Genetics of Gene Expression Chris Cotsapas [email_address]

Chris Cotsapas

[email_address]

Outline Background Significance Experimental design Definitions Basic findings Tissue specificity Population differences Biological insight (?)‏ Last thoughts

Background

Significance

Experimental design

Definitions

Basic findings

Tissue specificity

Population differences

Biological insight (?)‏

Last thoughts

In the beginning... King and Wilson, Science 1975 Chimp – human coding sequence differences don't match phenotypic distance Gene regulation responsible for rest? Basis for evo-devo, GGE

King and Wilson, Science 1975

Chimp – human coding sequence differences don't match phenotypic distance

Gene regulation responsible for rest?

Basis for evo-devo, GGE

Genetics of gene expression Find genetic variation that (partially) controls levels of RNA If RNA levels are different, maybe protein levels are also different If protein concentration is different, maybe there is a phenotype?

Find genetic variation that (partially) controls levels of RNA

If RNA levels are different, maybe protein levels are also different

If protein concentration is different, maybe there is a phenotype?

Experimental design Protein measurements 2D gels; quantitation hard; resolution? RNA measurements Expression chips ca. 1995 (Schena et al , Science)‏

Protein measurements

2D gels; quantitation hard; resolution?

RNA measurements

Expression chips ca. 1995 (Schena et al , Science)‏

Experimental design - methods Allelic discrimination SBE Pyrosequencing SNP arrays Whole-genome expression assays Genotypes?

Allelic discrimination

SBE

Pyrosequencing

SNP arrays

Whole-genome expression assays

Genotypes?

Experimental design - samples Pedigrees – multiplex/trios HapMap Population samples Inbred strain panels genotypes New crosses Inter-specific hybrids

Pedigrees – multiplex/trios

HapMap

Population samples

Inbred strain panels

genotypes

New crosses

Inter-specific hybrids

Definitions Cis Trans

Outline Background Basic findings Tissue specificity Population differences Biological insight (?)‏ Last thoughts

Background

Basic findings

Tissue specificity

Population differences

Biological insight (?)‏

Last thoughts

First forays Damerval et al 1994 Protein levels are different in 42/72 maize gene products 2D electrophoresis, eyeball spot quantitation Problems: genome coverage quantitation post-translational modifications

Damerval et al 1994

Protein levels are different in 42/72 maize gene products

2D electrophoresis, eyeball spot quantitation

Problems:

genome coverage

quantitation

post-translational modifications

Cowles et al 2002 F1 mice 69 genes in three tissues 4 variant; 2 only in liver

F1 mice

69 genes in three tissues

4 variant; 2 only in liver

Yeast WG approaches Brem et al Science 2002 Linkage in 40 offspring of lab x wild strain cross 1528/6215 DE between parents 570 map in cross multiple QTLs 32% of 570 have cis linkage 262 not DE in parents also map

Brem et al Science 2002

Linkage in 40 offspring of lab x wild strain cross

1528/6215 DE between parents

570 map in cross

multiple QTLs

32% of 570 have cis linkage

262 not DE in parents also map

Trans hotspots Brem et al Science 2002

Yvert et al Nat Genet 2003

Mammals I F2 mice on atherogenic diet Expression arrays; WG linkage Schadt et al Nature 2003

F2 mice on atherogenic diet

Expression arrays; WG linkage

Mammals II Chesler et al Nat Genet 2005 10% !!

Mammals III No major trans loci in humans Cheung et al Nature 2003 Monks et al AJHG 2004 Stranger et al PLoS Genet 2005, Science 2007 Further scans (WT, Cheung, Schadt etc)‏

No major trans loci in humans

Cheung et al Nature 2003

Monks et al AJHG 2004

Stranger et al PLoS Genet 2005, Science 2007

Further scans (WT, Cheung, Schadt etc)‏

What's the deal with trans ? Artefacts Normalization robustness Replication? Clusters of correlated genes Power trans often weak effects Small sample sizes

Artefacts

Normalization robustness

Replication?

Clusters of correlated genes

Power

trans often weak effects

Small sample sizes

Outline Background Basic findings Tissue specificity Population differences Biological insight (?)‏ Last thoughts

Background

Basic findings

Tissue specificity

Population differences

Biological insight (?)‏

Last thoughts

trans -acting factors may be tissue specific TFs, signalling molecules, etc etc cis regulatory sequences may be used by TS factors So, shouldn't genetic effects on GE be tissue specific? Logic

trans -acting factors may be tissue specific

TFs, signalling molecules, etc etc

cis regulatory sequences may be used by TS factors

So, shouldn't genetic effects on GE be tissue specific?

Examples Chesler vs Bystrykh (Nat Genet 2005)‏ 39/101 (39%) forebrain cis in HSC 297/1218 (24%) HSC cis in forebrain Hubner (Nat Genet 2005)‏ 311/4297 (7%) shared between fat and kidney Campbell et al (Gen Res 2008)‏ 12% of ~100 genes vary in at least one of three tissues

Chesler vs Bystrykh (Nat Genet 2005)‏

39/101 (39%) forebrain cis in HSC

297/1218 (24%) HSC cis in forebrain

Hubner (Nat Genet 2005)‏

311/4297 (7%) shared between fat and kidney

Campbell et al (Gen Res 2008)‏

12% of ~100 genes vary in at least one of three tissues

Correlation networks persist across tissues

Outline Background Basic findings Tissue specificity Population differences Biological insight (?)‏ Last thoughts

Background

Basic findings

Tissue specificity

Population differences

Biological insight (?)‏

Last thoughts

Slide courtesy Alkes Price

Population differences could have non-genetic basis • Differences due to environment? (Idaghdour et al. 2008)‏ • Differences in cell line preparation? (Stranger et al. 2007)‏ • Differences due to batch effects? (Akey et al. 2007)‏ (Reviewed in Gilad et al. 2008)‏ Slide courtesy Alkes Price

Gene expression experiment Does gene expression in 60 CEU + 60 YRI vary with ancestry? Does gene expression in 89 AA vary with % Eur ancestry? 60 CEU + 60 YRI from HapMap, 89 AA from Coriell HD100AA Gene expression measurements at 4,197 genes obtained using Affymetrix Focus array c Slide courtesy Alkes Price

Gene expression differences in African Americans validate CEU-YRI differences c = 0.43 (± 0.02)‏ ( P -value < 10 -25 )‏ 12% ± 3% in cis Slide courtesy Alkes Price

Outline Background Basic findings Tissue specificity Population differences Biological insight Last thoughts

Background

Basic findings

Tissue specificity

Population differences

Biological insight

Last thoughts

Does GGE matter? Where is the phenotype? Case/control differences? Does it translate to protein? Can we reconstruct networks/pathways?

Where is the phenotype?

Case/control differences?

Does it translate to protein?

Can we reconstruct networks/pathways?

Insight Mechanism? trans – coregulated groups? pathways Model QTs Molecular variation snapshots What does it mean? Ancillary information

Mechanism?

trans – coregulated groups?

pathways

Model QTs

Molecular variation snapshots

What does it mean?

Ancillary information

IRGM harbors a 20-kilobase deletion polymorphism immediately upstream IRGM Individuals Position on chr5 Slide courtesy Steve McCarroll McCarroll et al., Nature Genetics , Sept. 2008 rs13361189 (strongest Crohn’s-associated SNP)‏

IRGM structural haplotypes have altered expression Slide adapted, courtesy Steve McCarroll McCarroll et al., Nature Genetics , Sept. 2008

IRGM expression levels influence the efficacy of autophagy McCarroll et al., Nature Genetics , Sept. 2008 Slide adapted, courtesy Steve McCarroll

Outline Background Basic findings Tissue specificity Population differences Biological insight (?)‏ Last thoughts

Background

Basic findings

Tissue specificity

Population differences

Biological insight (?)‏

Last thoughts

Success? Baseline variation - tricky Perturbations and phenotypes mouse obesity crosses (Schadt et al )‏ Drug screening (Choi, Yelensky et al )‏ Crohn's disease (McCarroll et al )‏

Baseline variation - tricky

Perturbations and phenotypes

mouse obesity crosses (Schadt et al )‏

Drug screening (Choi, Yelensky et al )‏

Crohn's disease (McCarroll et al )‏

Stuff we haven't covered Coregulated sets Disease context Annotation/pathway analysis Modelling QTLs Regression WGAS Multiple data sets/types Post-transcriptional event variation

Coregulated sets

Disease context

Annotation/pathway analysis

Modelling QTLs

Regression

WGAS

Multiple data sets/types

Post-transcriptional event variation