eQTLs Chris Cotsapas cotsapasbroadinstituteorg 60476878HST507 Computational Biology Genomes Networks Evolution Module 4 Population Evolution Phylogeny L1516 Association mapping for disease and molecular traits ID: 931283
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Slide1
Lecture 16Regulatory variation and eQTLs
Chris Cotsapascotsapas@broadinstitute.org
6.047/6.878/HST.507
Computational Biology: Genomes, Networks, Evolution
Slide2Slide3Module 4: Population / Evolution / PhylogenyL15/16: Association mapping for disease and molecular traitsStatistical genetics: disease mapping in populations (Mark Daly)Quantitative traits and molecular variation:
eQTLs, cQTLsL17/18: Phylogenetics / Phylogenomics
Phylogenetics: Evolutionary models, Tree building,
Phylo
inference
Phylogenomics: gene/species trees, coalescent models, populations
L19/20: Human history, Missing heritabilityMeasuring natural selection in human populationsThe missing heritability in genome-wide associationsAnd done! Last pset Nov 11 (no lab), In-class quiz on Nov 20No lab 4! Then entire focus shifts to projects, Thanksgiving, Frontiers
Slide4Today: Regulatory variation and
eQTLs
Quantitative Trait Loci (QTLs), Regulatory Variation
Molecular phenotypes as QTs: expression, chromatin…
Discretization: a GWAS for each gene.
Cis
-/Trans-eQTLsUnderlying regulatory variation: eQTLs, GWAS, cis-eQTL
Finding trans-
eQTLs
(distal from gene that varies)
Challenges: Power, structure, sample size
Cross-phenotype analysis: trans QTLs affect many genes
Identifying underlying regulatory mechanisms
Cis-eQTLs
: TSS-distance, cell type specificity
eQTLs
vs. GWAS: Expression as intermediate trait
Population differences, emerging efforts
Shared associations, SNP-gene pairs, allelic direction
Confound: environment, preparation, batch, ancestry
Slide5Quantitative traits
- weight, height
- anything measurable
-
today: gene expression
QTLs (QT Loci)
-
The loci that control
quantitative traits
Slide6Regulatory variationWhat do trait-associated variants do?Genetic changes to:Coding sequence **Gene expression levels
Splice isomer levelsMethylation patternsChromatin accessibilityTranscription factor binding kinetics
Cell signaling
Protein-protein interactions
Regulatory
Slide7Basic ConceptsHistory, eQTL, mQTL, others
Slide8Slide9Within a population
Damerval
et al
1994
42/72
p
rotein
levels
differ in maize
2D electrophoresis, eyeball spot quantitation
Problems:
genome coverage
quantitation
post-translational
modifications
Solution: use expression levels instead!
Slide10Usual mapping tools available
Discretization approach
Slide11gene 3
Whole-genome
eQTL
analysis is an independent GWAS for expression of each gene
gene 2
gene N
gene 5
gene 4
gene 1
Slide12cis-eQTLThe 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)
Slide13Slide14Slide15Slide16eqtl – the array erayeast, mouse, maize, human
Slide17Yeast
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
Slide18trans
hotspots
Brem
et al
Science 2002
Slide19Yvert
et al
Nat Genet 2003
Slide20Mammals I
F2 mice on atherogenic diet
Expression arrays; WG linkage
Schadt
et al
Nature 2003
Slide21Mammals II
Chesler
et al
Nat Genet 2005
10% !!
Slide22Mammals 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
Slide23Today: Regulatory variation and
eQTLs
Quantitative Trait Loci (QTLs), Regulatory Variation
Molecular phenotypes as QTs: expression, chromatin…
Discretization: a GWAS for each gene.
Cis
-/Trans-eQTLsUnderlying regulatory variation: eQTLs, GWAS, cis-eQTL
Finding trans-
eQTLs
(distal from gene that varies)
Challenges: Power, structure, sample size
Cross-phenotype analysis: trans QTLs affect many genes
Identifying underlying regulatory mechanisms
Cis-eQTLs
: TSS-distance, cell type specificity
eQTLs
vs. GWAS: Expression as intermediate trait
Population differences, emerging efforts
Shared associations, SNP-gene pairs, allelic direction
Confound: environment, preparation, batch, ancestry
Slide24Where are the trans eQTLS?Open question
Slide25gene 3
Whole-genome
eQTL
analysis is an independent GWAS for expression of each gene
gene 2
gene N
gene 5
gene 4
gene 1
Slide26Issues with trans mappingPowerGenome-wide significance is 5e-8Multiple testing on ~20K genes
Sample sizes clearly inadequateData structureBias corrections deflate varianceNon-normal distributions
Sample sizes
Far too small
Slide27But…Assume that trans eQTLs affect many genes……and you can use cross-trait methods!
Slide28Association data
Z1,1
Z
1,2
…
…
Z1,pZ2,1
:
:
Z
s
,1
Z
s,p
Slide29Cross-phenotype meta-analysis
S
CPMA
~
L
(data | λ≠1)
L
(data | λ=1)
Cotsapas et al, PLoS Genetics
Slide30CPMA detects trans mixtures
Slide31Open research questionsDo trans effects exist?Yes – heritability estimates suggest so.Can we detect them?Larger cohorts?Most
eQTL studies ~50-500 individualsSee later, GTEx Project
Better methods?
Collapsing data?
PCA, summary statistics, modeling?
Slide32Today: Regulatory variation and
eQTLs
Quantitative Trait Loci (QTLs), Regulatory Variation
Molecular phenotypes as QTs: expression, chromatin…
Discretization: a GWAS for each gene.
Cis
-/Trans-eQTLsUnderlying regulatory variation: eQTLs, GWAS, cis-eQTL
Finding trans-
eQTLs
(distal from gene that varies)
Challenges: Power, structure, sample size
Cross-phenotype analysis: trans QTLs affect many genes
Identifying underlying regulatory mechanisms
Cis-eQTLs
: TSS-distance, cell type specificity
eQTLs
vs. GWAS: Expression as intermediate trait
Population differences, emerging efforts
Shared associations, SNP-gene pairs, allelic direction
Confound: environment, preparation, batch, ancestry
Slide33Can we learn regulatory variation from eQTL?
Slide34First, let’s define the questionCan 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?
Slide35Most significant SNP per gene
0.001
permutation
threshold
Significant associations are symmetrically distributed around TSS
Stranger
et al
.,
PLoS
Gen 2012
Slide36268
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 cell types with gene association
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
2009
Slide courtesy Antigone Dimas
Slide37Open research questionsDo 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
…
C
ommon regulators of coregulated genes?Tissue specificity?Mechanisms?
Slide38Application to GWASCandidate genes, perturbations underlying organismal phenotypes
Slide39eQTLs as intermediate traits
Schadt et al Nat Genet 2005
Slide40Modified 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 disease
relevant cell type for disease
Importance of cataloguing regulatory variation in multiple cell types
Slide courtesy Antigone Dimas
Slide41Barrett et al 2008
de Jager et al 2007
Slide42Slide43Slide44Franke et al
2010Anderson et al 2011
Slide45Today: Regulatory variation and
eQTLs
Quantitative Trait Loci (QTLs), Regulatory Variation
Molecular phenotypes as QTs: expression, chromatin…
Discretization: a GWAS for each gene.
Cis
-/Trans-eQTLsUnderlying regulatory variation: eQTLs, GWAS, cis-eQTL
Finding trans-
eQTLs
(distal from gene that varies)
Challenges: Power, structure, sample size
Cross-phenotype analysis: trans QTLs affect many genes
Identifying underlying regulatory mechanisms
Cis-eQTLs
: TSS-distance, cell type specificity
eQTLs
vs. GWAS: Expression as intermediate trait
Population differences, emerging efforts
Shared associations, SNP-gene pairs, allelic direction
Confound: environment, preparation, batch, ancestry
Slide46Population differences
Slide47Shared association in 8 HapMap populations
APOH:
apolipoprotein
H
Stranger
et al
., PLoS Gen 2012
Slide48Number of genes with cis-eQTL associations
8 extended HapMap populations
SRC: permutation threshold
Stranger
et al
.,
PLoS
Gen 2012
Slide49Direction of allelic effectsame SNP-gene combination across populations
AGREEMENT
OPPOSITE
Population 1
Population 2
log
2
expression
log
2
expression
log
2
expression
log
2
expression
Stranger
et al
.,
PLoS
Gen 2012
Slide50Slide courtesy
Alkes
Price
Slide51Population 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
Slide52Gene 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
Slide53Gene 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
Slide54Emerging effortsRNAseq, GTEx
Slide55RNAseq questionsStandard eQTLs Montgomery et al, P
ickrell et al Nature 2010Isoform eQTLsDepth of sequence!
Long genes are preferentially sequenced
Abundant genes/isoforms ditto
Power!?
Mapping biases due to SNPs
Slide56Strategies for transcript assembly
Garber
et al. Nat Methods
8:469 (2011)
Slide57GTEx – Genotype-T
issue EXpressionAn NIH common fund project
Current: 35 tissues from 50 donors
Scale up: 20K tissues from 900 donors.
Novel methods groups: 5 current + RFA
Slide58RNAseq combined with other techsRegulons: TF gene sets via CHiP/seq
Look for trans effectsOpen chromatin states (Dnase
I; methylation)
Find active genes
Changes in epigenetic marks correlated to RNA
Genetic effects
RNA/DNA comparisons Simultaneous SNP detection/genotypingRNA editing ???
Slide59Today: Regulatory variation and eQTLs
Quantitative Trait Loci (QTLs), Regulatory VariationMolecular phenotypes as QTs: expression, chromatin…
Discretization: a GWAS for each gene.
Cis
-/Trans-
eQTLs
Underlying regulatory variation: eQTLs, GWAS, cis-eQTLFinding trans-eQTLs (distal from gene that varies)Challenges: Power, structure, sample size
Cross-phenotype analysis: trans QTLs affect many genes
Identifying underlying regulatory mechanisms
Cis-eQTLs
: TSS-distance, cell type specificity
eQTLs
vs. GWAS: Expression as intermediate trait
Population differences, emerging efforts
Shared associations, SNP-gene pairs, allelic direction
Confound: environment, preparation, batch, ancestry