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Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
1The screen versions of these slides have full details of copyright and acknowledgements
1
Genetic Variation in Gene Regulation
Prof. Jonathan K. Pritchard
Departments of Genetics & Biology
Howard Hughes Medical Institute
Stanford University
Web: pritchardlab.stanford.edu
2
It is now clear that much of the genetic basis of complex traits is noncoding –
presumably due to regulatory variants
Genes
Figure from WTCCC study (2007)
A noncoding GWAS hit for Crohn’s disease
3 Figure from Pickrell, 2014 (AJHG)
Only a minority of GWAS hits are due to non-synonymous variants
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
2The screen versions of these slides have full details of copyright and acknowledgements
4
eQTLs: expression Quantitative Trait Loci -linking genetic variation to changes
in gene regulation
(Early key work on eQTLs by Leonid Kruglyak, Vivian Cheung,Manolis Dermitzakis and others)
Expre
ssion: +
/-S
Ds fro
m m
ean
Expression levels at HLA-C
5
DNA sequence encodes cis-acting regulatory
information
(Output is cell-type or context specific)
Steady state mRNA levels
Trans-acting factors in the cell
How do genetic variants influence expression?
Cis-eQTLs presumably disrupt this encoded
information
Affect expression of other genesTrans-eQTLs
6
Question: how do SNPs impact
gene regulation?
Ultimately we want to get much better
at interpreting noncoding variants
that affect phenotypes
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
3The screen versions of these slides have full details of copyright and acknowledgements
7
One major source of eQTL data is from GTEx
GTEx is collecting expression data from dozens
of tissue sites in hundreds of individuals
http://www.gtexportal.org/home/
8
• Immortalized B cells: There are now >1000 cell lines genotyped
and sequenced by HapMap and the 1000 Genomes Project
• Numerous studies have shown strong overlap
between HapMap eQTLs and GWAS signals for a variety of traits
Ima
ge:
ha
pm
ap
.org
HapMap cell lines as a model system for studying expression variation
See work by Cheung, Dermitzakis, Snyder and Gilad/Pritchard groups
9
RNA-seq studies in HapMap samples have so far identified several thousand
cis-eQTLs
[Montgomery et al., 2010, Pickrell et al., 2010, Lappalainen et al., 2013]
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
4The screen versions of these slides have full details of copyright and acknowledgements
10
Example cis-eQTL from HapMap samplesRNA-seq read depth at TSP50, stratified by genotype
at associated SNP
Pickrell et al, Nature (2010)
11
Some eQTLs affect individual exons only
12
What is the molecular basis for cis-eQTLs?
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
5The screen versions of these slides have full details of copyright and acknowledgements
13
eQTLs are detected because genotype correlates with steady state expression
Figure: Athma Pai
DNaseI, Pol II,
DNA methylation,
H3K4me1, H3K4me3, H3K27me3,
H3K27ac, MNase
mRNA transcription
rates (4SU metabolic
labeling)
Alternative splicing
from RNA-seq
Steady state expression
from RNA-seq
mRNA decay experiments
14
Most analyses of eQTLs are complicated by the fact that there is uncertainty
about which site is causal
Gaffney et al. (Genome Biology 2012)
15Pickrell et al (2010)
Model: Veyrieras, PLOS Gen (2008)
Most eQTLs lie inside, or very near target genes (long-range eQTLs >100kb do exist,
but these are unusual)
Distribution of top signals
with respect to affected genes
Using a model to correct for LD
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
6The screen versions of these slides have full details of copyright and acknowledgements
16
Most eQTLs affect regulators of chromatin function including
promoters and enhancers
Much of this is by altering transcription factor binding sites
17
DNaseI footprinting was first used by Galas & Schmitz (1979); genome-wide
assays developed by Crawford and Stamatoyannopoulos labs
Average DNaseI profile at NRSF binding sites
(Pique-Regi et al, 2011, Genome Research)
DNaseI sequencing
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Degner et al. (2012) performed DNase-seq in 70 HapMap cell lines
They identified ~9000 DNase-QTLs
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
7The screen versions of these slides have full details of copyright and acknowledgements
19
Example dsQTL: C haplotype has DNaseIhypersensitive site; only weak cutting
of T haplotypeAssociated SNP
(rs4953223)
20
This dsQTL appears to be due to disruption of an NF-KB binding site
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NF-kB ChIP-seq data show that binding is virtually eliminated from the T haplotype
Degner, Nature (2012)
Data from Kasowski, Science (2010)
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
8The screen versions of these slides have full details of copyright and acknowledgements
22
dsQTL SNPs typically drive coordinated changes in multiple aspects of chromatin
architecture as well as TF occupancy
23
Example: a promoter SNP at SNX7 drives coordinated changes in chromatin
and transcription
McVicker et al. Science (2013)
rs12723363
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Increased DNaseI sensitivity at dsQTLscorrelates with higher TF occupancy…
Degner et al, 2012
DNase1: fraction of reads in heterozygote carrying major allele
Transcription
factor data:
fraction
of reads
in heterozygote
carrying major
allele
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
9The screen versions of these slides have full details of copyright and acknowledgements
25
Nucleosome midpoint density around dsQTLs
(MNase data)Gaffney et al, PLOS Gen (2012)
…And increased DNaseI sensitivity at dsQTLscorrelates with lower nucleosome occupancy
and stronger positioning
dsQTL center
High DNase genotypes
Low DNase genotypes
26
What types of regulatory information encoded in the DNA sequence are disrupted
by chromatin QTL SNPs?
eQTLs can potentially help us to determine causal links from DNA sequence to chromatin function
27
Degner (2012), Kilpinen (2013), McVicker(2013), Heinz (2013), Gutierrez-Arcelus (2013),Banovich (2014)
• In many cases chromatin QTLs act by disrupting
transcription factor binding sequences
• SNPs that change TF binding can play causal roles
in driving changes in DNaseI sensitivity, histone marking,
Pol II occupancy and DNA methylation
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
10The screen versions of these slides have full details of copyright and acknowledgements
28
A
G
C
T
G
C
A
G
T
G
TF SNP Change in histones (NULL) TF affects histones
1
2
3
4
5
+
-
-
+
+
-
-
+
-
+
+
-
+
-
+
-
+
-
+
-
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Difference in predicted TF binding: genome reference alleles vs. alternate alleles
Mean difference
in ChIP-seq read
depth between
the alleles (all lines significant)
Histone marksSNPs within binding sites for PU.1 and other ETS-box
TFs direct changes in marking of histone H3, and Pol II occupancy
K27me3
K4me3
K4me1
K27ac
Pol II
McVicker et al. (2013)See also Heinz et al. (2013),Kilpinen et al. (2013)
30
Overall significance of correlations between PWM changes and allele-specific mark changes
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
11The screen versions of these slides have full details of copyright and acknowledgements
31
Model:
• Low DNase sensitivity
• Increased H3K27Me3
• Weak nucleosome positioning
• Most regulatory regions have competition between active
and closed chromatin configurations
• Active configurations have high TF occupancy and recruit chromatin
remodelers that add ‘active’ histone marks
SNPs that weaken TF binding tend to push the system towards the closed
nucleosome configuration, thus producing highly correlated quantitative
changes in multiple experimental assays as observed
• TF binding
• High DNase sensitivity
• Strong nucleosome positioning
• Increased H3K27ac, H3K4me1
or H3K4me3, and Pol II at promoters
32
Next: how do dsQTLs affect promoters and gene expression?
33
DNaseI data
Example: a SNP in the first intron of the SLFN5 gene affects DNaseI sensitivity
in a ~200bp region
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
12The screen versions of these slides have full details of copyright and acknowledgements
34
This dsQTL also impacts expression of SLFN5
DNaseI data RNA-seq data
35
dsQTLs at distal enhancers frequently drive remote chromatin activation
at promoters to create eQTLs
rs2886870
36
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
13The screen versions of these slides have full details of copyright and acknowledgements
37
eQTLs drive organism-level phenotypes through their effects on proteins
See Wu…Snyder, Nature (2013) Battle et al, Science (2015)
38
It is possible to measure post-transcriptional regulation through ribosomal profiling (translation) and mass spec (protein levels)
See Wu…Snyder, Nature (2013) Battle et al, Science (2015)
39
Most eQTLs are preserved at protein level
Battle et al, Science (2015)
Read
s p
er
milli
on
Read
s p
er
milli
on
Lo
g2(s
am
ple
/sta
nd
ard
), S
ILA
C
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
14The screen versions of these slides have full details of copyright and acknowledgements
40
However in many cases the effect sizes are smaller on protein suggesting a layer
of buffering regulation
Battle et al, Science (2015)
41
There is also a significant class of protein-specific QTLs that act post-translationally
Battle et al, Science (2015)
Re
ad
s p
er
mil
lio
nR
ea
ds
pe
r m
illio
n
Lo
g2(s
am
ple
/s
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da
rd),
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ILA
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42
Summary
• Most cis-eQTLs lie inside or near to the target genes
• Most cis-eQTLs acts through changes in chromatin
• SNPs that change TF binding affinity drive changes
in diverse aspects of chromatin architecture
• Other QTLs act through other mechanisms including
splicing and other properties of mRNA
• A subset of QTLs act on protein abundance levels
independently of steady state mRNA levels
Genetic variation in gene regulation
Prof. Jonathan K. Pritchard
15The screen versions of these slides have full details of copyright and acknowledgements
43Yoav Gilad
• Yoav Gilad
• Matthew Stephens
• Athma Pai
• Joe Pickrell
• Jacob Degner
• Dan Gaffney
• Roger Pique-Regi
• Jordana Bell
• Alexis Battle
• Zia Khan
Acknowledgments:
• Graham McVicker
• Bryce van de Geijn
• Jean-Baptiste Veyrieras
• And others
• For Funding: NIH/HHMI
44