Training managment training 14-18 feb,2021---------- Dr. Mirza Mofazzal Islam Director General Bangladesh Institute of Nuclear Agriculture

1. Welcome
Bangladesh Institute of Nuclear
Agriculture

2. MARKER-ASSISTED
BREEDING
Dr. Mirza Mofazzal Islam
Director General
Bangladesh Institute of Nuclear Agriculture
E-mail: mirza_islam@yahoo.com
dg@bina.gov.bd

3. Genetic Markers
• Genetic markers represent ‘signposts’ or ‘landmarks’
within DNA along chromosomes.
• Genetic markers may be used as diagnostic ‘tools’ by
breeders and geneticists to characterize germplasm or to
assist in phenotypic selection.

4. Classification of Genetic Markers
Three broad classes of genetic markers:
• Morphological markers
• Biochemical markers
• DNA or molecular markers

5.  Morphological markers represent single gene traits
detected visually. Examples include plant height, flower
colour and seed shape.
 Breeders have long since used morphological markers
to aid in selection.
 Biochemical markers are allelic variants of proteins.
Examples are isozyme marker, IEF.
Morphological and Biochemical Markers

6.  Limited in number and are influenced by environmental
factors or the developmental stage of the plant.
 However, despite these limitations, morphological and
biochemical markers have been extremely useful to plant
breeders.
Disadvantages of morphological and
biochemical markers

7. Molecular Markers
 Molecular markers (also called DNA markers) represent
specific regions on chromosomes.
 DNA markers may represent genes or loci within non-coding
regions.
 The different forms produced from DNA markers at a locus
are called marker alleles.
 The great advantage of DNA markers compared to other types
of markers is their abundance.
 Furthermore, their detection is not influenced by
environmental factors or the developmental stage of the plant.

8.  Molecular marker is the heritable entity at the DNA level transmitted
from parents to offspring
 Marker identifies specific location on the genome like milestone
 Importantly it unveils the genetic constitution of the locus –
homozygous or heterozygous; if homozygous like which parent or
allele
Segregation of a marker in BC2F2
generation
LADDER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
IR40931
BR11
L A A H H H B H A H B B B B H H B A
500 bp
400 bp
300 bp
200 bp
100 bp

9. Markers must be
tightly-linked to target loci!
• Ideally markers should be <5 cM from a gene or QTL
• Using a pair of flanking markers can greatly improve
reliability but increases time and cost
Marker A
QTL
5 cM
RELIABILITY FOR
SELECTION
Using marker A only:
1 – rA = ~95%
Marker A
QTL
Marker B
5 cM 5 cM
Using markers A and B:
1 - 2 rArB = ~99.5%

10. Markers must be polymorphic
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
RM84 RM296
P1 P2
P1 P2
Not polymorphic Polymorphic!

11. Fig. 1. PARTIAL VIEW OF PS GELS Polymorphic Monomorphic
POLYMORPHIC VS. MONOMORPHIC MARKER

12. Markers based on PCR
(Polymerase Chain Reaction)
• Description:
– Developed in the late 1980s as a way to amplify a
specific fragment of DNA
– Involves three steps, repeated many times:
• Denaturation of the DNA (94°C)
• Annealing of a primer to the template DNA (55°C)
• Extension of the DNA fragment between the primers using
a heat-stable DNA polymerase such as Taq (72°C)
• Characteristics:
– Usually very specific DNA fragment is amplified
(the primers can be designed to be single copy)
– Large numbers of DNA copies can be amplified
from very small amounts of original DNA template

13. Different types of molecular markers
• Most popular markers:
– SSR markers: co-dominant, single copy (easy to interpret),
high polymorphism rates, PCR-based (requires little DNA),
high-throughput techniques available (but moderately
expensive to run)
– STS markers: such as indels and CAPs, co-dominant, single
copy, moderate polymorphism, PCR-based, inexpensive
(agarose gels)
– SNP markers: bi-allelic, super high-throughput techniques
available (reduces cost-per-sample, but requires high initial
investment), will be used more often in the future

14. Ideal Characteristics
• Technical aspects
– PCR-based, reproducible, robust, protocol transferable to other
labs, high-throughput, cost-effective (cost per sample/initial
set-up costs)
• Information/output
– Co-dominant, highly polymorphic and/or abundant, single-
copy, easy to score, precise allele scores, easily data-based and
comparable between labs
• SSRs are useful, but SNPs gaining momentum
– High throughput SNP genotyping is more efficient, provide
precise data, but has higher initial costs

15. SSRs
Simple sequence repeats (microsatellites)
• Description:
– Take advantage of the many short repeats existing in all
plant genomes
– Requires primers specific to the flanking sequence of an
SSR, to be used in PCR and acrylamide gels
• Characteristics:
– Co-dominant marker with clear genotypes
– High level of polymorphism
– Expensive to develop (they require sequence data)
– Relatively inexpensive and easily transferred between labs
once they are developed

16. Microsatellites, or Simple Sequence Repeats (SSRs), are
polymorphic loci present in nuclear DNA that consist of
repeating units of 1-4 base pairs in length.
They are typically neutral, co-dominant and are used as
molecular markers which have wide-ranging applications
in the field of genetics.
The size of the amplified fragments in SSR generally
range from 100 to 350 bp
Forward and reverse SSR primers are designed following
unique sequences of genome
SSR

17. POLYMORPHISMS IN SSR
 Polymorphism is obtained due to variable number
of motif units
 Motifs are tandem repeats e.g. ATT, GCC etc.
which are specific to the primer
 Different numbers of motifs are created in nature
due to the following factors
 Unequal crossing over
 Replication slippage
 Retrotransposons
 Point Mutation

18. SSR motif with flanking primers
http://www.weihenstephan.de/pbpz/bambara/html/ssr.htm

19. Leaf Collection DNA Extraction PCR
LADDER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
IR40931
BR11
L A A H H H B H A H B B B B H H B A
Gel Electrophoresis Visualization of DNA
Bands
Data Scoring
Steps of Marker Genotyping
1 2 3
4 5 6

20. Fig. 1. PARTIAL VIEW OF PS GELS Polymorphic Monomorphic
POLYMORPHIC VS. MONOMORPHIC MARKER

21. Genotyping with SSRs
RM17 across an RIL population (most loci are homozygous)
RM17 across an F2 population (many heterozygous loci)

22. STS markers
Sequence Tagged Site
• Description:
– Molecular marker based on DNA sequence
– The known DNA sequence can be used to design PCR
primers to develop new markers
• Different types of STS markers
– SCARs (indels)
– CAPS
– ESTs

23. SCARs and Indel markers
Sequence characterized amplified regions
• Description
– Often referred to simply as a “STS marker” or
“indel” marker (for Insertion-deletion)
– PCR primers from a DNA sequence amplify a
product with a significant size difference
• Characteristics:
– Visible using agarose gel electrophoresis
– Does not require restriction enzymes
– Useful for developing markers at known genes

24. CAPs
Cleaved amplified polymorphic sequences
• Description:
– Similar to a SCAR marker, except the PCR product is
treated with a restriction enzyme (RE) to visualize the
polymorphism
• Characteristics:
– After RE digestion, the different size products can be
visualized on agarose/polyacrylamide gels
– Different REs can be used to try to find polymorphic
sites (based on single nucleotide polymorphisms, not
insertion-deletions)

25. CONFIRMATION OF SUB1 QTL BY USING A CAPs MARKER GnS2

26. ESTs
Expressed sequence tags
• Description:
– Based on DNA sequence of a gene itself (derived
from the expressed mRNA sequence)
• Characteristics:
– Primer sequences from the EST can amplify a
marker with an insertion-deletion, or it can be
digested with an RE (like a CAPs marker)
– The only difference from a SCAR or CAPs marker
is that this is at the gene locus

27. SNPs
Single nucleotide polymorphisms
• Description:
– A site in the DNA that differs at a single base
– SNP variants or alleles:
• For example, at one nucleotide site across many
accessions: 30% have an A, and 70% have a G
• Characteristics:
– Thousands (or even millions) of SNP markers
can be developed and genotyped using high-
throughput techniques

28. SNP discovery: Sequencing PCR products
• SNP Marker Development:
– Using the complete rice genome sequence, PCR
primers can be designed to amplify small fragments
(700-900 bp) across the target region
– PCR products from a wide range of diverse
varieties are sequenced, and multiple sequence
alignments are performed to identify SNPs
• SNP Marker Genotyping:
– High-throughput SNP marker assays can then be
developed (i.e. primer extension method)

29. SNP markers
Single nucleotide polymorphisms
Advantages for breeding:
– SNPs can be quickly genotyped using high-
throughput techniques
– SNP costs are rapidly decreasing
– Functional SNPs for BB, GM, BPH, RTSV
resistance, Drought tolerance are now available
in rice

30. Chr1
Chr2
Chr3
Chr4
Chr5
Chr6
Chr7
Chr8
SNP genotype data
Chr9
Chr10
Chr11
Chr12
FL478/IR29 BC4 lines: IR29 = red, Pokkali alleles in FL478 = green; data from McCouch 1536-SNP assay at Cornell Univ.

31. Genotyping by Sequencing
(GBS)
Simple, highly multiplexed system for
constructing libraries for next generation
sequencing
• Reduced sample handling
• Few PCR & purification steps
• No DNA size fractionation
• Inexpensive barcoding system

32. Marker assisted selection (MAS) refers
to the use of DNA markers that are
tightly-linked to target loci as a
substitute for or to assist phenotypic
screening
Assumption: DNA markers can reliably
predict phenotype

33. • Identify tolerance QTLs
– Large effect, stable across
environments/backgrounds
• Fine-map the target QTL
– Closely-linked markers
– Ideally, functional markers from the cloned QTL
• Use markers for rapid backcross conversion
– Use popular varieties as recurrent parents
– Precision marker strategy to reduce negative linkage
drag
Molecular breeding strategy:
Marker-Assisted Backcrossing (MABC)

34. Conventional backcrossing
x P2
P1
Donor
Elite
cultivar
Desirable trait
e.g. disease resistance
HYV
Lacking for 1 trait
Called RP P1 x F1
P1 x BC1
P1 x BC2
P1 x BC3
P1 x BC4
P1 x BC5
P1 x BC6
BC6F2
Visually select BC1 progeny that resemble RP
Discard ~50% BC1
Repeat process until BC6
Recurrent parent genome recovered
Additional backcrosses may be required due to linkage drag

35. Backcross Method
High yielding but
disease susceptible
Recurrent
Parent
Donor Parent Disease resistance
P1 x P2
F1
F1 x P1
BC1F1 x P1
BC2F1 x P1
BC4F1
87.50%recovery genome of
RecurrentParent
93.75%recovery genome of
RecurrentParent
50%of genome from P1 +
50%of unrelatedgenome from P2]
75%recovery genome of
RecurrentParent BC1F1
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(50%)] = 75% P1
genome
BC2F1
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(75%)] = 87.5%P1
genome
BC3F1
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(87.5%) ] = 93.75%
P1 genome
BC3F1 x P1
BC2F1 x P1
96.875%recovery genome of
RecurrentParent
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(93.75%)] = 96.875%
P1genome
BC5F1
98.4375%recoverygenome of
RecurrentParent
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(96.875%)] =
98.4375 P1 genome
BC5F1 x P1
BC6F1
100%recoverygenome of
RecurrentParent
50%of genome fromP1 + 50% of genome fromF1, which
itself is 50% P1 , therefore [50%+50%(98.4375%)] =
98.4375 P1 genome
Generalequation for average recovery of the recurrentparent:
1 - (½) n+1
where,nis the number of backcrossesto the recurrentparent.
for the F1, n= 0; for BC1, n=1; for the BC2, n=2; for the BC3, n=3, etc.

36. Advantages of MABC
Effective selection for target loci
Minimize linkage drag quickly and efficiently
Accelerate recovery of recurrent parent genome
efficiently
IR64 IR64 -
Sub1

37. P1 x F1
P1 x P2
CONVENTIONAL
BACKCROSSING
BC1
VISUAL SELECTION OF BC1 PLANTS THAT
MOST CLOSELY RESEMBLE RP
BC2
MARKER-ASSISTED
BACKCROSSING
P1 x F1
P1 x P2
BC1
USE ‘BACKGROUND’ MARKERS TO SELECT PLANTS
THAT HAVE MAX RP GENOME
BC2

38. Gel picture for Parental Survey Polymorphic Monomorphic
Around 60-80 polymorphic markers evenly distributed
throughout the genome are required
Primer Survey for Polymorphic Markers

39. F1s must be confirmed by molecular markers

40. MAB: 1ST LEVEL OF SELECTION –
FOREGROUND SELECTION
• Selection for target gene or
QTL
• Useful for traits that are difficult
to evaluate
• Also useful for recessive genes
1 2 3 4
Target locus
TARGET LOCUS
SELECTION
FOREGROUND SELECTION

41. Single Gene Transfer :
Linkage Drag with Traditional Backcross Breeding
Donor
variety
Resistance
Gene
New Variety
L Linkage Drag
Improved variety
X
Resistance
Gene

42. Donor/F1 BC1
c
BC3 BC10
TARGET
LOCUS
RECURRENT PARENT
CHROMOSOME
DONOR
CHROMOSOME
TARGET
LOCUS
LINKED
DONOR
GENES
Concept of ‘linkage drag’
• Large amounts of donor chromosome remain even after
many backcrosses
• Undesirable due to other donor genes that negatively
affect agronomic performance

43. Conventional backcrossing
Marker-assisted backcrossing
F1 BC1
c
BC2
c
BC3 BC10 BC20
F1
c
BC1 BC2
• Markers can be used to greatly minimize the amount
of donor chromosome….but how?
TARGET
GENE
TARGET
GENE
Ribaut, J.-M. & Hoisington, D. 1998 Marker-assisted selection:
new tools and strategies. Trends Plant Sci. 3, 236-239.

44. MAB: 2ND LEVEL OF SELECTION -
RECOMBINANT SELECTION
• Use flanking markers to
select recombinants
between the target locus and
flanking marker
• Linkage drag is minimized
• Require large population
sizes
– depends on distance of
flanking markers from target
locus)
• Important when donor is a
traditional variety
RECOMBINANT
SELECTION
1 2 3 4

45. OR
Step 1 – select target locus
Step 2 – select recombinant on either side of target locus
BC1
OR
BC2
Step 4 – select for other recombinant on either side of target locus
Step 3 – select target locus again
* *
* Marker locus is fixed for recurrent parent (i.e. homozygous) so does not need to be selected for in BC2

46. MAB: 3RD LEVEL OF SELECTION -
BACKGROUND SELECTION
• Use unlinked markers to
select against donor
• Accelerates the recovery of
the recurrent parent genome
• Savings of 2, 3 or even 4
backcross generations may
be possible
1 2 3 4
BACKGROUND
SELECTION

47. Background selection
Percentage of RP genome after backcrossing
Theoretical proportion of
the recurrent parent
genome is given by the
formula:
Where n = number of backcrosses,
assuming large population sizes
2n+1 - 1
2n+1
Important concept: although the average percentage of
the recurrent parent is 75% for BC1, some individual
plants possess more or less RP than others

48. Generation RP genome content (%) Donor genome content (%)
F1 50 50
BC1 75 25
BC2 87.5 12.5
BC3 93.8 6.3
BC4 96.9 3.1
BC5 98.4 1.6
BC6 99.2 0.8
BC7 99.6 0.4
BC8 99.8 0.2
Expected recovery of recurrent parent genome conventional
backcrossing in subsequent generations

49. % recurrent parent genome
Backcross
generation
Number of
individuals
Marker-
assisted
backcross
Conventional
backcross
BC1 70 79.0 75.0
BC2 100 92.2 87.5
BC3 150 98.0 93.7
BC4 300 99.0 96.9
Source: Hospital, 2003
Expected recovery of recurrent parent genome comparing conventional
and marker assisted backcrossing in subsequent generations

50. P1 x F1
P1 x P2
CONVENTIONAL BACKCROSSING
BC1
VISUAL SELECTION OF BC1 PLANTS THAT
MOST CLOSELY RESEMBLE RECURRENT
PARENT
BC2
MARKER-ASSISTED BACKCROSSING
P1 x F1
P1 x P2
BC1
USE ‘BACKGROUND’ MARKERS TO SELECT PLANTS
THAT HAVE MOST RP MARKERS AND SMALLEST %
OF DONOR GENOME
BC2

51. How many crossovers per chromosome per meiosis?
Cytogenetic studies observed 0, 1 or 2 chiasmata per
chromosome per meiosis
Roughly proportional to chromosome length
> 5 or 6 crossovers per chromosome extremely rare (Kearsey &
Pooni, 1996)

52. S
a
lt
Phenotyping at the reproductive stage
Seeding
Genotyping
Phenotypic
Evaluation
Salinization @
EC 5dS/m
Sampling

53. S
a
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Genotyping
Samples
DNA extraction
Stock DNA
DNA dilution
PCR
Visualization
PAGE
Data Analysis
Data process
Scoring

54. S
a
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Molecular characterization of RILs:
Polymorphism survey and
genotyping of the RILs using 640
SSR markers representing the 12
rice chromosomes.

55. S
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SAS
Data analysis
One-way
ANOVA
MapMaker QGENE
QTL
Cartographer

56. S
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Linkage Analysis
MapMaker (version 3.0) used.
Linkage group were determined using
“group” command with LOD >3.0.

57. S
a
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Single Marker Analysis
Detecting the association of a marker
with QTL lying at or close to the
marker.
One-way ANOVA for Proc GLM in SAS
was undertaken.
The proportion of the total phenotypic
variation explained by each marker
associated with a QTL was calculated as
R2 value.

58. S
a
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QTL Analysis
QGene used to identify the markers associated to QTL
for salinity tolerance.
A LOD score of 3 and interval map distance based on
the result on the MapMaker linkage map analysis.
Each putative QTL was identified using stepwise
regression based on single marker analysis (P<0.001).
Putative QTLs were re-evaluated using IM and CIM to
control background genetic effects by WinQTL
Cartographer.
GGT analysis was performed using QGene program.

59. S
a
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Data analysis
Genotyping
RILs development
Phenotyping

60. S
a
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Construction of Microsatellite
Map
Linkage Analysis
To complement the interval analysis
SMA was performed.
QTL Analysis – Interval Analysis and
Single Marker Analysis (SMA).

61. QTL for salt tolerance traits on the linkage map of microsatellite markers identified in
80 F8 RILs from the cross IR29/Pokkali.
S
al
t
(28)RM221
26.9
(21)RM48
7.8
(20)RM29
4.5
(24)RM154
38.2
(37)OSR17
14.0
(29)RM233A
4.2
(27)RM211
10.5
(22)RM109
22.9
(32)RM279
50.9
(36)OSR14
21.1
(30)RM240
30.7
(33)RM341
34.8
(31)RM263
18.8
(35)RM526
29.2
(34)RM406
55.2
(26)RM208
7.8
(25)RM207
9.5
(23)RM138
2 3
RFGWT
RBWT
RTBWT
Seedling stage
tolerance
(39)RM148
59.9
(38)RM36
44.2
(40)RM227
79.1
(41)RM231
(6) RM243
28.7
(18) RM6613
21.4
(9) RM490
28.4
(2) RM23
5.5
(13) RM1287
11.5
(16) CP6224
5.5
(10) RM493
5.3
(14) CP3970
1.8
(17) RM6386
4.2
(12) RM594
1.4
(19) RM7075
21.6
(4) RM140
15.3
(3) RM24
15.1
(11) RM562
3.6
(7) RM449
29.0
(1) RM9
38.9
(5) RM220
1
LOD=5.78
LOD=5.44
LOD=4.30
LOD=3.37

62. S
a
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Continued.
(58) RM508
8.7
(53) RM190
22.1
(55) RM204
5.7
(62) RM585
25.7
(61) RM584
19.2
(56) RM253
8.3
(57) RM276
24.1
(52) RM121
27.7
(59) RM527
29.7
(60) RM528
(49) RM249
16.1
(47) RM169
69.6
(44) RM26
62.6
(46) RM122
66.2
(45) RM31
69.3
(50) RM274
31.1
(51) RM334
34.7
(48) RM233B
5 6
RFGWT
RBWT
RTBWT
Seedling stage
tolerance
4
(42) RM127
44.4
(96) OSR30
87.7
(43) RM307
LOD= 4.33
LOD=
3.18

63. S
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Continue.
(73) RM223
19.2
(72) RM210
61.0
(74) RM256
3.5
(70) RM80
64.3
(68) RM25
20.4
(75) RM310
35.3
(76) RM337
7.2
(71) RM152
56.0
(69) RM32
8
RFGWT
RBWT
RTBWT
Seedling stage
tolerance
(67) RM445
25.1
(63) RM11
26.8
(65) RM51
41.0
(64) RM18
9.2
(66) RM248
7
(81) RM316
15.6
(78) RM219
45.6
(80) RM296
46.8
(79) RM242
9
LOD= 3.24
LOD= 3.70
LOD= 3.49
LOD= 4.63
LOD= 8.07
LOD= 3.89

64. S
a
lt
(82) RM171
26.8
(85) RM304
32.9
(84) RM228
34.4
(83) RM222
51.0
(86) OSR33
10
(87) RM21
53.0
(91) RM473E
58.2
(88) RM206
34.8
(89) RM209
37.9
(90) RM224
11
(93) RM19
21.3
(95) RM247
22.2
(94) RM155
23.4
(92) RM17
12
RFGWT
RBWT
RTBWT
Seedling stage
tolerance
Continued.

65. S
a
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RM248
3.89
3.0
RM18
RM51
RM11
RM445
0.0
7
RFGWT
RM242
RM296
RM219
RM316
3.24
3.0 0.0
9
Chromosome location of associated of salinity tolerance genes
of RFGWT at reproductive stage based on the threshold LOD
3.0. The QTLs possible a position is indicated by peak value
greater than the threshold LOD score.

66. S
a
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3.37
3.0
RM231
RM227
RM36
RM148
0.0
3
4.33
3.0
RM307
OSR30
RM127
0.0
4
RBWT
Chromosome location of associated of salinity tolerance genes of
RBWT at reproductive stage based on the threshold LOD 3.0.
The QTLs possible a position is indicated by peak value greater
than the threshold LOD score.

67. S
a
lt
8.07
3.0
RM248
RM18
RM51
RM11
RM445
0.0 3.70
3.0
RM242
RM296
RM219
RM316
0.0
7 9
RBWT
Chromosome location of associated of salinity tolerance genes of
RBWT at reproductive stage based on the threshold LOD 3.0.
The QTLs possible a position is indicated by peak value greater
than the threshold LOD score.

68. S
a
lt
3.18
3.0
RM307
OSR30
RM127
0.0
4.63
3.0
RM248
RM18
RM51
RM11
RM445
0.0
RTBWT
4 7
Chromosome location of associated of salinity tolerance genes
of RTBWT reproductive stage based on the threshold LOD 3.0.
The QTLs possible a position is indicated by peak value greater
than the threshold LOD score.

69. S
a
lt
3.49
3.0
RM242
RM296
RM219
RM316
0.0
9
RTBWT
Chromosome location of associated of salinity tolerance genes of
RTBWT at reproductive stage based on the threshold LOD 3.0.
The QTLs possible a position is indicated by peak value greater
than the threshold LOD score.

70. S
a
lt
5.78
3.0
RM5365
RM220
RM9
RM449
RM562
RM24
RM140
RM7075
RM594
RM6386
CP3970
RM493
CP6224
RM1287
RM23
RM490
RM6613
RM243
0.0
1
Seedling stage tolerance
Chromosome location of associated of salinity tolerance genes
at seedling stage based on the threshold LOD 3.0. The QTLs
possible a position is indicated by peak value greater than the
threshold LOD score.

71. S
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RFGWT
RBWT
RTBWT
Traits Flanking
Markers
Chromosome R2 (%) LOD P-VALUE
Seedling
Tol.
7
9
3
4
7
9
4
7
9
1
1
1
21.25
19.45
17.84
24.19
39.07
21.88
18.40
24.74
20.78
28.60
27.17
22.17
3.89
3.24
3.37
4.33
8.07
3.70
3.18
4.63
3.49
5.78
5.44
4.30
RM445-RM11
RM316-RM219
RM36-RM231
RM127-OSR30
RM445-RM11
RM316-RM296
RM127-OSR30
RM445-RM11
RM316-RM296
RM243-RM490
RM594-RM140
RM449-RM220
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0002
0.0001
0.0001
0.0001
0.0001
0.0001
QTL identified interval analysis

72. S
a
lt
Interval Analysis for QTLs showed
higher phenotypic variations (>17%)
with high LOD Score (>3.0).
Interval mapping (IM) and composite
interval mapping (CIM) gave same
output which are agreement with
interval analysis and single marker
analysis.

73. S
a
lt
QTL analysis
2 QTLs for RFGWT at chrom 7 & 9
4 QTLs for RBWT at chrom 3, 4, 7 & 9
3 QTLs for RTBWT at chrom 4, 7 & 9
3 QTLs for seedling stage tolerance at
chrom 1

74. S
a
lt
The markers RM11, RM18, RM21, RM127,
RM242, OSR14 & OSR17 showed
significant association with salinity tolerance
traits.
Single marker analysis
Could detect possible QTLs located at the
terminal end of the chromosome.
Showed high phenotypic variations

75. S
a
lt
Graphical Genotypic map – parental
contributions to the genome of the
progenies
Confirmed the detected salinity
tolerance QTLs from the single marker
analysis and interval analysis.
Facilitated selection and evaluation of
desirable individuals in breeding
population.

76. S
a
lt
9.2
41.0
26.8
25.1
40 41 23 75 36 45 76 53 37 10 32 55 35 19 4 59 71 68 14 48
RM248
RM18
RM51
RM11
RM445
44
9.2
41.0
26.8
25.1
60 61 63 56 9 38 39 54 30 70 42 73 57 74 79 7 16 64 82 12
RM248
RM18
RM51
RM11
RM445
51
9.2
41.0
26.8
25.1
21 24 62 18 78 31 58 49 8 77 66 67 65 43 15 25 80 6 17 50
RM248
RM18
RM51
RM11
RM445
46
9.2
41.0
26.8
25.1
34 72 52 5 33 81 47 11 29 28 26 20 13 22 1 2 27 3
RM248
RM18
RM51
RM11
RM445
69
Chromosome 7
AA aa -
Graphical genotyping of chromosomal segments for the RFGWT.

77. S
a
lt
Yield and yield components were
reduced in saline conditions.
Salt stress might increase or induce the
expression of specific genes and
repress or suppress the expression of
others.
Classical Approach:
Reaction to salinity at the seedling
stage may not be the same reaction
at the reproductive stage

78. S
a
lt
2 QTLs for RFGWT in chromosomes 7 and 9.
4 QTLs for RBWT in chromosomes 3, 4, 7, and 9.
3 QTLs for RTBWT in chromosomes 4, 7, and 9.
3 QTLs for Seedling stage tolerance in
chromosome 1.
QTLs for salinity tolerance genes at seedling
stage are different from reproductive stage.
Molecular Approach:

79. Graphical genotypes
• Graphical view of the genome (i.e. 12
chromosomes of rice) displaying the marker
genotype of each locus for an individual plant
– Originally described by Young and Tanksley, 1989
(TAG 77:95-101)
• Provides a convenient method to visualize
introgressions from each parent across the
genome
– Especially useful when developing NILs, to see at a
glance how many background introgressions remain

80. GGT
Important Concept :
• In advance backcross generation, there is accidental chance of introgression of donor
chromosomal segment in few position of genome
• Because, Recombination frequencies accumulate upon generation advancement
• Additional background markers are essential for identifying these introgressions,
however, SNPs are best solution

81. MODIFIED MABC APPROACH: RAPID
CONVERSION
Recipient background genome similar to donor
To produce 1000 BC1F1 plants using a cross like
BR44/BR11-Sub1//BR44
To select a best plant with 5 heterozygous linkage groups
(including target regions) in different chromosomes
To recover the recipient genome and target gene in BC1F2
(>1000).
Fixed line by only one backcrossing and one selfing

82. Graphical genotype of IR64-Sub1/PSBRc18//IR64-Sub1 BC1F1
Plants

83. IMPORTANT CONSIDERATIONS IN MABC
 ALWAYS TO PRODUCE BACKCROSS SEEDS FROM SOME BACK-UP PLANTS
 ROGUING OFF-TYPE PLANTS
 TO AVOID OFF-TYPE POLLEN LOAD DURING DUSTING
 NOT TO MAKE MISTAKE DURING LEAF COLLECTION
 TO CONFIRM SELECTION BY RECOLLECTION OF LEAF SAMPLES
 WE SUGEST DNA EXTRACTION INSIDE TUBES : ALTERNATIVE METHODS OF
HAND CRUSHING IS NOW AVAILABLE
 UPROOTING OF BEST PLANTS IN POT AND KEPT INSIDE CROSSING HOUSE
 REPROPAGATION OF BEST PLANTS BY RATOONING
 MAXIMUM CARE DURING DNA DILUTION, PCR & GEL LOADING
 TO BE AWARE OF RATS, BIRDS, VIRUS, MAJOR PESTS & ALSO CYCLONES

84. Alternate MAB Approaches
• Foreground and Phenotypic selection
• Background selection in advanced backcross
generations
• Quick homozygosity in F3 generation

85. Pyramiding Multiple Traits
• Widely used for combining multiple disease resistance
genes for specific races of a pathogen
• Pyramiding is extremely difficult to achieve using
conventional methods
– Consider: phenotyping a single plant for multiple forms of
seedling resistance – almost impossible
• Important to develop ‘durable’ disease resistance against
different races

86. F2
F1
Gene A + B
P1
Gene A
x P2
Gene B
MAS
Select F2 plants that
have Gene A and
Gene B
Genotypes
P1: AAbb P2: aaBB
F1: AaBb
F2
AB Ab aB ab
AB AABB AABb AaBB AaBb
Ab AABb AAbb AaBb Aabb
aB AaBB AaBb aaBB aaBb
ab AaBb Aabb aaBb aabb
Process of combining several genes, usually from 2 different
parents, together into a single genotype
x
Breeding plan

87. Early generation MAS
• MAS conducted at F2 or F3 stage
• Plants with desirable genes/QTLs are selected and
alleles can be ‘fixed’ in the homozygous state
– plants with undesirable gene combinations can be discarded
• Advantage for later stages of breeding program
because resources can be used to focus on fewer lines

88. F2
P2
F1
P1 x
large populations (e.g. 2000 plants)
Resistant
Susceptible
MAS for 1 QTL – 75% elimination of (3/4) unwanted
genotypes
MAS for 2 QTLs – 94% elimination of (15/16) unwanted
genotypes

89. P1 x P2
F1
PEDIGREE
METHOD
F2
F3
F4
F5
F6
F7
F8 – F12
Phenotypic
screening
Plants space-
planted in rows for
individual plant
selection
Families grown in
progeny rows for
selection.
Preliminary yield
trials. Select single
plants.
Further yield
trials
Multi-location testing, licensing, seed increase
and cultivar release
P1 x P2
F1
F2
F3
MAS
SINGLE-LARGE SCALE
MARKER-ASSISTED
SELECTION (SLS-MAS)
F4
Families grown in
progeny rows for
selection.
Pedigree selection
based on local
needs
F6
F7
F5
F8 – F12
Multi-location testing, licensing, seed increase
and cultivar release
Only desirable F3
lines planted in
field
Benefits: breeding program can be efficiently scaled
down to focus on fewer lines

90. QTL Pyramiding
• Combining multiple QTLs into a single line
– Combine two or more QTLs for a single trait
– Combine QTLs for different traits into a line
• Goal of breeding is to select best combination of alleles:
markers enable process to be precise
– MABC/NIL development to isolate desirable allele
– Use same recurrent parent background in parallel
– Cross NILs for different QTLs and use foreground markers to
select combination of QTL alleles

91. Pathway of QTL pyramiding

92. Marker Assisted Recurrent
Selection (MARS)
• De novo QTL detection in breeding
populations
• Recombine selected lines each cycle to
concentrate positive QTLs in subsequent
generations
• Increases the probability of combining key
alleles from both parents

93. Parent 1 X Parent 2
Population
development
F1
F2
F3
F3:4
F3:5 (if needed)
Single seed descent
300 F3 progenies
300 progenies
Multilocation phenotyping
1
st
Recombination cycle A B C D E F G H
F1 F1 F1 F1
F1 F1
F1
F2
F3
2
nd
Recombination cycle
3
rd
Recombination cycle
Multilocation phenotyping
F3:4
Recombination
Population
development
10 plants/family (A-H), 6 sets of 8 families/cross
Bi-parental population
QTL detection
Genotyping
Genotyping
Genotyping
Genotyping
Genotyping

94. Multiparent Advanced Generation Inter-
Cross (MAGIC)
• MAGIC population is genetically very diverse depending
upon founder parents
• Established by intercrossing multiple founder lines;
Intermated populations are then cycled through multiple
generations of crossing

95. PSBRc82
Sanhuangzhan-2
Fedearroz 50
IR77298-14-1-2-10
PSBRc 158
IR4630-22-2-5-1-3
IR45427-2B-2-2B-1-1
Sambha Mahsuri +
Sub1
MAGIC Parents
Colombia
China
India (IRRI)
IRRI
IRRI
IRRI
IRRI
IRRI
Indica / tropical japonica background
95

96. Genetic diversity of founder parents
Fingerprints of the 16 MAGIC founder lines using SSR markers
Using GCP panel of 50 SSR markers for diversity study

97. Bandillo et al. 2013 Rice

98. DNA extractions
DNA EXTRACTIONS
LEAF SAMPLING
Porcelain grinding plates
High throughput DNA extractions “Geno-Grinder”
Mortar and pestles
Wheat seedling tissue sampling in
Southern Queensland, Australia.

99. PCR-based DNA markers
• Generated by using Polymerase Chain Reaction
• Preferred markers due to technical simplicity and cost
GEL ELECTROPHORESIS
Agarose or Acrylamide gels
PCR
PCR Buffer +
MgCl2 +
dNTPS +
Taq +
Primers +
DNA template
THERMAL CYCLING

100. Agarose gel electrophoresis
http://arbl.cvmbs.colostate.edu/hbooks/genetics/biotech/gels/agardna.html
UV light
UV transilluminator

101. UV light
UV transilluminator
Acrylamide gel electrophoresis 1

102. Acrylamide gel electrophoresis 2

103. Examples of marker-assisted backcrossing in some crops
Species Trait(s) Gene/QTLs Reference
Barley Yield QTLs on 2HL and
3HL
Schmierer et al., 2004
Bean Common bacterial
blight
QTLs on LGs B6
& B8
Mutlu et al., 2005
Maize Drought adaptation
(anthesis silking
interval)
QTLs on chr. 1, 2,
3, 8 and 10
Riabut and Ragot 2007
Rice Bacterial blight xa5, xa13, and
Xa21
Sanchez et al., 2000
Rice Heading date Hd1, Hd4, Hd5,
Hd6
Takeuchi et al., 2006
Rice Submergence
tolerance
SUB1 QTL Mackill et al., 2006 ; Neeraja
et al., 2007, Septiningsih et al.
2013, Iftekharuddaula et al.
2015
Wheat Powdery mildew 22 Pm genes Zhou et al., 2005

104.  Marker Assisted Breeding has been a widely-used
scheme in plant breeding and this will undoubtedly
continue.
 MAB can be used in order to trace the introgression of
the transgene into elite cultivars during backcrossing.
 Accurate background selection is impossible using
conventional methods.
 The cost of molecular breeding will continue to be a
major obstacle for its application in crop improvement.
 Costs for marker assays need to be considerably
reduced to apply Marker Assisted Breeding on a larger
scale.
CONCLUSION

105.  New SNP high-throughput genotyping methods may
also be cheaper than current methods, although a large
initial investment is required for the purchase of
equipment.
 SNP markers, because of their widespread abundance
and potentially high levels of polymorphism, and the
development of SNP genotyping platforms will have a
great impact on MAB in the future.
 The use of molecular makers in plant Breeding will
accelerate the potential for crop improvement in the
new millennium.
CONCLUSION (CONTD.)

106. 07/11/2015 106
BINA is committed to
the advancement of
agriculture
Thank you