New quantitative trait loci for enhancing adaptation to salinity in rice from Hasawi, a Saudi...

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EuphyticaInternational Journal of Plant Breeding ISSN 0014-2336 EuphyticaDOI 10.1007/s10681-014-1134-0

New quantitative trait loci for enhancingadaptation to salinity in rice from Hasawi,a Saudi landrace into three Africancultivars at the reproductive stage

Isaac Kofi Bimpong, Baboucarr Manneh,Bathe Diop, Kanfany Ghislain, AbdulaiSow, Nana Kofi Abaka Amoah, GlennGregorio, et al.

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New quantitative trait loci for enhancing adaptationto salinity in rice from Hasawi, a Saudi landrace into threeAfrican cultivars at the reproductive stage

Isaac Kofi Bimpong • Baboucarr Manneh • Bathe Diop • Kanfany Ghislain •

Abdulai Sow • Nana Kofi Abaka Amoah • Glenn Gregorio •

Rakesh Kumar Singh • Rodomiro Ortiz • Marco Wopereis

Received: 27 December 2013 / Accepted: 24 April 2014

� Springer Science+Business Media Dordrecht 2014

Abstract Salinity is a major constraint affecting rice

productivity in rainfed and irrigated agro-ecosystems.

Understanding salinity effects on rice production at

the reproductive stage could improve adaptation for

this trait. Identifying quantitative trait loci (QTLs)

controlling adaptation to salinity may also accelerate

breeding rice germplasm for environments prone to

this stress. We used the salt tolerant landrace ‘Hasawi’

as a donor parent to generate three F2 offspring

(consisting each of 500 individuals) with three African

cultivars (‘NERICA-L-19’, ‘Sahel 108’ and ‘BG90-

2’) used as recipient parents (RP). The F2s and F2:3s

were evaluated for grain yield and other traits in saline

fields. Salinity caused reduction in all measured traits

across the F2-derived offspring, e.g. grain yield

reduced between 65 and 73 %, but some offspring

had twice the RP’s grain yield. QTL analysis revealed

75 QTLs for different traits in all 3 genetic back-

grounds (GBs): 24 of them were common among all

the 3 GBs while 31 were noted in 2 GBs, and 17 in one

GB. ‘Hasawi’ contributed on average 49 % alleles to

these QTLs. Two yield and yield related QTLs (qGY11

and qTN11) common in all 3 GBs were mapped on the

same chromosomal segment suggesting these QTLs

might be stable across different GBs. Four other QTLs

were strongly associated with salinity tolerance with

peak marker RM419, representing a potential candi-

date for MAS due to high LOD score and relatively

large effect QTLs.

I. K. Bimpong (&) � B. Manneh � B. Diop �K. Ghislain � A. Sow � N. K. A. Amoah

Africa Rice Centre, Sahel Regional Station, B.P 96,

Saint Louis, Senegal

e-mail: [email protected]; [email protected]

B. Manneh

e-mail: [email protected]

B. Diop


K. Ghislain


A. Sow


N. K. A. Amoah


G. Gregorio � R. K. Singh

International Rice Research Institute (IRRI),

DAPO Box 7777, Metro Manila, Philippines


R. K. Singh


R. Ortiz

Department of Plant Breeding, Swedish University of

Agricultural Sciences (SLU), Box 101, 23053 Alnarp,

Sweden


M. Wopereis

Africa Rice Centre, 01 BP 2031, Cotonou, Benin


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Euphytica

DOI 10.1007/s10681-014-1134-0

Author's personal copy

Keywords Landrace �Marker-aided breeding �Field phenotyping �QTL � Rice �Microsatellites �Salinity

Abbreviations

CIM Composite interval mapping

MAS Marker-aided selection

PCR Polymerase chain reaction

QTL Quantitative trait loci

SES Standard evaluation system

SMR Single-marker regression

SSR Simple sequence repeats

Background

Rice (Oryza sativa L.) productivity continues to be

affected by a series of biotic (blast, bacterial blight,

Africa gall midge and Rice yellow mottle virus) and

abiotic (drought, cold, submergence, salinity and prob-

lem soils) stresses. Salinity is considered as one of the

important abiotic factors influencing rice production. It

is the second most widespread soil problem in rice

growing areas after drought and is considered as a

serious constraint to increased rice production world-

wide (Sabouri et al. 2009). Salt stress (both salinity and

alkalinity or sodicity) is a serious problem in some

African inland areas of Burundi, Ethiopia, Liberia, Mali,

Senegal and Sierra Leone, because salt builds up as a

result of the excessive use of irrigation water with

improper drainage, coupled with the use of poor-quality

irrigation water or sodic soils developed from salt-

bearing rocks. More than 4.4 million ha of arable land

are under rice cultivation in West Africa of which 25 %

of the area (irrigated, mangrove swamp and deep-water

systems) is directly affected by salinity (Lancon and

Erenstein 2002). In addition, it has been estimated that

1.5 million ha of cultivable mangrove swamps in West

Africa are affected by salinity (Jones 1986). The

problem of salinization is expected to increase due to

poor agricultural management practices and climate

change. The decrease in rice productivity in salt-

affected areas can be addressed by plant breeding as it

contribution to food security and poverty alleviation has

well been documented (Singh 1999; Evenson and Gollin

1997; Hossain et al. 2003). Furthermore, other inte-

grated approach combining land reclamation and crop

management can be used. Management practices are

however not always feasible in the long term, as in

coastal areas where salt stress is seasonal or in inlands

where cost of reclamation are high (Ismail and Tuong

2009).

Although various rice landraces and primitive

cultivars are often less desired than modern cultivars

due to their phenotypes, they have been included for

breeding genetically enhanced germplasm showing

adaptation to salty soils. Among the most successful

examples is the use of ‘Pokkali’and Nona Bokra ‘for

adaptation of early seedling stage to salinity and to

many traits including shoot K? concentration and

shoot Na? concentration (Lin et al. 2004; Ren et al.

2005). Landraces have not been however used signif-

icantly for breeding adaptation to salinity at the

reproductive stage, besides; it has been difficult to

utilize traditional landraces for the improvement of

quantitatively inherited traits, such as yield, because

the superior trait of interest cannot be identified

phenotypically in the accessions. There is therefore a

need to broaden the rice breeding pool for adaptation to

salinity especially at the reproductive stage of the crop

growth and development.

‘Hasawi’ is a landrace adapted to the climate of

eastern Saudi Arabia and characterized by its strong

adaptability to soil salinity and drought (Al-Mssallem

et al. 2011). It has some undesired traits such as

susceptibility for lodging, delayed maturity, and

photoperiod sensitivity. Allelic diversity studies using

single nucleotide polymorphisms (SNPs) have

revealed that Hasawi belongs to the Aus cultivar

subgroup, thereby suggesting that there could be a

high level of polymorphism in offspring derived by

crossing this Saudi landrace and indica cultivars

(Thomson et al. 2010).

The identification of quantitative trait loci (QTLs)

conferring adaptation to salinity at the reproductive

stage and the elucidation of its genetic control are

necessary for the developing marker-aided selection

(MAS) and pyramiding breeding with the aim of

improving the efficiency of rice genetic enhancement

for this trait. The best known and most robust QTLs

are Saltol on the short arm of chromosome 1, which

explains most of the variation for ion uptake under salt

stress, and SKC1/OsHKT8 that regulates K homoeo-

stasis and shoot Na? concentration in salt-tolerant

indica cultivar ‘Nona Bokra’ (Lin et al. 2004; Ren

et al. 2005). There are other QTLs that are putatively

Euphytica

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https://www.researchgate.net/publication/253748797_New_evidence_of_QTLs_attributed_to_salinity_tolerance_in_rice?el=1_x_8&enrichId=rgreq-c6400ade-098c-4316-a33e-54e0f6472664&enrichSource=Y292ZXJQYWdlOzI2MzM3NTg0MDtBUzoxMTIwMDIxNDM4ODczNjBAMTQwMzcxNDgwMTQ0Ng==

https://www.researchgate.net/publication/225154782_Characterizing_the_Saltol_Quantitative_Trait_Locus_for_Salinity_Tolerance_in_Rice?el=1_x_8&enrichId=rgreq-c6400ade-098c-4316-a33e-54e0f6472664&enrichSource=Y292ZXJQYWdlOzI2MzM3NTg0MDtBUzoxMTIwMDIxNDM4ODczNjBAMTQwMzcxNDgwMTQ0Ng==

associated with performance under salt stress, e.g. for

root Na?/K? ratio (Lang et al., 2001; Sabouri and

Sabouri 2009; Islam et al. 2011).

Even though rice is sensitive to salt stress during the

early seedling and reproductive stages (Lutts et al.

1995), more research has been conducted on the former

than in the latter because of the difficulty in using a

reliable phenotyping in the reproductive stage (Sabouri

et al. 2009). The reproductive stage in rice life cycle is

most important as it is the stage that will give the

ultimate source in terms of yield and yield parameters

such as productive panicles, fertility, and spikelets per

panicle. It has been observed that salt stress at panicle

initiation reduces the number of fertile florets, grain

weight, panicle length, and grain yield (Khatun et al.

1995). There is an urgent need to breed rice cultivars

that can adapt to salt-prone environments particularly

during the reproductive stage. The objective of this

research was therefore to identify QTL(s) conferring

adaptation to salt stress in the field at the reproductive

stage in offspring derived from crossing African rice

cultivars (‘NERICA-L-19’, ‘Sahel 108’ and ‘BG90-2’)

with ‘Hasawi’, to determine grain yield and yield-

enhancing loci from ‘Hasawi’ in their segregating

offspring, and to find common QTL(s) across the

different genetic backgrounds (GBs) when being under

salinity in the field, which could be further use in MAS

for breeding rice cultivars aiming agro-ecosystems

affected by saline soils.

Materials and methods

Parental lines and population development

Three large F2 populations derived by crossing

popular African cultivars (‘NERICA-L-19’, ‘Sa-

hel108’ and ‘BG90-2’) with ‘Hasawi’ were used in

this research. Genetic analysis revealed that ‘Hasawi’

has alleles that differ from the already known salinity

tolerance QTL Saltol derived from ‘Pokkali’ (Fig. 1).

‘NERICA-L-19’ is one of the New Rice for Africa

(NERICA) cultivars adapted to the lowlands and

irrigated areas, which was developed after crossing

Asian rice and African rice (O. glaberrima). NERICA

cultivars are poorly adapted to saline soils. ‘Sahel 108’

(IR13240-108-2-2-3) is a semi-dwarf high yielding

indica cultivar bred for irrigated and favorable rainfed

lowlands and is sown in excess of 90 % on both sides

of Senegal and on the Mauritanian side of the Senegal

River. ‘BG90-2’ is regarded as having an excellent

yield potential and good grain quality, and it is grown

in 90 % of irrigated rice areas of Mali as well as in

Burkina Faso, Mauritania and Niger.

Hybridizations were made in the screenhouse of the

AfricaRice Sahel Regional Station near Saint Louis in

Senegal from 2010 to 2012. The three elite African

cultivars were used as female parents while ‘Hasawi’

was the male parent. The resulting F1 plants were

intermediate in morphological characteristics and

were selfed to produce the F2 generation. The F2

plants were examined for plant morphology and

fertility and those which looked good in plant type

were phenotyped for the salinity related traits and

genotyping using simple sequence repeats (SSR) or

microsatellites. The resulting F2-derived populations

are named in this article following the mother’s

cultivar name.

Experimental sites

The study was conducted at AfricaRice research farms

in Ndiaye, Senegal (16�32.141 N; 15�11.545 W) for

the non-saline environment and at Institut Senegalais

HASAWI

Fig. 1 Genetic analysis of Hasawi compared to Pokkali already

known salinity tolerance donor for QTL Saltol, FL478 an

international tolerant check and IR29 an International sensitive

check using RM 243

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de Recherche Agricoles (ISRA) research field in

Ndiol, Senegal for the saline environment. Both sites

are located in the Senegal River Valley and close to the

coast (about 40 km inland). Soil salinity at both sites

and in the river delta is generally high due to the

occurrence of marine salt deposits in the subsoil.

Electrical conductivity (EC) of the normal field is less

than 1 ds m-1 and it ranges between 6.5 and

9.5 dS m-1 for the saline fields. The soil profile at

AfricaRice research site corresponds to a typical

Orthithionic gleysol. The original soil contained at

least 10 mg C kg-1 of soil and 5 mg P kg-1 (P-

Bray1). The climate of both sites is characterized by a

wet season with approximately 200 mm rainfall per

year from July to October, a cold dry season from

November to February and a hot dry season from

March to June. Trials in lowlands under irrigated

conditions where no salinity stress was imposed are

referred to as non-stress trials.

Field screening of F2 progenies using the single

tiller approach

We used a single tiller approach because the annual

rice plant evolved from perennial wild species in

which a single or a few tillers removed from the

mother plant and planted could grow and produced

grains. We assessed the characters of replanted tillers

by comparing them with those of corresponding

undisturbed plants. Sufficient seeds were produced

for generation advancement and grain yield. The grain

yield for single-tiller crops does not represent the

genotype’s true potential but progeny testing on F2:3

derived-offspring could be used instead.

A total of 500 plants from each of the F2 derived-

populations were grown under controlled conditions

using the approach of single tiller at a spacing of

20 9 20 cm during the 2011–2012 wet seasons. A

total of 500 each of the two parents from each cross

were transplanted 25 days after seeding side by side of

each single F2 progeny. Four weeks after transplant-

ing, a single tiller was removed from each of the 500

mother plants and the two parents and transplanted in

the site for the saline stress trial. After transplanting,

approximately 5 cm of standing water was maintained

in the field until drainage before harvest for the non-

saline fields and until initiation of stress for saline

fields. Measurements were taken regularly to monitor

the salt concentration and adjusted accordingly.

Salinization was done by broadcasting measured

quantity of granular NaCl, representing the type of

salinity found in most parts of Senegal River valleys in

the standing water. Weeds were controlled by appli-

cation of post-emergence herbicide and by hand

weeding. Inorganic NPK fertilizer was applied at

150–60–60 kg ha-1 in both non-stress and stress trials.

The nitrogen was applied as 40 % each at basal and at

maximum tillering and 20 % at panicle initiation. P

and K were applied as basal fertilizers.

Trait evaluation

Data were recorded for the following traits:

1. Days to flowering/heading (days), i.e., the average

number of days from seeding until the panicles

had flowered;

2. Plant height (centimeter), Measured from the soil

surface to the tip of the tallest panicle (awns

excluded); this was recorded at eight consecutive

weeks after transplanting;

3. Number of tillers per plant; each tiller per plant

was counted. This was recorded at eight consec-

utive weeks after transplanting;

4. Panicle Sterility, i.e.; number of unfilled spikelets

deducted from the total number of spikelets per

plant and expressed as percentage

5. Grain yield (g m-2), i.e., the number of filled

spikelets per plant;

6. 1,000-grain weight (g), i.e., the average weight of

1,000 seeds from the sample harvested grains

from the plants;

7. Yield per plant (g m-2), for which each hills is

harvested to estimate the grain yield from each

plot. Seeds were then dried (50 �C), weighed, and

adjusted to a moisture content of 14 %;

8. Yield-component data for each plot, based on the

number of tillers, panicle counts, and filled, and

unfilled grain;

9. Salt tolerance score (SES score) were recorded at

83 and 100 days after transplanting using the

standard evaluation system (SES) of the Interna-

tional Rice Research Institute, in which a score of 1

was for a tolerant plant, 3 for moderately tolerant,

and a score above 5 was for a susceptible);

The percent reduction for each trait in stressed

plants was calculated relative to the nonstressed

control.

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Field screening of F2:3 offspring

We also assessed the performance of the selected

F2:3 derived-offspring during the 2nd season, using

an alpha lattice design with 3 replications under

saline stress and 2 replications under non-saline

conditions during the 2011–2012 dry seasons. Rows

were 2 m long and spaced at 0.2 m between rows

and between hills. One seedling was in each hill.

The same procedure used for the single tiller

approach in the 2011–2012 wet seasons was used.

Field plots were measured at 3 days intervals

regularly to monitor the salt concentration and

adjusted accordingly. Data were recorded as per

those used in previous F2 trials with some modi-

fications. Four hills per entry were harvested and

used for grain yield and its components. Days to

50 % flowering was recorded when at least half of

the plants in the plot had flowering tillers. Grain

yield was determined on a 0.4 m2 area in the

middle of each plot after removing the borders. In

addition the following parameters were taken

Panicle m-2 (g), estimated as the total number of

panicles used for yield component analysis and

expressed in per square meter.

Number of Grain/Panicles (g): mean weight of all

the grains countered from the total number of

panicles harvested for yield component analysis

Panicles sterility (%): this is estimated as the total

number of unfilled grains in a panicle from the total

number of filled grains on the panicles and

expressed as percentage.

Statistical analysis

SAS v9.2 (SAS Institute Inc 2004) was used for

analyzing the data. The analysis was based on mixed

models in which progenies were considered as fixed

factors whereas replicates and blocks within replicates

(wherever applicable) were random factors. Least

squares estimates of each trait on each progeny were

obtained using the Proc GLM option of SAS software

(SAS Institute Inc 2004). Broad sense heritability

(H)—i.e., the ratio of genetic to phenotypic variation

was estimated using variance components of the

analysis of variance.

Selection criteria for molecular

and QTL(s) detection

Genotyping was carried out using the F2 progenies that

performed better in terms of grain yield and SES score

than the mean of the 500 parents used in the first

season trials. This resulted in selecting 113 progenies

for F2 derived-NERICA-L-19, 114 progenies from F2

derived-Sahel 108, and 130 progenies from F2

derived-BG90-2.

To eliminate any bias, we randomly selected extra

10 % of the progenies from each of the F2 populations

to add to the initial populations that were selected

based on yield and SES score. A total of 153 progenies

were selected from F2 derived-NERICA-L-19, 154

progenies from F2 derived-Sahel 108, and 168 prog-

enies from F2 derived-BG90-2.

DNA extraction, PCR amplification and marker

analysis

DNA was extracted from 21-day old seedlings from

the screenhouse using the protocol of Dellaporta et al.

(1983) with some modifications. DNA was dissolved

in 200 ll of 1X TE buffer and stored at -20 �C and

was diluted to 25 ng ll-1 of double-distilled water

and used as working stock. Polymerase chain reaction

(PCR) amplification and detection for SSR markers

was performed on a G-storm system (96 and 384-well

alpha unit). The protocols for PCR amplification and

detection of SSR markers is basically the same as

described in Chin et al. (2003). We used a non-

denaturing polyacrylamide gel electrophoresis

(PAGE) to obtain a high resolution following a

modified procedure by AfricaRice Biotechnology

Laboratory at Senegal. The whole vertical rig assem-

bly was then set in place with 1XTBE as running

buffer. Four microliters of PCR product of which 4 ll

of 6 9 loading dye has been added to each well were

loaded on the wells with 1 kb ladder marker. Voltage

was set at 100 V and running time for electrophoresis

was variable ranging from 1.5 to 3.5 h depending on

the expected size of the PCR product for the marker.

The gels were stained with ethidium bromide for

10–15 min. DNA bands were visualized under UV

light using the gel documentation systems. Gel scoring

was done manually.

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Polymorphism and parental genome coverage

We used 203 microsatellite markers (SSR markers)

distributed all over the genome at approximately

10 cM intervals for a parental polymorphism survey

using 8 % high-resolution PAGE. Additional markers

were analyzed around the region that showed

polymorphism.

Marker analysis and QTL identification

Statistical analysis was performed using QGene

program (Nelson 1997). QTL analysis was conducted

on each of the F2 by regressing field performance

using the genotype information based on SSR markers

and a linkage map previously developed by Temnykh

et al. (2001) and McCouch et al. (2002). The

chromosomal locations of QTL were determined by

single-marker regression (SMR) and composite inter-

val mapping (CIM). In SMR, a QTL was declared if

the phenotype was associated with a marker locus at

P \ 0.001 or with two adjacent marker loci at

P \ 0.05. Significance thresholds for CIM were

determined using 10,000 permutations for each trait

with 4 iterations. For CIM, the experiment-wise

significance level of P \ 0.01 and P \ 0.05 corre-

sponds to an average LOD of the traits. When two

LOD peaks fell in a common support interval, only

one QTL was considered to be present, and its

approximate position was taken as the highest peak.

QTL detected by SMR or CIM were in agreement with

each other and were detected by both methods. The

proportion of observed phenotypic variation attribut-

able to a particular QTL was estimated by the

coefficient of determination (R2). QTL results in the

current study were compared with previously detected

rice QTLs for traits by using RICE QTL in TRAITS

option on the Gramene Web site (www.gramene.org/).

QTL Nomenclature

This article follows the naming described by

McCouch et al. (1997) where a 2- or 3-letter abbre-

viation is followed by the number of chromosome on

which the QTL is found and a terminal suffix,

separated by a period, provides a unique identifier to

distinguish multiple QTL on a single chromosome.

Results

Saline levels in the experimental fields

Salinity levels were continuously monitored in all

plots after maximum tillering until maturity. Soils

were sampled at a depth of 20 cm at five different spot

within each plot three times in a week and analyzed to

determine the saline levels. Average EC readings for

all replications for each of the 2 experimental sites are

shown in Figs. 2, 3 and 4. Average electrical conduc-

tivity of soils under non saline control conditions was

1.03 dS m-1 for the ‘NERICA-L-19 GB plots and

ranged from 0.17 to 1.98 dS m-1, while in the saline

stress fields salinity levels ranged from 0.94 to

12.8 dS m-1 and with a mean of 5.83 dS m-1. In

the ‘Sahel 108’ GB non-saline plots the mean salinity

level was 1.26 dS m-1 and with a range between 0.23

and 2.59 dS m-1. The salinity level ranged between

0.73 and 8.04 dS m-1 (mean = 5.65 dS m-1) in the

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

43 47 54 58 63 68 72 77 82 86 91 96 100 107

Number of days after transplanting (DAT)

Ele

ctri

cal c

on

du

ctiv

ity

(dS

m-1

)

0

2

4

6

8

10

12

38 40 42 45 47 52 54 59 61 63 66 68 70 73 75 77 80 82 84 89 91 94 96 115

EC (dsm-1)


Ele

ctri

cal c

on

du

ctiv

ity

(dsm

-1)

EC (dsm-1)

EC (dsm-1)

a

b

Fig. 2 a Salinity levels measured under the non-stress condi-

tions in the NERICA-L-19 GB, b salinity levels measured under

the saline stress conditions in the NERICA-L-19 GB

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http://www.gramene.org/

saline plots. Mean salinity levels in the ‘BG90-2’ GB

control plots was 0.9 dS m-1 with a range between

0.12 and 1.67 dS m-1, while those under saline

stressed-plots ranged from 1.78 to 13.4 dS m-1

(mean = 7.91 dS m-1).

Trait Analysis and Field Performance of F2:3

Offspring

The means, standard deviations and heritability of the

quantitative traits measured in the F2:3 offspring are

given in Tables 1, 2 and 3. The data were approxi-

mately normally distributed, indicating the feasibility

of QTL mapping for all traits in the 3 GBs. Significant

variations for all traits was observed as well as

transgressive segregation.

NERICA-L-19 9 Hasawi GB

Salinity stress caused significant reduction for all

measured traits in the NERICA-L-19 GB (Table 1).

Yield of recurrent parent (NERICA-L-19) was

reduced by 95 % under saline stress while in the F2:3

progenies mean reduction of 65 % over the non-saline

condition was observed. Heritability values were

relatively higher for days to flowering and number of

tillers in the experiments. Of the 153 progenies studied

33 showed at least 15 % increase over NERICA-L-19;

the recurrent parent for three or more yield

components.

Sahel 108 9 Hasawi GB

The mean values showed that the grain yield and its

components, except for the number of tillers, and

panicle sterility of the recurrent parent (Sahel 108)

were higher than the corresponding values of the F2:3

progenies; even though wide range of yield response

was observed in the progenies with some progenies

having better yields than the recurrent parent under

both saline and non-stress conditions (Table 2). The

progenies were also relatively taller than the recurrent

parent. The heritability values were high for all

measured traits.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6a

b

50 51 54 56 61 63 65 68 70 73 75 77 79 82 84 86 89 91 93


Ele

ctri

cal c

on

du

ctiv

ity

(dsm

-1)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

52 54 56 59 61 66 68 73 75 77 80 82 84 87 89 91 94


Ele

ctri

cal c

on

du

ctiv

ity

(dsm

-1)

EC (dsm-1)

EC (dsm-1)


tions in the Sahel108 GB, b salinity levels measured under the

saline stress conditions in the Sahel108 GB

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6a

b

50 51 54 56 61 63 65 68 70 73 75 77 79 82 84 86 89 91 93


Ele

ctri

cal c

on

du

ctiv

ity

(dsm

-1)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

45 47 49 52 54 59 61 66 68 70 73 75 77 80 82 84 87 89 91 96 98101122


Ele

ctri

cal c

on

du

ctiv

ity

(dsm

-1)

EC (dsm-1)

EC (dsm-1)


tions in the BG90-2 GB, b salinity levels measured under the

saline stress conditions in the BG90-2 GB

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BG90-2 9 Hasawi GB

Wide variation for yield was observed among the

progenies ranging from 1.50 to 542.50 g m-2 with an

average yield of 120.48 g m-2 under stress conditions.

Salinity caused a yield reduction of 96 % in the

recurrent parent and 72.83 % among the progenies.

Even though 23 of the 167 progenies had higher yields

or were similar in yield to the recurrent parent, BG90-2

under non saline conditions, the mean yield of the

Table 1 Agronomic performance of F2:3 progenies from ‘NERICA-L19’ 9 ‘Hasawi’ and female parent under saline and non-saline

stress

Treatment Mean SD Variance Range Heritability (%) ‘NERICA-L-19’a

Grain yield (g m-2) Stress 171.38 67.45 4,549.80 0–356.5 43 27.02

No stress 494.00 290.00 83,822.50 70–1,148.9 579.44

Panicle m-2 Stress 135.80 129.64 16,805.40 12.5–418.75 48 52.00

No stress 584.38 217.50 47,302.71 0–1,150 543.75

Grain per panicles Stress 5.28 3.76 14.15 1–13.3 – 4.00

No stress 31.50 17.09 292.16 6.2–78.1 41.90

Days to 50 % flowering Stress 121.10 15.15 230.00 90–147 76 –

No stress 108.22 17.34 300.59 83–144 104.50

Plant height (cm) Stress 89.69 13.67 187.00 64–112 45 –

No stress 154.38 24.50 599.99 95–204.3 125.88

Tiller numbers Stress 22.68 6.2 38.00 19–37 72 –

No stress 26.84 3.90 15.22 37–184 27.25

1,000 seed weight (g) Stress 22.46 0.22 2.52 20–25 – 21.00

No stress 27.21 4.17 17.39 17.1–35 28.00

Panicles sterility (%) Stress 60.10 21.62 467.00 5.7–97.5 –

No stress 50.30 19.90 396.14 3.5–97.48 80.00

a The parent ‘Hasawi’ did not flower hence was discarded from this trial

Table 2 Agronomic performance of F2:3 progenies from ‘Sahel 108’ 9 ‘Hasawi’ and female parent under saline and non-saline

stress

Treatment Mean SD Variance Range Heritability (%) ‘Sahel 108’a

Grain yield (g m-2) Stress 125.94 94.27 8,886.83 6.90–386.25 58 269.50

No stress 369.46 348.46 121,426 6.88–1,397.50 584.38

Panicle m-2 (g) Stress 134.32 116.99 13,687 6.78–606.7 72 311.63

No stress 599.99 224.64 5,046.45 56.25–1,481.25 493.75

Harvest Index (%) Stress 11.06 5.50 0.30 4.5–30.70 49 14.00

No stress 24.90 18.70 3.50 4.30–75.40 60.00

Days to 50 % flowering (day) Stress 99.80 22.26 495.0 53.0–139.0 78 86.50

No stress 95.19 21.90 479.00 44.0–144.0 79.50

Plant height (cm) Stress 94.51 16.98 288 62.25–138.75 92 71.13

No stress 125.07 34.95 1,221.33 62.27–198.50 93.75

Tiller numbers Stress 18.37 5.72 33 5.50–29.00 64 20.25

No stress 22.30 6.80 46.16 5.50–38.00 26.25

Panicles sterility (%) Stress 60.88 20.59 423.79 11.94–97.47 74 44.41

No stress 50.28 19.90 396.14 3.50–97.475 45.28


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progenies was statistically similar to the recurrent parent

(Table 3). Heritability values were relatively higher for

most of the measured traits in the experiments

Construction of linkage map and QTL

identification

Of the 203 SSR tested, 70 (35 %) were polymorphic

for NERICA-L-19 GBs, out of which 65 were then

applied in the population. For the Sahel 108 GB, 37 %

of the 203 SSR tested were polymorphic, of which 70

were used in the population and the construction of the

linkage map. There were 70 polymorphic markers

(35 %) in the BG90-2 GB.

Identification of putative QTL

NERICA-L-19 GB

QTL mapping was carried out using 153 progenies

comprising of 59 progenies that had better grain yield

and SES score than the mean of the 500 parents and 94

randomly selected progenies. A total of 30 QTLs were

detected for nine different traits. These include 4

QTLs each for SES tolerance score, plant height at

maturity, days to heading and grain number per

panicle; 3 QTLs each for number of tillers at maturity,

percent fertile panicle, 1,000-grain weight and grain

yield, and 2 QTLs for number of panicles (Table 4).

All QTLs were located on 5 chromosomes, namely 1,

6, 8, 10 and 11. The contribution of the phenotypic

variation explained by each QTL ranged from 14.8 to

49.5 % with LOD scores ranging from 5.3 to 22.7. The

additive effects of 50 % out of the 30 QTLs were

negative suggesting that alleles from ‘Hasawi’ con-

tributed to enhancing these traits (Table 4).

Sahel 108 GB

For this population QTL mapping was carried out

using 154 progenies comprising 60 progenies that had

better grain yield and SES score than the mean of the

500 parents and 94 randomly selected progenies.

There were 21 QTLs for six different traits spanning

across 7 chromosomes (1, 2, 3, 7, 9, 10 and 11). Four

QTLs each were detected for heading date, number of

panicles and percent fertile panicle; and 3 QTLs each

for plant height at maturity, number of tillers at

maturity and grain yield. The contribution of the

phenotypic variation explained by each QTL ranged

from 7.3 to 31.9 % with LOD scores ranging from 2.1

to 11. Six QTLs had negative additive effects indicat-

ing the contribution of alleles from ‘Hasawi’ to these

traits (Table 5).

Table 3 Agronomic performance of F2:3 progenies from ‘BG90-2’ 9 ‘Hasawi’ and female parent under saline and non-saline stress

Treatment Mean SD Variance Range Heritability (%) ‘BG90-2’a

Grain yield (g m-2) Stress 120.484 95.19 9,060.41 1.50–542.50 41 19.00

No stress 443.58 237.96 56,624.94 105.13–1,208.16 486.80

Panicle m-2 (g) Stress 417.08 209.153 43,744.90 100.00–993.76 56 297.50

No stress 540.09 135.293 18,304.31 237.50–806.25 350.00

Number of Grain/Panicles (g) Stress 15.05 9.50 90.192 1.00–44.40 53 7.00

No stress 34.807 15.38 236,461 10.10–85.00 54.2

Harvest Index (%) Stress 17.3 8.20 0.70 5.10–43.70 49 –

No stress 36.8 9.70 0.90 13.60–72.40 43.80

Days to 50 % flowering (day) Stress 71.95 9.90 97.94 51.0–94.0 45 85.0

No stress 56.63 4.35 18.93 50.0–71.0 60.0

Plant height (cm) Stress 82.198 21.29 453.03 41.50–131.0 56 63.40

No stress 138.68 26.987 728.29 66.30–196.00 112.63

Tiller numbers Stress 22.57 7.05 49.68 6.0–37.0 60 13.15

No stress 26.820 11.252 126.62 12.50–179.50 23.13

Panicles sterility (%) Stress 50.53 18.27 333.74 9.60–95.22 38 74.0

No stress – – – – –


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BG90-2 GB

QTL mapping was carried out using 167 progenies

comprising 73 progenies that had better grain yield

and SES score than the mean of the 500 parents and 94

randomly selected progenies. There were 24 QTLs for

5 different traits of which 6 QTLs were for grain yield

and 5 QTLs were for number of tillers at maturity.

Table 4 Quantitative trait loci (QTL) identified in each chromosome (chr.) of ‘NERICA-L-19’ 9 ‘Hasawi’ F2 under saline stress

QTL Chr. Peak marker Flanking markers LOD R2 Additive effect Source

SES tolerance score

qSES1 1 RM8094 RM8094-RM582 7.3 19.6 0.14 NERICA-L-19

qSES6 6 RM586 RM586-RM253 16.8 39.7 0.10 NERICA-L-19

qSES10 10 RM228 RM228-RM333 12.2 30.8 0.08 Hasawi


Plant height

qPH1 1 RM8094 RM8094-RM582 9.5 24.9 13.54 NERICA-L-19

qPH8 8 RM419 RM547-RM419 9.2 24.3 11.60 NERICA-L-19

qPH10 10 RM228 RM228-RM333 19.8 44.9 1.41 Hasawi


Tiller number

qTN6 6 RM586 RM586-RM253 15.4 37.1 0.98 Hasawi


qTN11 11 RM536 RM536-RM287 11.2 28.6 0.35 NERICA-L-19

Heading date

qDTF8 8 RM419 RM547-RM419 9.0 23.9 4.82 Hasawi

qDTF10.1 10 RM6691 RM184-RM228 13.7 34.0 55.13 Hasawi

qDTF10.2 10 RM228 RM228-RM333 16.9 40.1 0.54 NERICA-L-19

qDTF11 11 RM536 RM536-RM287 17.8 41.6 2.49 NERICA-L-19

Panicle number

qPN1 1 RM8094 RM8094-RM582 5.7 15.7 15.14 NERICA-L-19

qPN6 6 RM586 RM586-RM253 5.3 14.8 3.64 Hasawi

Panicle sterility (%)

qPS1 1 RM8094 RM8094-RM582 10.2 26.5 9.03 Hasawi

qPS10 10 RM228 RM184-RM590 15.2 36.9 1.54 NERICA-L-19

qPS11 11 RM536 RM536-RM287 19.4 44.5 6.51 NERICA-L-19

Grains per panicle

qGPP1 1 RM8094 RM8094-RM582 6.9 18.7 7.98 NERICA-L-19

qGPP6 6 RM586 RM586-RM253 17.9 41.7 2.82 NERICA-L-19

qGPP10 10 RM228 RM184-RM590 12.5 31.4 0.49 Hasawi


1,000-grain weight

qTGW6 6 RM586 RM586-RM253 12.9 33.4 0.17 NERICA-L-19

qTGW10 10 RM228 RM184-RM228 13.4 34.5 0.38 Hasawi

qTGW11 11 RM536 RM536-RM287 13.1 33.9 0.04 NERICA-L-19

Grain yield

qGY6 6 RM586 RM586-RM253 12.8 32.1 17.78 NERICA-L-19

qGY10 10 RM228 RM228-RM333 9.1 24.0 41.66 Hasawi


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Four QTLs each were detected for the number of

panicles m-2 and the number of grains per panicle.

One QTL each was detected for the SES tolerance

score and heading date (Table 6). All QTLs are on 8

different chromosomes, namely 1, 4, 5, 6, 8, 9, 11 and

12. The contribution of the phenotypic variation

explained by each QTL ranged from 9 to 25 % and

the LOD scores ranged from 3.39 to 11.34. The

additive effects of 7 out of the 20 QTLs were negative

suggesting that alleles from ‘Hasawi’ contributed to

enhancing these traits (Table 6).

Common QTL and peak markers associated

with the 3 GBs

Altogether, 75 putative QTLs were identified in the 3

GBs with Hasawi as donor for both yield and yield

related traits. Thirty-two percent (24 QTLs) were

found in overlapping regions for the same or related

traits in all the 3 GBs; this comprises of 9 QTLs on

chromosome 1 and 15 QTLs on chromosome 11.

Under 2 GBs, 14 QTLs were found overlapping on

chromosome 10 in both NERICA-L-19 and Sa-

hel108 GB (9 QTLs in NERICA-L-19 GB and 5

QTLs in Sahel108 GB) with marker intervals RM228-

RM333 and RM330A-RM216. Between the NERI-

CA-L-19 and BG90-2 GB, 11 QTLs were identified

on 2 chromosomes (7 QTLs on chromosome 6 and 2

QTLs on chromosome 8). Seven of the 11 QTLs were

found in NERICA-L-19 GB (5 on chromosome 6 and

2 on chromosome 8) and 4 in BG90-2 GB (2 each on

chromosome 6 and 8). For the common QTLs between

Sahel 108 and BG90-2 GB, 6 were identified with all

located on chromosome 9. Five of the QTLs were

Table 5 Quantitative trait loci (QTL) identified in each chromosome (chr.) of ‘Sahel 108’ 9 ‘Hasawi’ F2 under saline stress


Plant height


qPH10 10 RM228 RM216-RM330A 9.4 28.1 5.47 Sahel 108

qPH11 11 RM332 RM332-RM441 8.5 25.9 3.80 Sahel 108

Tiller number

qTN2 2 RM318 RM263-RM318 4.5 13.4 0.92 Sahel 108


qTN11 RM341 RM209-RM341 6.9 19.7 0.77 Sahel 108

Heading date

qDTF1 1 RM220 RM220-RM529 5.0 15.9 1.21


qDTF10 10 RM216 RM216-RM330A 10.4 30.2 4.52 Sahel 108

qDTF11 11 RM441 RM332-RM229 4.4 14 9.50 Sahel 108

Panicle number



qPN7 7 RM336 RM418-RM336 8.2 24.9 98.46 Sahel 108

qPN10 10 RM228 RM216-RM330A 11.0 31.9 68.82 Sahel 108



qPS3 3 RM148 RM251-RM468 6.7 20.9 6.09 Sahel 108

qPS10 10 RM228 RM216-RM330A 6.2 19.6 2.46 Hasawi


Grain yield




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identified on the BG90-2 GB and one in the GB of

Sahel 108

Single background QTL identified with peak

markers

A total of 17 QTLs covering 6 chromosomes were

identified under a single GB in both Sahel 108 and

BG90-2. Eight of the 17 QTLs were identified in the

GB of Sahel 108 with 3 QTLs each located on different

locations on chromosome 3 (qTN3, qPN3 and qPS3)

and chromosome 7 (qPH7, qDTF7 and qPN7). Two

QTLs were also identified on chromosome 2 (qTN2

and qPS2). For the BG90-2 GB, a total of nine QTLs

with 7 of them located on chromosome 5 (qTN5,

qDTF, qPN5, qPS5, qGG5, qGY5.1 and qGY5.2). One

QTL each were detected on chromosomes 4 (qSES4)

and 12 (qGPP12). None of the QTLs identified on

NERICA-L-19 GB were specific to this GB.

Discussion

1. Effect of salinity on yield

Very few studies has been reported on the effects of

salinity on yield and yield components at the

Table 6 Quantitative trait loci (QTL) identified in chromosomes of ‘BG90-2’ 9 ‘Hasawi’ F2 under saline stress


SES tolerance score


Tiller number

qTN5 5 RM274 RM274-RM334 3.5 9.0 1.57 BG90-2

qTN6 6 RM527 RM527-RM253 3.4 9.0 0.61 BG90-2

qTN8 8 RM419 RM407-502 4.2 11.0 1.93 BG90-2


qTN11 11 RM206 RM457-RM254 5.0 13.0 3.30 BG90-2

Heading date


Panicle number

qPN5 5 RM274 RM274-RM334 20.2 7.98 43.90 BG90-2

qPN8 8 RM419 RM407-RM502 5.0 13.0 14.93 BG90-2


qPN11 11 RM206 RM457-RM254 9.0 21.9 98.65 BG90-2




qPS9 9 RM434 RM434-419 2.8 7.3 6.07 Hasawi

Grains per panicle


qGPP11 11 RM254 RM457-RM254 6.6 17.0 4.43 BG90-2

qGPP11 9 RM410 RM434-RM242 5.6 14.0 0.36 BG90-2


Grain yield

qGY1 1 RM582 RM270-RM212 4.7 12.0 32.56 BG90-2

qGY5.1 5 RM267 RM267-RM289 8.2 20.0 20.33 Hasawi

qGY5.2 5 RM274 RM274-RM334 7.7 19.0 33.16 BG90-2


qGY9 9 RM242 RM410-RM278 7.3 18.0 0.61 BG90-2

qGY11 11 RM254 RM457-RM254 11.3 27.0 66.64 BG90-2

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reproductive stage of rice as most research has been

limited to the early vegetative stages (Moradi and

Ismail 2007; Zang et al. 2008). However; the few

studies that have been conducted have inconclusive

results which might be explained by the use of

different genotypes as demonstrated by Zeng et al.

(2002). The threshold of salinity at which the yield of

rice is affected by salt stress is as low as 3 dS m-1 EC

(Maas and Hoffiman 1997); but the salinity levels in

our experiments were on average above 5.65 dS m-1.

Salt stress resulted in a substantial decrease in grain

yield by 65, 66 and 73 % in NERICA-L-19, Sahel 108

and BG90-2 GBs respectively. Yield loss ranging

from 30 to 77 % has been reported under field

conditions (Nakhoda et al. 2012; Mahmood et al.

1999). Nonetheless, there was a wide range of grain

yield response in the progenies when grown under salt

stress with some of them showing better grain yield

than their African parents (356.50 vs 27.02 g m-2 in

NERICA-L-19 GB, and 542.50 vs. 19.0 g m-2 in

BG90-2 GB). In Sahel 108 GB, yield increase was

50 % in the progenies relative to the recurrent parent.

2. Hasawi derived alleles are associated with

improved yield

Selection under extreme stress to get surviving

genotypes could be a potential approach for identify-

ing germplasm sources for improving adaptation to

stressful environments. Some progenies in the exper-

iments showed transgressive performance for yield

under stress to the better parents; this suggests that

some positive alleles have been transferred from the

Saudi landrace ‘Hasawi’ into them. Our results

suggest that genes introgressed from Hasawi into elite

African cultivars can improve key agronomic traits,

even though Hasawi itself is phenotypically inferior to

these cultivars. Based on grain yield ([200 g m-2)

and final SES tolerance score, in excess of 20 superior

progenies from each of the three African-derived

offspring were selected for further use in breeding

adaptation to salt stress.

3. Beneficial alleles introgressed from Hasawi

Although Hasawi is phenotypically inferior to those

of O. sativa, we found that the former contributed

49 % of the beneficial alleles to QTLs identified in all

the 3 GBs for nearly all traits. Hasawi alleles

constituted 50 % in the NERICA-L-19 GB, 52 % in

the Sahel 108 GB and 46 % in the BG90-2 BG.

Beneficial alleles from landraces and wild rice species

to increased yield of rice under several abiotic stress

conditions have been reported in other studies; For

example, Bimpong et al. (2011) reported for drought,

Neeraja et al. 2007 and Septiningsih et al. (2009) have

reported for submergence and Ren et al. (2005) have

also reported for enhancing salinity tolerance to early

seedling stage. Three yields enhancing QTLs (qGY6,

qGY9 and qGY10) were identified in both NERICA-L-

19 and BG90-2 GBs; Sahel 108 and BG90-2 GBs and

NERICA-L-19 and Sahel 108 GBs respectively; hav-

ing an LOD score of between 2.1 and 12.8 and

explaining 7.3–32.1 % of the phenotypic variations

that was observed. These QTLs from Hasawi intro-

gressed could be new source of variation for genetic

improvement for salinity tolerance.

4. QTLs identified in more than one GB under salt

stress conditions

Most QTL research has been based on single GB. It

would be therefore expected that their QTL effects

will depend on the parentage, furthermore, one of the

major hurdles for the efficient use of MAS has been the

inability of using the favorable effects of QTL from

one GB to another (Xu and Crouch 2008). Besides,

since salinity tolerance is a complicated physiological

process in rice; it will be important if major QTLs

expressing in more than one GB can be detected at the

reproductive stage so as to facilitate the use of such

QTLs in MAS for salinity. In the present study we

identified 38 QTLs affecting many traits in more than

one GB (24 or 32 % QTLs in 3 GBs and 14 or 18.7 %

QTLs in 2 GBs). Two yield and yield related QTLs

(qGY11 and qTN11) were all mapped unto the same

chromosome in all the 3 GBs suggesting these QTLs

might be stable across different GBs. Two significant

QTLs associated with yield components under saline

stress (qPH10 and qPS10) were mapped to the same

positions as one major yield enhancing QTL (qGY10)

in both the NERICA-L-19 and Sahel 108 GB. This

locus is associated with an increase in grain yield per

plant under stress. Similarly, the region associated

with qTN6 which controlled an increase in number of

fertile tillers under saline stress was linked to qGY6

controlling an increase in yield per plant in both

NERICA-L-19 and BG90-2 GB. One QTL (qGY9)

associated with grain yield was mapped to the same

positions in the 2 GBs of Sahel 108 and the BG90-2.

Chromosomal regions associated with more than one

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trait in rice have been reported (Reddy et al. 2005;

Bimpong et al. 2011). There is the need to further test

the stability of the identified QTLs being expressed in

more than one GB before making a conclusion.

Four QTLs (qDTF8 and qPH8) from NERICA-L-

19 GB and qTN8 and qPN8 from BG90-2 GB) on

chromosome 8 were found to be strongly associated

with salinity tolerance with peak marker RM419; with

an LOD score from 4.19 to 9.20; and explaining

11.0–23.9 % of the variance observed. It has long been

suggested by some authors that there might be an

important QTL(s) for salinity tolerance in this region

of the chromosome 8 (Ammar et al. 2009; Islam et al.

2011). More than four QTLs with flanking markers

RM3395 and RM281 on the same chromosome at both

the vegetative and reproductive stages have been

detected by Ammar et al. (2009) and Islam et al.

(2011). This region represents a potential candidate for

MAS because of the high LOD score and relatively

large effects QTLs consistently detected in salinity

studies.

A total of 14 QTLs strongly associated with salinity

tolerance were detected on chromosome 10 in 2 GB (9

in NERICA-L-19 GB and 5 in Sahel 108 GB); with an

LOD ranging between 2.4 to 19.8. Earlier work by

Islam et al. (2011) have implicated this same region to

harbor a major QTL(s) for salinity tolerance with an

LOD peak tightly linked with RM25217. Consistent

detection of QTLs in these regions of chromosome 10

in rice with different salinity tolerance donors under

different GB suggests that the QTLs located in this

segment might be relatively stable. It will therefore be

interesting to fine map this regions to identify the

major QTLs and also validate their effects.

To date most of the QTL work for salinity tolerance

in rice have all been on seedling stage under hydro-

ponic conditions (Singh and Flower 2010; Negrao

et al. 2011); except the work by Takehisa et al. (2004)

where QTLs for traits contributing to salinity tolerance

on chromosomes 3 and 7 under field conditions were

identified in addition, to one QTL (qLB3) for leaf

bronzing. In the current study; 3 QTLs each were

identified in similar positions on both chromosomes 3

(qTN3 qPS3 and qPN3) and 7 (qPH7,qPN7 and

qDTF7) in the Sahel 108 9 Hasawi GB with an LOD

score ranging between 6.1 and 10.9 and explaining

between 18 and 31.8 % of the phenotypic variation

observed. QTLs for multiple traits have also been

identified in similar regions of chromosome 3 and 7

(Sabouri and Sabouri 2009). These regions seem to be

a hotspot for salinity tolerance and further research

need to be conducted in other to confirm the kind of

hotspot present.

5. QTLs identified under single background

There were 17 QTLs on six chromosomes observed

under a single GB in all the 3 populations. Eight of it is

from Sahel 108 GB located on chromosomes 2, 3 and

7 (qTN2, qPS2, qTN3, qPN3 and qPS3. The rest are

qPH7, qDTF7 and qPN7). There was only one QTL

(qSES4) on chromosome 4 and seven QTLs (qTN5,

qDTF, qPN5, qPS5, qGG5, qGY5.1 and qGY5.2) on

chromosome 5 and one on chromosome 12 (qGPP12)

in the BG90-2 GB. Takehisa et al. (2004) identified

QTL for salinity tolerance overlapping in the same

chromosomal regions of qTN2 (from ‘Sahel 108 GB),

which enhances the production of tillers under field

conditions. Most of the QTLs for plant height

identified in this study have already been mapped to

similar positions at early seedling stage tolerance

(Gramene database 2014). None of the QTLs identi-

fied on NERICA-L-19 9 Hasawi GB were specific to

this GB.

6. QTLs identified in relation to earlier QTLs for

salinity tolerance

Our objective in the current study was to identify

QTLs tolerant to salt stress during the reproductive

stage under field condition as a step toward marker-

assisted breeding. Of the 75 QTLs identified in the

experiment, 28 (from NERICA-L-19 GB) are cluster-

ing on 5 chromosomes (1, 6, 8, 10 and 11); 20 QTLs

(from Sahel 108 GB) and are clustered on 7 chromo-

somes (1, 2, 3, 7, 9, 10 and 11); whilst 24 QTLs (from

BG90-2 GB) spans across 8 chromosomes (1, 4, 5 6, 8,

9 11 and 12). The regular detection of salinity

tolerance QTLs on certain chromosomes especially

1, 4 and 6 were common in this study; where 9, 1 and 7

QTLs each were detected on chromosomes 1, 4 and 6

respectively.

A large number of QTLs for salinity tolerance have

been identified to be concentrated on the short arm of

chromosome 1 (Claes et al. 1990; Flowers et al. 2000,

Lang et al. 2001; Takehisa et al. 2004; Lin et al. 2004,

Ren et al. 2005; Zang et al. 2008, Sabouri et al. 2009,

Ammar et al. 2009). Similar observations were made

in this study where a total of 9 different QTLs were

identified in all the 3 GB clustering on chromosome 1.

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Five of the QTLs (qSES1, qPH1, qPN1, qPS1 and

qGPP1) were identified in the NERICA-L-19 GB and

2 QTLs each in the Sahel 108 GB (qDTF1 and qPN1)

and BG90-2 GB (qPS1 and qGY1). The QTLs iden-

tified on this chromosome controls yield and yield

related traits which might suggest that most QTLs

controlling salinity tolerance in both the early seedling

and reproductive stage are located on chromosome 1.

Even though we used more than 50 SSR markers

each on chromosomes 2, 3, 5 and 12 across all the

3 GB (59, 58 and 57 SSR markers for NERICA-L-19,

Sahel 108 and BG90-2 GBs respectively), relatively

few QTLs were detected compared with other chro-

mosomes. For example only 2 QTLs were found on

chromosome 2, and 3 on chromosome 3 (all the QTLs

were in Sahel 108 GB), 7 QTLs on chromosome (all

QTLs in the BG90-2 GB) and only 1 QTL on

chromosome 12 (in the BG90-2 GB). Similar obser-

vations have been made by Negrao et al. (2011) where

they indicated that no repeated QTLs have been

detected on chromosome 8 and 11 with very few on

chromosomes 2, 3, 5 and 12. This might suggest that

some chromosome might be gene rich for salinity

tolerance compared to others; the availability of high

density molecular markers such as SNPs offer the

opportunity to saturate the chromosomes in which

fewer QTLs are usually detected to confirm these

observations.

Our research shows that ‘Hasawi’ despite having

undesired traits such as lodging susceptibility, delayed

maturity, and photoperiod sensitivity contains alleles

that are likely to significantly improve grain yield

under salinity. Alleles from ‘Hasawi’ could also

account for most of the high visual scores of leaf

symptoms observed in some of the progenies showing

enhanced adaptation to salinity. Some progenies had

however a higher score than ‘Hasawi’, thereby sug-

gesting transgressive segregation that could result

from the accumulation of favorable alleles. Thomson

et al. (2010) found this transgressive segregation and

indicated that additional QTL from ‘Pokkali’ were

involved. They mapped QTLs for SES score on

chromosomes 3, 4, 5, 6, 9, 10 11 and 12, but did not

overlap with 5 QTLs identified in our research, which

suggest we found new QTLs for enhancing rice

adaptation to salinity. They were qSES1, qSES6,

qSES10 and qSES11 in the NERICA-L-19 GB, and

qSES4 with RM 252 as a peak marker in the BG90-

2 GB. Further research may elucidate need what are

their associated mechanisms for enhancing adaptation

of rice to salinity.

7. Conclusions

To the best of our knowledge, this is the first

research publication identifying grain yield enhancing

QTLs at the reproductive stage of rice grown at saline

soils in Africa. We still need testing their stability

across seasons to establish if they can be expressed

over time. The new QTLs described in this article are

also good candidates for fine mapping and positional

cloning research, while, those mapped to known

chromosome regions bearing QTL(s) enhancing adap-

tation of rice to salinity, could be useful for MAS. Last

but not least, in this omics era, the availability of

complete sequence information dense DNA marker

systems for saturating linkage map will assist further

on understanding these QTLs.

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