CS 374: Relating the Genetic Code to Gene Expression Sandeep Chinchali

Outline 1.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.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 ymposia/3/1/1957_dennise-5.gif

2. GENE REGULATION AND DISEASE

Bejerano Lab Disease Implications SHH MUTATIONS Brain Limb Other

Bejerano Lab Limb Enhancer 1Mb away from Gene SHH limb

Bejerano Lab SHH Enhancer Deletion limb DELETE Limb

Bejerano Lab SHH Enhancer 1bp Substitution limb MUTATIONS Limb Lettice et al. HMG :

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.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.Future Trends

MEASUREMENT TECHNOLOGIES

eQTLs: Correlating Genotype with Expression GTEX RNA-seq, Microarray SNP Array, WGS

Measuring Open Chromatin

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.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.Future Trends

PAPER 1: DISSECTING THE REGULATORY ARCHITECTURE OF GENE EXPRESSION QTLS

Overview HapMap cells G 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 Inactive Active Promoter/En hancer

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

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

References 30: 06/which-genetic-variants-determine-histone- marks/