Class presentation on QTLmapping

1. QTL mapping
PP 601: Functional genomics and genes associated with a few
physiological processes (2+0)
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Presented by
Ekatpure Sachin Chandrakant
PhD research scholar

2. Introduction
• Many agriculturally important traits such as yield, quality and some
forms of disease resistance are controlled by many genes
• Those are known as quantitative traits (also ‘polygenic,’
‘multifactorial’ or ‘complex’ traits)
• Eg: yield, seed weight, fruit length, some forms of disease resistance
etc
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3. Definition
The regions in the genomes that contain genes related to a particular
quantitative trait are known as Quantitative Trait Loci/ Locus (QTLs)
Term first coined by Gelderman in 1975
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4.  Identification of QTLs based only on conventional phenotypic evaluation is not
possible
 In 1980 discovery of molecular markers tremendously increase the identification of
QTLs
 The use of DNA markers in plant breeding has opened a new field in agriculture
called molecular breeding
 Also DNA markers were used in the construction of linkage maps
 Linkage maps can be utilised for identifying chromosomal regions that contain
genes controlling simple traits (controlled by a single gene) and quantitative traits
using QTL
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5. QTL mapping
• The process of constructing linkage maps and conducting QTL analysis
to identify genomic regions associated with traits with the help of
molecular markers is known as QTL mapping
• Also called as genetic, gene or genome mapping
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6. Section I: Genetic markers
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7. What is mean genetic markers?
 Genetic markers represent genetic differences between individual
organisms or species
 They do not represent the target genes themselves but act as ‘signs’ or
‘flags’
 Genetic markers that are located in close proximity to genes may be
referred to as ‘gene tags’
 All genetic markers occupy specific genomic positions within
chromosomes called ‘loci’
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8. Major types of genetic markers
Morphological/classical /visible markers:
 Which themselves are phenotypic
traits or characters
 Eg: flower colour, seed shape, growth
habits or pigmentation
Biochemical markers:
 Which include allelic variants of
enzymes called isozymes
 Detected by electrophoresis and
specific staining
DNA or Molecular markers:
 Which reveal sites of variation in DNA
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Main disadvantage is they are
limited in number and are
influenced by environmental
factors or the developmental
stage of the plant

9. DNA markers
 Most widely used type of marker predominantly due to their abundance
 Selectively neutral because they are non-coding sequences
 Not affected by environmental factors and/or the developmental stage of
the plant
 Numerous applications in plant breeding such as assessing the level of
genetic diversity within germplasm and cultivar identity
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10. CLASSIFICATION OF DNA MARKERS
Based on the method of their detection broadly divided into
 Hybridization-based
 PCR based
 DNA sequence-based
Visualises the genetic differences by gel electrophoresis
Staining with chemicals (ethidium bromide or silver)
Detection with radioactive or colourimetric probes
 Polymorphic DNA markers : helps in revealing the differences between individuals of the same or
different species.
 Monomorphic DNA markers: Markers that do not differentiate between genotypes are called
monomorphic markers
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11. Polymorphic vs. Monomorphic
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Monomorphic
Polymorphic
Monomorphic
Polymorphic
Polymorphic

12. Types of polymorphic markers
Polymorphic markers
 The different forms of a DNA marker (e.g. different sized bands on gels) are
called marker ‘alleles’
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Codominant Dominant
Differentiate in homozygous or heterozygous Do not differentiate the homo and heterozygous
Indicate differences in size On the basis of present or absent
Have many different alleles Only two alleles

13. Codominant and Dominant
Comparison between
(a) Codominant
(b) dominant markers
• Codominant markers can clearly
discriminate between homozygotes
and heterozygotes
• Whereas dominant markers do not
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DominantCodominant

14. Section II: Construction of linkage maps
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15. What are linkage maps?
 It is the ‘road map’ of the chromosomes derived from two different
parents
 Indicate the position and relative genetic distances between markers
along chromosomes (signs or landmarks along a highway)
 Helps to identify chromosomal locations containing genes and QTLs
associated with traits of interest; such maps may then be referred to
as ‘QTL’ /or ‘genetic’ maps
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16. Mapping principle
 Genes and markers segregate via chromosome recombination (called
crossing-over) during meiosis
 Genes or markers that are close together or tightly-linked will be
transmitted together from parent to progeny more frequently than
genes or markers that are located further apart
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17. Contd….
 In a segregating population, there is a mixture of parental and recombinant
genotypes
 The frequency of recombinant genotypes : infer the genetic distance between
markers
 The lower the frequency of recombination between two markers, the closer
they are situated on a chromosome and the higher the frequency of
recombination between two markers, the further away they are situated on a
chromosome).
 Markers that have a recombination frequency of 50% are described as ‘unlinked’
and assumed to be located far apart on the same chromosome or on different
chromosomes
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18. 18
e f
E F
E F
e f
G H
g h
g h
G H
G h
G H
g H
g h
E F
E f
e F
e f
Gametes
P
R
R
P
P
R
R
P
Frequency
45%
5%
5%
45%
30%
20%
20%
30%
Recombination events between homologous chromosomes

19. How linkage map constructed?
The three main steps of linkage map construction are
(1) Production of a mapping population
(2) Identification of polymorphism
(3) Linkage analysis of markers
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20. 1. Production of a mapping population
Requires a segregating plant population (i.e. a population derived from sexual
reproduction)
The parents selected will differ for one or more traits of interest
Population sizes: generally range from 50 to 250 individuals
For high-resolution mapping larger populations are required
If map is used for QTL studies then the mapping population must be
phenotypically evaluated (i.e. trait data must be collected) before subsequent
QTL mapping
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21. mapping population contd……
In self pollinated species, parents that are both highly homozygous (inbred)
In cross pollinating species, the situation is more complicated being
heterozygous
The following populations can be used
Backcross (BC) populations (F1 X one of the parents)
F2 population (selfing of F1 hybrids) and RI (Recombinant Inbreds)
Double Haploids
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23. mapping population contd……
 The major advantage of BC and F2 is that it can be produced in short period of
time
 RI obtained from F2 inbreeding consist of a series of homozygous lines
 Each containing a unique combination of chromosomal segments from the
original parents
 But it takes 6-8 generations for development
 Doubled haploid (DH) populations may be produced by induction of
chromosome doubling from pollen grains
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24. 2. Identification of polymorphism
• Select an appropriate marker that shows differences between parents (polymorphic
markers)
• It is critical that sufficient polymorphism exists between parents
• In Cross pollinating species, higher DNA polymorphism exits compared to inbreeding
species
• So in inbreeding species, parents selected should be distantly related
• Then marker should be screened across the entire mapping population, including the
parents : marker genotyping
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26. Expected segregation ratios for markers in
different population types
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Population Codominant markers Dominant markers
F2 1:2:1 (AA:Aa:aa) 3:1 (B:bb)
BC 1:1 (Cc:cc) 1:1 (Dd:dd)
RI/DH 1:1 (EE:ee) 1:1 (FF:ff)

27. Identification of polymorphism contd..
 Generally, markers will segregate in a Mendelian fashion although
distorted segregation ratios may be encountered
 Significant deviations from expected ratios can be analysed using chi-
square tests
 In polyploid species, identifying polymorphic markers is more complicated
 So mapping of diploid relatives of polyploid species is done
 However, diploid relatives do not exist for all polyploid species
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28. 3. Linkage analysis of markers
• Code data for each DNA marker on each individual of a population and conduct
linkage analysis using computer programs
• Linkage between markers is usually calculated using
• Odds ratios: the ratio of linkage versus no linkage
• LOD value/ LOD score: Logarithm of the odd ratio
• An LOD value of 3 between 2 markers: linkage is 1000 times more likely than no
linkage (1000:1)
• Manual analysis is not feasible, so computer programmes needed
• Commonly used software programs
• Mapmaker/ EXP, MapManager QTX, JoinMap etc 28

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30. Linkage map
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31. Linkage map
• Linked markers are grouped together into ‘linkage groups,’ which
represent chromosomal segments or entire chromosomes
• Polymorphic markers detected are not necessarily evenly distributed
over the chromosome, but clustered in some regions and absent in
others
• The accuracy of measuring the genetic distance and determining
marker order is directly related to the number of individuals studied
in the mapping population, min 50 individuals
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32. Genetic distance and mapping functions
• Greater the distance between markers, the greater the chance of recombination
occurring during meiosis
• Distance along a linkage map is measured in terms of the frequency of recombination
between genetic markers
• Mapping functions are required to convert recombination fractions into centiMorgans
(cM)
• When map distances are small (<10 cM), the map distance equals the recombination
frequency
• However, this relationship does not apply for map distances that are greater than 10
cM
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33. Mapping Function
• Kosambi mapping function, which assumes that recombination
events influence the occurrence of adjacent recombination events
• Haldane mapping function, which assumes no interference between
crossover events
• There are recombination ‘hot spots’ and ‘cold spots,’ which are
chromosomal regions in which recombination occurs more frequently
or less frequently, respectively
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34. Section III: QTL analysis
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35. Principle of QTL analysis
• Based on the presence or absence of a particular marker loci, the
mapping population is partitioned into different genotypic groups and
these groups are analyzed for significant differences with respect to the
trait
• A significant difference between phenotypic means of the groups
indicates that the particular marker locus is linked to a QTL controlling
the trait
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37. Principle of QTL analysis contd…
• If, QTL & marker is closely linked, chance of recombination will be less
• So QTL and marker will be inherited together and mean of the group will
have significant difference
• If loosely linked or unlinked, there is independent segregation of the
marker and QTL
• There will be no significant difference between means of the genotype
groups
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38. Methods to detect QTLs
 Single-Marker analysis
 Simple Interval mapping
 Composite Interval mapping
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39. 1. Single-marker analysis / single point analysis
Simplest method for detecting QTLs associated with single markers.
The statistical methods used : t-tests, analysis of variance (ANOVA) and linear
regression
Linear regression is most commonly used: Coefficient of determination (R2) :
explains the phenotypic variation arising from the QTL linked to the marker
Does not require a complete linkage map and can be performed with basic
statistical software programs
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40. Single-marker analysis
• QGene and MapManager QTX are commonly used computer programs to
perform single-marker analysis
Disadvantage
• A QTL is from a marker, the less likely it will be detected because recombination
may occur between them
• Large no of segregating DNA markers may be used for overcoming this issue
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Marker linkage group P value R2
E 2 <0.0001 91
F 2 0.0001 58
G 2 0.0230 26
H 2 0.5701 2

41. 2. Simple Interval Mapping
• Instead of analyzing single markers, the intervals between adjacent
pairs of linked markers along chromosomes is analysed
• This compensates for recombination between the markers and the
QTL, and is considered statistically more powerful
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42. 3. Composite interval mapping
• Combines interval mapping with linear regression and includes
additional genetic markers in the statistical model in addition to an
adjacent pair of linked markers for interval mapping
• More precise and effective at mapping QTL
• QTL Cartographer, MapManager QTX and PLABQTL are commonly
used softwares
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43. Understanding interval mapping results
• SIM and CIM produce a profile of the likely sites for a QTL between
adjacent linked markers
• The statistical results are typically presented using a logarithmic of odds
(LOD) score or likelihood ratio statistic (LRS)
• LRS = 4.6 × LOD
• Position for a QTL: position where the highest
• LOD value is obtained

45. Understanding interval mapping results contd…
• Real QTL: The peak must also exceed a specified significance and
is determined using permutation tests
• The phenotypic values of the population are ‘shuffled’ whilst the
marker genotypic values are held constant and QTL analysis is
performed some 500-1000 times to assess the level of false
positive marker-trait associations and significant levels are
determined
• Previously, LOD score of between 2.0 to 3.0 (most commonly 3.0)
was usually chosen as the significance threshold

46. Reporting and describing QTLs from Interval mapping
• Based on most closely linked markers on linkage maps
• The chromosomal regions represented by rectangles are usually the region
that exceeds the significance threshold
• “flanking” markers: Pair of markers that are most tightly-linked markers on
each side of a QTL and this selection is more reliable than selection based on a
single marker
• Reason: There is a much lower chance of recombination between two
markers and QTL compared to the chance of recombination between a single
marker and QTL

48. •QTLs can only be detected for traits that segregate between the
parents
•Several criteria may be used for phenotypic evaluation of a single
trait
•QTLs that are detected in common regions (based on different
criteria of a single trait) are likely to be important OTL for
controlling that trait

49. •Mapping populations may also be constructed based on parents
that segregate for multiple traits.
•This is advantageous because QTLs controlling the different traits
can be located on a single map
•May be difficult with completely or semi-destructive bioassays
(e.g. screening for resistance to necrotrophic fungal pathogens)

50. QTL can be major or minor based on the proportion of the phenotypic
variation i.e.. R2 value
Major QTLs: >10%), stable QTL
Minor QTLs: <10%. environmentally sensitive especially (disease
resistance)
(1) Suggestive;
(2) Significant;
(3) Highly significant to “avoid a flood of false positive claims” and to
ensure that “true hints of linkage” were not missed
Significant and highly-significant: significance levels of 5 and 0.1%,
respectively
Suggestive: Expected to occur once at random in a QTL mapping study
MapManager QTX reports QTL mapping results with this classification

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Molecular Diversity
Screening for molecular polymorphism
Parental Selection Phenotypic Difference
Good contrast between parent for
target trait (e.g. Resistance vs. Susceptibility
Segregating Population
Back cross, DH, RIL, NIL
Genotyping
Assay of mapping population using
polymorphic molecular markers
Linkage Map
MAPMAKER, JOINMAP, LINKAGE
Phenotypic Evolution
Measurements of target traits in
field/ green house replicated trials
QTL analysis
MAPMAKER-QTL, QTL-cartographer, QGene
QTL validation
Repetition of experiment in crosses Involving different
base germplasm
Breeding Applications
Marker Assisted Selection, Recurrent genome selection, QTL introgression, Differential QTL selection,

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Thank You