In this presentation, we will delve into the principles of QTL mapping and explore various strategies for mapping QTLs in plants. We will also discuss the advantages and limitations, and provide insights into how QTL mapping is advancing our understanding of genetics.

1. QTL mapping and analysis
Presented by:
S. Sarath,
I M.Sc. Genetics and Plant Breeding,
Dept. of Genetics and Plant Breeding

2. Introduction
 Many traits important to crop production, such as yield potential, end-use quality characteristics,
and stress tolerance, are quantitatively inherited, i.e., they are controlled by many genes acting
together to produce the desired plant type.
 These quantitative traits are characterized by continuous variation, in contrast to qualitative traits,
which show discrete variation and are controlled by one or two major genes.
 Because of the often subtle differences among individuals for quantitative traits, they are evaluated
by precise measurement rather than by classification.

3. Conti…
 Until recently, it has been difficult to focus on the individual genes (known as polygenes or QTLs)
that contribute to quantitative traits.
 However, the advent of molecular markers and the chromosome maps derived from them has
enabled the genetic control of quantitative traits to be dissected into its component parts.
 By applying the methodology known as QTL analysis in an appropriate population, a researcher can
locate the genes of interest in specific chromosome regions, estimate the size of their effects, and
determine whether their gene action is additive or dominant.
 These are initial steps in manipulating the genes in breeding programs to produce superior
varieties.

4. Quantitative trait locus (QTL)
 The term QTL was coined by Gelderman in 1975.
 Quantitative trait loci (QTL) a gene or chromosomal region that affects a quantitative trait
 In other words, Quantitative trait loci (QTL) are genetic regions that influence phenotypic variation
of a complex trait, often through genetic interactions with each other and the environment.
 These are commonly identified through a statistical genetic analysis known as QTL
mapping

5. Quantitative trait locus (QTL) analysis
 Quantitative trait locus (QTL) analysis is a statistical method that 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.
 QTL analysis allows researchers in fields as diverse as agriculture, evolution, and medicine to link
certain complex phenotypes to specific regions of chromosomes.
 The goal of this process is to identify the action, interaction, number, and precise location of these
regions.

6. Objective of QTL
• Locate and characterize genetic
factors that influence
quantitatively inherited traits, i.e.,
those that are controlled by a few
to many genes acting together to
produce a phenotype.
5

7. Steps involved in QTL
1. Population development (Mapping population).
2. Phenotypic evaluation.
3. Genotypic evaluation.
4. Linkage Mapping.
5. Analyzing the data using software.
Sarath S

8. 1. Population Development
 QTLs can be detected both in natural populations and in populations developed from controlled
crosses.
 Developing a population for QTL mapping involves selecting the parents, crossing them with each
other, then advancing the progeny in an appropriate manner to obtain a set of individual plants or
lines segregating for the traits of interest.
 A typical QTL population consists of 100 to 300 lines or individuals, each of which is evaluated both
for phenotypic traits and for molecular markers.
 However, when resources are available, larger population sizes are recommended and are
sometimes used (Beavis, 1994, 1998).
 Development of three common types of QTL mapping population:
 F2 segregating population,
 Recombinant inbred line, and
 Backcross populations.

10. i. F2 population
 An F2 population is quickly and easily developed by self-pollinating the F1 hybrid between two
parents.
 However, only a single plant represents each genotype, so replications over time or space cannot be
carried out.
 Also, for some traits like yield, evaluations on single plants are usually not considered reliable.
 This limitation of F2 populations can be overcome in species that are easily cloned, such as some
turfgrass and horticultural crops.
 In these cases, the cloned F2 plants can be used in replicated trials.
 Another possibility is to self-pollinate F2 plants to produce F3 families, each consisting of multiple
plants, which are used for phenotypic evaluation.

11. ii. Recombinant Inbred Line (RIL)
 A recombinant inbred line (RIL) population is developed through single seed descent from the F2
generation.
 The result is a set of homogeneous, homozygous lines for which large amounts of seed can be
produced for replicated trials.
 However, advancing the population for six or more generations is expensive in time and labor.

12. iii. Backcross Population
 The backcross population is easily developed by crossing an F1 plant to one of its parents.
 Like the F2 population, each primary backcross genotype is represented by a single plant.
 However, each backcross-derived plant can be self-pollinated or cloned to obtain multiple plants for
evaluation.
 Compared to an F2 population, a backcross population is less informative for linkage mapping
because recombination among markers occurs in only one set of gametes (either male or female)
(Lander et al., 1987).
 An adaptation of backcross QTL mapping is called the Advanced Backcross method (Tanksley and
Nelson, 1996).
 It is especially useful for identifying beneficial QTL alleles in wild germplasm.

13. 2. Phenotypic evaluation
Steps:
1. Recording phenotypic data:
 To grow the mapping population and evaluating for the target traits based on the phenotype.
 It should be done in replicated trials over locations and years.
2. Phenotypic data analysis:
 Frequency distribution – bell shaped
 ANOVA of the phenotypic data
 Heritability estimates
 Correlation analysis
 Mean calculation

15. 3. Genotypic evaluation:
(Molecular Marker Evaluation)
 Genotype of all the individuals/lines of the mapping population is studied with the selected
polymorphic markers.
 Examples of Polymorphic markers include:
 Single Nucleotide Polymorphisms (SNPS),
 Simple Sequence Length Polymorphisms (SSLPS),
 Restriction Fragment Length Polymorphisms (RFLPs).
 Scoring the bands on the electrophoresis gel should be uploaded as per the need of target
software.
 Steps:
1. Parental screening.
2. Evaluating the population for markers.
3. Segregation distortion.

16. 4. Linkage Mapping
 Scores from all markers are organized in a data file in a format that can be imported into a linkage
mapping program. The most commonly used software for linkage mapping is Mapmaker
 Objective of linkage mapping is to develop an ordered, linear representation of marker loci for each
chromosome, based upon the observed recombination rates between loci.
 There are two outputs from the marker evaluation component of a QTL study:
 A file of marker scores
 A genetic linkage map

17. A file of marker scores
A genetic linkage map

18. 5. Analyzing the data using software
 R software packages are that are commonly used are:
1. R/qtl
2. R/qtlDesign
 Other QTL mapping software's are:
1. MapMaker/QTL
3. MapQTL
5. GeneNetwork
7. The QTL Café
9. Multimapper
11. JoinMap
13. LINKAGE1
2. QTL Cartographer
4. HAPPY
6. MultiQTL
8. QTLMap
10. qGENE
12. Hypergene

19. QTL mapping strategies
 QTL mapping can be done using various statistical techniques, such as linkage analysis, association
mapping, and genome-wide association studies (GWAS).
 The choice of technique depends on the specific characteristics of the trait and the population
being studied.
All marker-based mapping experiments have same basic strategy:
 Select parents that differ for a trait
 Screen the two parents for polymorphic marker loci
 Generate recombinant inbred lines (can use F2-derived lines)
 Phenotype (screen in field)
 Contrast the mean of the MM and mm lines at every marker locus
 Declare QTL where (MM-mm) is greatest

20. QTL mapping strategies:
 Single QTL mapping:
1. Single marker analysis (SMA).
2. Simple interval mapping(SIM).
 Multiple QTL mapping:
1. Composite interval mapping (CIM).
2. Inclusive composite interval mapping (ICIM).
3. Joint inclusive composite interval mapping (JICIM).
4. Multiple interval mapping (MIM).
5. Bayesian multiple QTL mapping.

21. 1. Single-marker analysis
 Divide population into subpopulations based on allelic segregation of individual loci
 Measure phenotypic trait and average in each subpopulation
 Determine if statistically significant differences by:
1. t-test,
2. ANOVA,
3. Linear Regression (R2)
 Disadvantage:
1. Underestimation of the effect of a linked QTL due to recombination between marker and QTL
 Solution: use markers at spacing < 10-15 CM

22. 2. Interval mapping
 Developed by Lander and Botstein
 Marker interval = the segment between 2 markers
 Interval mapping methods use information on values of 2 flanking markers to estimate QTL position
 The probability that the data could be obtained assuming a QTL at several positions between the
markers is calculated
 QTL = declared where the probability of obtaining the observed data is highest
Finding the position of QTL with molecular markers:
 DNA markers can be used to map useful genes using recombination frequencies of linked genes.

23. LOD score method for estimating
recombination frequency
 LOD score analysis (Morton, 1955)
 Logarithm of the odds ratio (LOD score):
 Used to determine if the genes are linked or not.
 The LOD score often used for linkage analysis in plant populations.
 LOD of 2 means that it is considered evidence to exclude linkage.
 LOD of 3 means that is considered evidence of linkage.
Probability of sequence with a given linkage
Probability of sequence with no linkage
LOD = Z = Log10

24. 3. Composite interval mapping (CIM)
 In this method, one performs interval mapping using a subset of marker loci as covariates.
 These markers serve as proxies for other QTLs to increase the resolution of interval mapping, by
accounting for linked QTLs and reducing the residual variation.
 Analyzes intervals between adjacent markers + additional markers unlinked to the interval markers
to focus on the interval and eliminate confounding effects from other QTLs:
1. Elsewhere in the genome: "background noise"
2. Linked to QTL: biases location of QTL
Sarath S

25. 4. Inclusive Composite Interval Mapping (ICIM)
 It was proposed for additive, dominant and epistatic QTL mapping in biparental populations.
 ICIM applies a two-step mapping strategy:
1. Firstly, stepwise regression is conducted to select the significant markers for additive QTL mapping or
marker-pairs for epistatic QTL mapping considering all marker information simultaneously.
2. Secondly, the phenotypic values are adjusted by the marker variables retained in the regression equation
except the two markers flanking the current scanning position(s) for background control.
 The adjusted phenotypic values are subsequently used in interval mapping.

26. 5. Multiple Interval Mapping (MIM)
 It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for
mapping QTL.
 The MIM model is based on Cockerham's model for interpreting genetic parameters and the
method of maximum likelihood for estimating genetic parameters.
 With the MIM approach, the precision and power of QTL mapping could be improved.
 Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits
can be readily estimated and analyzed.
 A stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to
identify QTL.

27. Family-pedigree based mapping
 Family based QTL mapping, or Family-pedigree based mapping (Linkage and association mapping),
involves multiple families instead of a single family.
 Family based QTL mapping has been the only way for mapping of genes where experimental
crosses are difficult to make.
 However, due to some advantages, now plant geneticists are attempting to incorporate some of the
methods pioneered in human genetics.

28. Applications:
1. MAS in Breeding:
 To enhance plant breeding efforts to speed up the creation of cultivars.
2. Pre-breeding/ germplasm enhancement:
 It also unveils masked, interesting wild alleles and
 Easier introgression of genes from wild species, without the usual associated drawbacks.
3. QTL cloning.

29. Limitations:
Numerous QTL studies but few have resulted in application of MAS:
 Major QTLs often not found.
 Uncertainty of the QTL position (population size, magnitude of the QTL).
 Deficiencies in QTL analysis leading to an inaccurate estimation of number and magnitude of QTLs.
 Limited usefulness of markers across backgrounds and environments.
 Epistatic effects.
 QTL x environment effects.