CS 374: Relating the Genetic
Code to Gene Expression
Sandeep Chinchali
Outline
1.
2.
3.
4.
5.
Basic Gene Regulation
Gene Regulation and Human Disease
Measurement Technologies
Papers
Future Trends
1. BASIC GENE REGULATION
Human Genome
• 3 billion bases – 2% coding, 5-10% regulatory
• Organism’s complexity NOT correlated with
number of genes!
– Human (20-25k genes) vs. Rice (51k genes)
• 1 million Regulatory elements enable:
– Precise control for turning genes on/off
– Diverse cell types (lung, heart, skin)
Regulatory Elements
• ~ 20-25k genes
– Expression Modulated by ~ 1 Million cis-reg
elements
– Enhancer, Promoters, Silencers
Controlling Gene Expression
• Transcription factors (TFs):
– Proteins that recognize sequence motifs in
enhancers, promoters
– Combinatorial switches that turn genes on/off
Modulating Gene Expression
Expression Quantitative Trait Locus (eQTL):
– Regions where different genotypes correlate with
changes in gene expression
Chromatin Remodelling
http://www.cropscience.org.au/icsc2004/s
ymposia/3/1/1957_dennise-5.gif
2. GENE REGULATION AND DISEASE
Disease Implications
SHH
MUTATIONS
•Brain
•Limb
•Other
Bejerano Lab
Limb Enhancer 1Mb away from Gene
limb
Bejerano Lab
SHH
Enhancer Deletion
limb
DELETE
•Limb
Bejerano Lab
SHH
Enhancer 1bp Substitution
limb
SHH
MUTATIONS
•Limb
Lettice et al. HMG 2003
12: 1725-35
Bejerano Lab
Genome Wide Assocation Study
(GWAS):
80% of GWAS SNPs are noncoding (many are eQTLs)
Bejerano Lab
From eQTL to Disease
T
Allele specific binding may alter gene expression
Outline
1.
2.
3.
4.
5.
Basic Gene Regulation
Gene Regulation and Human Disease
Measurement Technologies
Papers
Future Trends
MEASUREMENT TECHNOLOGIES
eQTLs: Correlating Genotype with
Expression
RNA-seq, Microarray
SNP Array, WGS
GTEX
Measuring Open Chromatin
http://hmg.oxfordjournals.org
Measuring open chromatin – DNase
Seq
Sequence open chromatin – map enhancers, promoters …
wikipedia
Statistical Overview
• Given: Genotype + Expression Matrix
• Problem: Determine eQTLs
• Possible Solutions:
– Regress homozygous/het genotypes with
expression
• Key Problem:
– Of many linked SNPs, what is the causal variant?
Enhancer
Outline
1.
2.
3.
4.
5.
Basic Gene Regulation
Gene Regulation and Human Disease
Measurement Technologies
Papers
Future Trends
PAPER 1: DISSECTING THE REGULATORY
ARCHITECTURE OF GENE EXPRESSION
QTLS
Overview
• HapMap cells + 1000G genotypes
• Bayesian Model
– Uncertainty over functional SNP
– Prior: Whether SNP hits a functional element
(TFBS, promoter, etc)
– Upweight effect of SNPs in functional regions
• Results:
– eQTLs often in TFBS, open chromatin, not
specifically overrepresented in TATA box
METHODS
1. Associate SNPs with Gene
Expression
2. Functional Annotation
3. Adjust p-value based on annotation
RESULTS
eQTNs are enriched in enhancers,
promoters
Active
Promoter/En
hancer
Inactive
eQTNs are enriched in enhancers,
promoters (2)
What is the distribution of eQTNs in regulatory sites?
eQTNs enriched in TF binding sites
What TF families show the highest eQTN
enrichments?
PAPER 2: DNASE1 SENSITIVITY QTLS
ARE A MAJOR DETERMINANT OF
HUMAN EXPRESSION VARIATION
Overview
• If an allele is correlated with changes in open
chromatin, how often does it actually
modulate gene expression?
• dsQTL – DNase sensitive QTL
• dsQTL vs eQTL
– Functional link between changes in chromatin
accessibility, gene expression
DNase Hypersensitive Region
http://hmg.oxfordjournals.org
dsQTL – genotype correlates with
extent of open chromatin
How does a dsQTL look?
RESULTS
In what proximity of gene’s TSS do
dsQTLs occur?
Changes in open chromatin associated
with gene expression levels
How might a dsQTL be an eQTL?
Mechanisms of dsQTLs
In which conformations are dsQTLs also eQTLs?
CONCLUSION
Future Trends
• Denser genotyping + more expression
measurements in variety of cell lines
– Better power to detect eQTLs with more people
• eQTLs with small effect sizes that additively
disrupt disease pathways
– Common disease, common variant hypothesis
• Better annotating + understanding genome
enhances selection of causal eQTNs
EXTRA SLIDES
Connections to GWAS
Joe Pickrell,, Joint analysis of functional
genomic data and genome-wide
association studies of 18 human traits
Joe Pickrell,, Joint analysis of functional
genomic data and genome-wide
association studies of 18 human traits
References
• 30:
http://stanfordcehg.wordpress.com/2013/12/
06/which-genetic-variants-determine-histonemarks/