Genetic Interactions With the Laboratory Environment

1. Genetic Interactions With the Laboratory Environment Elissa J. Chesler, Ph.D. University of Tennessee Health Science Center

2. Studying Individual Differences in the Mouse Individual differences are due to both environmental and genetic effects. Evidence for a strong role of the laboratory environment comes from multiple sources: experimentalists woe direct examination heritability estimates

3. Experimentalist Woe:Now you see it, now you don’t ! • Anecdotal evidence of failures to replicate • A file-drawer problem • Data driven evaluation of the laboratory environment must be performed

4. Trading Spaces • Genetic Architecture of Selected Lines: • open field activity test • High and low activity lines bred selectively (Flint et al, 1995) • Two replicates to determine whether the same loci are selected (Turri et al, 2001). • The 2001 lines had the same selected loci. • Only two highly significant loci were replicated across 1995 and 2001 experiments.

5. A Direct Examination:Three labs, same mice • Crabbe, Wahlsten and Dudek (1999) • 8 behavioral traits studied in Portland, Edmonton and Albany laboratories. • Strains had similar relative phenotypes • Magnitude of effects varied by lab • What were the relevant environmental factors?

6. Heritability Estimation: The Tail Withdrawal Test of Thermal Nociception 49°C 49°C

7. Estimating Heritability • Heritability is the proportion of trait variance accounted for by genetic factors.

8. Inbred Mice—A diverse genetic resource Beck et al, 2000

9. Estimating Heritability

10. Organismic Influences on Tail-Withdrawal Latency: Genotype

11. Variability in Tail-Withdrawal Latency:Something in the Air? 600 0.10 h2 = 24% n = 8034 Mean: 3.1s SD: 1.3 s 0.08 400 0.06 Count Proportion per Bar 0.04 200 0.02 0 0.00 0 1 2 3 4 5 6 7 8 9 10 Tail-Withdrawal Latency (s)

12. Data Sheet Records 11 Experimenters 40 Genotypes including RI, Mutant, Selected, Inbred, Outbred 4 Seasons 9:30 – 17:00 h Both Sexes Cage Populations Order of testing within cage Merged by date with animal colony records Temperature Humidity Cage changes Food lots. Contruction of the TW Data Archive

13. Organismic Influences on Tail-Withdrawal Latency: Sex

14. Organismic Influences on Tail-Withdrawal Latency: Weight

15. Environmental Influences on Tail-Withdrawal Latency: Experimenter

16. Environmental Influences on Tail-Withdrawal Latency: Season

17. Environmental Influences on Tail-Withdrawal Latency: Cage Density

18. Environmental Influences on Tail-Withdrawal Latency: Time of Day

19. Environmental Influences on Tail-Withdrawal Latency: Order of Testing

20. Which of these factors actually matter? A “Messy Data” Problem • Large sample sizes preclude meaningful planned comparisons—everything is “significant”! • Data are unbalanced with respect to the many predictors. • Some observations are missing. • Insufficient data for comparing variable importance through hierarchically related models. • Linear modeling fits a single structure to data, when many complex structures may exist.

21. "To consult a statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of." - R. A. Fisher, 1938

22. Which factors actually matter? • Archive analysis • Data Mining • Modeling • Planned Experimentation

23. Which factors actually matter? • Archive analysis • Data Mining • Modeling • Planned Experimentation

24. Data Mining the GE interaction • Classification And Regression Trees (CART) • Develops rules for splitting data into groups using the many predictors. • Partitions are chosen that maximally reduce the variability in the resulting subsets. • Variables are ranked based on the degree to which they reduce variability. • This method allows for many complex data structures to co-exist.

25. Detail of the regression tree

26. █ Experimenter █ Genotype █ Season █ Cage Density █ Time of Day █ Sex █ Humidity █ Order Entire tree is available online at: http://www.nature.com/neuro/journal/v5/n11/extref/nn1102-1101-S1.pdf

27. The resulting regression tree accounts for 42% of the variance in trait data

28. Assessing the Environmental Influence • In the presence of sex differences, females were more sensitive than males. • The first mouse from each cage has a higher latency than other mice. • Lower latencies • late in the day • in the spring • in higher humidity

29. Humidity and Season • Humidity fluctuates with season • This is true even in a “climate controlled” environment. • TW Baselines drop with increasing humidity within spring, summer and fall.

30. Which factors actually matter? • Archive analysis • Data Mining • Modeling • Planned Experimentation

31. Modeling of Fixed-Effects • All factors interact with genotype except for within cage order of testing.

32. Strain Differences in Tester Effects

33. Which factors actually matter? • Archive analysis • Data Mining • Modeling • Planned Experimentation

34. Experimenter P <.05

35. Genotype P <.05 P <.05

36. Time of Day P <.05

37. Cage Density

38. Sex

39. Order of Testing

40. Planned Experiments: Order of Testing • Within-cage order of testing is a main effect. • The order influence can be eliminated. • The order influence is even greater in studies of analgesia than in studies of nociception.

41. Nature, Nurture or Both? • Genotype accounts for less than 1/3 of the trait variance. • Two-thirds of the variance is accounted for by environmental effects and their interactions with genotype. Genotype 27% Residual 13% Genotype by Environment 15% Environment 45%

42. Why is the laboratory environment more important than ever? • Expansion of the scope of projects • Multiple staff turnovers – transience of undergraduates, graduate students, and post-docs • Long-term Experiments (mapping studies, special breeding) • Multi-lab, multi-site collaboration (TMGC) • Data sharing projects (e.g. WebQTL, MPD) • Distributed Mouse Reagents (TMGC) • Later addition of data (fickle dissertation committee, pilots of costly studies) • Small sample studies (microarray)

43. Laboratory influence on gene expression? • Many factors can vary systematically with a grouping variable (Confounds) • Unplanned is not the same as random. • Careful balancing of important factors is the best approach. • Small samples can easily become confounded. Morning Afternoon B6 D2 C3H

44. Integrating Data Across Laboratories www.webQTL.org

45. High Correlation Across Laboratories for this Trait

46. A highly heritable behavioral trait Chromosome 18 Locomotor Activity

47. Fix laboratory conditions for the entire study Cost effective for high throughput studies Results may only apply to a specific environment Perform experiment across a limited set of known conditions Cost increase or power decrease Increases ability to generalize findings to multiple environments Standardization vs. Systematic Variation

48. Acknowledgements Data Archive and Analysis Dr. Jeffrey S. Mogil Dr. Sandra L. Rodriguez-Zas Dr. Lawrence Hubert Dr. William R. Lariviere Dr. Sonya G. Wilson …and the Mogil Lab Dr. John C. Crabbe Dr. Robert W. Williams Dr. Daniel Goldwitz