genetic interactions with the laboratory environment
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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. - PowerPoint PPT PresentationTRANSCRIPT
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
aten
cy (s
)
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
cy (s
)
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
cy (s
)
P <.05
Cage Density
1-3 (Low) 4-6 (High)0
1
2
3
4
5LS Means
TW L
aten
cy (s
)
Sex
Female Male0
1
2
3
4
5LS MeansPlanned Experiment
TW L
aten
cy (s
)
Order of Testing
First Second Third Fourth0
1
2
3
4
5LS MeansPlanned Experiment
TW L
aten
cy (s
)
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
aten
cy (
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