QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis All the above steps are described in these slides very briefly along with two case studies.

2. QTL Mapping for Crop
Improvement
Presented by:
SANDEEP KUMAR SINGH
Adm. No. -02/PBG/Ph.D./17
Ph.D. Research scholar
(Plant Breeding and Genetics)
DEPARTMENT OF Plant Breeding and Genetics
COLLEGE OF AGRICULTURE,
ORISSA UNVERSITY OF AGRICULTURE AND TECHNOLOGY,
BHUBANESWAR, ODISHA-751003
Credit seminar
On
Chairman:
Dr. P. N. Jagdev
(Professor, Department of Plant Breeding and
Genetics, CA, OUAT, BBSR.)

3. Qualitative Quantitative
Qualitative Vs Quantitative traits
Few genes
Low environmental Influence
Distinct classes
Discontinuous variation
Polygenes
High environmental Influence
No distinct classes
Continuous variation

4.  A gene or chromosomal region that affects a quantitative trait
 Must be polymorphic (have allelic variation) to have an effect
in a population
 Must be linked to a polymorphic marker allele to be detected
 Term first coined by Gelderman in 1975.
What a QTL is?

5. QTLs have the following characteristics
These traits are controlled by multiple genes, each segregating
according to Mendel's laws.
These traits can also be affected by the environment to
varying degrees.
Many genes control any given trait and Allelic variations are
fully functional.
Individual gene effects is small &The genes involved can be
dominant, or co-dominant.
The genes involved can be subject to epistasis or pleiotrophic
effect.

6. A statistical method links
two types of information
Genotypic data
(Genetic Marker)
A linkage map of
polymorphic markers
Phenotypic data
(Quantitative Trait)
Variation within
a mapping population
QTL Analysis
Requirement of
QTL Analysis

7. Construction of Linkage
map

9. Types of mapping population
Secondary MPPrimary MP
F2 Populattion
F2 derrived F3
Back cross
Multi Parent Advanced Generation Intercross (MAGIC) population
Double haploid (DH)
Recombinant Inbred Lines (RILs)
Near Isogenic Lines (NILs)
Chromosomal segment substitutional lines(CSSLs)
Advanced inter crossed lines
Immortalised F2
Recurrent selection back cross (RSB)
Inter connected mapping population
Mortal
Population
Immortal
Population
mortal
Population

10. •NIL: Introgression of a gene by repeated backcrossing
combined with selection for the gene.
** CSSL: Repeated backcrossing without selection; each
line has a distinct chromosome segment from the donor
parent.
@ RSB: The donor parent has high value for a quantitative
trait. In each back cross generation, the individual with the
highest value for the trait is selected and backcrossed to
the recurrent parent
A schematic
representation of the
various biparental
mapping populations
Adopted from Marker assisted plant breeding: Principle and Practices by B.D.Singh A.K.Singh

12. F2 derived F3(F2:3) population
AA aa
F1
F2 AA Aa aa
All AA AA, Aa, aa All aaF3
Aa
Parents
Suitable for
 Mapping quantitative
traits
 Mapping recessive
genes
 Useful for reconstitution
of individual F2
genotypes
Demerit
Like F2 population, it is
mortal

17. Immortalized F2 population
Parent 1 Parent 2
AAbb X aaBB
F1 AaBb
Conventional F2 population
Immortalized F2 population
(by open pollinating the RILs)
RILs produced from AAbb X aaBB
AABB, AAbb
aaBB, aabb
Six possible RIL
combinations
Six
heterozygous
genotypes
AABB X AAbb
X aaBB
X aabb
AABb
AaBB
AaBb
AAbb X aaBB
X aabb
AaBb
Aabb
aaBB X aabb aaBb
AB Ab aB ab
AB AAB
B
AABb AaBB AaBb
Ab AABb AAbb AaBb Aabb
aB AaBB AaBb aaBB aaBb
ab AaBb Aabb aaBb aabb
F2
Advantages
 Population identical to the
conventional F2 population
can be produced and
replicated ‘n’ number of
times
 Individual F2 genotypes
can be evaluated over the
years and locations
 No need for genotyping
the immortalized F2s. Their
genotype can be deduced
based on their parental
RILs genotypes. Thus
economizing the cost of
mapping
 It is possible to estimate
the additive X dominance
(j) and dominance X
dominance (l) effects

18. Chromosomal segment substitutional lines(CSSLs)
 Phenotypic
characterization of each
line can reveal which
chromosome fragment
from the donor has the
gene(s) associated with an
interesting trait.

19. Advanced Inter crossed Lines (AIL)
Developed by intermating the individuals of F2 and subsequent generations from a
suitable cross.
Intermating in the segregating generations maintains heterozygosity in the population
and allows recombination between the QTLs and the markers linked to them in every
generation leading to a more precise location of the QTLs.
Advantages:
It was estimated that the confidence interval of QTLs would be reduced by up to five-
fold in AILs as compared to that in an F2 population (Darvasi and Soller 1995).
Disadvantages:
Appropriate statistical methods for modeling and analysis of the data from AILs are not
available

20. Recurrent selection back cross (RSB)
 Given by Wright (1952).
 F1 obtained from a cross between a homozygous line with high value for a quantitative
trait (the DP) and a homozygous line with low value for the trait (the RP) and the
subsequent backcross progeny are backcrossed to the RP.
 In each backcross generation, a predetermined number of individuals with the top
phenotypic values (i.e., DP phenotype) for the trait are selected and backcrossed to the RP.
Advantages:
Used for high-resolution QTL mapping
Disadvantages:
 High effort, resources, and time consuming.
 RSB is suited for localization of large effect QTLs, while important quantitative traits like
yield are governed by moderate to low effects QTL.

21. Inter connected mapping population
 Given by Gilbert, 1985

22. Multi Parent Advanced Generation Intercross (MAGIC) population
 Extension of AIL, proposed by Darvasi and Soller (1995) in Mice Mackay and Powell (2007)
 It is differ from AIL with involvement of multi-parent
Disadvantages:
 Large number of crossing
progrmme.
 Time and labour consuming.

23. Comparison of different mapping population

24. F2 BC RILs NILs CSSLs Immortalised F2

26. Genetic
map
Linkage
map
Physical
map
Cytological
map
 Sturtevant (1913) - Developed the first genetic map – on fruitflies
 The recombination frequency - measure of the distance between two genes
 Coined the term Recombination Fraction.

27. Linkage mapping
Finding those genes/markers that are linked together and co-inherited to
the next generation
Markers are mapped relative to one another on chromosomes and used
as signposts against which to map genes of interest that are linked with
marker
The distance between two genes - determined by their recombination
fraction
The map units centimorgan (cM)
1 cM = distance over which 1 crossover occurs (on average) per meiosis
(no general relationship between genetic distance and physical distance
in base pairs)

28. Mapping Functions
A mapping function translates recombination frequencies between two loci into a map
distance
Within small distances, a mapping function is simply:
map distance (d) = recombination fraction (r)
Two types of mapping functions
1. Haldane mapping function – When no interference exist (all crossovers occurs
independently of one another)
2. Kosambi mapping function – Allows some positive interference (one chiasma
deters the occurrence of the second in close proximity to the first)

29. Testing for Linkage – LOD (Log of Odds) scores
When 2 genes are segregating independently or not can be known by 2 method
1) Chi square test
2) LOD Score
 Performs the likelihood of a certain recombination fraction (r) versus the
likelihood of no linkage ( r= 0.5)
 LOD score - the log10 of this likelihood ratio
LOD score >3 --- null hypothesis (no linkage r= 0.5) is rejected (ratio of likelihoods
of 1000 to 1 ---- among the 1,000 plants, the chance of cross over is 1)

30. Mapping of genetic markers Genetic Segregation Ratio in
Different Marker-Population
Combinations

31. Bulk segregant analysis (BSA)
Resistant Parent Susceptible ParentX
F1
F2 individuals
R P S P R B S B R R S R S S

34. A
E
H
F
J
0.2cm
0.2cm
0.2cm
0.2cm
D
I
C
B
G
0.2cm
0.2cm
0.2cm
0.2cm

36. QTL mapping
Single QTL
mapping
Single Marker
Analysis
(SMA)
Simple
Interval
Mapping
(SIM)
Multiple QTL
mapping
Composite
Interval
Mapping
(CIM)
Multiple
Interval
Mapping
(MIM)
Bayesian
Multiple QTL
mapping

37.  Single point analysis
 Simplest and earliest method of QTL detection
 In this method each marker is separately tested for its association with the targeted traits
based on linear model:
yj = μ + f (markerj) + ɛj, where
yj is trait value of the jth individual in the population, μ is population mean, f (markerj) is a function
of marker genotype, ɛj is the residual associated with the jth individual
SMA: (Soller and Brody, 1976)

38. • Marker genotypes treated as classification variable
- for a backcross (2 genotypes/ Classes): use t-test
- for F2 population (up to 3 genotypes/classes): use ANOVA
- For t-test individual in the population are classified according to the classes of genotype and
tested for its significance.
- Significant difference indicates the marker to be associated with the QTL affecting the trait.
- The chance of detection of QTL depends on:
1)the magnitude of the effect size of QTL (=yQq-yqq )
2) The recombination rate (r) between the trait and the marker
yMm-ymm=(1-r) (yQq-yqq)
So, for a given magnitude of QTL effect, larger the value of r, smaller will be the difference
in phenotypic mean of the 2 marker classes, same time the smaller will be the likelihood of
this difference being significant.
M Qr

39. ANOVA for the differences among marker classes

40. 1. Conceptually and computationally
simple
2. Genetic linkage map
information not needed
3. Easily incorporates covariates
4. Informative when markers
sufficiently cover the genome
5. Can be extended to multiple
regression for multiple QTL model
1. Location and effects of detected QTLs are
Confounded larger QTL effect could be because the
marker is close to a QTL or farther from the QTL, but
the QTL contributes much significantly to the trait
2. QTL position cannot be precisely detected
3. Power to detect QTL is low when marker density
is low
4. Multiple comparison increases false positives
5. Missing genotypes are totally excluded from
analysis
6. Limited ability to separate linked QTLs and
no ability to assess interacting QTLs
Advantages Limitation

41. SIM: Lander & Botstein (1989)
Concept:
Based on joint segregation of a pair of adjacent markers and a putative QTL
within an interval flanked by the marker pair.
 SIM makes a systematic linear or one dimensional search for a QTL at
several location say, at every 1 or 2 cm within each marker interval.
 Genetic Model: yi=µ+axi+ei where, yi =trait phenotype of ith individual,
µ= Grand phenotypic mean of the population, a=QTL effect, xi=indicator
of QTL genotype, ei =random error term with σ2 as variance and mean as
0.
 Xi represent the no. of positive allele at QTL locus for eg: 1 for Qq
genotype, 0 for qq genotype
M1 M2Q
r
r2r1

42.  A linear regression programme use to estimates the (MLEs) Maximum Likelihood
estimates for µ, σ2 , and a of xi
 The MLEs for these parameter are calculated again assuming that there is no QTL in the
marker interval.
 The above MLEs are used to calculate the LOD score.

43. LOD curve

49. Limitation:
1) The arbitrariness in selection of co-factor for QTL analysis.
2) Unable to detect the interacting QTL. So, inefficient when epistasis is
present.
 Using multiple marker intervals simultaneously to identify multiple putative
QTLs.
 Study epistatic effects of QTLs.
MIM: Kao et al, 1999

50. Bayesian Multiple QTL mapping
 Here a prior distribution is selected, from which the posterior
distribution is derived and inference are drawn from the posterior
distribution (here it is QTL).
 It treat the QTL as random variable.
 It has very little practical utility in case of bi-parental mapping
population
Limitation:
 Difficulties in choosing the prior distribution
 Complexities of the computation
 Lack of user friendly software

54. Parent-HP2216 (Susceptible) x Tetep (Resistant)
Mapping population- 127 RIL
Marker- 940 SSR markers

55. Result:
A total of 12 QTLs were identified for sheath blight resistance using composite interval mapping. These QTLs
were located on chromosomes 1, 3,7, 8, 9 and 11 and the respective alleles explain 8.13– 26.05%, of the
total phenotypic variation

57. Parent-Cocodrie (High yield in stress condition) x Vandana (Low yield under stress condition)
Mapping population-187 F2 : 3 families
Marker- 330 SSR markers

58. FIGURE 3 | Quantitiative trait loci on chromosomes 1, 5, 8, and 9 associated with grain yield under greenhouse
drought. QTLs (in green) represent the genomic regions associated with grain yield in non-stressed control
conditions. Markers identified through single marker analysis and within the QTL interval are depicted in bold
red fonts.
RESULT:

59. Table: Potential QTL’s mapped in rice using different mapping populations for various growth, physiological
and yield traits
Trait QTL Marker Population References

60. Trait QTL Marker Population References

61. Trait QTL Marker Population References

62.  Identification of novel genes
 Good alternative when mutant screening is laborious and Expensive
 Small additive effects / epistatic loci are not detected and may require further analyses.
 No. of QTLs detected, their position and effects are subjected to statistical error.
Future Prospects
 Constant improvements of Molecular platforms
 New Types of genetic materials( e.g. introgression lines: small effect QTLs
can be detected)
 Advances in Bioinformatics

63. References:
Allard, R. W. 1960. Principles of Plant Breeding.John Wiley and Sons Inc, New York, USA.
Arraudeau M, Harahap Z (1986). Relevant upland breeding objectives. In: Progress in upland rice research. IRRI, Manila, pp 189-197
Benjamin JG, Nielsen DC (2006).Water deficit effects on root distribution of soybean, field pea and chickpea. Field Crops Res., 97: 248-
253.
Collard, Bertrand & Jahufer, Zulfi & Brouwer, J.B. & Pang, Edwin. (2005). An introduction to markers, quantitative trait loci (QTL)
mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica. 142. 169-196. 10.1007/s10681-005-
1681-5.
David CC (1991). The world rice economy: challenges ahead. In: Khush GS, Toenniessen GH (eds) Rice biotechnology. IRRI, Manila,
pp 1-18
Dixit, S., Swamy, B. M., Vikram, P., Bernier, J., Cruz, M. S., Amante, M., ...& Kumar, A. (2012). Increased drought tolerance and wider
adaptability of qDTY 12.1 conferred by its interaction with qDTY 2.3 and qDTY 3.2. Molecular breeding, 30(4), 1767-1779.
O’Toole JC (1982). Adaptation of rice to drought-prone environments. In: Drought Resistance in Crops with the Emphasis on Rice.
Manila: IRRI, pp 195–213
Pandey S (2007). Economic costs of drought and rice farmers’ coping mechanisms. International. Rice Research Notes, 1:5–11.
Singh, B.D. &A.K.Singh. 2015.: Marker assisted plant breeding: Principle and Practices. Springer, New Delhi, Heidelberg, New York,
USA.
Sofi, Parvaze & A.G, Rather. (2007). QTL Analysis in Rice Improvement: Concept, Methodology and Application. Biotechnology. 6.
10.3923/biotech.2007.1.13.

64. THANK YOU!!!