1. Harmonizing technological advances in phenomics and
genomics for enhanced salt tolerance in rice
Speaker: Satasiya Pratik N
Reg. no.: 1010119040
Major guide: Dr. R. K. Patel
Minor guide: Dr. V. B. Parekh
Course no: GP-691
Date: 21/05/2021
Time: 10:00 to 11:00

2. Contents
6
5
4
7
2
1
3
Introduction
Timeline for rice salt tolerance breeding
Rice under saline condition
Breeding for salt tolerance : Conventional and modern approaches
Phenotyping approaches for salt tolerance
Imaging technologies for high throughput phenotyping
Genomics for salt tolerance
8 Case studies
9 Conclusion 1

3. 2

5. Saline soils: A brief account
• 6 % of the global area is salt affected (20 % cultivated).
• Salt affected soils, likely to increase to 16.2 million ha by 2050 in India.
• Maximum salt affected soils are in Gujarat (2.22 m ha) followed by Uttar Pradesh (1.37 m ha).
• It has been estimated that 50 % of the aerable land will be saline due to salt accumulation.
• More than 80 Mha of irrigated land have already damaged by salt (40 % of irrigated land).
Saline soils
(Mha)
Sodic soils
(Mha)
Total (Mha)
World 376 434 810
India 2.956 3.771 6.727
Gujarat 1.68 0.54 2.22
Curtesy: https://www.salineagricultureworldwide.com/salinization
4
Table 1: Status of saline soils

6. (Arora and Sharma, 2017)
Area
(Mha)
Production
(Mt)
Productivity
(kg/ha)
India 43.70 112.91 2432
Gujarat 0.84 1.92 2283
• Rice is very sensitive to salinity stress and is currently listed as the most salt sensitive cereal crop.
• Floodwater with EC more than 2 dSm−1 leads to yield loss up to 1 t/ha.
• Even at EC as low as 3.5 dSm−1, rice loses about 10% of its yield.
• 50% yield loss was recorded for rice at EC 7.2 dSm−1.
• Excess salt caused both ionic toxicity and osmotic stress in rice plants.
• Under high salinity, rice plants show various morphological, physiological or biochemical alterations and
symptoms and even may die when the salt stress becomes very high.
Rice under saline condition: A brief account
5
Table 2: Status of rice cultivation
FAO stat, 2018-19

7. Roadmap with success in salt tolerance breeding
Tamimi et al.
International Rice Genome
Sequencing Project
The map based sequence of
rice genome
Zhang A et al. (2019)
1969
1990s
Work on genetic dissection of
traits started
Found additive and dominance
effects and low heritability.
Senadhira et al.
Somaclonal variant of
pokkali with
improved ST
Transfer of traits form donor
Linkage drag is
major problem
1994
1996
Anther culture for ST
8 AC derived ST
lines developed in
just 3 years
1990
Claes et al.
First QTL for
ST: salT on
chromosome 1
1980s
Gregorio et al.
Seedling stage phenotyping for
ST under phytotron
Saltol : for Na/K homeostasis
1997
2005
Screened 138000
Genotypes for salt
tolerance.
GEU Program
at IRRI
mutagenesis in OsRR22 without
exogenous T-DNA using
CRISPR-Cas
Agrisea (2020)
Private start up to develop sea
rice floating fields
2018
Frouin et al.
Used GBS approach of
GWAS for QTL mapping
Use of high throughput
phenotyping for ST
2019
2016
6

8. 7

9. Fight for survival under saline conditions
Cl-
Na+
Na+ Na+
Cl-
Cl-
Cl-
Cl-
Cl-
Battle lost: Susceptibility Battle won: Tolerance
Stomatal closure, Reduced TR
and increased leaf temp.
1st Response: Osmotic stress
High apoplastic and
Symplatsic Na+ absorption
Na+ K+ K+ K+
Na+Na+ K+ K+
K+ K+ Na+ K+
NaK++K+Na+
K+ K+ K+ K+ K+
High proliferation of root cap:
Na+ exclusion
Intracellular compartment-
talization of Na+ to vacuole
Accumulation of GH (ABA, JA, CK,
IAA & BRs), osmolytes (Proline &
GB), Sugars (Trehaloes &
myoinositol) and aqaporins
Accumulation of GH (ABA, JA, CK,
IAA & BRs), osmolytes (Proline &
GB), Sugars (Trehaloes &
myoinositol) and aqaporins
High Na+ accumulation to
Flag leaf
2nd Response: Ionic stress
Na+
Higher rate of lower
leaf death
Na+K+Na+Na+N
a+Na+NaK++K+
Na+Na+Na+Na+
K+Na+Na+Na+K
+Na+Na+Na+
Intercellular compartmentalization
resrtiction of Na+ to root zone
and ion exclusion
Na
+
Exclusion
Tissue
tolerance
2nd Response: Ionic stress
8

10. Conventional breeding for salt tolerance: Dark side
Salinity tolerance: A
complex trait
phenotype
Why conventional
Breeding alone is not an
option ??
Large G × E
interactions
Early generation
selection is not
possible
Plant
population
would not
be sufficient if
we wait
Seed germination, early seedling
vigour, chlorophyll content, root
and shoot Na/K ratio,
Salinity scoring, Leaf area, Number
of productive tillers, Pollen
fertility, Spikelet fertility,
Chaffy panicles, leaf rolling, flag
leaf length, Flag leaf Na/K ratio,
Harvest index, Biomass content
and ultimately yield
Phenotyping
accuracy is
very low
9

11. Conventional Breeding: Bright side
1
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Inter-specific variability
• O. rufipogon (GP1)
• O. nivara (GP1)
• O. coarctata (GP3)
• O. latifolia (GP2)
• O. meridionalis (GP1)
• O. australiensis (GP2)
Prebreeding
• FL-478
• IR 4630-22-2-5-1-3
• IR 4595-4-1-13
• IR 9884-54-3
• IR 10167-129-3-4
• IR 55179-3B-11-3 and so many
Released varieties
• CSR-10
• CSR-13
• BR-11
• BRRI dhan 28
• PB6
• Pusa basmati 1121
(saltol) etc.
Intra-specific variability
• Pokkali
• Nona bokra
• Cheriveruppu
• SR26B
• Getu
• Dandi
Quest for donors
Genome size ~ 400 mb
10

12. Salt stress breeding and rice: 21st century
Multi-disciplinary approach
Advancement in
Plant Molecular
biology
Advances in
plant
physiology
Advances in
breeding
systems
2
Functional
Genomics
Transgenics,
Epigenetics
Genomics,
Phenomics,
Transcriptomics,
Proteomics
Metabolomics
Metabolite
profiling
Genome
editing
Use of
Mi-RNA
Association
mapping
GWAS
Improved
Screening
methods for
salinity
tolerance
QTL
mapping
GBS
MAS
TILLING
And
Eco-TILLING
Candidate
gene
MABC
Genomic
selection
Soil
metagenomics
Advances in
Microbiology
11

13. Phenomics and Genomics: Two sides of a single coin
• Phenomics is systemic genome wide
study of phenotypes of an organism.
• Germplasm evaluation and varietal
development through field
evaluation and novel phenotyping
protocols for target soil stress factors
have not taken the expected
advantage of spiraling developments
in molecular and computational
biology.
Phenomics Genomics
2
• Genomics is an interdisciplinary field
of biology focusing on the structure,
function, evolution, mapping and
editing of genomes.
• The genomics and transgenic
technologies have so far provided little
success in developing successful salt
tolerance rice varieties for actual field
conditions.
Harmonization between these two is a key to achieve
history making accomplishments
12

14. Phenotyping to phenomics: A step towards precision breeding
Phenotyping
• Determination of type and level
of expression of concerned trait.
• As salt tolerance is an complex
quantitative trait, it is challenging
task to carryout accurate data.
• Accurate phenotyping is far more
difficult than genotyping.
• Become a bottleneck for progress
in basic plant science and plant
breeding.
Phenomics
• Concerned with the measurement of
phenomes where a phenome is the set of
phenotypes (physical and biochemical
traits).
• Integrative approach of Plant biology,
Mathamatics, Engineering and Computer
science.
• Forward phenomics: methods to screen
germplam for valuble traits.
• Reverse phenomics: Detailed analysis
of a trait to unravel various biophysical,
physiological and biochemical processes,
and genes involved in that.
13

15. Seedling float: salinity screening at seedling stage
Plastic tray: 12 L
Nylon net
Styrofoam sheet
Materials
• pH meter
• EC meter
• Balance
• NaOH and HCI
• NaCl
• Reagents
• Convection oven
• Germinator oven
• Volumetric flasks
• Graduated cylinders
• Plastic trays
• Beaker
• Styrofoam sheets
• Nylon net
Making of seedling floats
• Rectangular Styrofoam of size 28 × 32 × 1.25 cm
• Drill 100 holes (10 × 10 cm)
• Fix nylon net at the bottom
• Rectangular plastic tray with 12-L capacity and 4
× 30 × 35 cm size
Preparation of stock solutions and salinization
• Nutrient solution for hydroponics is prepared by the
method suggested by Yoshida et al. (1976).
• pH adjusted and maintained at 5.0
• Replace the solutions at every week
• As the time arrives salinize the solution with
analytical grade NaCl
(SES, IRRI, 2002) 14
Fig. 1: Seedling float

16. Seedling float: salinity screening at seedling stage
Handling of seedlings
• Seeds are heat treated in oven at 50 ºC for 5 days to break seed dormancy.
• Place the sterilized seeds to Petri plate with moisten germination paper.
• Incubate 30 ºC for 48 hrs for germination.
• Place single seed per hole in Styrofoam sheet.
• Sheet is then transferred to tray having distilled water for 3 days.
• After 3 days, change water with Yoshida solution.
• Nutrient solution should be changed weekly.
Salinization method and its modifications
• If only visual observations taken, salinization cand be started from the
3rd day and continued for 14 days.
• When we need tissue sample for further analysis, salinization must be
delayed till 14th day.
• It is essential to add sodium meta-silicate 9-hydrate to avoid the
seedlings from lodging.
• Sampling and salinization done when susceptible check scores 7 which
is 20-25 days after salinization.
• In another modification we can use green house instead of phytotron
but there are many environmental factors affecting the screening.

17. Characters under consideration at seedling stage
Physio-biochemical
parameters
• Chlorophyll content
• Shoot Na+ content
• Shoot K+ content
• Root Na+ content
• Root K+ content
• Na+/K+ ratio for shoot
• Na+ /K+ ration for root
Agro-morphological
parameters
• Plant survival
• Injury score
• Shoot length
• Root length
• Seedling fresh weight
• Shoot biomass
• Root biomass
Score Observation Tolerance
1
Normal growth, no leaf
symptoms
HT
3
Nearly normal growth but leaf
tips or few leaves whitish and
rolled
T
5
Growth severely retarded;
most leaves rolled;
few are elongating
MT
7
Complete cessation of growth;
most leaves dry;
some plants dying
S
9
Almost all plants are dead or
about to die.
HS
Modified standard evaluation score
16

18. Screening at reproductive stage
Plastic tray
Fig. 2: Set up for salinity screening at reproductive stage
Plastic pot
Materials
• Plastic pots (15 cm height 11 cm diameter)
• Plastic trays (22 cm height)
• Cloth bags (cotton)
• EC meter
• Well ground soil from paddy field
• NaCl and fertilizers
Preparation of pots and cloth bags
• The wall of plastic pot should be drilled with 3-4 mm
diameter holes 2 cm apart.
• The top most circle of holes should be at least 3 cm
below the rim of plastic pot.
• stitch cloth bags that fit well inside the plastic pot.
Seeding and salinization
• Place a cloth bag inside the pot and fill with fertilized soil.
• With leveled soil place pots in the plastic tray filled with
tap water.
• Four to five pregerminated seeds of test entries are placed
on soil surface of each pot.
• Salinity treatment given to 21 days old seedling. (SES, IRRI, 2002)
17

19. Introduction to novel phenotyping method
Curtesy: Ahmadizadeh M (2016)
Materials required
• Plastic pots 30 cm height
• EC meter
• Well ground soil from paddy field
• NaCl and fertilizers
• Normal water source (< 4 EC)
Methodology
• Test seeds have to be heat-treated for 5 d in a
convection oven set at 50 ºC to break seed dormancy.
• Sowing of the treated seeds in the small homogeneous
patch of the field.
• Transplant three healthy seedlings per pot after 21 days
• Appropriate fertilizers and water level maintained in the
pots.
• At the time of booting stage, salinization treatment
given to the pots.
• At that time, prune the lower leaves leaving only flag
leaf and penultimate leaf.
• Salinity level of the pot is maintained daily for 10-15
days.
• After the completion of the salinization period, irrigate
the pots with normal water till harvesting.
18
Fig. 3: Leaf cutting experiment

20. 1
2
3
4
Stress susceptibility index (S) Stress tolerance index (STI)
Tolerance index (TOL)
Mean Productivity
𝑆 =
1 − (𝑌𝑠 𝑌𝑝)
𝐷 Ys = Mean grain yield of genotype under stress
Yp = Mean grain yield of genotype under non stress
Xp= Mean yield of all genotypes under non stress
Higher the STI---Tolerance
Lower the STI---Suceptibility
Fischer and Maurer, 1978
𝑆𝑇𝐼 = (𝑌𝑝 × 𝑌𝑠)/(𝑋𝑝)2
Fernandez, 1992
Ys = Mean grain yield of genotype under stress
Yp = Mean grain yield of genotype under non stress
D = Stress intensity (1-(Xms-Xmp))
Xms= Mean yield of all genotypes under stress
Xmp= Mean yield of all genotypes under non stress
Higher the S---susceptibility
Lower the S---Tolerance
𝑀𝑃 = (𝑋𝑠 + 𝑋𝑝)/2
Rosielle and Hamblin, 1981
Xs = Yield of given genotype under stress
Xp = Yield of same genotype under non stress
Rosielle and Hamblin, 1981
𝑇𝑂𝐿 = (𝑌𝑝 − 𝑌𝑠)
Ys = Yield of given genotype under stress
Yp = Yield of same genotype under non stress
19

21. Flag leaf area
14
12
10
7
6
3
2
Flag Na+/K+ ratio
Pollen fertility (%)
Panicle length (cm)
Grain yield per
plant
(gm/plant)
Plant height (cm)
Spikelet sterility (%)
16
Seeds/ Panicle
Traits under consideration at reproductive stage
Productive tillers/ plant
• Flag leaf length
• Width
• Area in cm2
• Ratio of unfilled grains to total
number of grains
• Counting papery spikelets and
chaffy panicles
• Number of filled seeds per
panicle
• Spikelet fertility by deriving
ratio of filled grains to total no.
of grains
1
4
5
8
9
11
13
15
ABA content
TUE (%)
Relative Growth Rate
Transpiration rate
Net photosynthesis rate
Proline content
Stomatal Conductance
20

22. • Absorbs light of given wavelength and
emits light of a longer wavelength
• Majorly used for chlorophyll fluorescence
Fluorescence imaging
04
The imaging technologies: In respect of salinity tolerance
(Hairmansis et al., 2014)
RGB
images
Fluorescent
images
01 • 400-700 nm
• Quantitative estimation of leaf area, leaf
senescence, rolling, yellow and green areas
• Relative growth rate (Time lapse visual
imaging)
• Prediction of biomass production
• No of chaffy and papery panicles
RGB (visible image range)
03
• 8000-14000 nm
• Estimation of canopy temperature which
indirectly indicates stomatal conductance and
relative photosyntatic ability
• Assay the crop water use
Infrared imaging
• 800-1300 nm
• Water index
• Information about various pigments like
chlorophyll, carotenoids and xanthophyll
• Leaf color differences
Near infrared imaging
02
21
Fig. 4: Different images captured with different wavelength

23. Genomics for salinity tolerance
• Recently, the development of high quality reference genome of rice has facilitated the high density genotyping (McCouch
et al., 2016) and re-sequencing of more than 3000 rice varieties made addition to it.
• Therefore, genomic dissection of salt tolerance and its components can pave the way for systematic and precise gene
transfer in salt sensitive cultivars through marker assisted breeding.
• Though there have been numerous attempts to review work on genes/ QTLs for salt tolerance, genomic data offers insights
of QTL mapping (Linkage and population mapping), Transgenic approaches, genome editing and soil metagenomics
approaches for salt tolerance in rice.
Fig. 5: Geographical distribution of sequenced rice genomes
Ganie et al, 2019
Song et al., 2018
Fig. 6: Genomic resources related to salinity tolerance
22

24. 2
QTL mapping
Development of mapping
population
• F2 (very limited scope)
• DHs (recombination: 1 gen)
• RILs (mostly used)
• NILs (Fine mapping of QTLs)
• CSSLs (Ideal for QTL mapping)
• AILs (5x reduced CI then F2 )
Phenotyping of mapping
population along with parents
• Should be done in replicated trials
over the locations and year.
• High throughput phenotyping
provides better accuracy.
• The accuracy of phenotyping
(increases heritability) determines
how realistic the QTL mapping
results are.
Genotyping of parents to
identify polymorphism and
use it on individuals of
mapping population
• Done with molecular
markers like RFLP, RAPD,
SSRs, SNPs etc. or GBS
approach
Marker genotype and trait
phenotype data are analyzed
to detect association between
them
• QTL mapping parameters:
Regression and Maximum
likelihood method
• Softwares: SAS, Qgene, QTL
Cartographer, PLABQTL,
MQTL, MapMaker/QTL
Term QTL by: Gelderman 1975
A chromosomal region that contains
gene(s) that affects a quantitative trait
2
1 4
3
Methods for QTL mapping :
• Single Marker Analysis (SMA)
• Simple Interval Mapping (SIM)
• Composite Interval Mapping (CIM)
• Multiple Interval Mapping (MIM)
Factors affecting:
• Genetic properties of QTLs
• Genetic background
• Size of the mapping population
• Environmental effects
• Experimental error
 LOD score: > 3
23

25. QTL mapping and salt tolerance in rice
• To date, the best-known QTL studied for rice salt tolerance is
Saltol (to maintain K+/Na+ homeostasis.).
• Maximum number of salt tolerance associated QTLs are
reported on chromosome 1, followed by 3, 4, 6, 7, 2 and 9.
(Ganie et al., 2019) 24
Fig. 8: Chromosomal location of salt stress-related QTLs in rice
Fig. 7: Different traits correlated with salt
responsive QTLs of rice

26. Association mapping Association
mapping
population/
germplasm
Precise
phenotyping
Genotyping
(population
structure
analysis)
Structure and
kinship
analysis
Genotyping
(LD
analysis)
AM and LD
analysis
6
5
4
3
2
1
• A population-based survey used to identify trait-
marker relationships based on linkage
disequilibrium by exploiting historical and
evolutionary recombination events.
• Two types: GWAS and Candidate gene
association mapping
• Power of AM and statistical analysis
Abdurakhmonov and Abdukarimov, 2008
1. Extent of LD in the genomic region
2. Type of gene action involved
3. Size and composition (structure) of
population
4. Field design and data analysis (Genotyping
and phenotyping)
5. Structured association (SA) to avoid false
positives
• Softwares used in AM: STRUCTURE 2.3, TASSEL,
BAPS, mStruct, GenStat, JMP genomics 25

27. • A recent genome-wide association study has led to the identification of 20 significant SNPs for K+/Na+
ratio on four rice chromosomes (1, 4, 6 and 7), whereas 44 significant SNPs were detected for different
traits such as yield per plant, filled grains, spikelet fertility, productive tillers and stress susceptibility
index observed under salt stress (Kumar et al., 2015).
• Most of the SNPs for Na+/K+ ratio were located on chromosome 1 near saltol locus.
• During reproductive stage, the significant SNPs increased under salt stress as compared to the unstressed
condition, since most of the SNPs for Na+/K+ ratio were detected in saltol (affecting salt tolerance at rice
seedling stage).
• Seven SNP markers associated with the salt responsive QTLs have been identified (Bimpong et al.,
2014).
• In this prospect, a recent genome-wide association analysis in rice has identified 20 QTNs associated
with 11 salt tolerance traits using 395,553 SNP markers (Naveed et al., 2018).
Association mapping and salt tolerance in rice
26

28. Transgenic approach
• Genes are taken from other species in order to get insertion of gene(s) controlling traits without dilution of any desirable
trait of a recipient elite genotype
• Improve salinity tolerance in rice by various approaches like control of organic solutes, antioxidants detoxifying ROS,
transport of ion, late embryogenesis proteins (LEP) and heat-shock proteins, programmed cell death (PCD), signal
transduction and transcription factor (TF) etc.
Antioxidants and ROS detoxification
• CAT1 and GST from Suaeda salsa (Zhao and Zhang, 2006),
• GlyII from Oryza sativa (Singla-Pareek et al., 2008),
• GS2 from Oryza sativa (Hoshida et al., 2000),
• katE from Escherichia coli (Nagamiya et al., 2007; Moriwaki
et al., 2008),
• Mn-SOD from Saccharomyces cerevisiae (Tanaka et al., 1999),
• Sod1 dismutase - Avicennia marina (Prashanth et al., 2008)
Ion homeostasis and compartmentation
• nhaA from Escherichia coli (Wu et al., 2005),
• AgNHX1 from Atriplex gmelini (Ohta et al., 2002),
• OsNHX1 Vacuolar from Oryza sativa (Fukuda et al., 2004;
Biswas et al., 2015; Chen et al., 2007),
• SOS2 from Schizosaccharomyces pombe (Zhao et al., 2006),
• PgNHX1 from Pennisetum glaucum (Verma et al., 2007)
• OsKAT1 from Oryza sativa (Obata et al., 2007).
• Recently, a salt-tolerant transgenic rice has been developed using over-expressing a gene, OsIF (Oryza
sativa intermediate filament) from wild rice (Porteresia coarctata) which is native to India, Bangladesh, Sri Lanka and
Myanmar (Soda et al., 2018).
28

29. Genome editing in rice for salinity tolerance
• CRISPR/Cas has been used widely in many crops and in rice also it
has shown huge success in making desirable changes against
various biotic and abiotic stresses (Khang 2018).
• Zhang A et al. (2019) obtained two homozygous mutant lines that
harbor mutagenesis in OsRR22 without exogenous T-DNA and
found salinity tolerance at the seedling stage.
Fig. 10: General strategy for improving rice through genome editing
Steps:
• The first step is gRNA design and construct development.
• After confirmation of the construct it is transferred either
in Agrobacterium or coated on gold particles for biolistic
bombardment.
• The construct is transformed to rice callus. The plants are
regenerated from this callus.
• The regenerated plants are confirmed through PCR and
restriction digestion, followed by sequencing.
• The plants are then screened for desired characteristics and
moved to the greenhouse and then to field trials.
(Zafar K. et al., 2020)
(curtesy:
Mounadi
et
al.,
2020)
27
Fig. 9: CRISPR-Cas9 mediated gene-editing mechanisms.

30. Other approaches
1
EcoTILLING
• A significant method for SNP and InDel discovery of
haplotypes at specific loci.
• Study of 392 rice accessions has resulted in the discovery of
11 significant salinity-related SNPs from five salt-
responsive genes, such as K24E (in salt), T67 K and P140A
(in OsHKT1;5) (Negrão et al., 2013).
Allele mining
• Identifying the effective allelic forms is the prerequisite for
utilizing the full potential of a gene affecting a specific
trait.
• 52 SSRs and 124 SNP markers linked with different
salinity-related QTLs and candidate genes have been used
to genotype the European Rice Core collection (ERCC)
(Ahmadi et al., 2011).
Whole-genome re-sequencing
• Whole-genome re-sequencing has enabled the discovery of
many SNPs and InDels in QTL regions, which may act as
functional markers to screen the large rice germplasm for
salinity tolerance (Jain et al., 2014).
• 3000 rice genotypes, have been re-sequenced (Li et al.,
2014), with the aim of finding valuable genomic resources
in the form of allelic variations and eQTLs.
Transcriptomic profiling
• Global transcriptome profiling gives a snapshot of the
genes involved in the different biosynthetic pathways and
metabolic processes operating in a particular tissue under a
specific condition.
• Interestingly, transcript expression profiling has been
correlated with the genetic diversity between two
contrasting rice genotypes (Jiang et al., 2013).
29

31. Ocean Agriculture
• However, despite all this progress, hopes rest on the
CRISPR-based efforts of ‘Agrisea’ (a Canadian start-up
company; https://www.agrisea.co.uk/), who intend to grow
rice in the ocean in a practice called ‘Ocean Agriculture’ and
to overcome the seawater tolerance barrier.
• The company’s approach to producing genetically
engineered salt-tolerant rice is based on editing a network of
genes (not just one gene), having edited eight different
genes that are only switched on in plants that are naturally
adapted to high salinity.
• The floating ocean rice farms are expected to be ready by
the end of 2021.
Transgene free

32. 31

33. Case study I
32
Thuwal, Saudi Arabia Al-Tamini et al. (2016)

34. • Two Smarthouses, north-east (NE) and north-west (NW),
with conveyor systems are connected to one imaging hall
with five camera stations (LemnaTec Scanalyzer 3D) for
the daily non-destructive phenotyping of plants.
• The imaging stations providing fluorescence imaging,
near-infrared imaging, near-infrared imaging of pots (soil),
red-green-blue visible imaging and far infrared (thermal)
imaging.
Double-pot design to
enable growth of rice in
controlled waterlogged
conditions, to which salt
could be applied.
Materials and methods
• Used two diversity panels composed of two major rice
subpopulations, indica and aus, which contain 297 and 257
accessions, respectively.
• Salt treatment was applied at 24 DAT (29 days after
germination) with final concentration of 150 mM NaCl in
the soil solution after drying down to 950 ml.
• Daily imaging and watering were continued for 13 days after
treatment until 37 DAT.
Experimental design
• split-plot design
• No of pots: 1,056 pots
(plants)
• main-plot was a partially
replicated, blocked, row-and-
column design.
2
Plant material
1
33

35. RGB image capture and image analysis
• Plant imaging started at 23 DAT and continued until 37
DAT (which was 13 days after treatment).
• Shoot images were taken using the LemnaTec 3D
Scanalyzer system (LemnaTec GmbH, Germany).
• Plants were imaged daily in an imaging chamber using
two 5-megapixel visible/RGB cameras.
Data preparation for main traits and their derived indices
• Data corresponding to the Projected Shoot Area (PSA), the
weight before imaging, the weight after imaging and the weight
of the evaporation cart were recorded daily between 23 and 37
DAT.
Genome-wide association analysis
• GWAS performed with diversity panels of indica and aus independently and also with a combined single set (INDAUS).
• A 700k SNP array designed by McCouch’s laboratory at Cornell University was used for genotyping.
• As a result, a total of 397,659 SNPs in the indica subpopulation, 394,785 SNPs in the aus subpopulation and 304,877 SNPs in the
combined INDAUS subpopulations for GWAS.
• TASSEL software (http://www.maizegenetics.net/tassel) : For response for each salt treatment in a separate model.
• ASReml-R: The interaction model, combine each response for both treatments.
3
2
1
34

36. • RGR was lower in the salt-treated accessions than in the control plants for both rice panels.
• Indica lines maintained better growth than aus lines.
• As expected, RGR decreases through time to a greater extent under saline conditions than in control conditions.
• More specifically, a rapid reduction in biomass production was observed immediately after salt application, suggesting
that the rice plants responded to the ‘osmotic component’ of salt stress, before a build-up of salt in the leaves could
impact plant growth, as occurs after several days, in the later ‘ionic phase’. 35
Fig. 11: Relative growth rate (RGR) of salinity-induced responses comparing indica and aus
Results

37. Association analysis of salinity-induced responses
• The early time interval (2-6 days
after treatment) had multiple
significant peaks associated with
TUE under saline conditions (on
chromosome 11 of indica and
chromosomes 5, 9 and 11 of aus),
that disappeared from later time
intervals.
• The interval 2-6 days after treatment
represents the early response phase
to salinity, while the later intervals,
6-9 and 9-13 days after treatment,
appear to be affected by different
processes, which may represent a
shift in the relative importance of
‘osmotic’ and ‘ionic’ processes
between these intervals.
Fig. 12: SNPs associated with TUE are shown at the different time intervals
36

38. Candidate genes
underlying QTLs in
early salinity responses
Table 3: Candidate genes and local linkage disequilibrium region underlying the most
significant using the interaction model
• In the indica panel, one of the most promising regions is located at 3.62-3.76 Mb on chromosome 11 (harbors several candidate
genes, Os11g07230 (encoding a receptor kinase) and Os11g07240 (encoding a serine/threonine-protein kinase).
• Regions significant for TUE traits in the aus panel under salt stress harbor genes related to signaling and signal transduction, such as
Os03g16130 (encoding a calcium/calmodulin-dependent kinase), Os05g39870 (encoding OsCIPK28 and CAMK_KIN1,
calcium/calmodulin-dependent protein kinase).
• Hence, results show that candidate genes found in the early intervals, 2-6 and 2-9 days after treatment, are mainly encoding for
signaling proteins, further supporting the hypothesis that the early responses to salt stress are related to signaling mechanisms.
37

39. Table 3: Candidate genes and local linkage disequilibrium region underlying the most significant using the
interaction model (continued)
38

40. • Advances in image-based phenotyping that use new methods to recover the structure of a plant and
build a three-dimensional (3D) model are likely to play a useful role in determining tiller number
per plant, a key trait for rice yield.
• High-throughput phenotyping produces multiple measurements over time, which require new
methods of analyses that are flexible in their quantification of plant growth.
• Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analyzed using a
new association model that takes into account the interaction between treatment (control and salt)
and genetic marker.
• This model allows the identification of previously undetected loci affecting TUE on chromosome 11,
providing insights into the early responses of rice to salinity, in particular into the effects of salinity
on plant growth and transpiration.
Conclusion
39

41. Case study II
40
LA, USA Puram et al. (2018)

42. Materials and methods
3
2
1 Plant Materials:
Rice variety ‘Cheniere’
• Long grain
• High yielding
• Early maturing
• semi-dwarf variety
• Susceptible to salinity
Rice variety ‘Nona Bokra’
• Photosensitive
• Late maturing
• salinity-tolerant indica
landrace
BC3F4 introgression lines (ILs) were generated after three back crosses followed by selfing for three generations
using single seed descent method.
Screening for Salinity Tolerance
• 112 introgression lines along with parents were phenotyped for seedling stage salinity tolerance.
• Randomized complete block design with three replications.
• Pregerminated seeds transferred to nutrient solution.
• Seedlings at two leaf stage were subjected to salt stress at EC of 6 dSm−1 for 2 days followed by EC of 12 dSm−1.
• Phenotypic observations under both stress and control were recorded on five uniform plants.
• The visual salt injury score (SIS) was recorded on 1 to 9 scale.
41

43. 6
5
4 Statistical Analyses
• Analysis of variance (ANOVA) with IL and replication as fixed effect and random effect, respectively.
• The relationship among different morpho-physiological traits by computing Pearson correlation coefficients.
• The distribution pattern of the ILs visualized from the histograms constructed in Microsoft Excel 2010.
Genotyping of Introgression Lines Using SSR Markers
• Genotyping of ILs was done using SSR markers.
QTL Mapping
• CSSL finder v.0.9.7.2.2 was used to estimate the genome composition of the ILs (Lorieux 2005).
• Linkage map was constructed using QTL IciMapping software v. 4.1 using common SSR markers in both ILs
(Cheniere × Nona Bokra and Jupiter × Nona Bokra) (Wang et al., 2016).
• Significant QTLs were detected at LOD threshold of 2.0 using interval mapping.
• The candidate genes were obtained from the Rice Genome Annotation Project Database.
42

44. ILs developed
Fig. 14: Jupiter (B) × Nona Bokra (W)
Fig. 13: ILs from Cheniere (B) × Nona Bokra (W)
43

45. Fig. 15: Frequency distribution of ILs for eight morpho-physiological traits under salt stress (EC 12 dSm−1) and salt tolerance indices
SIS: Salt injury score,
Na+: Na+ concentration,
K+: K+ concentration,
NaK: Na+/K+ ratio,
CHL: Chlorophyll content,
SHL: Shoot length,
RTL: Root length,
DWT: Dry weight,
SRI-Na+: Relative shoot sodium concentration,
SRI-K+: Relative shoot potassium concentration,
SRI-NaK: Relative shoot sodium to potassium ratio,
SRI-SHL: Relative shoot length,
SRI-RTL: Relative root length,
SRI-DWT: Relative shoot dry
weight
44

46. QTLs
Fig. 16: Comparative map positions of QTLs for traits related to seedling stage salinity tolerance
45

47. QTLs identified
• The QTLs for shoot Na+ concentration were identified on the chromosomes 2, 3, and 8 in the JN IL
population, whereas only one QTL was identified on chromosome 7 in this study.
• For K + concentration in the shoot, all three QTLs had favorable effect from ‘Nona Bokra’ alleles. The
QTL qK1.1 was close to the QTL identified in the JN IL population, qSKC-1 and Saltol QTL.
• The QTLs identified for shoot and root lengths, chlorophyll content, and shoot dry weight also
contributed toward salinity tolerance. Only one QTL identified for chlorophyll content on chromosome 6
in this study was on map location different from that in the JN IL population.
• In total, 18 different QTLs were identified for salt tolerance indices computed for several morphological
traits.
46

48. Table 4: Additive QTLs detected in ILs for seedling stage salinity tolerance Table 5: Summary of QTLs co-localizing to published QTLs
47

49. • The results from this study suggested that Na+ and K+ homeostasis, Na+ exclusion, and Na+
compartmentation are key mechanisms for salt tolerance response in ‘Nona Bokra’ ILs developed
in two genetic backgrounds.
• The ILs with enhanced salt tolerance in both populations will be useful for fine mapping and
positional cloning of salt tolerance QTLs.
• Since the selected prebreeding lines do not possess any undesirable domestication traits such as tall
plant stature, photosensitivity, red pericarp, and awn, these lines can be valuable resource for the
rice breeding programs to design salt tolerant varieties in the future.
48
Conclusion

50. Case study III
49
Lubbock, TX, USA Pabuayon et al. (2021)

51. Phenotypic evaluation of the RIL Population
• The RILs of IR29 (Xian/Indica; salt-sensitive) and Pokkali (Aus; salt-tolerant) consisted of 123 individuals as the core QTL
mapping population.
• 14 day-old seedlings were transplanted to 0.6-gallon buckets with 1 g/L Peter’s Professional 20-20-20 Fertilizer (JR Peters Inc.,
Allentown, PA, United States) at pH 5.8, supplemented with 0.4 g L-1 FeSO4.7H2O.
• Salinity stress at tillering stage (V4 to V12) with EC = 12 dS m-1.
• Samples for physiological assays, RNA extraction, and biomass measurements were collected at 0 h (control) and after 24, 48,
72, and 144 h of stress.
Materials and methods
3
2
1
Aspects under consideration
• Biomass, Stomatal Conductance
• Standard Evaluation Score (SES)
• Electrolyte Leakage Index
• Peroxidase Activity Assay
• Lipid Peroxidation Assay
• Na+ and K + Quantification
• Proline Content
• Aggregate Phenotypic Scores (APS)
Real-Time Plant Growth Profiling
• IR29 (S), Pokkali (T), FL478 (T-RIL), FL510 (ST-RIL), FL454 (S-
RIL), and FL499 (SS-RIL) subjected to digital growth profiling with
the LemnaTec Scanalyzer 3D platform.
• Plants were digitally imaged daily for 18 days with RGB and
hyperspectral cameras.
• The RGB images were analyzed using PhenoImage (Zhu et al.,
2020).
• Plant size was initially reported as number of pixel squares,
converted into cm2 using the Fiji software (Schindelin et al., 2012). 50

52. Transcriptome Profiling by RNA-Seq and Transcriptional Network Modeling
Statistical Analysis
2
1
• Total RNA was extracted from frozen leaves of each replicate with the miRVanaTM miRNA Isolation Kit to construct
time-course (0, 24, 48, 72, and 144 h) RNA-Seq libraries.
• Strand-specific 150-bp paired-end libraries were sequenced twice on Illumina HiSeq3000.
• Gene expression clusters were identified and visualized using the “MBCluster.Seq” package in R.
• Pearson’s correlation coefficient (PCC) matrix was established for each cluster.
• Genetic networks were constructed and visualized using Cytoscape (Smoot et al., 2010).
• The annotation applied to RNA-Seq dataset was based on UniProt (The UniProt Consortium, 2018).
• Statistical analyses were conducted with R software.
• Pearson’s correlation coefficient (PCC) matrices, gene ranks, and mutual ranks for genetic network and PCA were scripted
in R-package.
• The “ggplot2” package was used for data visualization (Wickham and Chang, 2008).
51

53. Table 6: Standard Evaluation Scores (SES) and qualitative phenotypic
ranking at vegetative growth/tillering stage
52

54. Relatedness Among RILs Based on
Physio-Morphometric Matrix
53
Fig. 17: Comparison of plant health in
control (C) and stress (S)
Fig. 18: Physio-morphometric scores were normalized and combined as Aggregate Phenotypic Score (APS)
Fig. 19: Heat map showing the gradients of the normalized physio-morphometric scores across the comparative panel

55. Real-time growth profiling Variation in hyperspectral variance
Fig. 19: Growth curves as a function of changes in plant area and plant height and plant area
Fig. 20: Variation in hyperspectral variance at 243
wavelength bands ranging from 550 to 1,700 nm
54

56. Fig. 21: Parallel comparison of transcriptomes across genotype
shown for 14,696 unique transcript loci.
Network Rewiring in Transgressive Segregants Based on Transcriptome Profiles
• The super-tolerant FL510 had the highest number of
genes that did not change in expression at EC = 12
dS m-1.
• Upregulation in FL510 occurred gradually but
progressively compared to others.
• In the super-sensitive FL499, only subtle changes
occurred during the early stages of stress, but many
genes were drastically upregulated or
downregulated at 144 h/6-day, coinciding with
severe leaf senescence and necrosis.
• The tolerant FL478 and sensitive FL454 had large
numbers of early, late, sustained, and transiently
upregulated and downregulated genes similar to
IR29 and Pokkali.
55

57. Comparison of transcriptional co-expression networks across parents and their RILs
• The OsCML27 profile was most distinct
in the super-tolerant FL510 with
sustained upregulation. The parents
IR29 and Pokkali did not share any
cohort genes with any progeny, while
only minimal overlaps at best occurred
between FL510, FL478, FL499 and
FL454.
• These trends illustrate the non-additivity
(network rewiring) of parental gene
expression in the progenies, where
outlier trends were evident. The
OsCML27 cohorts in FL510 were
enriched with salicylic acid (SA) and
jasmonic acid (JA) associated genes
with sustained upregulation through 144
h. Fig. 22: Comparison of transcriptional co-expression networks across parents and their RILs. 56
OsCML27
OsMT14A27
OsHKT27 OsMPS27

58. Hypothetical model of physiological coupling and
uncoupling in transgressive segregants
• Proposes that the novelties of FL510 and FL499 are due
to the coupling in the progeny of the good properties
coming from either parent or uncoupling of bad
properties from the good properties from the same
parent.
• On top of the core mechanisms that contribute to a large
proportion of phenotypic variance for defense
potentials, each parent has their own characteristics that
may or may not be beneficial under stress.
• Benefit from IR29 would be its superior growth and
development potentials.
• Pokkali offers many stress defense mechanisms
including salt exclusion.
• Combining the physiological potentials of parents with
the reconfigured (non-parental) properties led to
positive or negative coupling and uncoupling effects in
RILs. 57
Fig. 23: Hypothetical model of physiological coupling and uncoupling in transgressive
segregants for salinity tolerance

59. • The phenomenon of transgressive segregation, where a small minority of recombinants are outliers relative to
the range of parental phenotypes, is commonly observed in plant breeding populations.
• By systems-level profiling of the IR29 × Pokkali recombinant inbred population of rice, the hypothesis that
novel salinity tolerance phenotypes are created positive or negative coupling-uncoupling of developmental and
physiological attributes of each parent.
• Real-time growth and hyperspectral profiling distinguished the transgressive individuals in terms of stress
penalty to growth.
• Non-parental network signatures that led to either optimal or non-optimal integration of developmental with
stress-related mechanisms were evident at the macro-physiological, biochemical, metabolic, and transcriptomic
levels.
• This study also supports a breeding paradigm rooted on genomic modeling from multi-dimensional genetic,
physiological, and phenotypic profiles to create novel adaptive traits for new crop varieties of the 21st century.
Conclusion
58

60. Seminar conclusion
• Harmonizing the high throughput techniques for phenomics and genomics is both a challenge and opportunity.
• There is no replacement of the conventional breeding, but its limitations in terms of speed and accuracy can be
overcome by molecular breeding programmes.
• The conventional phenotyping and breeding approaches are sound, the advantages and opportunities thrown
open by automated phenotyping should be availed for faster gains.
• Since modern genotyping protocols are well developed and high throughput in rice, phenotyping models need
more consideration because capturing “right QTL” largely depends upon right phenotyping.
• In molecular breeding for salinity tolerance, initial success has been made by the discovery of many QTLs and
several rice salinity GWAS reports, but still there is a considerable gap between knowledge discovery and
actual use of molecular breeding in realization of field oriented salt tolerant rice varieties.
• Stage-specific and stress-specific QTLs may be identified for need based deployment for which, the screening
methodology should be simple and high throughput, reproducible and representative of near-field conditions.
59

61. Thank You