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Finding “the gene” for cystic
fibrosis
Finding “the gene” for cystic
fibrosis
Why is this in quotes?
A. CF is not caused by a gene, it’s caused by
multiple genes.
B. CF is not caused by genetic factors.
C. CF is not caused by a gene, it’s caused by a
mutation.
How to find genetic
determinants of naturally
varying traits?
Genetic markers
(microsatellite)
Fig. 10.3
Genetic markers
(microsatellite)
Fig. 10.3
Table 11.1
Lots of benign variation
between us.
How do you find polymorphisms?
Fig. 11.6
How do you find polymorphisms?
Introduced in lecture 9/15.
Fig. 11.6
How do you find polymorphisms?
Fig. 11.6
How do you find polymorphisms?
Fig. 11.6
How do you find polymorphisms?
Fig. 11.6
How do you find polymorphisms?
Fig. 11.6
How do you find polymorphisms?
Fig. 11.6
Hybrid mapping: location of probe
mouse
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
www3.mdanderson.org/depts/cellab/fish1.htm
human/mouse hybrid
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Hybrid mapping: location of probe
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Back then, no
technique to see 6kb
at cytological
resolution.
Who cares about benign
polymorphisms?
Remember Sturtevant?
Fig. 5.10
Who cares about benign
polymorphisms?
We are going to do a twopoint cross.
One of our genetic loci is represented by
phenotype; the other is a DNA marker.
Mapping a disease locus
(Autosomal dom)
A1
A2
Fig. 11.A
Mapping a disease locus
(Autosomal dom)
phenotype
(variation in
locus 1)
A1
A2
Fig. 11.A
Mapping a disease locus
(Autosomal dom)
phenotype
(variation in
locus 1)
A1
A2
marker
genotype
(variation in
locus 2)
Fig. 11.A
Mapping a disease locus
(Autosomal dom)
phenotype
(variation in
locus 1)
A1
A2
marker
genotype
(variation in
locus 2)
Fig. 11.A
How close are they in genetic distance?
Mapping a disease locus
(Autosomal dom)
A1
A2
D
d
A1
A2
Fig. 11.A
Mapping a disease locus
(Autosomal dom)
A1
A2
D
d
(assume phase)
A1
A2
Fig. 11.A
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
Fig. 11.A
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
A2
Fig. 11.A
d
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
A1
Fig. 11.A
D
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
A2
Fig. 11.A
d
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
?
Fig. 11.A
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
(sperm)
A1
A2
?
Fig. 11.A
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
(sperm)
A1
A2
A2
Fig. 11.A
D
d
d
Mapping a disease locus
A1
A2
D
d
A1
A1
A1
A2
Fig. 11.A
d
d
In total, 7 of the kids are
non-recombinants and 1
is a recombinant.
Mapping a disease locus
A1
A2
Fig. 11.A
What is the apparent
RF between the DNA
marker and the
disease mutation?
In total, 7 of the kids are
non-recombinants and 1
is a recombinant.
A. 1/10
B. 1/8
C. 1/20
Mapping a disease locus
1/8 = 12.5 m.u.
A1
A2
Fig. 11.A
What is the apparent
RF between the DNA
marker and the
disease mutation?
In total, 7 of the kids are
non-recombinants and 1
is a recombinant.
A. 1/10
B. 1/8
C. 1/20
Why do I say “apparent RF?”
What if…
True distance 30 cM
Diseasecausing
mutation
observed recombination
fraction = 1/8 = 12.5 cM
Restriction
fragment
length
polymorphism
What if…
True distance 30 cM
Diseasecausing
mutation
observed recombination
fraction = 1/8 = 12.5 cM
Restriction
fragment
length
polymorphism
You could say this will
never happen. But…
What if…
True distance 30 cM
Diseasecausing
mutation
Restriction
fragment
length
polymorphism
observed recombination
fraction = 1/8 = 12.5 cM
this is our observation
What if…
True distance 30 cM
Diseasecausing
mutation
Restriction
fragment
length
polymorphism
observed recombination
fraction = 1/8 = 12.5 cM
this is our observation
The observed number of
recombinants is just a
point estimate, with some
error associated.
12 cM, 18 cM…who cares?
Further experiments need to find the causal variant,
not just a marker. If distances are wrong, could be
hunting for years.
Mapping a disease locus
1/8 = 12.5 m.u.
A1
A2
We now know the mutation is near (linked
to) the marker.
Fig. 11.A
Mapping a disease locus
marker (known)
1/8 = 12.5 m.u.
A1
A2
We now know the mutation is near (linked
to) the marker.
Mapping a disease locus
marker (known)
1/8 = 12.5 m.u.
A1
window
containing
causative
mutation
A2
We now know the mutation is near (linked
to) the marker.
Mapping a disease locus
1/8 = 12.5 m.u.
A1
A2
How significant?
Mapping a disease locus
1/8 = 12.5 m.u.
A1
A2
How significant?
If RF = 0.5 (unlinked), would be like flipping a coin 8 times.
How likely would you be to get 7 heads and 1 tail?
If RF = 0.5 (unlinked), would be like flipping a coin 8 times.
How likely would you be to get 7 heads and 1 tail?
How much MORE likely is a model of RF < 0.5?
If RF = 0.5 (unlinked), would be like flipping a coin 8 times.
How likely would you be to get 7 heads and 1 tail?
How much MORE likely is a model of RF < 0.5?
For large cross between known parents,
would use 2 to evaluate significance.
Here we can’t.
LOD scores
r = genetic distance between marker and disease locus
1 recomb, 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
1 recomb, 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
“How likely are the
data given our
model?”
1 recomb, 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5n • 0.5k
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
We have an idea of true r,
but it is imprecise.
k = 1 recomb, n = 7 non-recomb.
A1
A2
Remember?
True distance 30 cM
Diseasecausing
mutation
Restriction
fragment
length
polymorphism
observed recombination
fraction = 1/8 = 12.5 cM
this is our observation
The observed number of
recombinants is just a
point estimate, with some
error associated.
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
This formalism allows
any r value. Let’s
guess r = 0.3.
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
Odds =
0.77 • 0.31
This formalism allows
any r value. Let’s
guess r = 0.3.
0.58
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
This formalism allows
any r value. Let’s
guess r = 0.3.
0.5(total # meioses)
Odds =
0.77 • 0.31 = 6.325
0.58
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r = genetic distance between marker and disease locus
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
Odds =
0.77 • 0.31 = 6.325
0.58
Data >6 times more
likely under LINKED
hypothesis than
under UNLINKED
hypothesis.
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scores
r
0.1
0.2
0.3
0.4
0.5
odds
12.244
10.737
6.325
2.867
??
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
k = 1 recomb,
n = 7 non-recomb.
LOD scores
r
0.1
0.2
0.3
0.4
0.5
odds
12.244
10.737
6.325
2.867
??
Odds =
P(pedigree | r)
P(pedigree | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # meioses)
Odds at r=0.5?
A. 2.5
B. 0
C. 1
D. 10
LOD scores
r
0.1
0.2
0.3
0.4
0.5
odds
12.244
10.737
6.325
2.867
1
What’s the best (most likely) value of r?
A. 0.1
B. 0.2
C. 0.3
D. 0.4
E. 0.5
What problems will look like
A1
A2
1,2 1,2 1,1 1,2 1,1 1,2 1,1 1,2
1,2 1,1
What problems will look like
1,2 1,2 1,1 1,2 1,1 1,2 1,1 1,2
1,2 1,1
What problems will look like
1,2 1,2 1,1 1,2 1,1 1,2 1,1 1,2
1,2 1,1
Count number of recombinants, calculate odds.
Reading and chapter
problems on web site.