Post on 31-Dec-2015
description
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.
Hybrid mapping: location of probe
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www3.mdanderson.org/depts/cellab/fish1.htm
mouse human/mouse hybrid
Hybrid mapping: location of probe
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Back then, no technique to see 6kb
at cytological resolution.
Who cares about benign polymorphisms?
We are going to do a two-point cross.
One of our genetic loci is represented by phenotype; the other is a DNA marker.
Mapping a disease locus
Fig. 11.A
(Autosomal dom)phenotype (variation in locus 1)
marker genotype (variation in locus 2)
A1
A2
Mapping a disease locus
Fig. 11.A
(Autosomal dom)phenotype (variation in locus 1)
marker genotype (variation in locus 2)
How close are they in genetic distance?
A1
A2
Mapping a disease locus
Fig. 11.A
A1 d
A1 d
A1 D
A2 d
A1
A2
In total, 7 of the kids are non-recombinants and 1
is a recombinant.
Mapping a disease locus
Fig. 11.A
What is the apparent RF between the DNA marker and the disease mutation?
A. 1/10B. 1/8C. 1/20
A1
A2
In total, 7 of the kids are non-recombinants and 1
is a recombinant.
Mapping a disease locus
Fig. 11.A
What is the apparent RF between the DNA marker and the disease mutation?
1/8 = 12.5 m.u.
A1
A2
A. 1/10B. 1/8C. 1/20
In total, 7 of the kids are non-recombinants and 1
is a recombinant.
What if…
observed recombination fraction = 1/8 = 12.5 cM
Disease-causing mutation
Restriction fragment length polymorphism
True distance 30 cM
What if…
observed recombination fraction = 1/8 = 12.5 cM
Disease-causing mutation
Restriction fragment length polymorphism
True distance 30 cM
You could say this will never happen. But…
What if…
observed recombination fraction = 1/8 = 12.5 cM
Disease-causing mutation
Restriction fragment length polymorphism
True distance 30 cM
this is our observation
What if…
observed recombination fraction = 1/8 = 12.5 cM
Disease-causing mutation
Restriction fragment length polymorphism
True distance 30 cM
The observed number of recombinants is just a point estimate, with some error associated.
this is our observation
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
Fig. 11.A
We now know the mutation is near (linked to) the marker.
1/8 = 12.5 m.u.
A1
A2
Mapping a disease locus
We now know the mutation is near (linked to) the marker.
marker (known)
1/8 = 12.5 m.u.
A1
A2
Mapping a disease locus
We now know the mutation is near (linked to) the marker.
window containing causative mutation
1/8 = 12.5 m.u.marker (known)
A1
A2
Mapping a disease locus
1/8 = 12.5 m.u.
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?
A1
A2
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
1 recomb, 7 non-recomb.
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
A1
A2
LOD scores
1 recomb, 7 non-recomb.
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
“How likely are the data given our model?”
A1
A2
LOD scores
k = 1 recomb, n = 7 non-recomb.
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5n • 0.5k
A1
A2
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5(total # meioses)
A1
A2
k = 1 recomb, n = 7 non-recomb.
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
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?
observed recombination fraction = 1/8 = 12.5 cM
Disease-causing mutation
Restriction fragment length polymorphism
True distance 30 cM
The observed number of recombinants is just a point estimate, with some error associated.
this is our observation
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5(total # meioses)
k = 1 recomb, n = 7 non-recomb.
A1
A2
This formalism allows any r value. Let’s guess r = 0.3.
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5(total # meioses)
Odds = 0.77 • 0.31
0.58
k = 1 recomb, n = 7 non-recomb.
A1
A2
This formalism allows any r value. Let’s guess r = 0.3.
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5(total # meioses)
Odds = 0.77 • 0.31
0.58
= 6.325
k = 1 recomb, n = 7 non-recomb.
A1
A2
This formalism allows any r value. Let’s guess r = 0.3.
LOD scores
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
r = genetic distance between marker and disease locus
Odds = (1-r)n • rk
0.5(total # meioses)
Odds = 0.77 • 0.31
0.58
= 6.325
Data >6 times more likely under LINKED hypothesis than under UNLINKED hypothesis.
k = 1 recomb, n = 7 non-recomb.
A1
A2
LOD scoresr odds
0.1 12.244
0.2 10.737
0.3 6.325
0.4 2.867
0.5 ??
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
Odds = P(pedigree | r)
P(pedigree | r = 0.5)
Odds = (1-r)n • rk
0.5(total # meioses)
Odds at r=0.5?A. 2.5B. 0C. 1D. 10
r odds
0.1 12.244
0.2 10.737
0.3 6.325
0.4 2.867
0.5 ??
LOD scores
What’s the best (most likely) value of r?
A. 0.1B. 0.2C. 0.3D. 0.4E. 0.5
r odds
0.1 12.244
0.2 10.737
0.3 6.325
0.4 2.867
0.5 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.