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Genetic Interactions With the Laboratory Environment Elissa J. Chesler, Ph.D. University of Tennessee Health Science Center 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 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 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. 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? Heritability Estimation: The Tail Withdrawal Test of Thermal Nociception 49°C 49°C Estimating Heritability • Heritability is the proportion of trait variance accounted for by genetic factors. h 2 2 G E 2 2 G Inbred Mice—A diverse genetic resource Beck et al, 2000 Estimating Heritability Table 2. One-way ANOVA table used to estimate heritability of tail withdrawal baselines. Source of Variance Strain Error Total d.f. S-1 28 Observed b Sums of Mean Expected Squares Squares Mean Squares SSbs SSbs / (a-1) ws+kbs 198.89 7.10 ws+186.32 bs N-S 5543 SSws 647.10 a SSws/(N) 0.12 ws ws = .11674 N-1 SStotal 5571 845.99 a S is the number of strains and N is the total number of individuals. The coefficient, k, is the number of individuals in each strain in a balanced design. b 2 In an unbalanced design, k = (1/S-1)*{N – (ni /N)}, where ni is the number of th individuals in the i strain. Organismic Influences on Tail-Withdrawal Latency: Genotype TW Latency (s) 5 4 3 2 1 F2AF29P3 A KR /10 L/6 B/c /He /FeC58 BA A/2 IIIS SM KO KO KO KO bre e/eD-1 D4Sim HA LA AR AR 2 A BL 7B AL 3H eB C B R H L C -N D 3H 12 1BELT ND MU om D 6 W 7 W 5 T H B C3 E B C S S S C5 C 5H D C Variability in Tail-Withdrawal Latency: Something in the Air? 400 200 h2 = 24% n = 8034 Mean: 3.1s SD: 1.3 s 0.10 0.08 0.06 0.04 0.02 0 0.00 0 1 2 3 4 5 6 7 8 9 10 Tail-Withdrawal Latency (s) Proportion per Bar Count 600 Contruction of the TW Data Archive • 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. Organismic Influences on Tail-Withdrawal Latency: Sex TW Latency (s) 200 3.50 0.05 3.25 0.04 3.00 0.03 2.75 100 Male Female 0.02 0.01 0 0 1 2 3 4 5 6 7 TW Latency (s) 8 0.00 9 10 Proportion per Bar Count 300 Organismic Influences on Tail-Withdrawal Latency: Weight 10 9 8 TWBL 7 6 5 4 3 2 1 0 0 10 20 30 WT 40 50 60 Environmental Influences on Tail-Withdrawal Latency: Experimenter 3.5 3.0 2.5 KM HH BM JH SW 2.0 JM TW Latency (s) 4.0 Environmental Influences on Tail-Withdrawal Latency: Season TW Latency (s) 3.50 3.25 3.00 2.75 Winter Spring Summer Fall Environmental Influences on Tail-Withdrawal Latency: Cage Density 3.75 Males TW Latency (s) TW Latency (s) 3.75 3.50 3.25 3.00 Females 3.50 3.25 3.00 2.75 (32) 2.50 2.25 2.75 1 2 3 4 Cage Density 5 6 1 2 3 4 Cage Density 5 6 Environmental Influences on Tail-Withdrawal Latency: Time of Day TW Latency (s) 4.0 Albino Mice 3.5 3.0 Pigmented Mice 2.5 2.0 1000 1100 1200 1300 1400 Time of Day (h) 1500 1600 Environmental Influences on Tail-Withdrawal Latency: Order of Testing TW Latency (s) 3.50 3.25 3.00 2.75 1 2 3 4 5 Order of Testing 6 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. "To consult a statistician after an experiment is finished is often merely to ask him to conduct a postmortem examination. He can perhaps say what the experiment died of." - R. A. Fisher, 1938 Which factors actually matter? • Archive analysis – Data Mining – Modeling • Planned Experimentation Which factors actually matter? • Archive analysis – Data Mining – Modeling • Planned Experimentation 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. Detail of the regression tree █ █ █ █ █ █ █ █ 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 The resulting regression tree accounts for 42% of the variance in trait data Relative Error 0.9 0.584 0.8 0.7 0.6 0.5 0 100 200 300 Number of Nodes 400 500 600 Assessing the Environmental Influence Table 2. Factor importance rankings computed by CART. Factor Number of Levels Score Experimenter 11 100.0 Genotype 40 78.0 Season 4 35.8 Cage Density 7 20.4 Time of Day 3a 17.4 Sex 2 14.6 Humidity 4b 12.0 Order of Testing 7 8.7 a Time of day levels were: early (09:30-10:55 h), midday (11:00-13:55 h), and late (14:00-17:00 h). b Humidity levels were: high (60%), medium-high (40-59%), medium-low (20-39%), and low (<20%). • 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 Humidity and Season 80 •Humidity fluctuates with season 70 % H u m id it y 60 •This is true even in a “climate controlled” environment. 50 40 30 20 10 0 50 100 W inter 150 300 Fall Summer 3.5 3.5 3.5 3.5 3.0 3.0 3.0 3.0 2.5 2.5 2.5 2.5 2.0 2.0 2.0 <20% 20-39% 40-59% >60% <20% 20-39% 40-59% >60% 350 Fall 4.0 4.0 Spring Winter 250 Sum m er 4.0 4.0 200 Spring 2.0 <20% 20-39% 40-59% >60% <20% 20-39% 40-59% >60% •TW Baselines drop with increasing humidity within spring, summer and fall. Which factors actually matter? • Archive analysis – Data Mining – Modeling • Planned Experimentation Modeling of Fixed-Effects Table 3. The tail-withdrawal variability model Source df STRAIN SEX SEASON TIME CAGEPOP HUMIDITY ORDER PERSON STRAIN x SEX STRAIN x SEASON STRAIN x TIME STRAIN x CAGEPOP STRAIN x HUMIDITY STRAIN x PERSON TIME x SEASON SEASON x HUMIDITY SEX x CAGEPOP PERSON x TIME POPCAT x SEASON TIME x HUMIDITY CAGEPOP x HUMIDITY 10 7.19 1 20.12 3 0.82 2 4.51 1 3.82 3 0.44 5 27.84 4 33.99 10 4.18 30 3.46 19 1.80 10 2.09 30 1.64 35 3.25 4 3.10 6 3.23 1 4.08 4 3.16 3 5.37 4 7.93 3 3.15 a F P-value 0.0001 0.0001 0.4823 0.0111 0.0509 0.7268 0.0001 0.0001 0.0001 0.0001 0.0181 0.0224 0.0163 0.0001 0.0149 0.0037 0.0436 0.0135 0.0011 0.0001 0.0241 Fixed-Effects remaining in the final reduced model of tail-withdrawal variability based on 1772 subjects. b The denominator df = 1580. c Note that some numerator df's are lower than expected due to the empty cells. • All factors interact with genotype except for within cage order of testing. Strain Differences in Tester Effects 2 1.8 1.6 1.4 1.2 JM 1 0.8 0.6 0.4 SW RI IIS /2 DB A CB A C5 8 AK R BA LB /c C3 H/ He C5 7B L/ 10 C5 7B L/ 6 A 12 9/ P3 0.2 0 Which factors actually matter? • Archive analysis – Data Mining – Modeling • Planned Experimentation Experimenter TW Latency (s) 5 LS Means Planned Experiment P <.05 4 3 2 1 0 BM JH JM KM SW Genotype P <.05 TW Latency (s) 5 P <.05 4 LS Means Planned Experiment 3 2 1 0 129/P3 A/J AKR/J BALB/cJ C3H/HeJ C57BL/6J C57BL/10J C58/J CBA/J DBA/2J RIIIS/J Time of Day TW Latency (s) 5 4 LS Means Planned Experiment P <.05 3 2 1 0 08:00-10:55 11:00-13:55 14:00-17:00 Cage Density TW Latency (s) 5 LS Means 4 3 2 1 0 1-3 (Low) 4-6 (High) Sex TW Latency (s) 5 LS Means Planned Experiment 4 3 2 1 0 Female Male Order of Testing TW Latency (s) 5 LS Means Planned Experiment 4 3 2 1 0 First Second Third Fourth Planned Experiments: Order of Testing TW Latency (s) 7 Home Cage Holding Cage 6 5 * 4 3 1st 2nd 3rd 4th Order of Testing % Analgesia 100 1st (AD50: 2nd (AD50: 3rd (AD50: 4th (AD50: 80 60 40 20 0 5 10 20 40 Morphine Dose (mg/kg) 14.2 mg/kg) 16.6 mg/kg) 17.2 mg/kg) 22.0 mg/kg) * • 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. Nature, Nurture or Both? Genotype 27% STRAIN TESTER ERROR Residual 13% TIME ORDER STRAINxSEXxENV Genotype by Environment 15% STRAINxENV SEX ENVxENV SEXxENV STRAINxSEX Environment 45% • 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. 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) 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 Integrating Data Across Laboratories www.webQTL.org High Correlation Across Laboratories for this Trait A highly heritable behavioral trait Chromosome 18 Locomotor Activity Standardization vs. Systematic Variation • 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 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