTACC 55

47 RM325 8 GACGATGAATCAGG

AGAACG

GGCATGCATCTGAG

TAATGG 55

48 RM152 8 GAAACCACCACACC

TCACCG

CCGTAGACCTTCTTG

AAGTAG 55

49 RM447 8 CCCTTGTGCTGTCTC

CTCTC

ACGGGCTTCTTCTCC

TTCTC 55

50 RM72 8 CCGGCGATAAAACA

ATGAG

GCATCGGTCCTAAC

TAAGGG 55

51 RM25 8 GGCCCGTCCAAGAA

ATATTG

CGGTGAGACAGAAT

CCTTACG 55

52 RM256 8 TATGCTAGCTAAGT

CCAATGC

AAGTAATATGCTGT

TAGCTGGTG 55

53 RM210 8 GCTTCAGGTGCTTCT

TCACC

CCTCCTCCACATCTT

GGAAC 55

54 RM337 8 TTCTTCCCAGTTGGG

TTGAC

CATCTTGTTGATGGT

GGTGG 55

55 RM245 9 ATGCCGCCAGTGAA

TAGC

CTGAGAATCCAATT

ATCTGGGG 55

56 RM296 9 CACATGGCACCAAC

CTCC

GCCAAGTCATTCAC

TACTCTGG 55

57 RM189

6 9

GGACAGGGTAAAGT

GTTAGA

CCTAAGACCTATCA

ACTCCA 55

58 RM205 9 CTGGTTCTGTATGGG

AGCAG

CTGGCCCTTCACGTT

TCAGTG 55

59 RM553 9 AACTCCACATGATT

CCACCC

GAGAAGGTGGTTGC

AGAAGC 55

60 RM205 9 CTGGTTCTGTATGGG

AGCAG

CTGGCCCTTCACGTT

TCAGTG 55

61 RM171 10 AACGCGAGGACACG

TACTTAC

ACGAGATACGTACG

CCTTTG 55

62 RM216 10 GCATGGCCGATGGT

AAAG

TGTATAAAACCACA

CGGCCA 55

63 RM222 10 CTTAAATGGGCCAC

ATGCG

CAAAGCTTCCGGCC

AAAAG 55

64 RM496 10 GACATGCGAACAAC

GACATC

GCTGCGGCGCTGTT

ATAC 55

65 RM258 10 ATGGGCCATGAGAG

AGAGAG

ACACACACCTACCA

CCATGG 55

66 RM228 10 ACAGGTTGGCGATG

TTTCTCT

TTCTTTTTCGAATTC

ATTCCTTTT 55

67 RM184 10 GGCTTGAGAGCGTT

TGTAGG

TATCGGGTGGAGTT

AGAGCC 55

68 RM206 11 CCCATGCGTTTAACT

ATTCT

CGTTCCATCGATCCG

TATGG 55

69 RM21 11 ACAGTATTCCGTAG

GCACGG

GCTCCATGAGGGTG

GTAGAG 55

70 RM167 11 GATCCAGCGTGAGG

AACACGT

AGTCCGACCACAAG

GTGCGTTGTC 55

71 RM202 11 CAGATTGGAGATGA

AGTCCTCC

CCAGCAAGCATGTC

AATGTA 55

72 RM287 11 TTCCCTGTTAAGAG

AGAAATC

GTGTATTTGGTGAA

AGCAAC 55

73 RM209 11 TACCTCGGTGAGAT

AGGGAATGC

CTTCACATCCACACT

TGCACTCG 55

74 RM126

1 11

CCTCCATTTCAGCCA

CCAACC

CAGAGTACGCGCTG

ATTGACTGC 55

75 RM21 11 TTCCCTTATTCCTGC

TCTCC

GGGATTTGCAGTGA

GCTAGC 55

76 RM12 12 TGCCCTGTTATTTTC

TTCTCTC

GGTGATCCTTTCCCA

TTTCA 55

77 RM19 12 CAAAAACAGAGCAG

ATGAC

CTCAAGATGGACGC

CAAGA 55

78 RM235 12 AGAAGCTAGGGCTA

ACGAAC

TCACCTGGTCAGCCT

CTTTC 55

79 RM260 12 ACTCCACTATGACC

CAGAG

GAACAATCCCTTCT

ACGATCG 55

80 RM519 12 AGAGAGCCCCTAAA

TTTCCG

AGGTACGCTCACCT

GTGGAC 55

81 RM20 12 ATCTTGTCCCTGCAG

GTCAT

GAAACAGAGGCACA

TTTCATTG 55

82 RM333

1 12

CCTCCTCCATGAGCT

AATGC

AGGAGGAGCGGATT

TCTCTC 55

83 RM285

19 12

TTCAGAGCATGTAT

GTGAGTGAGC

AAGCTCGGAAACAA

TCAAGAGG 55

Fig. 4.1: Gel pictures of some of the polymorphic markers showing the amplified

products

(a) RM293

(b) RM 447b

(c) RM 429

(d) RM337

(e) RM245

(f) RM152

(g) RM216

Table 1:The major allele frequency, number of alleles per locus, gene

diversity, heterozygosity and PIC values of 53 SSR markers

Marke

r

Major

Allele

Frquenc

y

Genotyp

eNo

Sample

Size

No. of

obs.

Allele

No

Availab

ility

GeneDive

rsity

Heterozyg

osity

PIC

RM-

204

0.754 3.0 60.0 59.0 2.0 0.983 0.371 0.017 0.302

RM-

164

0.604 5.0 60.0 53.0 4.0 0.883 0.493 0.075 0.389

RM-

249

0.575 3.0 60.0 60.0 2.0 1.000 0.489 0.017 0.369

RM-

440

0.517 4.0 60.0 60.0 3.0 1.000 0.576 0.433 0.489

RM-12 0.767 3.0 60.0 58.0 2.0 0.967 0.357 0.259 0.293

RM-19 0.712 4.0 60.0 59.0 3.0 0.983 0.431 0.508 0.368

RM-

235

0.558 4.0 60.0 60.0 3.0 1.000 0.520 0.350 0.415

RM-

260

0.536 6.0 60.0 55.0 5.0 0.917 0.593 0.745 0.523

RM-

519

0.592 3.0 60.0 60.0 2.0 1.000 0.483 0.017 0.366

RM-11 0.764 2.0 60.0 55.0 2.0 0.917 0.361 0.000 0.296

RM-20 0.733 2.0 60.0 60.0 2.0 1.000 0.391 0.000 0.315

RM-44 0.567 3.0 60.0 60.0 2.0 1.000 0.491 0.033 0.371

RM-

318

0.983 2.0 60.0 60.0 2.0 1.000 0.033 0.000 0.032

RM-

530

0.767 2.0 60.0 60.0 2.0 1.000 0.358 0.000 0.294

RM-

3614

0.533 2.0 60.0 60.0 2.0 1.000 0.498 0.000 0.374

RM-

5361

0.550 2.0 60.0 60.0 2.0 1.000 0.495 0.000 0.372

RM-

206

0.500 5.0 60.0 60.0 4.0 1.000 0.585 0.933 0.501

RM-

252

0.950 2.0 60.0 60.0 2.0 1.000 0.095 0.000 0.090

RM-21 0.508 3.0 60.0 60.0 2.0 1.000 0.500 0.117 0.375

RM-

167

0.900 4.0 60.0 60.0 3.0 1.000 0.182 0.033 0.168

RM-

202

0.867 3.0 60.0 60.0 2.0 1.000 0.231 0.033 0.204

RM-

287

0.517 3.0 60.0 60.0 2.0 1.000 0.499 0.033 0.375

RM-50 0.897 2.0 60.0 58.0 2.0 0.967 0.185 0.000 0.168

RM-

520

0.850 2.0 60.0 60.0 2.0 1.000 0.255 0.000 0.222

RM-29 0.683 3.0 60.0 60.0 2.0 1.000 0.433 0.033 0.339

RM-60 0.950 2.0 60.0 60.0 2.0 1.000 0.095 0.000 0.090

RM-

261

0.867 2.0 60.0 60.0 2.0 1.000 0.231 0.000 0.204

RM-

273

0.842 4.0 60.0 60.0 3.0 1.000 0.271 0.083 0.242

RM-

325

0.858 3.0 60.0 60.0 2.0 1.000 0.243 0.050 0.214

RM-

545

0.442 4.0 60.0 60.0 3.0 1.000 0.627 0.017 0.549

RM-

573

0.967 3.0 60.0 60.0 3.0 1.000 0.065 0.000 0.064

RM-

1038

0.658 5.0 60.0 60.0 3.0 1.000 0.508 0.083 0.455

RM-

3331

0.850 2.0 60.0 60.0 2.0 1.000 0.255 0.000 0.222

RM-

3742

0.783 3.0 60.0 60.0 3.0 1.000 0.352 0.367 0.309

RM-

28519

0.867 2.0 60.0 60.0 2.0 1.000 0.231 0.000 0.204

RM-

127

0.575 3.0 60.0 60.0 2.0 1.000 0.489 0.017 0.369

RM-

171

0.783 2.0 60.0 60.0 2.0 1.000 0.339 0.000 0.282

RM-

178

0.558 2.0 60.0 60.0 2.0 1.000 0.493 0.883 0.372

RM-

216

0.725 3.0 60.0 60.0 2.0 1.000 0.399 0.017 0.319

RM-

222

0.767 2.0 60.0 60.0 2.0 1.000 0.358 0.000 0.294

RM-

293

0.400 6.0 60.0 60.0 4.0 1.000 0.700 0.983 0.645

RM-

438

0.983 2.0 60.0 60.0 2.0 1.000 0.033 0.000 0.032

RM-

496

0.408 6.0 60.0 60.0 4.0 1.000 0.706 0.600 0.655

RM-

1352

0.917 2.0 60.0 60.0 2.0 1.000 0.153 0.000 0.141

RM-

15669

0.917 2.0 60.0 60.0 2.0 1.000 0.153 0.000 0.141

RM-

152

0.483 3.0 60.0 60.0 3.0 1.000 0.546 0.000 0.442

RM-

245

0.517 3.0 60.0 60.0 3.0 1.000 0.555 0.000 0.458

RM-

296

0.792 3.0 60.0 60.0 2.0 1.000 0.330 0.017 0.275

RM-

1896

0.608 3.0 60.0 60.0 2.0 1.000 0.477 0.017 0.363

RM-

429

0.850 2.0 60.0 60.0 3.0 1.000 0.266 0.150 0.250

RM-

447

0.475 5.0 60.0 60.0 3.0 1.000 0.636 0.717 0.564

RM-

347

0.558 3.0 60.0 60.0 3.0 1.000 0.500 0.017 0.383

RM-

334

0.550 5.0 60.0 60.0 4.0 1.000 0.614 0.400 0.561

Mean 0.701 3.1 60.0 59.6 2.5 0.993 0.387 0.152 0.323

Major allele frequency

The major allelic frequency revealed by the SSR markers across the 60

rice genotypes ranged from 0.400 to 0.983, with a mean of 0.701. Generally,

the allele frequency for a maximum number of markers was below 0.95, indicating that they

were all polymorphic. [74] obtained major allele frequencies ranging from 0.50 for Assam to

0.99 for Tripura. [75] reported the average of major allele frequency from 0.42 to 0.10, while

Mvuyekure et al. (2018) observed 0.76; all these findings were quite similar to the present

results.

Number of alleles

The SSR markers have significantly superior allelic diversity of microsatellites

(McCouch et al. 1997) and a high allele number for rice microsatellite markers. The present

study detected 133 alleles across the 60 rice genotypes by 53 polymorphic SSR markers with

2.5 average alleles per locus. The number of alleles generated per locus by each marker ranged

from 2 (RM296, RM1896, RM204, RM249, RM12, RM519, RM11, RM20, RM44, RM318,

RM530, RM3614, RM5361, RM21, RM202, RM287, RM50, RM520, RM29, RM60, RM261,

RM-325, RM28519, RM127, RM171, RM178, RM216, RM222, RM252, RM3331, RM1352,

RM15669 and RM438 to 5 (RM260). These numbers were comparable to 2.0-5.5 alleles per

SSR locus a different set of rice germplasm. A range of 2-4 alleles per locus was common in

rice [75-78]. The average number of alleles was 6.72 [79-85], 4.90 [80]), 4.50 [81] , 8.00 [82],

4.30 [83], 7.40 [84] and 6.40 [85] because they use of a more diverse set of rice accessions in

their study or due to the use of highly polymorphic markers.

Gene diversity

Gene diversity ranged from 0.033 in RM204 to 0.706 in RM334, with a mean

of 0.387. [75,77] reported mean gene diversity values of 0.390 and 0.325, respectively, similar

to the present mean values. Watanabe et al. 2016, reported a gene diversity mean of 0.84, and

[74] obtained gene diversity ranging from 0.006 in Arunachal Pradesh to 0.50 in Manipur.

Observed heterozygosity

The observed heterozygosity ranged from 0.017 for RM287 to 0.983 for

RM334, with a mean of 0.152. Most of the SSR markers exhibited heterozygosity, either zero

or low value. [78,75] reported heterozygosity of 0.690 and 0.386,

respectively.[74] reported heterozygosity ranging from 0.002 in Nagaland to 0.420 in

Mizoram. The present results suggested that the majority of rice germplasm were pure and

completely homozygous for SSR markers, which might result from the self-pollinated mode of

reproduction of rice. The observed heterozygosity (0.152) was far lower than the total

expected heterozygosity (0.387) which further supported a low gene flow value for the

majority of the loci, except RM 440, RM 19, RM 260, RM 206, RM 178, RM 293, RM 447

and RM 334.

Polymorphism information content (PIC)

In the present investigation, the mean PIC value for all the markers RM 293,

RM 334 and RM 545 showed higher discriminatory power to distinguish genotypes due to its

high PIC values of 0.645, 0.561 and 0.549, respectively. The primer RM 60 and RM 573

showed lower PIC values of 0.090 and 0.064, respectively, suggesting

less discriminatory power of these primers. A microsatellite marker with a PIC value greater

than 0.50 is highly informative [79]. The average PIC values of 0.33 [80], 0.32 [76], 0.32 [75]

and 0.26 [77] reported previously. [81] obtained a mean PIC value of 0.37 in sets of 14

improved varieties and 27 landraces of rice collected from different zones of seven Indian

states, which were comparable to the present result. All these findings indicated that the

rice accessions used in the present study had a broad genetic diversity. The average PIC values

reported were 0.57 [79], 0.57 [80], 0.66 [82], 0.46 [83], 0.53 [81], 0.603 [83], 0.57 [84], 0.665

[86] and 0.47 [87] respectively. These values were comparatively higher than the earlier

reports, which might be due to the high diversity of rice accessions used in their studies or

highly polymorphic markers.

Fig. 1 presents the dendrogram showing hierarchical horizontal clustering of

60 ahu rice genotypes of Assam using unweighted neighbour-joining (UNJ) method based on

chord distances estimated from 53 polymorphic SSR marker data. The dendrogram

indicated three major clusters' dissimilarity values ranging from 0.111 to 0.695. Cluster I had

two sub-clusters, I-A and I-B, containing 18 and 3 genotypes, respectively. Similarly, cluster

II was the most significant cluster with 34 genotypes which consisted of two sub-clusters, II-

A having 16 genotypes and II-B with 18 genotypes. Cluster III contained

five genotypes belonging to landraces only. All the genotypes of cluster I belonged to

landraces except Nagina 22, which was a selection from the landraceRajbhog. Cluster II-A was

an admixture composed of both improved varieties and landraces. All the maintainer lines of

WA CMS belonged to cluster II A. Bau Murali and Krishna were the

most distantly apart genotypes among all the 60 genotypes.

Fig. 2 showed 53 SSR markers used for hierarchical clustering using the

Unweighted Neighbour-joining (UNJ) method of 11 Ahu rice genotypes used in the present

investigation. The grouping resulted from the chord genetic distances among the 11

parental genotypes. All the genotypes included in each cluster represent a heterotic group. As

shown in Table 4, three broad heterotic groups were apparent using 53 polymorphic SSR

markers. Group I contained Bormekohi Dhan and Mayamoti, group II comprised Luit, Lachit,

IR 58025B, IR 68888B, IR 68897B, IR 79156B and IR 80555B, group III included Suryamukhi

and Lal Aus. All the maintainers of WA CMS belonged to group II and Luit and Lachit.

Landraces belonged to two different groups I and III. Thus, a fair degree of similarity was

evident among genotypes of the same group. Also, the genotypes having a similar combining

ability performance and the heterotic response would categorize them into one heterotic group

[18].

Fig. 1: Hierarchical horizontal clustering of 60 Ahu rice genotypes of Assam using

Unweighted Neighbour-joining (UNJ) method based on chord distances estimated from SSR

marker data

Fig 2: Hierarchical axial clustering of 11 Ahu rice genotypes using Unweighted Neighbour-

joining (UNJ) method based on genetic distances estimated from SSR markers

Clustering based on genetic distances

The heterotic pattern increases the efficiency of hybrid

development, inbred recycling, and population improvement. Recognition and determination

of heterotic groups and patterns are essential for breeding hybrid varieties, as shown in various

studies on rice [88,30]. Inbred varieties are then often developed from crosses within heterotic

groups. Promising hybrids result from crossing the inbred lines developed between different

heterotic groups. Grouping of germplasm in divergent pools is advantageous to maximize the

expected heterosis [89].. The success of the hybrid rice that resulted from the utilization of

heterosis depends on the genetic divergence of germplasm, basically

on geographic divergence for three-line hybrid rice and sub-specific genetic divergence for

two-line hybrid rice. Heterotic groups for hybrid crops could be determined by marker-based

groups as studied in maize [31,34,32], as well as in other crops [50].[88] investigated the

genetic diversity of hybrid rice parents developed at IRRI and evaluated with simple sequence

repeats and single-nucleotide polymorphism markers where they confirmed that heterotic

hybrids could be formed based on marker-based parent groups to increase the efficiency of

hybrid rice breeding[30]. Fig. 3 showed the frequency distribution of genetic distances (GDs)

of the 60 rice genotypes and the selected 11 parents for the diallel crosses based on 53 SSR

markers. The mean GD for the 60 genotypes was 0.507 and for the 11 parents was 0.475. This

11 parents’ sample accounted for 83.3% of the allelic variation of the 60 genotypes and

reasonably represents the population panel because of the high allelic coverage and similar

cluster structure. This result was closely related to [11].

Fig. 3: Frequency distribution of genetic distances of the 60 rice genotypes and the selected

11 parents for the diallel crosses based on 53 SSR markers

Pooled analysis of variance (ANOVA)

The pooled analyses of variance (ANOVA) for the various traits evaluated

under 40 and 60 kg N ha-1(Supplementary Table 4a-4c) revealed highly significant (p<0.01)

differences among the genotypes for all the traits, indicating that the material under

investigation was diverse for the traits in question. The environment source of variation due

to Nitrogen (N) doses showed significant differences (p<0.01) for all the traits except

only seedling establishment, culm length, panicle length, harvest index, amylose content.

The genotype x environment (GE) interaction exhibited highly significant differences (p<0.01)

for all traits except seedling establishment, suggesting differential behaviour of

the genotypes in the two N doses.

Table 4a: Pooled ANOVA for the traits of the 66 genotypes including 11 parents and

their 55 hybrids evaluated over the two nitrogen doses.

Source of

Variations DF

Mean Squares

Seedling

length

(cm)

Leaf

number

Seedling

establishm

ent (%)

Days to

panicle

initiation

Days to

50%

flowering

Flag leaf

area (cm2)

Replicates/N Doses 2 0.03 (1) 0.02 (1) 9.85 0.76 0.47 6.72

Nitrogen Doses (N)

Doses) 1 - - 151.52 992.97** 1605.31** 1052.00**

Genotypes (Gen) 65 33.16** 0.61** 488.25** 62.59** 64.69** 46.92**

N Doses*Gen 65 - - 95.36 18.54** 16.68** 16.17**

Pooled Error 130 1.91 (65) 0.20 (65) 82.16 2.94 2.54 3.07

CV (%) 6.63 15.32 11.36 2.27 1.96 1.61

Fre

qu

en

60 genotypes GD

11 parents GD

11 parents represent 83.3% of the

Figures in parentheses are degrees of freedom for replication and error, respectively for the traits recorded in the

seedling stage. *, ** Significant at 5% and 1% level

Table 4b: Pooled ANOVA for the traits of the 66 genotypes including 11 parents and

their 55 hybrids evaluated over the two nitrogen doses.

Source of

Variations DF

Mean Squares

Days to

maturity

Culm

height

(cm)

Productive

tillers

Plant-1

Average

panicle

weight (g)

Panicle

length (cm)

Filled

grains

Panicle-1

Replicates/N

Doses 2 47.31 11.12 5.17 0.21 0.43 11.95

Nitrogen

Doses (N 1 62.06** 21.31 293.80** 9.58** 0.21 10363.81**

Genotypes

(Gen) 65 57.46** 1173.89** 95.43** 5.07** 41.17** 9046.83**

N Doses*Gen 65 20.64** 133.20** 30.24** 1.85** 21.36** 709.23**

Pooled Error 130 5.62 20.39 2.46 0.26 3.35 11.09

CV (%) 2.20 6.54 10.29 16.45 7.21 3.03

*, ** Significant at 5% and 1% level

Table: 4c: Pooled ANOVA for the traits of the 66 genotypes including 11 parents and

their 55 hybrids evaluated over the two nitrogen doses.

Source of Variations DF

Mean Squares

Spikelet

fertility

(%)

Straw

yield

Plant-1

(g)

Grain

yield

plant-1

(g)

Biologic

al yield

Plant-1

Harvest

index

(%)

Amylo

se

conten

t (%)

Gel

consisten

cy (mm)

Replicates/N Doses 2 1.42 57.93 4.52 25.80 5.78 14.70 3.63

Nitrogen Doses (N

Doses) 1 110.80** 2176.21*

*

448.58*

*

4444.81*

* 0.02 0.10 1581.72*

* Genotypes (Gen) 65 778.82** 1276.49*

*

383.76*

*

1741.82*

*

357.12*

*

53.56*

* 705.01**

N Doses*Gen 65 96.83** 56.74** 46.27** 124.79** 39.70** 11.64*

* 980.82**

Pooled Error 130 1.78 16.99 4.24 24.93 5.38 2.84 7.41

CV (%) 1.72 6.89 7.57 5.74 7.35 11.46 5.76

*, ** Significant at 5% and 1% level

Mean comparison

The traits days to panicle initiation (77.7), days to 50% flowering (83.6), flag

leaf area (27.1 cm2), days to maturity (111.1), productive tillers plant-1 (16.3), average panicle

weight (3.3 g), grains panicle-1 (116.1), spikelet fertility (78.0%), straw yield plant-1 (62.7 g),

grain yield plant-1 (28.5 g), biological yield plant-1 (91.0 g), gel consistency (49.8 mm) in 60

kg N ha-1 were significantly higher (P<0.05) than their respective trait means at 40 kg N ha-1

(Table 2). Thus, increasing the N-dose from 40 to 60 kg had a positive correlation with grain

yield and its components traits. Increasing grain yield and biomass could be due to N-supply

increase chlorophyll content, leaf area index, and nutrient uptake and utilization. Significant

effect of increasing nitrogen rate on rice grain yield was also reported earlier by [90-94]. [95]

reported that nitrogen application increases the straw yields of transplanted rice. [94] obtained

that N fertilizer doses significantly increased the number of tillers in rice; late-

emerging tillers usually produce lower yields compared with early emerging tillers.Among the

two-hybrid groups involving WA CMS maintainers and the remaining parents, maintainer (M)

× landrace (LR) was the most productive for grain yield and other related traits, while LR ×

improved variety (IV) yielded the maximum with a concomitant increase in component traits

(Table 2a-2b). The hybrid group IV×IV was the least productive in terms of yield and most

yield component traits, followed by LR×LR and M×M, suggesting that improved varieties were

already selected for most desirable alleles for high yield and thus, hybridization among them

resulted in less yield improvement, due to few allelic differences between them than crosses

between landraces and improved varieties. Given the decrease in genetic diversity among the

improved rice varieties due to shifting of the breeding goal from high yield to biotic and abiotic

stresses, [96] suggested broadening the genetic base by incorporating more diverse donor

parents in the breeding programme for yield improvement in rice.

Table 2a: Mean performance of the two N-doses for the traits showing significant

environmental variation.

N-Dose

(kg ha-1)

Days to

panicle

initiation

Days to

50%

flowering

Flag leaf

area (cm2)

Days to

maturity

Productiv

e tillers

Plant-1

Average

panicle

weight (g)

40 73.8b 78.7b 26.7b 107.1b 14.2b 2.9b

60 77.7a 83.6a 27.1a 111.1a 16.3a 3.3a

CD 5% 0.4 0.4 0.2 0.4 0.4 0.1

Mean values with different superscript lowercase letters indicate significant difference at the

0.05 level.

Table 2b: Mean performance of the two N-doses for the traits showing significant

environmental variation.

N-Dose

(kg ha-1)

Filled

grains

Panicle-1

Spikelet

fertility

(%)

Straw

yield

Plant-1

Grain

yield

Plant-1

Biological

yield

Plant-1

Gel

consistenc

y (mm)

40 103.5b 76.7b 56.9b 25.9b 82.8b 44.9b

60 116.1a 78.0a 62.7a 28.5a 91.0a 49.8a

CD 5% 0.8 0.3 1.0 0.5 1.2 0.7

Mean values with different superscript lowercase letters indicate significant difference at the

0.05 level.

Range of mid-parent (HMP) and better-parent heterosis (HBP)

The range of mid-parent heterosis for grains yield per plant was from -6.82**

(LAL×BOR) to 31.11** (88B×LUI) (Table 3). The crosses, BOR×25B, LAL×55B,

SUR×LAC and MAY×LAC showed significant positive mid-parent heterosis for grain yield

per plant. Earlier studies reported significant positive heterosis for grain yield per plant [97-

100]. Better-parent heterosis for grain yield per plant ranged from -10.38** (LAL×BOR) to

29.20** (88B×LUI). The crosses, namely 88B×LUI, SUR×LAC, 88B×55B and MAY×LAC

exhibited significant positive better-parent heterosis for grains yield per plant. A similar finding

was also reported by [101].

Table 3: Range of mid-parent (HMP) and better-parent heterosis (HBP) for the seedling

traits of the 55 rice hybrids

Traits HMP HBP

Seedling

height (cm)

SUR*LAL(-8.48)-BOR*88B (8.48)

LAC*LUI (8.30)-LAL*LAC (-9.70)

Leaf number LAL*LUI (-2.0)-97B*LUI(1.25**)

25B*LUI(-2.0)-97B*LUI (1.0*)

Seedling

establishment

MAY*97B (-36.25**)- 55B*LAC

(12.5)

MAY*97B(-37.50**)-

SUR*25B(17.5)

Days to

panicle

initiation

97B*LAC(11.75**)-BOR*55B(

-6.50**

97B*LAC(11.75**)-BOR*55B(

-10.00**

Days to 50%

flowering

SUR*LUI(13.75**)-SUR*LAL(-

5.75**)

SUR*LUI(11.50**)-BOR*55B(-

9.00**)

Flag leaf area 25B*LUI(-9.90**)-

LAL*56B(26.28**)

25B*LUI(-11.06**)-

LAL*56B(21.60**)

Days to

maturity

BOR*55B(-4.88**)-

MAY*LAC(11.38**)

SUR*25B(-7.25**)-

MAY*LAC(9.75**)

Culm height

SUR*LAL(-21.11**)- 88B*97B(

45.78**)

SUR*LAL(-25.58**)-

SUR*97B(29.38**)

Productive

tillers

BOR*55B(-7.11*)-MAY*56B

(13.18**)

LAL*97B()-MAY*56B(11.45**)

Average

panicle weight

BOR*97B(-13.75**)-55B*LUI(

37.00**)

MAY*56B(-14.75**)-

55B*LUI(35.25**)

Panicle length 56B*55B(-11.00*)-

25B*LUI(48.63**)

LAL*LAC(-10.25*)-

25B*LUI(48.63**)

Filled grains

per panicle

25B*97B(-43.95**)-

BOR*55B(173.78**)

25B*97B(-62.40**)-

BOR*55B(164.30**)

Spikelet

fertility

LAL*LUI (-35.29**) -

LAL*56B(22.29**)

25B*LUI (-41.55**) -

LAL*56B(22.29**)

Straw yield

per plant

MAY*56B(-37.38**)-

25B*56B(91.10**)

MAY*56B(-37.38**)-

25B*56B(80.96**)

Grain yield

per plant

56B*LUI(-6.57**)-

BOR*88B(29.05**)

56B*LUI(-8.80**)-

BOR*88B(27.11**)

Biological

yield per plant

BOR*56B(-19.54**)-

25B*56B(96.20**)

BOR*56B(-32.44**)-

25B*56B(81.47**)

Harvest index 25B*56B(-21.13**)-

AY*55B(28.61**)

25B*56B(-22.67**)-

MAY*55B(26.60**) Amylose

content

97B*56B(-6.46**)-

BOR*LAC(13.34**)

BOR*55B(-10.65**)-

BOR*LAC(11.90**)

Gel

consistency

97B*56B(-36.13**)-

AY*LAC(23.13**)

LAL*MAY(-44.75**)-

BOR*MAY(16.50**)

Heterotic grouping based on genetic distances

Genetic distance based heterotic clustering of the 11 parental genotypes along

with mean yield, combining ability and heterosis estimates (Table 4) revealed that the intra-

group hybrid category G2×G2 contained the highest frequency of crosses (0.38), followed by

inter-group hybrid category G1×G2 (0.25) and G2×G3 (0.25). G1×G2 (0.55) registered the

highest genetic distance, followed by G1×G3 (0.48) and G2×G2 (0.40). G2×G3 (34.61)

recorded the highest grain yield, followed by G3×G3 (33.76) and G1×G1 (28.48). G3×G3

(7.03) showed the highest SUM-GCA (g Plant-1), followed by G2×G3 (3.05) and G1×G3

(1.63). G1×G1 (5.02) showed the highest SCA (g Plant-1), followed by G2×G3 (4.35) and

G1×G2 (1.72).

The highest 𝑯𝑴𝑷̅̅ ̅̅ ̅ (%) was observed for G2×G3 (63.70), followed by G1×G1

(51.74) and G3×G3 (46.05). G2×G3 (46.15) exhibited the highest 𝑯𝑩𝑷̅̅ ̅̅ (%), followed by

G1×G1 (43.56) and G3×G3 (36.00). G2×G3 (75.60) showed the highest 𝑯𝑺𝑷̅̅ ̅̅ (%), followed by

G3×G3 (71.26) and G1×G1 (44.51). G2×G3 had the highest heterosis, grain yield and genetic

distance; therefore, hybrids from this group are useful to produce superior hybrids, predicting

the heterosis and formed a heterotic pattern. But the highest SCA was shown by G1×G1, an

intra-group hybrid category. The lowest yielding hybrids and yield heterosis were evident in

the crossing pattern of G1×G3.

The parental group G2 recorded the highest frequency of crosses (0.88), genetic

distance (0.50), SCA (2.10), 𝑯𝑴𝑷̅̅ ̅̅ ̅ (46.51%) and 𝑯𝑩𝑷̅̅ ̅̅ (32.68%). G3 registered the highest grain

yield (31.13), SUM-GCA (3.90) and 𝑯𝑺𝑷̅̅ ̅̅ (57.93%).Mean of the inter-group hybrid categories

for the frequency of crosses (0.42), genetic distance (0.53), grain yield (28.74), SUM-GCA

(0.78), SCA (0.74), 𝑯𝑴𝑷̅̅ ̅̅ ̅ (40.77%), 𝑯𝑩𝑷̅̅ ̅̅ (28.20%) and 𝑯𝑺𝑷̅̅ ̅̅ (45.81%) were 0.42, 0.53, 28.74,

0.78, 0.74, 40.77%, 28.20% and 45.81%, respectively. Meansof the intra-group hybrid

categories were 0.58, 0.29, 29.59, 0.78, 1.59, 45.07%, 34.36% and 50.12% for the frequency

of crosses, genetic distance, and grain yield, SUM-GCA, SCA, 𝑯𝑴𝑷̅̅ ̅̅ ̅, 𝑯𝑩𝑷̅̅ ̅̅ and 𝑯𝑺𝑷̅̅ ̅̅ ,

respectively.

The inter-group parents were more diverse (GD=0.53) than the intra-group

parents (GD=0.29), which was in tune with the findings of [30]. However, the hybrids

originating from inter-group parents do not always give high yield or yield heterosis [11]. But

some specific intra-group hybrids, G3×G3 in the present case, having low genetic distance

produce a higher yield than the average yield of inter-group hybrids. The present study

suggested that molecular markers might be useful for the grouping of parents and heterotic

grouping. Based on this, an intra-heterotic group needs genetic improvement, and hybrids

between inter-heterotic groups used to produce the superior hybrids. The intra-group

hybridshad high grain yield, SCA and heterosis than inter-group hybrids. The

hybridcombinationsof an inter-group giving low yields but have high genetic distance, so

genetic distance cannot as such predict the heterosis. However, the use of functional markers

related to grain yield and its components would provide specific guidelines for combining

parents correctly to increase breeding efficiency. Broadening the hybrid rice parental gene

pools by introducing germplasm from other sources and integrating them into heterotic groups

are essential steps to further enhance the heterotic performance of hybrids in the state of Assam.

Table 4: Genetic distance based heterotic grouping of the 11 parental genotypes along with

mean yield, combining ability and heterosis estimates

Hybrid

category

Freq.

of

crosses

GD

GYP

(g

Plant-

1)

SUM-

GCA

(g

Plant-

1)

SCA

(g

Plant-

1)

HMP

(%)

HBP

(%) HSP (%)

Summarized by hybrid groups

G1*G1 0.02 0.29 28.48 -3.76 5.02 51.74 43.56 44.51

G2*G2 0.38 0.40 26.53 -0.93 0.24 37.41 23.51 34.58

G3*G3 0.02 0.19 33.76 7.03 -0.49 46.05 36.00 71.26

G1*G2 0.25 0.55 26.59 -2.35 1.72 38.40 28.39 34.90

G1*G3 0.07 0.48 25.02 1.63 -3.83 20.20 10.06 26.93

G2*G3 0.25 0.55 34.61 3.05 4.35 63.70 46.15 75.60

Summarized by inter- and intra-group hybrids

Inter-group 0.42 0.53 28.74 0.78 0.74 40.77 28.20 45.81

Intra-group 0.58 0.29 29.59 0.78 1.59 45.07 34.36 50.12

Summarized by parental groups involved in hybrids

G1 0.34 0.44 26.70 -1.49 0.97 36.78 27.34 35.45

G2 0.88 0.50 29.24 -0.08 2.10 46.51 32.68 48.36

G3 0.34 0.41 31.13 3.90 0.01 43.32 30.74 57.93

Mean comparison of heterotic groups based on genetic distances

Table 5 presents the mean performance of the hybrids in GD-based heterotic

groups for the different traits. A comparative evaluation of heterotic group means for different

traits could be suggested for improving specific characters. This comparison indicated that

G2×G3 had better cluster means for most of the characters and therefore, G2×G3 needs

consideration for selecting genotypes as parents in a hybridization programme. The hybrids of

G1×G3 group could lead to early maturing hybrid development.

Seedling length (cm), leaf number, seedling establishment (%), days to panicle

initiation, days to 50% flowering, days to maturity, culm length (cm), productive tillers,

grains/panicle, harvest index (%) and gel consistency (mm) recorded higher mean

performances in the inter-group hybrids than in intra-group hybrids, providing more chances

for segregation and recombination and thus, these traits concern priority in selecting promising

parents for hybridization programme. For the remaining traits, mean performances of the intra-

group hybrids were higher than that of the inter-group hybrids. The higher mean performance

was evident for grain yield (g Plant-1) in the intra-group hybrids than in the inter-group hybrids

and was because of the adverse indirect effects of yield contributing traits on yield in the inter-

group hybrids.

Table 5: Mean performance of the hybrids in GD-based heterotic groups for the

different traits

Character G1*G1 G2*G2 G3*G3

Intra-

group

mean

G1*G2 G1*G3 G2*G3

Inter-

group

mean

Seedling

height (cm)

20.05 20.24 18.70 19.66 21.84 24.31 20.88 22.34

Leaf number 3.00 2.71 2.00 2.57 2.86 2.75 3.07 2.89

Seedling

establishment

67.50 79.52 85.00 77.34 71.61 83.75 83.57 79.64

Days to

panicle

80.25 75.04 74.25 76.51 76.91 75.00 78.13 76.68

Days to 50%

flowering

84.50 80.54 79.25 81.43 82.75 80.81 83.63 82.40

Flag leaf area

(cm2)

35.52 27.16 40.25 34.31 27.77 23.40 26.51 25.89

Days to

maturity

111.50 108.48 109.00 109.66 111.02 107.19 110.88 109.69

Culm length

(cm)

66.15 55.86 56.98 59.66 83.08 81.56 79.39 81.34

Productive

tillers Plant-1

8.50 14.60 13.50 12.20 16.43 12.66 17.91 15.67

Average

panicle

5.80 3.31 3.38 4.16 2.89 3.37 3.03 3.10

Panicle

length (cm)

29.25 25.50 23.80 26.18 27.61 25.07 23.80 25.49

Grains

Panicle-1

102.18 95.97 165.35 121.17 97.19 146.90 142.48 128.86

Spikelet

fertility (%)

83.51 73.62 77.38 78.17 79.13 71.22 79.85 76.73

Straw yield

Plant-1

65.95 66.77 67.99 66.90 65.46 58.37 50.15 57.99

Grain yield

Plant-1

28.48 26.53 33.76 29.59 26.59 25.02 34.61 28.74

Biological

yield Plant-1

94.43 92.59 101.75 96.26 92.05 83.38 85.40 86.94

Harvest

index (%)

30.20 28.92 32.96 30.69 29.03 30.74 40.56 33.44

Amylose

content (%)

19.80 15.10 11.05 15.32 14.62 14.94 14.26 14.61

Gel

consistency

31.25 41.07 65.00 45.77 50.93 39.38 48.07 46.13

Correlation between heterosis and genetic distances

A perusal of Table 6 revealed that the parameters, namely, mean yield, 𝑯𝑴𝑷̅̅ ̅̅ ̅, 𝑯𝑩𝑷̅̅ ̅̅ and 𝑯𝑺𝑷̅̅ ̅̅ , were strongly correlated among themselves. Genetic distances showed no

correlation with any of the mean yield and heterosis parameters. Grain yield per plant had

positive and highly significant correlation (p<0.01) with 𝑯𝑴𝑷̅̅ ̅̅ ̅ (g Plant-1), 𝑯𝑴𝑷̅̅ ̅̅ ̅ (%), 𝑯𝑩𝑷̅̅ ̅̅ (g

Plant-1), 𝑯𝑩𝑷̅̅ ̅̅ (%) and 𝑯𝑺𝑷̅̅ ̅̅ (%). 𝑯𝑴𝑷̅̅ ̅̅ ̅ (g Plant-1) had positive and significant association with 𝑯𝑴𝑷̅̅ ̅̅ ̅ (%), 𝑯𝑩𝑷̅̅ ̅̅ (g Plant-1), 𝑯𝑩𝑷̅̅ ̅̅ (%) and 𝑯𝑺𝑷̅̅ ̅̅ (%). 𝑯𝑴𝑷̅̅ ̅̅ ̅ (%) showed a positive and significant

association with the 𝑯𝑩𝑷̅̅ ̅̅ (g Plant-1), 𝑯𝑩𝑷̅̅ ̅̅ (%) and 𝑯𝑺𝑷̅̅ ̅̅ (%). 𝑯𝑩𝑷̅̅ ̅̅ (g Plant-1) exhibited positive

and significant association with 𝑯𝑩𝑷̅̅ ̅̅ (%) and 𝑯𝑺𝑷̅̅ ̅̅ (%). 𝑯𝑩𝑷̅̅ ̅̅ (%) had a positive and significant

association with the 𝑯𝑺𝑷̅̅ ̅̅ (%). The correlation coefficient of genetic distance was negative and

non-significant with grain yield per plant (-0.0067). [11] also found a negative correlation of

grain yield with the genetic distance.[102] found a weak association of grain yield with the

genetic distance in maize. Many of the studies on rice found a negative correlation of genetic

distance with the heterosis [103, 104]. The association of marker-based GD and hybrid

performance was too small to be used for predicting hybrid breeding [47]. [50-51] also

confirmed the non-significant correlation of genetic distance with heterosis in maize. Thus, the

molecular marker-based genetic distance may not always be reliable for rice hybrid breeding

programme and prediction of heterosis. The information about functional markers related to

yield heterosis might provide precise direction or guideline for combining parents definitely to

increase breeding efficiency of rice. [105] suggested the prediction of heterosis based on yield-

related functional genes. As the heterosis is measured mainly in terms of yield, [106] suggested

the reliability of markers within genes (EST-SSRs) in heterosis prediction. However, the

present results showed that the association and prediction could be enhanced when the parental

groups are formed first by molecular markers, which may not predict the best hybrid

combination, but it reveals a practical value of assigning existing and new hybrid rice

germplasm into heterotic groups and increasing opportunities to develop desirable hybrids from

the best heterotic groups, which is consistent with a previous study in maize [107]. [106]

suggested the use of molecular marker heterozygosity, combining ability, high mean

performance for different traits, the morphological and molecular marker-based grouping of

parental lines to identify heterotic patterns in rice.

Table 6: Pearson correlation matrix among mean grain yield, combining ability, heterosis and

genetic distance estimates for the hybrids

Variables

GYP

(g Plant-

1)

SUM-

GCA

(g Plant-1)

SCA

(g Plant-1)

HMP

(g Plant-

1)

HMP

(%)

HBP

(g Plant-

1)

HBP

(%)

HSP

(%)

SUM-GCA (g

Plant-1) 0.4469**

SCA (g Plant-1) 0.9318** 0.0917

HMP (g Plant-1) 0.9713** 0.2793* 0.9678**

HMP (%) 0.9277** 0.1838 0.9580** 0.9857**

HBP (g Plant-1) 0.9514** 0.2331 0.9644** 0.9890** 0.9789**

HBP (%) 0.9013** 0.1412 0.9460** 0.9641** 0.9834** 0.9825**

HSP (%) 1.0000** 0.4469** 0.9318** 0.9713** 0.9277** 0.9514** 0.9013**

GD -0.0067 -0.0949 0.0311 0.0055 0.0034 0.0351 0.0168 -0.0067

Conclusions

In genetic distance based heterotic grouping, the intra-group hybrids recorded a

higher frequency of crosses, GYP, SCA, 𝑯𝑴𝑷̅̅ ̅̅ ̅, 𝑯𝑩𝑷̅̅ ̅̅ and 𝑯𝑺𝑷̅̅ ̅̅ values than those of inter-group

hybrids. No correlation between heterosis and genetic distance could be attributable to the use

of a subset of markers not linked to yield or concerned. Gene linked markers or high-density

markers (genome-wide markers) or markers within yield genes should be used for genetic

distance analysis and for heterotic grouping based on genetic distances. Apart from the further

study on the genetic aspects, it might be interesting to integrate epigenomics, metabolomics,

proteomics, and systems biology approaches for gaining better understandings into the

heterotic gene pools of rice. A careful selection of parents with desirable

traits constellation contributing to high yield of the parents for better complementation should

be emphasized along with a considerable genetic distance for augmentation of yield heterosis.

Acknowledgements: We are thankful to the Assam Agricultural University in Jorhat, Assam,

and the DBT-AAU centre in Jorhat for providing field and molecular marker work in their

laboratories, respectively.

Authors’ contributions All the authous helped me writtering and conceptualized this

manuscript. All authors read and approved the fnal manuscript.

Declarations

Competing interests

The authors state that they do not have any competing interests.

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Funding

Not applicable.

Author details

1,2,3,4,5,6 Department of Plant breeing and genetics, The Assam Agricultural University,

Jorhat, 785013, India.

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