office hours wednesday 3-4pm 304a stanley hall review session 5pm thursday, dec. 11 gpb100
Post on 22-Dec-2015
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Association vs. linkage
Strong, easy to detect, but rare in population;may not be reflective of common disease.Also, hard to collect family data.
Common but weak effects; need 1000’s of samples to detect.If no common cause, can fail.
Unrelatedindividuals
Relatedindividuals
Association vs. linkagesmall number of generations; individuals share big chunks of genome; can get co-inheritance between distant markers
many recombinations have happened since common ancestor;shared region is small; no co-inheritance 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.
“Regulon” expression response
http://www.mcb.mcgill.ca/~hallett/GEP/Lecture2/Image17.gif
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(e.g. cortisol)
“Regulon” expression response
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http://www2.kenyon.edu/Depts/BioEllipse/courses/biol114/Chap06/week08a_files/regulon.gif
Oligo expression array
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transcripts
(soybean component)
Expression profiles
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Time since drug administered
Expression profiles
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Time since drug administered
Time since drug administered
Expression profiles
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Time since drug administered
Time since drug administered
Each color is a regulon, or “cluster,” of co-regulated genes
Expression effects of cancer
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Cancer classification
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Cancer classification
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Diagnosis via transcriptional profile
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Diagnosis via transcriptional profile
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transcripts
patient samples
Diagnosis via transcriptional profile
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Diagnosis via transcriptional profileQuickTime™ and a
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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?
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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Natural variation among “normals”
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Natural variation among “normals”
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Many mRNA differences at once
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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
Microarrayeach F2 liver
…
Linkage mapping of mRNA levels
“Black 6” mouse x “DBA” mouse
111 F2 progeny
Microarrayeach F2 liver
…
Genotypeeach F2
Linkage mapping of mRNA levels
“Black 6” mouse x “DBA” mouse
111 F2 progeny
Microarrayeach F2 liver
…
Genotypeeach F2
Looking for linkage (coinheritance) between marker and mRNA level.
Marker is linked to polymorphism in expression regulation cascade
ORFTFTF
G
kinaseTF
G
G One allele = high mRNA,the other = low mRNA
Marker is linked to polymorphism in expression regulation cascade
ORFTFTF
G
kinaseTF
mRNA level shows linkage to locus of polymorphic regulator(s).
Marker is linked to polymorphism in expression regulation cascade
ORFTFTF
G
kinaseTF
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
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Polymorphism responsible for mRNA difference is at the locus of the gene itself
Locally acting polymorphisms
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Polymorphism responsible for mRNA difference is at the locus of the gene itself
~25% of varying mRNAs are caused by locally acting polymorphism
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
Microarrayeach F2 liver
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Genotypeeach F2
Measure fat pad each F2
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Clinical applications
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Clinical applications
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Colored curves = fat mass at different body locations
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
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Clinical applications
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Counts allele 1/allele 2, casesCounts allele 1/allele 2, controls
= 1.5
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Linkage of human transcripts
Linkage of human transcripts
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Association of human transcripts
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Association of human transcripts
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linkage (families)
assoc (unrelated)
Association in multiple populations
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Han Chinese and Japanese
European-Americans in Utah