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 woedirect examinationheritability 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 G

G E

22

2 2

Beck et al, 2000

Inbred Mice—A diverse genetic resource

Estimating HeritabilityTable 2. One-way ANOVA table used to estimate heritability of tail withdrawal baselines.

Source of Variance ad.f.

Sums of Squares

Observed Mean

Squares

bExpected Mean Squares

Strain S-1 SSbs SSbs / (a-1) ws+kbs

28 198.89 7.10 ws+186.32 bs

Error N-S SSws SSws/(N)

ws 5543 647.10 0.12

ws = .11674

Total N-1 SStotal 5571 845.99

aS 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. bIn an unbalanced design, k = (1/S-1)*{N – (ni

2/N)}, where ni is the number of individuals in the ith strain.

Organismic Influences on Tail-Withdrawal Latency:

Genotype

1

2

3

4

5

TW L

aten

cy (s

)

Variability in Tail-Withdrawal Latency:Something in the Air?

h2 = 24%n = 8034Mean: 3.1sSD: 1.3 s

0 1 2 3 4 5 6 7 8 9 10Tail-Withdrawal Latency (s)

0

200

400

600C

ount

0.00

0.02

0.04

0.06

0.08

0.10 Proportion per B

ar

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.

0 1 2 3 4 5 6 7 8 9 10TW Latency (s)

0

100

200

300

Cou

nt

0.00

0.01

0.02

0.03

0.04

0.05

Proportion per B

arOrganismic Influences on

Tail-Withdrawal Latency: Sex

Male Female2.75

3.00

3.25

3.50

TW L

aten

cy (s

)

Organismic Influences on Tail-Withdrawal Latency: Weight

0 10 20 30 40 50 60WT

0

1

2

3

4

5

6

7

8

9

10TW

BL

Environmental Influences on Tail-Withdrawal Latency:

Experimenter

2.0

2.5

3.0

3.5

4.0

TW L

aten

cy (s

)

Environmental Influences on Tail-Withdrawal Latency: Season

Winter Spring Summer Fall2.75

3.00

3.25

3.50

TW L

aten

cy (s

)

Environmental Influences on Tail-Withdrawal Latency: Cage Density

1 2 3 4 5 62.75

3.00

3.25

3.50

3.75 Males

Cage Density

TW L

aten

cy (s

)

1 2 3 4 5 62.25

2.50

2.75

3.00

3.25

3.50

3.75

(32)

Females

Cage Density

TW L

aten

cy (s

)

Environmental Influences on Tail-Withdrawal Latency: Time of Day

1000 1100 1200 1300 1400 1500 16002.0

2.5

3.0

3.5

4.0

TW L

aten

cy (s

)

Time of Day (h)

Albino Mice

Pigmented Mice

Environmental Influences on Tail-Withdrawal Latency: Order of

Testing

1 2 3 4 5 62.75

3.00

3.25

3.50

Order of Testing

TW L

aten

cy (s

)

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 post-mortem 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 treeNode 3

GENOTYPE =(sombre,e/e,5HT1BKO,

MUKO,HA,LA,HAR,LAR,C57BL/6,C3H/He,C3HeB/Fe,SJL,SM,SWR,CXBK,NOD,

OFQKO)Avg = 2.360

N = 2821

Terminal Node3

Avg = 2.275N = 334

Node 7 HUMIDITY = (4)

Avg = 2.358 N = 945

Terminal Node 1

Avg = 1.929 N = 315

Terminal Node 2

Avg = 2.157 N = 306

Node 8CAGE DENSITY =

(1,2,3,4,5,7) Avg = 2.264

N = 303

Node 12EXPERIMENTER=

(JH,AK) Avg = 2.403

N = 642

Node 9SEASON = (fall,winter)Avg = 2.236

N = 294

Terminal Node 8

Avg = 3.189N = 9

Terminal Node 9

Avg = 1.925 N = 63

Node 13TIME = (late)Avg = 2.455

N = 579

Terminal Node 4

Avg = 1.414N = 7

Node 10EXPERIMENTER= (SW,KM,BM,AK)

Avg = 2.256 N = 287

Terminal Node 10

Avg = 2.311 N = 257

Node 14HUMIDITY = (1,2)

Avg = 2.569N = 322

Node 6 SEX = (female)

Avg = 2.481 N = 1866

Terminal Node 16

Avg = 2.363 N = 266

Node 4ORDER = (2,3,4,5,6)

Avg = 2.123N = 955

Node 5TIME =

(early,late)Avg = 2.041

N = 621

█ 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 data0.584

Rel

ativ

e E

rror

Number of Nodes

0.5

0.6

0.7

0.8

0.9

0 100 200 300 400 500 600

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

aTime of day levels were: early (09:30-10:55 h), midday(11:00-13:55 h), and late (14:00-17:00 h).bHumidity 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

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300 350

% H

umid

ity

Spring Summer FallWinter

<20%20-39%40-59%>60%2.0

2.5

3.0

3.5

4.0

Spring

<20%20-39%40-59%>60%2.0

2.5

3.0

3.5

4.0

Winter

<20%20-39%40-59%>60%2.0

2.5

3.0

3.5

4.0

Summer

<20%20-39%40-59%>60%2.0

2.5

3.0

3.5

4.0

Fall

•Humidity fluctuates with season•This is true even in a “climate controlled” environment.•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

• All factors interact with genotype except for within cage order of testing.

Table 3. The tail-withdrawal variability model

Source df F P-value

STRAIN 10 7.19 0.0001SEX 1 20.12 0.0001SEASON 3 0.82 0.4823TIME 2 4.51 0.0111CAGEPOP 1 3.82 0.0509HUMIDITY 3 0.44 0.7268ORDER 5 27.84 0.0001PERSON 4 33.99 0.0001STRAIN x SEX 10 4.18 0.0001STRAIN x SEASON 30 3.46 0.0001STRAIN x TIME 19 1.80 0.0181STRAIN x CAGEPOP 10 2.09 0.0224STRAIN x HUMIDITY 30 1.64 0.0163STRAIN x PERSON 35 3.25 0.0001TIME x SEASON 4 3.10 0.0149SEASON x HUMIDITY 6 3.23 0.0037SEX x CAGEPOP 1 4.08 0.0436PERSON x TIME 4 3.16 0.0135POPCAT x SEASON 3 5.37 0.0011TIME x HUMIDITY 4 7.93 0.0001CAGEPOP x HUMIDITY 3 3.15 0.0241

aFixed-Effects remaining in the final reduced model oftail-withdrawal variability based on 1772 subjects.bThe denominator df = 1580.c Note that some numerator df's are lower thanexpected due to the empty cells.

Strain Differences in Tester Effects

00.20.40.60.8

11.21.41.61.8

2

JM

SW

Which factors actually matter?

• Archive analysis– Data Mining– Modeling

• Planned Experimentation

BM JH JM KM SW0

1

2

3

4

5LS MeansPlanned Experiment

TW L

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Experimenter

P <.05

Genotype

129/P3 A/J AKR/J BALB/cJ C3H/HeJ C57BL/6J C57BL/10J C58/J CBA/J DBA/2J RIIIS/J0

1

2

3

4

5LS MeansPlanned Experiment

TW L

aten

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P <.05

P <.05

Time of Day

08:00-10:55 11:00-13:55 14:00-17:000

1

2

3

4

5LS MeansPlanned Experiment

TW L

aten

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P <.05

Cage Density

1-3 (Low) 4-6 (High)0

1

2

3

4

5LS Means

TW L

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Sex

Female Male0

1

2

3

4

5LS MeansPlanned Experiment

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Order of Testing

First Second Third Fourth0

1

2

3

4

5LS MeansPlanned Experiment

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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.

3

4

5

6

7Home Cage

Holding Cage

*

1st 2nd 3rd 4th

Order of Testing

TW L

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s)

0

20

40

60

80

100 1st (AD50: 14.2 mg/kg)2nd (AD50: 16.6 mg/kg)3rd (AD50: 17.2 mg/kg)4th (AD50: 22.0 mg/kg) *

5 10 20 40

Morphine Dose (mg/kg)

% A

nalg

esia

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.

STRAIN

TESTER

TIME

ORDER

ERROR

STRAINxENV SEXSTRAINxSEXSEXxENV

STRAINxSEXxENVENVxENV

Environment 45%

Genotype 27%

Genotype by Environment 15%

Residual 13%

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

AcknowledgementsData Archive and Analysis

Dr. Jeffrey S. MogilDr. Sandra L. Rodriguez-

ZasDr. Lawrence HubertDr. William R. LariviereDr. Sonya G. Wilson

…and the Mogil LabDr. John C. CrabbeDr. Robert W. WilliamsDr. Daniel Goldwitz