2. 2
Background
Genotypes influence phenotypes
– phenotype determined by a single gene
Mendelian rules e.g. colour of flowers
– phenotype determined by multiple genes
e.g. coat colour in horses
– phenotype determined by single gene and
environment e.g. cystic fibrosis
– multiple genes and environment
quantitative traits - traits with continuous
measurement e.g. milk yield, litter size,....
3. 3
History – classical breeding
Aim : select parents to breed best possible
offspring
selection decision is based on:
– phenotypes
– relationship among animals
– simple genetic model
– complex statistical models to seperate
G and E
4. 4
Basic model
infinite number of genes
each with a very very small effect
all additive
G = A
In most cases this model works very well
P = G + E
5. 5
QTL – what is it ?
QTL = quantitative trait locus
A junk / segment of DNA (not necessarily a
gene) that affects a quantitative trait
6. 6
Though not necessarily genes themselves,
quantitative trait loci (QTLs) are stretches of DNA
that are closely linked to the genes that underlie the
trait in question. QTLs can be molecularly identified
(for example, with PCR) to help map regions of the
genome that contain genes involved in specifying a
quantitative trait. This can be an early step in
identifying and sequencing these genes.
Moreover, a single phenotypic trait is usually
determined by many genes. Consequently, many QTLs
are associated with a single trait.
7. 7
Mapping QTL
Determining the location of a gene in the
genome
Determining the effect of the alleles and
mode of action
8. 8
QTL – why map it?
To provide knowledge of individual gene
actions and interactions
To build a more realistic model of phenotypic
variation, response to selection and
evolutionary processes
To improve breeding value estimation and
selection response / reduce cost of breeding
programmes through marker assisted
selection
9. 9
Why map QTL?
The detection and localization of QTL is valuable for
several reasons
Firstly, we still know very little about the genetic
background of quantitative traits such as growth,
muscular development, milk yield, disease resistance
etc
Mapping of QTL gives us better insight into the
action and interaction of individual genes, which will
give us opportunities to refine the genetic models
used to describe the variation in quantitative traits.
10. 10
Secondly, associations between genetic markers and
QTL can be utilized to improve the efficiency of
selection schemes
Thirdly, mapping of QTL will eventually allow us to
identify some of the genes and to study the molecular
biology underlying the traits
This knowledge may in the near future be used for
genetic modification of genes that are important in
breeding programs, for development of efficient
vaccines etc
11. 11
Basic principles of QTL mapping
To detect genes (segments of DNA) that
cause variation in quantitative traits
using phenotypic data
molecular markers
pedigree information
12. 12
A full genome scan for QTL
includes five steps:
Choice of a mapping population
Collection of phenotype data
Genotyping
Setting up a genetic model for QTL
Drawing statistical inference from data
13. 13
Linkage
2 loci on 2 different chromosomes segregate
independently from each other. Their chance
to be inherited together (co-inherit) is 0,5.
These loci are unlinked.
2 loci are said to be linked if they are located
on the same chromosome and segregate
together.
Due to recombination 2 loci on the same
chromosome have got a chance to be not
inherited together.
14. 14
Recombination
During meiosis, the chromosome often breaks
up and rejoins with its homologue
chromosome, resulting in new chromosomal
combinations (cross overs).
The greater the distance between 2 loci on a
chromosome the more likely it is that there
is a recombination between them.
15. 15
Recombination / cross over
A b
a b
a B
A B 1-r
1-r
r
r
a b
A B
2 homologue
chromosomes
possible gametes
no recombination
no recombination
recombination
recombination
19. 19
Mapping functions
The mapping function gives the relationship
between the distance of 2 chromosomal
locations on a genetic map and their
recombination frequency.
The distance between 2 loci is determined by
their recombination fraction.
The mapping unit is Morgan.
1 Morgan is the distance over which on
average 1 cross over /recombination occurs
per meiosis.
20. 20
Principles of QTL mapping
M Q
m q
paternal haplotype
maternal haplotype
linkage cross over /
recombination
M
Q
m
q
M
q
m
q
observed markerlocus unobserved QTL locus
21. 21
Principles of QTL mapping
M Q
m q
paternal haplotype
maternal haplotype
M
Q
m
q
observed markerlocus unobserved QTL locus
Linkage / co-segregation of QTL
alleles and linked marker alleles
22. 22
Principles of QTL mapping
Molecular Markers enable us to follow the
inheritance of segments in the genome from
parent to offspring.
– i.e. we know which of the two alleles has been inherited.
If a QTL is linked to a marker it will tend to
segregate with it.
If an individual is heterozygous both at the
marker and QTL we expect to see a
difference in the mean performance of those
having inherited one allele vs. those having
inherited the other QTL allele.
23. 23
Principles of QTL mapping
Q m
q m
q M
Q M 1-r
1-r
r
r
q m
Q M
Compare
mean
phenotype
(1-2r)
parental
haplotypes
gametes
24. 24
Markers
Genetic marker can be defined as any stable and inherited
variation that can be measured or detected by a suitable
method, and can be used subsequently to detect the
presence of a specific genotype or phenotype other than
itself
phenotpyic markers:
– coat colour
– blood type
– polledness
genetic / molecular markers:
– DNA
– can be made visible with molecular methods
– microsatellites, SNP
25. 25
QTL mapping methods: Analysis of
variance
The simplest method for QTL mapping is
analysis of variance at the marker loci.
In this method, in a backcross, one may
calculate a t-statistic to compare the
averages of the two marker genotype groups.
For other types of crosses (such as the
intercross), where there are more than two
possible genotypes, one uses a more general
form of ANOVA, which provides a so-called F-
statistic.
26. 26
The ANOVA approach for QTL mapping has three
important weaknesses.
First, we do not receive separate estimates of QTL
location and QTL effect. QTL location is indicated
only by looking at which markers give the greatest
differences between genotype group averages, and
the apparent QTL effect at a marker will be smaller
than the true QTL effect as a result of
recombination between the marker and the QTL.
Second, we must discard individuals whose genotypes
are missing at the marker.
Third, when the markers are widely spaced, the QTL
may be quite far from all markers, and so the power
for QTL detection will decrease.
27. 27
Interval mapping
Lander and Botstein developed interval mapping,
which overcomes the three disadvantages of analysis
of variance at marker loci.
Interval mapping is currently the most popular
approach for QTL mapping in experimental crosses
The method makes use of a genetic map of the typed
markers, and, like analysis of variance, assumes the
presence of a single QTL. Each location in the genome
is positioned, one at a time, as the location of the
putative QTL.
28. 28
Composite interval mapping
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
The key problem with CIM concerns the choice of
suitable marker loci to serve as covariates; once
these have been chosen, CIM turns the model
selection problem into a single-dimensional scan.
Not surprisingly, the appropriate markers are those
closest to the true QTLs, and so if one could find
these, the QTL mapping problem would be complete
anyway
29. 29
Multiple QTL methods
Methods that consider multiple QTLs simultaneously
have three advantages:
– Greater power to detect QTLs
– Greater ability to separate linked QTLs
– The ability to estimate interactions between QTLs
These more complex methods may facilitate the
identification of additional QTLs and assist in
elucidating the complex genetic architecture
underlying many quantitative traits
Model selection is the principal problem in multiple
QTL methods; the chief concern is the formation of
appropriate criteria for comparing models
30. 30
The simplest multiple QTL method, multiple
regression, should be used more widely, although, like
analysis of variance, it suffers in the presence of
appreciable missing marker genotype data.
A forward selection procedure using interval mapping
is appropriate in cases of QTLs that act additively,
and makes proper allowance for missing genotype
data.
MIM is an improved method, that, although
computationally intensive, can, in principle, map
multiple QTLs and identify interactions between
QTLs.
