Download Review

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
(sperm)
A1
A2
A2
Fig. 11.A
D
d
d
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
Just a point estimate
True distance 30 cM
Diseasecausing
mutation
Restriction
fragment
length
polymorphism
observed recombination
fraction = 1/8 = 12.5 cM
this is our observation
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.
Combining families
1,2
2,3
1,2
Given r
2,3
Given r
Odds2
Odds1
2,3
1,2
2,3
1,3
1,2
1,3
How to get an overall estimate of
probability of linkage?
A. Multiply odds together
B. Add odds together
C. Take the largest odds
D. Take the average odds
1,2
1,3
2,3
1,3
1,3
2,3
1,3
1,2
2,3
Given r
Odds3
2,3
2,2
2,2
More realistic situation: in dad,
phase of alleles unknown
A1
A2
D
d
or
A1
A2
d
D
A1
A2
A1
d
A1
d
More realistic situation: in dad,
phase of alleles unknown
P(pedigree|r)
Odds =
1/2[(1-r)n • rk] +
assume one phase for dad
1/2[(1-r)n • rk]
assume the other phase for dad
A1
D
A1
d
A2
d
A2
D
7 non-recomb, 1 recomb
1 non-recomb, 7 recomb
(k = # recomb, n
= # non-recomb)
A1
A2
In real life this correction does matter…
family 1: 10 meioses, 1 (or 9) apparent recombinants
family 2: 10 meioses, 4 (or 6) apparent recombinants
family 3: 10 meioses, 3 (or 7) apparent recombinants
family 4: 10 meioses, 3 (or 7) apparent recombinants
total LOD = LOD(family 1) + LOD(family 2) + LOD(family 3) + LOD(family 4)
Using only one phase
best r = 0.2771
Accounting for both phases
best r = 0.2873
Modern genetic scans
(single family)
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Age of onset in breast cancer
age of onset
Coins
r = intrinsic probability of coming up heads (bias)
Odds =
P(your flips | r)
P(your flips | r = 0.5)
Odds =
(1-r)n • rk
0.5(total # flips)
Coins
r = intrinsic probability of coming up heads (bias)
0 heads
1 heads
2 heads
3 heads
4 heads
r
odds
r
odds
r
odds
r
odds
r
odds
0
16
0
0
0
0
0
0
0
0
0.1
10.498
0.1
1.1664
0.1
0.1296
0.1
0.0144
0.1
0.0016
0.2
6.5536
0.2
1.6384
0.2
0.4096
0.2
0.1024
0.2
0.0256
0.3
3.8416
0.3
1.6464
0.3
0.7056
0.3
0.3024
0.3
0.1296
0.4
2.0736
0.4
1.3824
0.4
0.9216
0.4
0.6144
0.4
0.4096
0.5
1
0.5
1
0.5
1
0.5
1
0.5
1
0.6
0.4096
0.6
0.6144
0.6
0.9216
0.6
1.3824
0.6
2.0736
0.7
0.1296
0.7
0.3024
0.7
0.7056
0.7
1.6464
0.7
3.8416
0.8
0.0256
0.8
0.1024
0.8
0.4096
0.8
1.6384
0.8
6.5536
0.9
0.0016
0.9
0.0144
0.9
0.1296
0.9
1.1664
0.9
10.498
1
0
1
0
1
0
1
0
1
16
Coins
By chance, can get good LOD score for just
about anything.
The more students you have flipping coins,
the more likely you are to see this “unlikely”
combination.
The multiple testing problem
Coins
Probability of one student observing 0 heads and 4 tails: 1/16
Estimated number of students out of 70 observing 4 tails:
70*(1/16) = 4
Probability of one student observing 1 head and 3 tails: 4/16
Estimated number of students out of 70 observing 1 heads and 3
tails: 70*(4/16)= 17.5
Probability of one student observing 2 heads and 2 tails: 6/16
Estimated number of students out of 70 observing 2 heads and 2
tails: 70*(6/16) = 26.3
…
We would need to see >4 students
get 0 heads and 4 tails before we
believe any coins are biased.
Simulation/theory
Expect 0.09 of
a locus to
reach LOD=3
by chance.
Simulation/theory
But this would
change in a
different
organism, with
different
number of
markers, etc.
So in practice,
everyone does
their own
simulation
specific to their
own study.
Candidate gene approach
Hypothesize that causal variant will be in
known pigment gene or regulator. NOT
randomly chosen markers genome-wide.
Candidate gene approach
Red progeny have
RFLP pattern like
red parent
Affected sib pair method
2,2
2,3
4,4
1,3
…
Total # families
2,2
2,2
1,4
1,4
Sib pairs
Observed
Expected
under null
Same
allele
Different
allele
2
(1/2)*2
0
(1/2)*2
2 =  (O - E)2
E
Test for significant
allele sharing.
Qualitative but polygenic
Two loci.
Fig. 3.12
Need one dominant allele at each
locus to get phenotype.
“A weak locus”: need lots of data
AAbb
aaBB
AaBb
inter-mate
Two loci.
Need one
dominant allele
at each locus to
get phenotype.
AABb AaBb
aaBb
AaBB
aaBB
Aabb
Flower color
Genotype at
marker close to A
locus
purple
white
Top
allele
3
1
Bottom
allele
2
3
More generally (one locus):
AA x BB
AB
(F1)
AB x AB
AA
AB
BA
BB
(F2)
1 locus, incomplete dominance
AB
BA
AA
25%
BB
50%
25%
“Effect of having a
B”
AA
AB
BA
BB
Effect of a B allele is the same regardless of genotype: additive
1 locus, complete dominance
AB
BA
AA
BB
75%
25%
Dominance is a kind of epistasis: nonadditive
A real example
CC x SS
(F2’s)
CS
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
CS x CS
CC
SS
CS
SC
(F2’s)
Quantitative trait linkage test
(F2’s)
kTime™ and a
essed) decompressor
o see this picture.
Not counting
recombinants.
Statistical test for
goodness of fit.
Locus effect vs. parents
Homozygotes
do not look like
parent.
What do you
infer?
C3H
parent
F2’s,
F2’s,
C/C at C/S at
marker marker
F2’s,
SWR
S/S at parent
marker
A single varying locus does not explain the data
>1 locus controlling trait
(One mouse family)
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
A weak locus
“Effect of having an S allele”
Most loci
underlying
human
disease look
like this.
C3H
parent
F2’s,
F2’s,
C/C at C/S at
marker marker
F2’s,
SWR
S/S at parent
marker
Heritability in exptal organisms
 e
t
Genetic variance = total var - “environmental var”
 g  =  t -  e 
Heritability H2 = g/t
http://www.sciam.com/media/inline/15DD5B0E-AB4123B8-2B1E53E8573428C5_1.jpg
http://www.twinsrealm.com/othrpics/twins16.jpg
http://www.twinsrealm.com/ot
hrpics/sarahandsandra.jpg
http://www.twinsinsurance.net/images/twins.jpg
Heritability in humans: MZ twins
Mean each pair = zi
Each individual = zij
Total mean sq =
  (zij - z)
Within pairs mean sq = 
2
T
2
(z
z
)
ij
i

N
Between pairs mean sq =  (zi - z)2
N-1
=
 t2
= w2
=  b2
h2 =
b2 w2
 t2
Linkage mapping (quantitative)
me™ and a
ed) decompressor
see this picture.
intolerant
tolerant
Transgenic test
QuickTime™ and a
Uncompressed) decompressor
needed to see this picture.
Fine-mapping: new markers
Position of true causal variant
Because you have to
hunt through by hand to
find the causal gene, and
test experimentally. The
smaller the region, the
better.
Best marker
Fine-mapping: new markers
Position of true causal variant
Increased
marker
density
Two loci, incomplete dominance
0.5
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
1
1.5
2
2-locus interaction
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Effect
of J at
locus 2
Locus 2 is epistatic to locus 1: effects of locus 1 are
masked in individuals with JJ or JL,LJ at locus 2
Locus 2 follows a dominance model: JJ and JL,LJ have
the same phenotype, LL differs
“The dominant allele of locus 2 does the masking”
NO progeny as extreme as diploid hybrid
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Three mutant genes
From pathogenic strain
From pathogenic strain
From pathogenic strain
Alleles from the
same strain at
different genes/loci
can have different
effects.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Linked mutations of opposite effect
Path
Lab
Very unlikely
Fine-mapping
WT uninjected
Golden uninjected
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Inject into golden larvae
No truncation in humans, but…
No other species have the Thr allele: what does this mean?
Could be deleterious, just an accidental mutation.
Could be advantageous for some humans, no other species.
Correlates with human differences
Allele is rare
AA
Thr
AG
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Perhaps explains
phenotypic
variation among
people of African
ancestry
Thr, Ala
GG
Ala
Association mapping (qualitative)
Blue alleles at
markers are
on the same
haplotype as
the M allele of
the disease
locus
Fig. 11.26
Association scan, qualitative
2 test
osteoarthritis
controls
C’s
141
797
G’s
47
433
-log(2 p-value)
Fine-mapping
rs377472
Beginnings of molecular confirmation
coding
polymorphisms
Association scan, quantitative
Association vs. linkage
Unrelated
individuals
Common but weak
effects; need 1000’s
of samples to detect.
If no common cause,
can fail.
Related
individuals
Strong, easy to detect,
but rare in population;
may not be reflective of
common disease.
Also, hard to collect family
data.
Association mapping causal loci
“Gm is protective against diabetes?”
diabetes control
Gm
23
no Gm 1343
270
3284
Association and admixture
these are all the
Caucasians…
Association and admixture
Cases
Controls
=
=
Don’t believe any one locus is causative!
Genotyping by array
Fig. 11.8
Coding sequence array
Fig. 1.13
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
G
G
TF
ORF
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
TF
ORF
mRNA level shows linkage to locus of polymorphic
regulator(s).
Clinical applications
Colored curves
= fat mass at
different body
QuickTime™ and a
TIFF
(Uncompressed)
decompressor
locations are needed to see this
picture.
Association of human transcripts
linkage
(families)
assoc
(unrelated)
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Protein inheritance
Genetically distinct S. cerevisiae strains
PSI+ phenotypes
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
pressor
ure.
50°C