MARKER-ASSISTED BREEDING

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: [email protected]
[email protected]

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
Molecularmarkers(alsocalledDNAmarkers)represent
specificregionsonchromosomes.
DNAmarkersmayrepresentgenesorlociwithinnon-coding
regions.
ThedifferentformsproducedfromDNAmarkersatalocus
arecalledmarkeralleles.
ThegreatadvantageofDNAmarkerscomparedtoothertypes
ofmarkersistheirabundance.
Furthermore,theirdetectionisnotinfluencedby
environmentalfactorsorthedevelopmentalstageoftheplant.

8. MolecularmarkeristheheritableentityattheDNAleveltransmitted
fromparentstooffspring
Markeridentifiesspecificlocationonthegenomelikemilestone
Importantlyitunveilsthegeneticconstitutionofthelocus–
homozygousorheterozygous;ifhomozygouslikewhichparentor
allele
Segregation of a marker in BC
2F
2
generationLADDER 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 IR40931 BR11
LAAHHHBHAHBBBBHHBA
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 –r
A= ~95%
Marker A
QTL
Marker B
5 cM 5 cM
Using markers A and B:
1 -2 r
Ar
B= ~99.5%

10. Markers mustbe polymorphic
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
RM84 RM296
P
1P
2
P
1P
2
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,orSimpleSequenceRepeats(SSRs),are
polymorphiclocipresentinnuclearDNAthatconsistof
repeatingunitsof1-4basepairsinlength.
Theyaretypicallyneutral,co-dominantandareusedas
molecularmarkerswhichhavewide-rangingapplications
inthefieldofgenetics.
ThesizeoftheamplifiedfragmentsinSSRgenerally
rangefrom100to350bp
ForwardandreverseSSRprimersaredesignedfollowing
uniquesequencesofgenome
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 PCRLADDER 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 IR40931 BR11
LAAHHHBHAHBBBBHHBA
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 Indelmarkers
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 SUB1QTL 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 BC
4lines: 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 barcodingsystem

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 P
2P
1
DonorElite
cultivar
Desirable trait
e.g. disease resistance
HYV
Lacking for 1 trait
Called RP P
1 x F
1
P
1x BC1
P
1x BC2
P
1x BC3
P
1x BC4
P
1x BC5
P
1x 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 resistanceP
1x P
2
F
1
F
1x P
1
BC
1F
1x P
1
BC
2F
1x P
1
BC
4F
1
87.50% recovery genome of
Recurrent Parent
93.75% recovery genome of
Recurrent Parent
50% of genome from P
1+
50% of unrelated genome from P
2]
75% recovery genome of
Recurrent Parent
BC
1F
1
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(50%)] = 75% P
1
genome
BC
2F
1
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(75%)] = 87.5% P
1
genome
BC
3F
1
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(87.5%) ] = 93.75%
P
1genome
BC
3F
1x P
1
BC
2F
1x P
1
96.875% recovery genome of
Recurrent Parent
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(93.75%)] = 96.875%
P
1 genome
BC
5F
1
98.4375% recovery genome of
Recurrent Parent
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(96.875%)] =
98.4375 P
1genome
BC
5F
1x P
1
BC
6F
1
100% recovery genome of
Recurrent Parent
50% of genome from P
1 + 50% of genome from F1, which
itself is 50% P
1, therefore [50% + 50%(98.4375%)] =
98.4375 P
1genome
General equation for average recovery of the recurrent parent:
1 -(½)
n+1
where, n is the number of backcrosses to the recurrent parent.
for the F
1, n= 0; for BC
1, n=1; for the BC
2, n=2; for the BC
3, 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. P
1 x F
1
P
1 x P
2
CONVENTIONAL
BACKCROSSING
BC
1
VISUAL SELECTION OF BC1 PLANTS THAT
MOST CLOSELY RESEMBLE RP
BC
2
MARKER-ASSISTED
BACKCROSSING
P
1 x F
1
P
1 x P
2
BC
1
USE ‘BACKGROUND’ MARKERS TO SELECT PLANTS
THAT HAVE MAX RP GENOME
BC
2

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. F
1s must be confirmed by molecular markers

40. MAB: 1
ST
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: 2
ND
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: 3
RD
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
2
n+1
-1
2
n+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. GenerationRPgenomecontent(%)Donorgenomecontent(%)
F
1 50 50
BC
1 75 25
BC
2 87.5 12.5
BC
3 93.8 6.3
BC
4 96.9 3.1
BC
5 98.4 1.6
BC
6 99.2 0.8
BC
7 99.6 0.4
BC
8 99.8 0.2
Expected recovery of recurrent parent genome conventional
backcrossing in subsequent generations

49. %recurrentparentgenome
Backcross
generation
Number of
individuals
Marker-
assisted
backcross
Conventional
backcross
BC
1 70 79.0 75.0
BC
2 100 92.2 87.5
BC
3 150 98.0 93.7
BC
4 300 99.0 96.9
Source:Hospital,2003
Expected recovery of recurrent parent genome comparing conventional
and marker assisted backcrossing in subsequent generations

50. P
1 x F
1
P
1 x P
2
CONVENTIONAL BACKCROSSING
BC1
VISUAL SELECTION OF BC1 PLANTS THAT
MOST CLOSELY RESEMBLE RECURRENT
PARENT
BC2
MARKER-ASSISTED BACKCROSSING
P
1 x F
1
P
1 x P
2
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
lt
Genotyping
Samples
DNA extraction
Stock DNA
DNA dilution
PCR
Visualization
PAGE
Data Analysis
Data process
Scoring

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

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

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

57. S
a
lt
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
R
2
value.

58. S
a
lt
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
lt
Dataanalysis
Genotyping
RILs development
Phenotyping

60. S
a
lt
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 F
8RILs from the cross IR29/Pokkali.
S
al
t
(28)RM221
26.9
(21)RM487.8
(20)RM294.5
(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)RM2087.8
(25)RM2079.5
(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)CP62245.5
(10)RM493
5.3
(14)CP3970
1.8
(17)RM6386
4.2
(12)RM5941.4
(19)RM707521.6
(4)RM140
15.3
(3)RM24
15.1
(11)RM5623.6
(7)RM44929.0
(1)RM9
38.9
(5)RM220
1
LOD=5.78
LOD=5.44
LOD=4.30
LOD=3.37

62. S
a
lt
Continued.
(58)RM508
8.7
(53)RM190
22.1
(55)RM2045.7
(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
a
lt
Continue.
(73)RM223
19.2
(72)RM210
61.0
(74)RM2563.5
(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
lt
RM248
3.89
3.0
RM18
RM51
RM11
RM445
0.0
7
RFGWT
RM242
RM296
RM219
RM316
3.24
3.0 0.0
9
Chromosomelocationofassociatedofsalinitytolerancegenes
ofRFGWTatreproductivestagebasedonthethresholdLOD
3.0.TheQTLspossibleapositionisindicatedbypeakvalue
greaterthanthethresholdLODscore.

66. S
a
lt
3.37
3.0
RM231
RM227
RM36
RM148
0.0
3
4.33
3.0
RM307
OSR30
RM127
0.0
4
RBWT
Chromosomelocationofassociatedofsalinitytolerancegenesof
RBWTatreproductivestagebasedonthethresholdLOD3.0.
TheQTLspossibleapositionisindicatedbypeakvaluegreater
thanthethresholdLODscore.

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
Chromosomelocationofassociatedofsalinitytolerancegenesof
RBWTatreproductivestagebasedonthethresholdLOD3.0.
TheQTLspossibleapositionisindicatedbypeakvaluegreater
thanthethresholdLODscore.

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
a
lt
RFGWT
RBWT
RTBWT
TraitsFlanking
Markers
ChromosomeR
2
(%)LODP-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
40412375364576533710325535194 5971681448
RM248
RM18
RM51
RM11
RM445
44
9.2
41.0
26.8
25.1
606163569 383954307042735774797 16648212
RM248
RM18
RM51
RM11
RM445
51
9.2
41.0
26.8
25.1
21246218783158498 77666765431525806 1750
RM248
RM18
RM51
RM11
RM445
46
9.2
41.0
26.8
25.1
3472525 338147112928262013221 2 273
RM248
RM18
RM51
RM11
RM445
69
Chromosome 7
AAaa-
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 BC
1F
1plants 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 BC
1F
2
(>1000).
Fixed line by only one backcrossing and one selfing

82. Graphical genotype of IR64-Sub1/PSBRc18//IR64-Sub1 BC
1F
1
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 homozygosityin 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. F
2
F
1
Gene A + B
P
1
Gene A
xP
2
Gene B
MAS
Select F2 plants that
have Gene A and
Gene B
Genotypes
P
1: AAbb P
2: aaBB
F
1: AaBb
F
2
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. F
2
P
2
F
1
P
1 x
large populations (e.g. 2000 plants)
ResistantSusceptible
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
P
o
p
u
l
a
t
i
o
n

d
e
v
e
l
o
p
m
e
n
t

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


R
e
c
o
m
b
i
n
a
t
i
o
n

P
o
p
u
l
a
t
i
o
n

d
e
v
e
l
o
p
m
e
n
t

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
Fedearroz50
IR77298-14-1-2-10
PSBRc158
IR4630-22-2-5-1-3
IR45427-2B-2-2B-1-1
SambhaMahsuri+
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 +
MgCl
2 +
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. Examplesofmarker-assistedbackcrossinginsomecrops
SpeciesTrait(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 Mackillet al., 2006; Neeraja
et al., 2007, Septiningsihet al.
2013, Iftekharuddaulaet al.
2015
Wheat Powdery mildew 22Pm 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