1. 1 Paper Outline Specific Aim Background & Significance Research Description Potential Pitfalls and Alternate Approaches Class Paper: 5-7 pages (with figures) Scope is 1 aim (one main topic, can have multiple sub aims) * Topic must utilize at least one genomic sequence in some way Short description of topic: due THURSDAY, March 25 Final paper due: THURSDAY, April 29

2. 2 What contributes to the evolution of gene expression? How many loci underlie expression variation? Few major effectors or many minor contributors? What are the mechanisms of expression evolution? Relative prominence of cis vs. trans effects? How much of expression variation has been selected for?

3. 3 Expression QTL (eQTL) mapping Treat expression levels as a quantitative trait. * With DNA microarrays (and now next-gen sequencing) can simultaneously phenotype (measure) ALL genes at once. Can quickly measure all the reproducible (heritable) expression differences between two different parental lines.

4. 4 eQTL mapping in yeast spore clones From Rockman & Kruglyak 2006 First done by Rachel Brem et al.

5. 5 eQTL mapping in yeast spore clones From Rockman & Kruglyak 2006 First done by Rachel Brem et al.

6. 6 eQTL mapping in yeast spore clones From Rockman & Kruglyak 2006 First done by Rachel Brem et al.

7. 7 LOD threshold in standard QTL mapping (* 1 trait) 1000 permutations 10% LOD score threshold: 3.19 5% LOD score threshold: 3.52 Challenge with eQTL mapping is that there are thousands of traits.

8. 8 Challenge of multiple testing Imagine doing a single t-test with p = 0.01 the significance threshold. * at this p-value: 1 in 100 change data could be randomly generated But if you do 10,000 t-tests and EACH has a p = 0.01 … expect 100 positive tests to have occurred by chance In genomics it is common to do a Multiple-Test Correction on the p-value cutoff * Simplest is the Bonferroni correction but it is way too stringent Divide p-value cutoff by number of tests. eg. 0.01 / 10,000 tests = 10 -6 is new cutoff * Better methods adjust for False Discovery Rate (FDR) (eg. Benjamini & Hochberg or Storey’s Qvalue) Out of total set of what was called significant, how many of those are likely to be false positives.

9. 9 Lessons for eQTL studies Only ~25% of heritable expression traits can even be mapped - on average they explain only 30% of heritable variation Most traits explained by many loci - only 3% explained by 1 locus - Alan Orr exponential QTL model: few big effectors with lots of modifiers Majority of traits explained by transgressive segregation - distribution of F2 phenotypes extends beyond parental phenotypes - indicates many small effectors - suggests stabilizing selection - also consistent with epistasis

10. 10 Lessons for eQTL studies Fewer traits show directional segregation - Phenotypic distribution of F2’s between the parents - Also implies many minor effectors - Suggests directional selection by ‘tweaking’

11. 11 ~16% were directional ~23% showed epistasis ~82% were transgressive Brem et al. 2005. PNAS epistatic 583 directional 406 highly heritable 3546 985 319 72 68 438 1640 transgressive 2093 Lessons for eQTL studies

12. 12 Local vs. Distant and cis vs. trans ORF Local QTL that work in cis: TF binding site affects transcription 3’ UTR affects RNA stability Local eQTL: “near” the affected gene Distant eQTL: “far” from the affected gene cis effect: often taken to mean on the DNA molecule affected trans effect: often taken to mean takes effect through the protein/RNA

13. 13 Local vs. Distant and cis vs. trans Local eQTL: “near” the affected gene Distant eQTL: “far” from the affected gene cis effect: often taken to mean on the DNA molecule affected trans effect: often taken to mean takes effect through the protein/RNA ORF Local QTL that work in trans: Coding polymorphism that affects TF activity

14. 14 Local vs. Distant and cis vs. trans ORF Distant QTL that work in trans: Local eQTL: “near” the affected gene Distant eQTL: “far” from the affected gene cis effect: often taken to mean on the DNA molecule affected trans effect: often taken to mean takes effect through the protein/RNA ORF

15. 15 Local vs. Distant and cis vs. trans ORF Distant QTL that work in trans: Local eQTL: “near” the affected gene Distant eQTL: “far” from the affected gene cis effect: often taken to mean on the DNA molecule affected trans effect: often taken to mean takes effect through the protein/RNA ORF PHYSIOLOGY Most trans acting effects are likely secondary responses (distantly-acting loci are NOT enriched for TFs)

16. 16 Local vs. Distant and cis vs. trans Which is more prevalent? Estimates vary: - Brem et al papers: ~25% traits explained by local polymorphs - other studies say close to 100% - Many MORE individual genes explained by distant polymorphs * but because many link to same loci, there are fewer distantly acting loci But … statistical challenges likely enrich for local polymorphisms: - FDR hurdle is higher for trans acting loci - cis (local) polymorphisms may have larger effect size - also depends on how “local” is defined

17. 17 Local vs. Distant and cis vs. trans Which is more prevalent? Using hybrid diploids and allele-specific expression ORF-1 ORF-2 A cis acting polymorphism will affect only the allele it’s physically linked to

18. 18 Local vs. Distant and cis vs. trans Which is more prevalent? Using hybrid diploids and allele-specific expression ORF-1 ORF-2 A trans acting polymorphism will affect BOTH alleles

19. 19 Local vs. Distant and cis vs. trans Which is more prevalent? Tricia Wittkopp et al. 2004. Nature. - 29 differentially expressed genes between D. melanogaster & D. simulans: - Measured allele-specific expression in D. mel/D. sim hybrid with pyrosequencing 28 out of 29 show cis variation in expression 16 out of 29 affected by trans and cis variation Conclusion: cis-acting variation is more common to explain interspecific variation

20. 20 Local vs. Distant and cis vs. trans Which is more prevalent? Tricia Wittkopp et al. 2008. Nature Genetics. - 78 genes examined (48 within, 49 between species … 16 genes overlapping) 4 D. melanogaster strains and 4 D. simulans strains Conclusion: trans-acting variation is more common within species (over shorter time frames) but is more likely to have more pleiotropic and deleterious effects … trans-acting variation more likely to be removed over time -cis regulatory effects explained more variation between (64%) species rather than within (35%) … argues against neutrality, since effects should occur at same ‘rate’ over time - compensatory cis + trans effects also more common between species