Lecture 15 Regulatory variation and eQTLs
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Transcript of Lecture 15 Regulatory variation and eQTLs
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Lecture 15Regulatory variation and eQTLs
Chris [email protected]
6.047/6.878/HST.507Computational Biology: Genomes, Networks, Evolution
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Module 4: Population / Evolution / Phylogeny
• L15/16: Association mapping for disease and molecular traits– Statistical genetics: disease mapping in populations (Mark Daly)– Quantitative traits and molecular variation: eQTLs, cQTLs
• L17/18: Phylogenetics / Phylogenomics– Phylogenetics: Evolutionary models, Tree building, Phylo inference– Phylogenomics: gene/species trees, coalescent models, populations
• L19/20: Human history, Missing heritability– Measuring natural selection in human populations– The missing heritability in genome-wide associations
• And done! Last pset Nov 11 (no lab), In-class quiz on Nov 20– No lab 4! Then entire focus shifts to projects, Thanksgiving, Frontiers
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Today: Regulatory variation and eQTLs1. Quantitative Trait Loci (QTLs), Regulatory Variation
– Molecular phenotypes as QTs: expression, chromatin…– Discretization: a GWAS for each gene. Cis-/Trans-eQTLs– Underlying regulatory variation: eQTLs, GWAS, cis-eQTL
2. Finding trans-eQTLs (distal from gene that varies)– Challenges: Power, structure, sample size– Cross-phenotype analysis: trans QTLs affect many genes
3. Identifying underlying regulatory mechanisms– Cis-eQTLs: TSS-distance, cell type specificity– eQTLs vs. GWAS: Expression as intermediate trait
4. Population differences, emerging efforts– Shared associations, SNP-gene pairs, allelic direction– Confound: environment, preparation, batch, ancestry
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Quantitative traits- weight, height- anything measurable- today: gene expression
QTLs (QT Loci)- The loci that control
quantitative traits
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Regulatory variation
• What do trait-associated variants do?• Genetic changes to:
– Coding sequence **– Gene expression levels– Splice isomer levels– Methylation patterns– Chromatin accessibility– Transcription factor binding kinetics– Cell signaling– Protein-protein interactions
Regulatory
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BASIC CONCEPTSHistory, eQTL, mQTL, others
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Within a population
• Damerval et al 1994• 42/72 protein levels differ in maize• 2D electrophoresis, eyeball spot quantitation• Problems:
– genome coverage– quantitation– post-translational modifications
• Solution: use expression levels instead!
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Usual mapping tools available
• Discretization approach
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gene 3
Whole-genome eQTL analysis is an independent GWAS for expression of each
gene
gene 2
gene N
gene 5
gene 4
gene 1
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• cis-eQTL– The position of the eQTL maps near
the physical position of the gene.– Promoter polymorphism?– Insertion/Deletion?– Methylation, chromatin
conformation?
• trans-eQTL– The position of the eQTL does not
map near the physical position of the gene.
– Regulator?– Direct or indirect?
Modified from Cheung and Spielman 2009 Nat Gen
Genetics of gene expression (eQTL)
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eQTL – THE ARRAY ERAyeast, mouse, maize, human
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Yeast
• 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
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trans hotspots
Brem et al Science 2002
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Yvert et al Nat Genet 2003
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Mammals I
• F2 mice on atherogenic diet• Expression arrays; WG linkage
Schadt et alNature 2003
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Mammals II
Chesler et al Nat Genet 2005
10% !!
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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
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Today: Regulatory variation and eQTLs1. Quantitative Trait Loci (QTLs), Regulatory Variation
– Molecular phenotypes as QTs: expression, chromatin…– Discretization: a GWAS for each gene. Cis-/Trans-eQTLs– Underlying regulatory variation: eQTLs, GWAS, cis-eQTL
2. Finding trans-eQTLs (distal from gene that varies)– Challenges: Power, structure, sample size– Cross-phenotype analysis: trans QTLs affect many genes
3. Identifying underlying regulatory mechanisms– Cis-eQTLs: TSS-distance, cell type specificity– eQTLs vs. GWAS: Expression as intermediate trait
4. Population differences, emerging efforts– Shared associations, SNP-gene pairs, allelic direction– Confound: environment, preparation, batch, ancestry
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WHERE ARE THE TRANS eQTLS?Open question
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gene 3
Whole-genome eQTL analysis is an independent GWAS for expression of each
gene
gene 2
gene N
gene 5
gene 4
gene 1
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Issues with trans mapping
• Power– Genome-wide significance is 5e-8
– Multiple testing on ~20K genes– Sample sizes clearly inadequate
• Data structure– Bias corrections deflate variance– Non-normal distributions
• Sample sizes– Far too small
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But…
• Assume that trans eQTLs affect many genes…
• …and you can use cross-trait methods!
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Association data
Z1,1 Z1,2 … … Z1,p
Z2,1
::
Zs,1 Zs,p
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Cross-phenotype meta-analysis
SCPMA ~L(data | λ≠1)
L(data | λ=1)
Cotsapas et al, PLoS Genetics
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CPMA detects trans mixtures
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Open research questions
• Do trans effects exist?– Yes – heritability estimates suggest so.– Can we detect them?
• Larger cohorts?– Most eQTL studies ~50-500 individuals– See later, GTEx Project
• Better methods?– Collapsing data?– PCA, summary statistics, modeling?
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Today: Regulatory variation and eQTLs1. Quantitative Trait Loci (QTLs), Regulatory Variation
– Molecular phenotypes as QTs: expression, chromatin…– Discretization: a GWAS for each gene. Cis-/Trans-eQTLs– Underlying regulatory variation: eQTLs, GWAS, cis-eQTL
2. Finding trans-eQTLs (distal from gene that varies)– Challenges: Power, structure, sample size– Cross-phenotype analysis: trans QTLs affect many genes
3. Identifying underlying regulatory mechanisms– Cis-eQTLs: TSS-distance, cell type specificity– eQTLs vs. GWAS: Expression as intermediate trait
4. Population differences, emerging efforts– Shared associations, SNP-gene pairs, allelic direction– Confound: environment, preparation, batch, ancestry
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CAN WE LEARN REGULATORY VARIATION FROM eQTL?
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First, let’s define the question
• Can we use genetic perturbations as a way to understand how genes are regulated?
• In what groups, in which tissues? • To what stimuli/signaling events? • Do cis eQTLs perturb promoter elements?• Do trans perturb TFs? Signaling cascades?
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Most significant SNP per gene 0.001 permutation threshold
Significant associations are symmetrically distributed around TSS
Stranger et al., PLoS Gen 2012
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268 271 262
73 85 82
86 86 86
Cell type-specific and cell type-shared gene associations(0.001 permutation threshold)
cell type
No.
of c
ell t
ypes
with
gen
e as
soci
ation
69-80% of cis associations are cell type-specific
• cis association sharing increases slightly when significance thresholds are relaxed• Cell type specificity verified experimentally for subset of eQTLs
Dimas et al Science 2009
Dimas et al Science 2009Slide courtesy Antigone Dimas
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Open research questions
• Do cis eQTLs perturb functional elements?– Given each is independent, how can we know?
• Do tissue-specific effects correlate with the expression of a gene across tissues? Or a regulator?– Perhaps a gene is expressed, but in response to different
regulators across tissues?• If we ever find trans eQTLs…
– Common regulators of coregulated genes?– Tissue specificity?– Mechanisms?
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APPLICATION TO GWASCandidate genes, perturbations underlying organismal phenotypes
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eQTLs as intermediate traits
Schadt et al Nat Genet 2005
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Modified from Nica and Dermitzakis Hum Mol Genet 2008
Exploring eQTLs in the relevant cell type is important for disease association studies
cell type not relevant for diseaserelevant cell type for disease
Importance of cataloguing regulatory variation in multiple cell types
Slide courtesy Antigone Dimas
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Barrett et al 2008de Jager et al 2007
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Franke et al 2010Anderson et al 2011
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Today: Regulatory variation and eQTLs1. Quantitative Trait Loci (QTLs), Regulatory Variation
– Molecular phenotypes as QTs: expression, chromatin…– Discretization: a GWAS for each gene. Cis-/Trans-eQTLs– Underlying regulatory variation: eQTLs, GWAS, cis-eQTL
2. Finding trans-eQTLs (distal from gene that varies)– Challenges: Power, structure, sample size– Cross-phenotype analysis: trans QTLs affect many genes
3. Identifying underlying regulatory mechanisms– Cis-eQTLs: TSS-distance, cell type specificity– eQTLs vs. GWAS: Expression as intermediate trait
4. Population differences, emerging efforts– Shared associations, SNP-gene pairs, allelic direction– Confound: environment, preparation, batch, ancestry
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POPULATION DIFFERENCES
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Shared association in 8 HapMap populations
APOH: apolipoprotein H Stranger et al., PLoS Gen 2012
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Number of genes with cis-eQTL associations8 extended HapMap populations
Spearman Rank CorrelationEnsGene
0.01 0.001 0.0001# genes FDR # genes FDR # genes FDR
CEU 2869 0.06 657 0.03 313 0.01CHB 2832 0.06 774 0.02 378 0.00GIH 2959 0.06 698 0.03 300 0.01JPT 2900 0.06 795 0.02 386 0.00
LWK 3818 0.05 773 0.02 311 0.01MEX 2609 0.07 472 0.04 165 0.01MKK 4222 0.04 947 0.02 411 0.00YRI 3961 0.05 799 0.02 328 0.01
non-redundant 12494 3130 1132>2 pops 6889 0.55 1074 0.34 547 0.488 pops 151 0.01 63 0.02 28 0.02
4 PCA + 4 non-PCAGenes are Ensembl genes.Generated on July 22, 2009, modified July 28th because of MKK error.These numbers differ from v2 because all assocs where dist to true TSS > 1Mb were removed.This removed some genes. I redid the MostSigSnpEnsGene after removing those SNPs. This is in column MostSigSnpEnsGene_1
SRC: permutation threshold
Stranger et al., PLoS Gen 2012
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Direction of allelic effectsame SNP-gene combination across populations
AGREEMENT
OPPOSITE
Population 1
rs40915
THAP5
TTCTCC
8.00
7.75
7.50
7.25
7.00
6.75
6.50
Population 2
rs40915
THAP5
TTCTCC
8.00
7.75
7.50
7.25
7.00
6.75
6.50
log 2 e
xpre
ssio
n
log 2 e
xpre
ssio
n
rs40915
THAP5
TTCTCC
8.00
7.75
7.50
7.25
7.00
6.75
6.50
log 2 e
xpre
ssio
n
rs40915
THAP5
TTCTCC
8.00
7.75
7.50
7.25
7.00
6.75
6.50
log 2 e
xpre
ssio
n
Stranger et al., PLoS Gen 2012
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Slide courtesy Alkes Price
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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
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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 HD100AAGene expression measurements at 4,197 genes obtained using Affymetrix Focus array
c
Slide courtesy Alkes Price
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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
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EMERGING EFFORTSRNAseq, GTEx
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RNAseq questions
• Standard eQTLs – Montgomery et al, Pickrell et al Nature 2010
• Isoform eQTLs– Depth of sequence!
• Long genes are preferentially sequenced• Abundant genes/isoforms ditto• Power!?• Mapping biases due to SNPs
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Strategies for transcript assembly
Garber et al. Nat Methods 8:469 (2011)
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GTEx – Genotype-Tissue EXpressionAn NIH common fund project
Current: 35 tissues from 50 donors
Scale up: 20K tissues from 900 donors.
Novel methods groups: 5 current + RFA
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RNAseq combined with other techs
• Regulons: TF gene sets via CHiP/seq– Look for trans effects
• Open chromatin states (Dnase I; methylation)– Find active genes– Changes in epigenetic marks correlated to RNA– Genetic effects
• RNA/DNA comparisons – Simultaneous SNP detection/genotyping– RNA editing ???
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Today: Regulatory variation and eQTLs1. Quantitative Trait Loci (QTLs), Regulatory Variation
– Molecular phenotypes as QTs: expression, chromatin…– Discretization: a GWAS for each gene. Cis-/Trans-eQTLs– Underlying regulatory variation: eQTLs, GWAS, cis-eQTL
2. Finding trans-eQTLs (distal from gene that varies)– Challenges: Power, structure, sample size– Cross-phenotype analysis: trans QTLs affect many genes
3. Identifying underlying regulatory mechanisms– Cis-eQTLs: TSS-distance, cell type specificity– eQTLs vs. GWAS: Expression as intermediate trait
4. Population differences, emerging efforts– Shared associations, SNP-gene pairs, allelic direction– Confound: environment, preparation, batch, ancestry