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

2. Outline 1.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.Future Trends

3. 1. BASIC GENE REGULATION

4. 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)

5. Regulatory Elements ~ 20-25k genes – Expression Modulated by ~ 1 Million cis-reg elements – Enhancer, Promoters, Silencers

6. Controlling Gene Expression Transcription factors (TFs): – Proteins that recognize sequence motifs in enhancers, promoters – Combinatorial switches that turn genes on/off

7. Modulating Gene Expression Expression Quantitative Trait Locus (eQTL): – Regions where different genotypes correlate with changes in gene expression

8. Chromatin Remodelling http://www.cropscience.org.au/icsc2004/s ymposia/3/1/1957_dennise-5.gif

9. 2. GENE REGULATION AND DISEASE

10. Bejerano Lab Disease Implications SHH MUTATIONS Brain Limb Other

11. Bejerano Lab Limb Enhancer 1Mb away from Gene SHH limb

12. Bejerano Lab SHH Enhancer Deletion limb DELETE Limb

13. Bejerano Lab SHH Enhancer 1bp Substitution limb MUTATIONS Limb Lettice et al. HMG 2003 12: 1725-35

14. Genome Wide Assocation Study (GWAS): 80% of GWAS SNPs are noncoding (many are eQTLs) Bejerano Lab

15. From eQTL to Disease T Allele specific binding may alter gene expression

16. Outline 1.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.Future Trends

17. MEASUREMENT TECHNOLOGIES

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

19. Measuring Open Chromatin http://hmg.oxfordjournals.org

20. Measuring open chromatin – DNase Seq Sequence open chromatin – map enhancers, promoters … wikipedia

21. 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

22. Outline 1.Basic Gene Regulation 2.Gene Regulation and Human Disease 3.Measurement Technologies 4.Papers 5.Future Trends

23. PAPER 1: DISSECTING THE REGULATORY ARCHITECTURE OF GENE EXPRESSION QTLS

24. 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

25. METHODS

26. 1. Associate SNPs with Gene Expression

27. 2. Functional Annotation

28. 3. Adjust p-value based on annotation

29. RESULTS

30. eQTNs are enriched in enhancers, promoters Inactive Active Promoter/En hancer

31. eQTNs are enriched in enhancers, promoters (2) What is the distribution of eQTNs in regulatory sites?

32. eQTNs enriched in TF binding sites What TF families show the highest eQTN enrichments?

33. PAPER 2: DNASE1 SENSITIVITY QTLS ARE A MAJOR DETERMINANT OF HUMAN EXPRESSION VARIATION

34. 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

35. DNase Hypersensitive Region http://hmg.oxfordjournals.org

36. dsQTL – genotype correlates with extent of open chromatin How does a dsQTL look?

37. RESULTS

38. In what proximity of gene’s TSS do dsQTLs occur?

39. Changes in open chromatin associated with gene expression levels How might a dsQTL be an eQTL?

40. Mechanisms of dsQTLs In which conformations are dsQTLs also eQTLs?

41. CONCLUSION

42. 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

43. EXTRA SLIDES

44. Connections to GWAS Joe Pickrell,, Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

48. References 30: http://stanfordcehg.wordpress.com/2013/12/ 06/which-genetic-variants-determine-histone- marks/