Functional Information from Genetic Interactions

1. Functional Informationfrom Genetic Interactions Bernd Fischer

2. Genome Wide Association Studies • Currently ~400 variants that contribute to common traits and diseases are known • Individual and the cumulative effects are disappointingly small • Limitations to current genome-wide association studies: • Common SNPs miss rare variants with potentially huge effects • Many structural variants undetected, since not covered by SNP-arrays • Common variants with low penetrance also missed • Higher order effects are hard to estimate, due to combinatorial explosion • Epistasis may confound identification In this talk: Detection of Pairwise Interactions by Combinatorial RNAi knock-downs

3. Imaging • Cells grow for 5 days (Dmel2 cell line) • Fixate and stain with Hoechst (DNA content) • Image with Cytometry laser scanner (TTP LabTech Acumen Explorer) Joint Work with Thomas Horn, Thomas Sandmann, Michael Boutros, DKFZ

4. Image Processing B = conv. with ring Input image A = conv. with Gaussian A > B + epsilon Label image (cell area) Detected Cells

5. Feature Extraction • Features per cell: • intensity • area • Per well statistics: • number of cells • In total 52 features • median • sum • quantiles • histograms • intensity • area • of

6. Phenotypic Effects of Single Knock Downs #cells area

7. Image analysis Estimation of interaction terms Subset of 96 Drosophila kinases and phosphatases Screening in 384-well microscopy plates Design of Combinatorial knock-down Screen 192 reagents 192 reagents Expressed in Dmel2 cells (RNA-Seq) Two independent RNAi designs Validation of knock-down by qPCR Query 1 Query 2 96 plates (~37.000 wells) 4.600 distinct gene pairs Readout: Hoechst nuclear staining Multi-parametric analysis: number of nuclei; summary statistics of size, intensity distribution

8. Screen Plot of Read-out (Number of Cells) within screen replicates (cor=0.968) independent daRNA designs (cor=0.902) between screen replicates (cor=0.948)

9. Estimating Genetic Interactions • For many phenotypes, the main effects (single gene) are multiplicative for non interacting genes i, j: • Additive on logarithmic scale • Estimation of main effects (assume that interactions are rare) • Detect Genetic Interactions: Compare to (t-test) effect of control main effect of dsRNA j error term interaction term 0, for non interacting genes ≠0, for interacting genes measurement (nr cells, growth rate, …) main effect of dsRNA i 15/10/2014

10. Reproducibility of Main Effects Template dsRNA left vs. Template dsRNA right Template dsRNA left vs. Query dsRNA dsRNA design 1 vs. dsRNA design 2 High reproducibility between template dsRNA on left half-plate and right half plate • Worse correlation between • template dsRNA and • query dsRNA • Different spotters used!!! For a few genes the two dsRNA show very different main effects => Different efficiency or off-target effects!!!

11. Interaction Surfaces viability interactions area interactions

12. Interaction Surfaces viability interactions area interactions

13. Interaction Surfaces of Ras85D Ras85D CG42327 Ras85D Gap1 Ras85D drk Ras85D Ras85D no interaction pos. interaction strength, presence, and direction of interaction depends on knock-down level genes with neg. phenotype show pos. self-self interactions

14. Interaction Surfaces of Ras85D • The Strength, Presence, and Direction of Interaction depends on the knock-down level of the genes (comparable to drug-drug interactions)

15. Screen Plot of Interaction Score (#Cells) within screen replicates (cor=0.639) independent daRNA designs (cor=0.567) between screen replicates (cor=0.619)

16. Number of Interactions (q-value cut-off: 5%) Overlap with other networks (DroID database) correlation data genetic interactions Human Interologs viability genetic interactions p-value= 5.0*10-15 p-value= 1.4*10-2 p-value= 1.5*10-12 area genetic interactions p-value= 1.0*10-14 p-value= 1.6*10-15 p-value= 1.8*10-4

17. Interaction Profiles of CG3573 and rl Interactions of CG3573 Interactions of rl double knock-down level single knock-down level of second knock-down single knock-down level of second knock-down The viability effect of most genes is recovered, if Gap1 is knocked down in addition

18. Epistatis of Gap1 Interactions of Gap1 Interactions of PTP-ER double knock-down level single knock-down level of second knock-down single knock-down level of second knock-down The viability effect of most genes is recovered, if Gap1 is knocked down in addition

19. MapKinase Pathway MapKinase JAK/STAT JNK p38

20. Clustering of Interaction Map

21. Clustering of Interaction Map

22. Clustering of Interaction Map

23. Clustering of Interaction Map

24. Clustering of Interaction Map

25. Functional Classification Cka is classified as positive regulator of RasMap-Kinase pathway

26. Validation of Cka as MapK-regulator knock-down

27. Future Work • Predictive Modeling of Phenotype as Function of Genotype Can Genetic Interaction Screens help to fill the missing inheritance gap in GWAS? • What about higher order Interactions (complete screening impossible) • Dissection of Pathways with Combinatorial RNAi Thanks to Thomas Horn, Thomas Sandmann, Michael Boutros, DKFZ; Huber Lab Predict nrcells for previously unseen double-knock-down We can predict interactions (Additive) Linear model => network model cross validation error