genetic variation in gene regulation · genetic variation in gene regulation prof. jonathan k....

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


Departments of Genetics & Biology

Howard Hughes Medical Institute

Stanford University

Web: pritchardlab.stanford.edu

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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




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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

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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

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Question: how do SNPs impact

gene regulation?

Ultimately we want to get much better

at interpreting noncoding variants

that affect phenotypes




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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/

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• 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

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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]




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Example cis-eQTL from HapMap samplesRNA-seq read depth at TSP50, stratified by genotype

at associated SNP

Pickrell et al, Nature (2010)

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Some eQTLs affect individual exons only

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What is the molecular basis for cis-eQTLs?




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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

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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




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Most eQTLs affect regulators of chromatin function including

promoters and enhancers

Much of this is by altering transcription factor binding sites

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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




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Example dsQTL: C haplotype has DNaseIhypersensitive site; only weak cutting

of T haplotypeAssociated SNP

(rs4953223)

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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)




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dsQTL SNPs typically drive coordinated changes in multiple aspects of chromatin

architecture as well as TF occupancy

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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




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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

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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

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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




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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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+

-

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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)

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Overall significance of correlations between PWM changes and allele-specific mark changes




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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

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Next: how do dsQTLs affect promoters and gene expression?

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DNaseI data

Example: a SNP in the first intron of the SLFN5 gene affects DNaseI sensitivity

in a ~200bp region




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This dsQTL also impacts expression of SLFN5

DNaseI data RNA-seq data

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dsQTLs at distal enhancers frequently drive remote chromatin activation

at promoters to create eQTLs

rs2886870

36




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eQTLs drive organism-level phenotypes through their effects on proteins

See Wu…Snyder, Nature (2013) Battle et al, Science (2015)

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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)

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Most eQTLs are preserved at protein level

Battle et al, Science (2015)

Read

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However in many cases the effect sizes are smaller on protein suggesting a layer

of buffering regulation


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There is also a significant class of protein-specific QTLs that act post-translationally


Re

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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




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

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