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Mapping Basics
MUPGRET Workshop
June 18, 2004
Randomly Intermated
P1 x
P2

F1
 SELF
F2
1 2 3 4 5 6 7 ……
One seed from each used for next generation
Recombination.
After recombination self to create line.
Randomly Intermated.

Very high resolution.
 Accumulates recombination events across
generations and fixes them.
 Excellent for fine mapping
 Only homozygous genotypes.
Population Size

Dependent on type of population
 Generally 200-300 individuals
 If doing trait analysis, the number of
individuals determines the maximum
number of QTL you can find.
 Two samples from the same population will
produce different maps because they sample
different gametes.
Genetic Mapping Basics

Gene: a particular sequence of nucleotides
among a molecule of DNA which represents
a functional unit of inheritance. (Johannsen,
1909)
 Locus: the position of a gene on a
chromosome or a genetic map. (Morgan,
Sturtevant, Muller, and Bridges, 1915)
More terminology

Linkage: the association in inheritance of
certain genes and their associated
phenotypes due to their being localized in
the same chromosome. (Morgan, 1910)
 Linked: two genes showing less than 50%
recombination.
More terms

Recombination: Any process which gives
rise to cells or individuals (recombinants)
associating the alleles of two or more genes
in new ways. (Bridges and Morgan, 1923)
 Recombinants are the end products of
exchange of alleles from parental types as a
result of crossing-over.
Terminology

Phenotype: the observable properties of an
organism, produced by the interaction
between the organism’s genotype and the
environment (Johannsen, 1909).
 Genotype: the genetic constitution in
respect to the alleles at one or a few genetic
loci under observation. (Johannsen, 1909).
Recombination
Parental
Recombinant
Recombination and Mapping

Assume the frequency of crossing-over is
equal along the chromosome.
 Two genes that are very close to one another
will have a lower likelihood of having a
cross-over between them than two genes
that are far apart.
Recombination and Mapping

So, we can determine the relative distance
between genes by counting the number of
recombinant genotypes for each pair of
genes.
– Lots of recombinants = far apart
– Fewer recombinants = close together
Two Point Analysis

Parental Types
Tall, Green
42

Recombinant Types
Tall, White
7
Short, White
39
Short, Green
12
=81%
=19%
Map Units

1 map unit is equal to 1% recombination.

Map units are also called centimorgans after
geneticist Thomas Hunt Morgan who won
the Nobel Prize for discovering how
chromosomes govern inheritance.
Challenge

How do we merge the information about
each pair of genes together into one
common framework?

How do we order the genes relative to one
another?
Three-Point Analysis
A
B
C
a
b
c
Single cross-over
Double cross-over
Double cross-overs and Map
Distance
If we only look at the outer markers A and C
on the previous slide, we will underestimate
the true distance between them because we
have not accounted for the double crossovers.
Three-Point Analysis

Distance = # Singles +2 * Doubles
Total
 If cross-overs are equally likely along the
chromosome and closer genes have few
cross-overs, then the likelihood of two
cross-overs close to one another would be
small.
Double cross-overs

So mapping algorithms can order genes by
minimizing the number of double crossovers.
Maximum Likelihood Method

Gives an estimate of the distances and the
relative orders of the loci which would
maximize the probability that the observed
data would have occurred.
How Maximum Likelihood
Works
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MapMaker

Mapping program that uses maximum
likelihood method.
 Initially calculates what is linked (< 50%
recombination).
MapMaker

Works one linkage group at a time.
 Randomly picks two genes with the group
and calculates the distance between them.
 Adds another gene from the group and
determines the correct placement by using
maximum likelihood to minimize the
double cross-overs.
MapMaker

Does this by calculating a LOD value for
the placement of the gene in each of the
intervals.
 Accepts the placement with the highest
LOD value.
 Can be used for molecular markers or for
trait data.
LOD

Log likelihood.
 LOD = log 10 (Probability that the observed
data would have occurred /probability that
the gene is unlinked).