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Office hours
Wednesday 3-4pm
304A Stanley Hall
Review session
5pm Thursday, Dec. 11
GPB100
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 vs. linkage
small
number of
generations;
individuals
share big
chunks of
genome;
can get coinheritance
between
distant
markers
many
recombinations
have happened
since common
ancestor;
shared region
is small; no coinheritance
between
distant markers
So you need very high density
of markers to get signal in an
association study, but you get
very high spatial resolution.
Association and admixture
Cases
Controls
=
=
At any one of these loci, Caucasian-like allele will be
enriched in control samples.
Genotyping by array
Fig. 11.8
Expression microarrays
Fig. 1.13
Labeled DNA sample vs.
labeled mRNA (cDNA) sample
Labeled DNA sample vs.
labeled mRNA (cDNA) sample
(reverse transcription in the test tube)
Coding sequence array
Fig. 1.13
Coding sequence array
say, with chemotherapeutic
Fig. 1.13
Coding sequence array
Fig. 1.13
Coding sequence array
Fig. 1.13
Coding sequence array
expression
expression
expressed
not expressed
Fig. 1.13
Why measure expression of
all genes at once?
“Regulon” expression response
(e.g. cortisol)
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are needed to see this picture.
http://www.mcb.mcgill.ca/~hallett/GEP/Lecture2/Image17.gif
“Regulon” expression response
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http://www2.kenyon.edu/Depts/BioEllipse/courses/biol114/Chap06/week08a_files/regulon.gif
Finding regulatory
response on a large scale
Oligo expression array
(soybean component)
transcripts
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Expression profiles
Time since drug administered
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Expression profiles
Time since
drug administered
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Time since drug administered
Expression profiles
Time since
drug administered
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Each color is a
regulon, or
“cluster,” of coregulated genes
Time since drug administered
Expression effects of cancer
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Cancer classification
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Cancer classification
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Histological classification is
finicky: can we do better?
Diagnosis via transcriptional profile
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Diagnosis via transcriptional profile
patient samples
transcripts
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Diagnosis via transcriptional profile
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QuickTime™ and a
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Diagnosis via transcriptional profile
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Natural variation among “normals”
Two human chromosomes differ at ~1/1000 bases.
96% of these differences are not in protein-coding sequence.
Why?
Natural variation among “normals”
Two human chromosomes differ at ~1/1000 bases.
96% of these differences are not in protein-coding sequence.
Most protein coding mutations are deleterious;
appear but are culled by natural selection.
Natural variation among “normals”
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are needed to see this pic ture.
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are needed to see this pic ture.
Natural variation among “normals”
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Genetic variation in mRNA levels
G
kinase
TF
TF
TF
ORF
Genetic variation in mRNA levels
G
Likely to be a complex trait.
kinase
TF
TF
TF
ORF
Many mRNA differences at once
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Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
~10% mRNA levels significantly different
Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
111 F2 progeny
…
Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
111 F2 progeny
…
Microarray
each F2 liver
Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
111 F2 progeny
…
Microarray
each F2 liver
Genotype
each F2
Linkage mapping of mRNA levels
“Black 6” mouse
x
“DBA” mouse
111 F2 progeny
…
Microarray
each F2 liver
Genotype
each F2
Looking for linkage (coinheritance) between
marker and mRNA level.
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
TF
ORF
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
G
G
TF
ORF
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
G
TF
ORF
One allele = high mRNA,
the other = low mRNA
G
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
TF
ORF
Marker is linked to polymorphism in
expression regulation cascade
G
kinase
TF
TF
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).
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).
Locally acting polymorphisms
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Locally acting polymorphisms
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Locally acting polymorphisms
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Polymorphism responsible for mRNA difference is at the locus
of the gene itself
Locally acting polymorphisms
G
kinase
TF
TF
TF
ORF
Locally acting polymorphisms
G
kinase
TF
TF
TF
ORF
Locally acting polymorphisms
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Polymorphism responsible for mRNA difference is at the locus
of the gene itself
Locally acting polymorphisms
~25% of varying mRNAs
are caused by locally
acting polymorphism
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Polymorphism responsible for mRNA difference is at the locus
of the gene itself
Nonlocal polymorphisms
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Nonlocal polymorphisms
One polymorphism in a key regulator can affect a
regulon: 100’s of related mRNAs.
Clinical applications
“Black 6” mouse
x
“DBA” mouse
111 F2 progeny
…
Microarray
each F2 liver
Genotype
each F2
Measure fat
pad each F2
Quic kT i me™ and a
T IFF (Unc ompres s ed) dec ompres s or
are needed t o s ee thi s pi c ture.
Quic kT i me™ and a
T IFF (Unc ompres s ed) dec ompres s or
are needed t o s ee thi s pi c ture.
Quic kT i me™ and a
T IFF (Unc ompres s ed) dec ompres s or
are needed t o s ee thi s pi c ture.
Clinical applications
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Clinical applications
Colored curves
= fat mass at
different body
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(Uncompressed)
decompressor
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Clinical applications
Finding polymorphism responsible for
difference in macroscopic phenotype is hard
Clinical applications
Finding polymorphism responsible for
difference in macroscopic phenotype is hard
If mRNAs change too, can learn mechanism
from known function of encoded proteins
Clinical applications
Clinical applications
Clinical applications
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Clinical applications
Counts allele 1/allele 2, cases = 1.5
Counts allele 1/allele 2, controls
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Clinical applications
Clinical applications
Marker predicts quantitative expression level = association
Can we map expression traits
first, disease afterward?
Linkage of human transcripts
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Linkage of human transcripts
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Association of human transcripts
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Association of human transcripts
linkage
(families)
assoc
(unrelated)
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Association in multiple populations
European-Americans in Utah
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Han Chinese and Japanese
Association in multiple populations
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