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CS 374: Relating the Genetic Code to Gene Expression. Sandeep Chinchali. Outline. 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
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CS 374: Relating the Genetic Code to Gene Expression SandeepChinchali
Outline • Basic Gene Regulation • Gene Regulation and Human Disease • Measurement Technologies • Papers • Future Trends
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/symposia/3/1/1957_dennise-5.gif
Disease Implications SHH • MUTATIONS • Brain • Limb • Other Bejerano Lab
Limb Enhancer 1Mb away from Gene limb SHH Bejerano Lab
Enhancer Deletion limb SHH • DELETE • Limb Bejerano Lab
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 • Basic Gene Regulation • Gene Regulation and Human Disease • Measurement Technologies • Papers • Future Trends
eQTLs: Correlating Genotype with Expression RNA-seq, Microarray SNP Array, WGS GTEX
Measuring Open Chromatin http://hmg.oxfordjournals.org
Measuring open chromatin – DNaseSeq 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 • 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
eQTNs are enriched in enhancers, promoters Active Promoter/Enhancer 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 • dsQTLvseQTL • 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?
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?
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
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-histone-marks/