This document discusses QTL analysis for heat tolerance in wheat. It outlines the prerequisites for QTL analysis, which include a mapping population, phenotypic data, genotypic data, and a marker linkage map. Several methods for QTL analysis are described, including single marker analysis, interval mapping approaches like composite interval mapping, and association mapping. The document also provides details on using QTL analysis and marker-assisted selection to develop wheat varieties with improved heat tolerance.

2. QTL Analysis for Heat Tolerance on Wheat

3. QTL Analysis
Content:-
1. Introduction
2. Pre-requisites for QTL Analysis
 Mapping Population
 Phenotypic & Genotypic data
 Linkage Map
6. Methods of QTLs Analysis
7. Future Prospects

4. Continuous Traits
Quantitative
Traits ?
Meristic Traits
Categorical Traits Ordinal traits
Binary Traits

6. QTL ?
A gene/set of genes or genomic regions
associated with the expression of a
quantitative trait ; referred to as
Quantitative Trait Locus(QTL).
Type s of QTLs:-
 Main effect QTLs
 Small effect QTLs
 E-QTLs
 e-QTLs
 m-QTL
 p-QTLs
 h-QTLs etc.

8. Pre-requisites for QTL Analysis
1. Mapping Population
2. Genotypic data
3. Phenotypic data
4. Marker Linkage Map
5. Appropriate software
packages

9. Mapping Population

10. Genotypic data

11. Marker Linkage Map

15. QTLs Analysis & Its Approaches
• Quantitative trait locus (QTL) analysis: a statistical method links two types of
information—phenotypic data (trait measurements) and genotypic data (usually
molecular markers)—in an attempt to explain the genetic basis of variation in
complex traits (Falconer & Mackay, 1996; Kearsey, 1998; Lynch & Walsh, 1998).
Approaches for QTL Analysis:
1. Single Marker Analysis
2. Interval Mapping(IM):-
 Simple Interval Mapping (SIM)
 Composite Interval Mapping (CIM)-Including Inclusive IM
 Multiple Interval Mapping (MIM)
3. Association Mapping(AM):-
 Candidate Gene Approach
 Genome Wide Association Studies(GWAS)

16. Bulk Segregant Analysis

17. 1.Single Marker Analysis (SMA)
Soller, M., Brody, T. and Genizi, A., 1976.
 Detect associations between molecular
markers and traits of interest
 Genetic Model :- Linear model represents an
association between the pattern of variation in trait
and the pattern of marker segregation:
Four Methods:-
1. ‘t’ Test
2. Regression Analysis
3. ANOVA
4. Maximum Likelihood Approach
A
a
M
m
QTL Marker

18. 1. ‘t’ Test:-
2. Regression Analysis:-
Regression equation :
Trait
Yi
AA aa AA aa
Trait
Yi

19. Maximum Likelihood Estimations
A good estimate of the unknown parameter θ would be the value
of θ that maximizes the probability, that is, the likelihood... of
getting the data we observed.

20. Likelihood Ratio and LOD Score(Newton Marton 1955)

21. ADVANTAGES AND LIMITATIONS OF SMA
Advantages
• Simplest method of QTL detection.
• Use basic statistical software.
• Not need linkage map.
Disadvantages/Limitations
 Confounded QTL effects & position, no
determine
 Statistical Power is low
 Epistasis interaction can not be
determine
 So, many false positive

22. Simple Interval Mapping (SIM)
Genetic Model :-
yj = μ + bxj + ej
M1 A
m1 a
M2
m2

24. Regression approach:-
y = m + βl + e
Yield
yi
l l l l l l l l l l l l
l - expected recombination frequency

25. Composite Interval Mapping
 Use most significant markers as Cofactors
 CIM: Refinement of SIM
 CIM: Combining interval mapping with multiple
regression approach
 Power of QTL detection is increased, reduction of
bias in the estimation of QTL position and effects.
Genetic Model :-
yj= μ + bxij+ ∑bkxjk + ej
Inclusive Composite Interval Mapping(ICIM)

26. Two Locus Analysis
Multiple Interval Mapping(MIM)

28. Linkage Analysis & Association Mapping

29. Heat tolerant genotype Heat susceptible genotype×
F1DH population
BC1 and BC2
Phenotyping Genotyping
QTL analysis
Marker Assisted
Backcrossing (MABS)
QTL analysis and MAS program for heat tolerance

31. THANK YOU