The genomic regions associated with the expression of a quantitative trait is referred to as quantitative trait loci (QTL). A QTL may contain one or more genes affecting the concerned quantitative trait. Sax(1923) reported linkage between seed coat colour and seed size, which are qualitative and quantitative traits in common bean and the work highlighted the basic principles for mapping of polygenes based on the detection of association between a quantitative trait phenotype and a genetic marker. Thoday (1961) explored this QTL concept further by combining elaborate cytogenetic techniques with genetic analysis to map QTLs for several quantitative traits in Drosophila

1. PRESENTATION
ON
STRATEGIES FOR MAPPING GENES OF AGRONOMIC TRAITS IN PLANTS
COURSE NO: 603 – GENOMICS IN CROP IMPROVEMENT
SUBMITTED BY
M. TUSHARA
Ph. D.(Ag.) – I YEAR
DEPT. OF GENETIC AND PLANT BREEDING

2. AGRONOMIC TRAITS
 Agronomic traits in plants include:
 Days to 50 % flowering
Plant height
100 Seed weight
Grain yield
Panicle shape etc.,
 Most of the agronomic traits are quantitative in nature.

5. FAMILY MAPPING POPULATION MAPPING
QTL ANALYSIS
1. CANDIDATE GENE
APPROACH
2. GWAS

9. • The genomic regions associated with the expression of a quantitative trait is
referred to as quantitative trait loci (QTL).
• A QTL may contain one or more genes affecting the concerned quantitative
trait.
• Sax(1923) reported linkage between seed coat colour and seed size, which are
qualitative and quantitative traits in common bean and the work highlighted the
basic principles for mapping of polygenes based on the detection of association
between a quantitative trait phenotype and a genetic marker.
• Thoday (1961) explored this QTL concept further by combining elaborate
cytogenetic techniques with genetic analysis to map QTLs for several
quantitative traits in Drosophila.
What is a QTL ?

10. QTLs have been grouped into different categories on the basis of their
effect size, effect of the environment on their expression and the manner of
their action.
1. Main effect QTLs – produce direct effect on the expression of the
concerned traits.
2. Epistatic QTLs – interact with the main effect QTLs to influence the trait
phenotype. Thus, epistatic QTLs are considered as modifiers, and they
together constitute the genetic background.
3. Major QTL – A main effect QTL is described as a major QTL if it
explains 10 % or more of the phenotypic variance for the trait.
4. Minor QTL – A QTL with a smaller effect size.
Most of the quantitative traits are governed by few major QTLs and many
minor QTLs.

11. • QTL mapping is a method in which molecular markers are utilized to locate the
genes that affect the traits of interest.
• QTL analysis is a statistical method that links two types of information—
phenotypic data (trait measurements) and genotypic data (usually molecular
markers).
• Most agricultural traits of economic interest are polygenic and quantitative in
nature and are controlled by many genes on the same/different chromosome.
• The chromosomal regions having genes for these quantitative traits are referred
to as QTL.
QTL MAPPING

12. Procedure for QTL MAPPING

13. 1.
2.
Selectionof two diverse parents
Mapping populationdevelopment
Selection of two diverse parents having allelic variations that affect the trait of
interest and preferably they should have been developed by divergent
selection for the trait.
 Near-isogenic lines (NILs), DHs, backcrosses (BCs), recombinant inbred
lines (RILs) and F2 populations can be used as the mapping population.
 Practically 50–250 individuals are selected in a mapping population but for
high-resolution and fine mapping, a larger size of the mapping population
is required.

14. 3.
4.
Phenotyping
The mapping population is evaluated for the target trait in replicated trials
conducted, preferably over locations and years
Genotyping
 The two parents of the mapping population are tested with a large number of
markers covering the entire genome, and polymorphic markers are identified.
 It is important that polymorphic markers should cover the whole genome at a
sufficient density.
 All the lines of the mapping population are now analyzed using these polymorphic
markers

15. 5.
Linkage map construction& QTL Detection
 The marker genotype data are used to construct a framework linkage map for the
population, which depicts the order of the markers and the genetic distances
between marker pairs in terms of centimorgans.
 Finally, the marker genotype and the trait phenotype data are analyzed to detect
association between marker genotypes and the trait phenotype.
 The plants are divided into separate groups on the basis of their marker genotype.
For each of these groups mean and variance for the trait phenotype are estimated
and used for comparison between the groups.
 In case the genotype groups for a marker differ significantly for the trait of interest,
it is concluded that the concerned marker is associated with the trait, the marker is
most likely linked to a QTL controlling the trait phenotype.

17. 17

18. 7. QTLvalidation
 After the QTL detection, it is necessary to validate that particular QTL.
 For this purpose, diverse populations will be developed by crossing different
parents in order to check the presence of a particular QTL in other populations
with different genetic background.
 NILs are commonly used for the confirmation and validation of QTL.
 Confirmation of QTL provides the information about the marker to be used or
not for MAS.

19. PRINCIPLE OF QTL
QTL mapping is based on marker segregation via chromosome recombination during meiosis, in
which those markers which are tightly linked with each other will be transferred together more
commonly during recombination as compared to those which are away from each other.
This recombination frequency is used to calculate the recombination fractions. Through the
segregation analysis, the actual distance and relative order of markers can be calculated.
Odds ratios (the ratio of linkage versus no linkage) are used for the calculation of the
linkage between markers.
This value is called LOD, or logarithm of odds. For the construction of linkage maps, LOD
values of >3 are considered ideal.
The LOD score is a measure of the strength of evidence for the presence of a QTL at a particular
location.

21. SOFTWARES USED
• Map Maker/QTL
• QTL Cartographer
• MapManager
• R ,
• QTLNetwork,
• PLABQTL,
• QGENE and
• MapChart

22. 22

23. • It is also referred as single point analysis.
• It is the simplest method for detecting QTL associate with single markers.
• The statistical method used for the single point analyses includes T-test, analyses
of variance (ANOVA) and linear regression.
• SMA is done for each marker locus, independent of information for other loci.
SINGLE MARKERANALYSIS

24. • Simple Interval Mapping was first proposed by Lander and Botstein in 1989.
• SIM method makes use of linkage maps and analysis intervals between adjacent
pairs of linked markers along the chromosomes, simultaneously, instead of
analyzing single markers.
• Presence of a putative QTL is estimated if the logarithm of odds ratios (LOD)
exceeds a critical threshold which is more often fixed as > or =3.
• The use of linked markers for analysis compensates for recombination between
the marker and the QTL, and is considered statistically more powerful than SMA.
SIMPLE INTERVAL MAPPING

25. • Composite Interval Mapping is one of the popular methods used to detect QTLs.
• CIM was developed by Zeng (1993; 1994).
• This method combines interval mapping with linear regression. It considers a
marker interval plus a few other well-chosen single markers in each analysis.
• The main advantage of CIM is that it is more precise and effective at mapping
QTLs compared to SMA and SIM, especially when linked QTL are involved.
COMPOSITEINTERVALMAPPING

26. • Most recently MIM has become popular for mapping QTLs.
• MIM is the extension of interval mapping to multiple QTLs, just as multiple
regression extends analysis of variance.
• MIM allows one to infer the location of QTLs to position between markers makes
proper allowance for missing genotype data and can allow interaction between
QTLs.
MULTIPLEINTERVALMAPPING

27. FACTORS AFFECTING QTL MAPPING
1.
2.
3.
4.
5.
GENETIC PROPERTIES OF QTLs
GENETIC BACKGROUND
TYPE AND SIZE OF MAPPING POPULATION
ENVIRONMENTAL EFFECTS ON QTL EXPRESSION
EXPERIMENTAL ERROR

28. ADVANTAGES OF QTL MAPPING
• Linkage mapping detects and maps each of the QTLs governing the target trait
within relatively short confidence intervals.
• QTL mapping identifies markers flanking the QTL regions; these markers can be
used for MAS.
• It provides an estimate of the QTL effect size on the trait phenotype. Thus,
breeders get a rough idea of the usefulness of incorporating a given QTL in their
breeding programs.
• Selective DNA pooling can be combined with transcriptome analysis to identify a
limited number of candidate genes located in the genomic region harboring the
QTL for the target trait.

29. Limitations of QTL mapping
• Since the mapping population is initiated by crossing two parents selected for the
purpose, genetic variation in the quantitative traits of the population is limited to
the differences between the two parents.
• Multiple alleles of genes/ QTLs cannot be analyzed by QTL mapping but can be
analyzed by using interconnected populations like MAGIC and NAM.
• Low resolution power of mapping.
• QTL analysis (including population development, marker genotyping, trait
evaluation, and statistical analysis) is expensive in time and materials.
• It is difficult to distinguish two closely linked QTLs, those that are less than 20
cM apart.

30. ASSOCATION MAPPING
• Association mapping (AM) is significant association of molecular markers with a phenotypic
trait.
• Statistically, Association Mapping is the covariance between the polymorphism present in the
marker and the trait of interest.
• It is more time saving as compared to linkage mapping and provides greater mapping
resolution with a higher number of recombination events.
• AM facilitates the identification of a greater number of alleles due to availability of more
genetic variations with larger background; historically measured phenotypic data can also be
used for Association Mapping.
• It uses Linkage Disequilibrium between markers and the concerned genes/ QTLs for
identifying marker- trait associations.
• AM is also known as association analysis, LD mapping, population mapping and
structured association mapping.

31. PRINCIPLE OF ASSOCIATION MAPPING

39. GENERAL PROCEDURE OF ASSOCIATION MAPPING
1.
2.
3.
4.
5.
6.
Association Mapping Population/ Association panel
Phenotyping
Genotyping for population structure analysis
Structure and Kinship analysis
Genotyping for LD analysis
AM and LD analysis

40. 1.
2.
Association Mapping Population/ Association panel
A large number of random sample from a natural population, a germplasm core
collection, a collection of breeding lines including cultivars or a population
derived from multiparent crosses of the concerned species is used for AM.
Phenotyping
The selected sample is evaluated for the various traits of interest; this is called
phenotyping. It should be preferably based on replicated trials conducted over
locations and years to minimize environmental effects. The trials should be
conducted using a suitable experimental design like RBD, Augmented design,
nested design etc.

41. 3.
4.
Genotyping for population structure analysis
The sample is then genotyped, i.e., tested with a set of molecular markers
(preferably SSR markers) that are evenly distributed over the entire genome of the
species. These markers should be unlinked, i.e., should be located more than 40
cM apart in the genome.
Structure and Kinship analysis
The marker data are analyzed to detect and estimate the population structure of the
sample using the STRUCTURE program and the extent of kinship among the
individuals of the sample using the TASSEL program.

42. 5.
6.
Genotyping for LD analysis
The sample is also genotyped with a sufficiently large number of molecular
markers that cover entire genome as densely as is feasible so that LD between
markers and the loci of interest can be detected. SSR and SNP marker systems are
the most widely used for this purpose.
AM and LD analysis
A model based analysis of relatedness between the phenotype and the
genotype data is done to detect and quantify LD between the markers and the
genes/ QTLs governing the trait of interest.
The estimates of population structure and kinship are used as covariates in
the model to minimize the associations between the markers and the genes/ QTLs
of interest.

43. PROCEDURE FOR ASSOCIATION MAPPING

44. SOFTWARES USED FOR LD MEASUREMENT
 TASSEL
EMMAX
GenAMap
R
GenStat

45. TYPES OF ASSOCIATION MAPPING
CANDIDATEGENE APPROACH GENEOME WIDE ASSOCIATION STUDIES

46. CANDIDATE GENE APPROACH
• It is a very useful technique where scientists study the correlation present between
a trait of interest and the DNA polymorphism present in a gene.
• Candidate genes are generally genes having direct or indirect effect on the trait
of interest with known biological functions.
• The genotyping effort is focused in the genomic regions with the candidate genes.
• This great reduces the target genomic region, which can be analyzed with a high
density markers.
• The total number of markers used as well as the sample size will also be
considerably reduced.
• A limitation of this approach is that the involvement of genes not included in the
list of the trait phenotype cannot be assessed.

47. GENOME WIDE ASSOCIATIONSTUDIES
• In GWAS, the markers used for genotyping are distributed, preferably evenly and
densely over the whole genome.
• In this approach, all the loci involve in the control of all the traits showing
variation in the sample can be evaluated in one go.
• The number of markers used for genotyping would be much larger in cross
pollinated than in self pollinated species because the LD decays much faster in the
former than in the later.
• F1 derived mapping populations like RILs are highly suited for genome wide
scanning for QTLs.

48. FACTORS AFFECTING LD AND ASSOCIATION MAPPING
1. Mating pattern in the population
2. Population structure
3. Admixture
4. Genomic region
5. Kinship
6. Gene conversion
7. Marker mutation rate

49. MERITS OF ASSOCIATION MAPPING
• No need for the development of specific mapping population, this
saves time, effort and cost.
• The QTL linked markers identified by AM can be directly used for
MAS.
• AM has high resolution as it takes into account all the meiotic events
since the origin of new allele.
• The data on the target traits collected in earlier studies also can be used
for AM.

50. Demerits
• The results from AM are affected by several factors like selection
history, population structure, kinship etc., which may lead to false
associations between QTLs and markers.
• For high resolution, large number of markers would be required,
which is costly.
• The power of detection of marker trait association depends on allele
frequencies of the concerned gene/ QTL . Low frequency of alleles
have little effect on the phenotype of the concerned trait.