cs 374: relating the genetic code to gene expression sandeep chinchali
TRANSCRIPT
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CS 374: Relating the Genetic Code to Gene Expression
Sandeep Chinchali
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Outline
1. Basic Gene Regulation2. Gene Regulation and Human Disease3. Measurement Technologies4. Papers5. Future Trends
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1. BASIC GENE REGULATION
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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)
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Regulatory Elements
• ~ 20-25k genes– Expression Modulated by ~ 1 Million cis-reg
elements– Enhancer, Promoters, Silencers
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Controlling Gene Expression
• Transcription factors (TFs):– Proteins that recognize sequence motifs in
enhancers, promoters– Combinatorial switches that turn genes on/off
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Modulating Gene Expression
Expression Quantitative Trait Locus (eQTL):– Regions where different genotypes correlate with
changes in gene expression
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Chromatin Remodelling
http://www.cropscience.org.au/icsc2004/symposia/3/1/1957_dennise-5.gif
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2. GENE REGULATION AND DISEASE
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Bejerano Lab
Disease ImplicationsSHH
MUTATIONS
•Brain
•Limb
•Other
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Bejerano Lab
Limb Enhancer 1Mb away from Gene
SHHlimb
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Bejerano Lab
SHH
Enhancer Deletionlimb
DELETE
•Limb
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Bejerano Lab
SHH
Enhancer 1bp Substitutionlimb
MUTATIONS
•Limb
Lettice et al. HMG 2003 12: 1725-35
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Genome Wide Assocation Study (GWAS):
80% of GWAS SNPs are noncoding (many are eQTLs)
Bejerano Lab
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From eQTL to Disease
TAllele specific binding may alter gene expression
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Outline
1. Basic Gene Regulation2. Gene Regulation and Human Disease3. Measurement Technologies4. Papers5. Future Trends
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MEASUREMENT TECHNOLOGIES
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GTEX
eQTLs: Correlating Genotype with Expression
RNA-seq, Microarray
SNP Array, WGS
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Measuring Open Chromatin
http://hmg.oxfordjournals.org
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Measuring open chromatin – DNase Seq
Sequence open chromatin – map enhancers, promoters …wikipedia
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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
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Outline
1. Basic Gene Regulation2. Gene Regulation and Human Disease3. Measurement Technologies4. Papers5. Future Trends
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PAPER 1: DISSECTING THE REGULATORY ARCHITECTURE OF GENE EXPRESSION QTLS
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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
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METHODS
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1. Associate SNPs with Gene Expression
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2. Functional Annotation
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3. Adjust p-value based on annotation
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RESULTS
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eQTNs are enriched in enhancers, promoters
Inactive
Active Promoter/En
hancer
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eQTNs are enriched in enhancers, promoters (2)
What is the distribution of eQTNs in regulatory sites?
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eQTNs enriched in TF binding sites
What TF families show the highest eQTN enrichments?
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PAPER 2: DNASE1 SENSITIVITY QTLS ARE A MAJOR DETERMINANT OF HUMAN EXPRESSION
VARIATION
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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
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DNase Hypersensitive Region
http://hmg.oxfordjournals.org
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dsQTL – genotype correlates with extent of open chromatin
How does a dsQTL look?
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RESULTS
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In what proximity of gene’s TSS do dsQTLs occur?
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Changes in open chromatin associated with gene expression levels
How might a dsQTL be an eQTL?
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Mechanisms of dsQTLs
In which conformations are dsQTLs also eQTLs?
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CONCLUSION
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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
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EXTRA SLIDES
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Connections to GWAS
Joe Pickrell,, Joint analysis of functional genomic data and genome-wide
association studies of 18 human traits
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Joe Pickrell,, Joint analysis of functional genomic data and genome-wide
association studies of 18 human traits
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References
• 30: http://stanfordcehg.wordpress.com/2013/12/06/which-genetic-variants-determine-histone-marks/