Genetic Variation In Mice: Modeling Disease, Pharmacogenetics, And Basic Biology

Genetic variation in mice: modeling disease, pharmacogenetics, and basic biology Tim Wiltshire

School of Pharmacy University of North Carolina Chapel Hill

What do we know about gene function? How do we efficiently annotate the function of all the genes in the mammalian genome? Goal: “Genome-wide functional genomics”

TP53 TNF APOE MTHFR HLA-DRB1 IL6 ACE TGFB1 EGFR VEGFA

Fraction of all Citations Accounted for by Highly-Cited Genes

31672 genes (78%) have five or fewer linked references 19709 genes (49%) have zero linked references 40234 entries in Entrez Gene

How should we use the genetic variation in mice as a model for Annotating gene function and discovery in disease status, pharmacogenetics, and basic biology? Traditional genetics – F2 crosses, recombinant inbred strains (RI), knockouts, transgenics.

New RI initiatives - A new set of comprehensive RI strains

Inbred strains – genetic variation of the inbred strains, haplotype mapping.

Outbred strains – most closely model human populations

Genetic diversity through mating F2  Two parental strains are crossed to produce F1 generation. Brother-sister matings of F1 mice produce F2 generation, a random shuffling of parental strains genomes.  Requires a very large set of mice (~200), each genetically unique  Utility of genotype data, which is a huge undertaking for such a large set, is limited to the life of the mouse RI  Two parental strains are crossed to produce F1 generation. Brother-sister matings are carried out for 20 generations until genomic pattern is fixed.  Each mouse from a given RI line is genetically identical  Genotyping only has to be done once and can be applied to any phenotype  Number of lines and strain crosses available from an RI cross is limited, decreasing the possible resolution in mapping the trait and the number of traits that can be examined

Both methods require months or years to define candidate region

Mammalian Genome 13:175, 2002

Nature Genetics 36:1133, 2004

Randomization of Variation through Meiosis

Parental Strains 129S1/SvImJ

NOD/LtJ

A/J

NZO/HlLtJ

C57BL/6J

PWK/PhJ

CAST/EiJ

WSB/EiJ

A/J

C57BL6

129S1

NOD

NZO

PWK

CAST

Representative CC genome

WSB

The CC has many Independent Iterations High Statistical Power

Infinitely Reproducible

X

CC Population ~ Human Population

A/J

C57BL6/J

129S1/SvIm

NOD/Lt

NZO/HlLt

PWK/PhJ

CAST/EiJ

WSB/EiJ

Captures 90% of the variation present in the mouse! The variation is randomly distributed across the genome (there are no blind spots)

SNPs

Insertion/deletions

Human

20 x 106

1 x 106

CC

50 x 106

4 x 106 Yang et al. 2007 Nature Genetics 39, 1100 Roberts et al. 2007 Mammalian Genome 18, 473

How should we use the genetic variation in mice as a model for disease status, pharmacogenetics, and basic biology? Traditional genetics – F2 crosses, recombinant inbred strains (RI), knockouts, transgenics.

New RI initiatives - A new set of comprehensive RI strains

Outbred strains – most closely model human populations

Inbred strains – genetic variation of the inbred strains, haplotype mapping.  Whole animal studies  Cell-based studies – mouse embryonic fibroblasts (MEFs), hepatocytes, macrophages

Inter-strain phenotypic variance

12 Hamilton, Frankel (Cell, 2001)

C58/J

NZW

SJL/J

NZB/BINJ

KK/HIJ

SM/J

129S1/SvlmJ

NOD/LtJ

CBA/J

MA/MyJ

I/LnJ

PL/J

AKR/J

C57BL/6J

BUB/BnJ

BTBR T + tf/J

BALB/cByJ

FVB/NJ

NZO/HILtJ

RIIIS/J

A/J

P/J

SWR/J

MRL/MpJ

WSB/EiJ

CE/J

LG/J

60

C3H/HeJ

Tail Suspension Immobility » 70

DBA/2J

0

NON/LtJ

15

C57BR/cdJ

30

Pct Immobility

35

A/J CZECHII/EiJ 129S1/SvImJ NZO/HLLTJ LP/J BALB/cByJ KK/HlJ SEA/GnJ DBA/2J PWD/Ph LG/J RIIIS/J SM/J CE/J MRL/MPJ NZB/BlNJ CBA/J I/LnJ SJL/J PL/J AKR/J C3H/HeJ BUB/BnJ SWR/J DDY NON/LtJ MA/MyJ P/J WSB/EiJ BTBR_T+_tf/ FVB/NJ PERA/EiJ C58/J C57BL/6J NOR/LTJ C57BR/cdJ NOD/LtJ NZW/LacJ

Percent Time in Center

Clinical Phenotypes

Female Male

25

20

10

« Open Field Center Time

5

Female Male

50

40

30

20

10

0

0.214

0.426

LP/J

0.111

0.323

NZW/LacJ

0.111

0.323

CBA/J

0.105

0.315

DBA/2J

0.105

0.315

129S1/SvImJ

0.1

0.308

A/J

0.1

0.308

C57BLKS/J

0.0938

0.296

PL/J

0.0769

0.277

DBA/1J

0.0556

0.236

SEA/GnJ

0.0556

0.236

C57BR/cdJ

0.0526

0.229

0.04

0.2

AKR/J

0

0

I/LnJ

0

0

NZB/BlNJ

0

0

SM/J

0

0

BTBR T+ tf/J

1.2 1 0.8 0.6 0.4 0.2 0 SM/J

WSB/EiJ

I/LnJ

0.428

NZB/BlNJ

0.222

AKR/J

C3H/HeJ

Susceptibility to developing gallstones

C57BR/cdJ

0.444

BTBR T+ tf/J

0.25

SEA/GnJ

BALB/cJ

PL/J

0.487

DBA/1J

0.361

C57BLKS/J

C57BL/6J

Quantitative Traits

A/J

0.503

DBA/2J

0.6

129S1/SvImJ

C57BL/10J

CBA/J

0.47

LP/J

0.7

NZW/LacJ

C57L/J

WSB/EiJ

0.41

C3H/HeJ

0.8

BALB/cJ

C58/J

C57BL/6J

0

C57BL/10J

1

PERA/EiJ

C58/J

sd

C57L/J

mean

PERA/EiJ

strain

Haplotype Association Mapping

C A G T T C T C C T C C G A

C A G T T C T C C T C C G A

C A G T T C T C C T C C G A

C A G T T C T C C T C C G G

C A G T T C T C C T C C G A

C A G T T C T C C T C C G A

C A G T T C T C C T C C G A

G A G T T C T C C T C C G G

C A G T T C T C C T C C G G

C A G T T C T C C T C C G A

G T A T C C A C T A T T A G

C A G T T C T C C T C C G A

G A G T T T T T T T G G

C A G T T C T C C T C C G A

G A G G T C T C C T C C G G

C A G T T C T C C T C C G A

G A G G T C T C C T C C G G

C A G T T C T C C T C C G A

G A G G T C T C C T C C G G

G A G G T C T C C T C C G G

C A G T T C T C C T C C G A

C A G T T C T C C T C C G G

C G T A T A G A T T T T C C C T C A C C A T T C T C T G A A G

G C G C C A A A A A A A G G G G G G G T T T TT TT T T T T T C C C C C T C T C T C T C T C C T C T C T C T C T C C C C C C C C C C G G G G G G A G A A

Taking a 3 SNP window consecutively down the genome and asking “do these haplotypes associate with a specific phenotype”?

G A G T T C T C C T C C G G

G A G T T C T C C T C C G G

• Inferred haplotype patterns can then be related back to the observed phenotype values across the same set of strains Pos 1 1 1 1 1 1 1

171297027 171297120 171297250 171297364 171297418 171297467 171297468

CTG 129S1/SvImJ BALB/cByJ C3H/HeJ FVB/NJ NZB/BlNJ NZW/LacJ

129S1/SvImJ T G C T G C C

A/J C G T C G C C

AKR/J C A T C G T C

BALB/cByJ T G C T G C C

BTBR_T+_tf/J BUB/BnJ C C A G T T C C G G T C C C

C3H/HeJ T G C T G C C

TCG 120.7 105.4 120.1 116.5 165.5 130.7

A/J 67.3 AKR/J 84.6 BTBR_T+_tf/J 110.2 BUB/BnJ 67.8 C57BL/6J 71.7 C57BLKS/J 78.6 C57L/J 80 CAST/EiJ 67.1 CBA/J 85.4 CZECHII/EiJ 81.3 DBA/2J 63.4 I/LnJ 93.4 JF1/Ms 88.8 MA/MyJ 122.9 MOLF/EiJ 81.6 MSM/Ms 103.2 NOD/LtJ 103 PL/J 97 RIIIS/J 48.8 SEA/GnJ 82 SJL/J 76 SM/J 94.7 SWR/J 91.2

126.4833

84.34783

ANOVA analysis: Identify associations between shared haplotypes and phenotypes

logP

Chr

Genome Location

HDL phenotype analysis - measurement of HDL cholesterol levels 34 mouse strains

Inferred Haplotype Groups at ApoA2 locus IH Groups at ApoA2 Locus

T C (m g /d l)

200

100

0 CTG 129S1/SvImJ BALB/cByJ C3H/HeJ FVB/NJ NZB/BlNJ NZW/LacJ

120.7 105.4 120.1 116.5 165.5 130.7

126.4833

TCG AKR/J BTBR_T+_tf/J BUB/BnJ C57BL/6J C57BLKS/J C57L/J CAST/EiJ CBA/J CZECHII/EiJ DBA/2J I/LnJ JF1/Ms MA/MyJ MOLF/EiJ MSM/Ms NOD/LtJ PL/J RIIIS/J SEA/GnJ SJL/J SM/J SWR/J

84.6 110.2 67.8 71.7 78.6 80 67.1 85.4 81.3 63.4 93.4 88.8 122.9 81.6 103.2 103 97 48.8 82 76 94.7 91.2 85.12273

The use of haplotype association mapping to identify clinical QTL (cQTL)

35

Grm7

Female Male

30

logP

Percent Time in Center

25 20 15 10 5 0

Genome Location 30 250

Identification of clinical QTL and expression difference for open field behavior

Hap Group 1 Hap Group 2

200

15

10

5

* **

20

Intensity Level

12

9S

1/ A Sv /J I N KK mJ ZO / BA /H HlJ LB IL / tJ D cBy BA J /2 S J R M/ N III J ZB S / M /B J R lN L/ J M AK pJ R / BU CE J B/ /J B SJ nJ L/ LG J CB /J A / PL J BT C SW /J BR 3H R/ _T /H J + eJ _t f/ J Pct Time Center M P A / N /M J O y N J / C Lt W 58 J SB / J F /E C5 VB iJ 7B /N N L/ J C5 OD 6 7B /L J R tJ /c dJ

25

150

100

50

0 1

2

Haplotype Group

0 Nucleus Accumbens

Amygdala

Hippocampus

Prefrontal Cortex

Candidate lung tumor susceptibility genes identified through whole-genome association analyses in inbred mice. Liu et.al. Nature Genetics 38, 888 - 895 (2006)

Whole-genome association analysis of urethane-induced lung adenoma incidence in laboratory inbred mice. The scatter plots were drawn for -log(P) against SNP positions in the chromosomes. The two horizontal gray lines indicate the significance levels of -log(P) = 4.8 and -log(P) = 6.2. The arrows indicate the genomic regions with -log(P) > 4.8. These refined genomic regions with significant associations are within 10 Mb of one or more QTLs (such as Sluc18, Pas1, Sluc23 and Pas10, and Sluc26) for chemically induced lung cancer detected by previous linkage studies.

What phenotypes can be used? Whole organism phenotypes gene expression biomarkers identification of biological networks Gene expression analysis Clinical phenotypes

Anxiety and Depression Biomarker analysis

Haplotype association mapping

hippocampus 0 AKR/J

A/J

SJL/J

P/J

FVB/NJ

KK/HlJ

RIIIS/J

C57BR/cdJ

CBA/J

MA/MyJ

SWR/J

C3H/HeJ

NZW/LacJ

DBA/2J

C58/J

I/LnJ

WSB/EiJ

NOD/LtJ

600

SM/J

800

BUB/BnJ

129S1/SvImJ

PL/J

BTBR_T+_tf/J

C57BL/6J

Catechol-o-methyltransferase (Comt) »

CE/J

0

NON/LtJ

1000

BALB/cByJ

Intensity

B A LB /c B SW yJ R /J C B NO E/ T B N J R /L T t + J tf /J P FV L/ B J /N SJ J L/ J C SM 3H / C /H J 57 B eJ L M /6 N A/ J ZO M /H yJ 12 IL tJ 9 S1 /S P/ vI J m C B J A K /J K B /H U B lJ /B n J A / N I/ J ZW L n / J N La O cJ D /L C tJ 58 C AK /J 57 R B / R J M /c R L/ dJ R Mp II J W IS SB /J / D Ei B A J /2 J

Intensity

Gene Expression as a Phenotype Mendelian or complex?

2000

1800

1600

1400

1200

« Glutamate transporter (Slc1a1) hippocampus

400

200

3000

2500

2000

1500

1000

500

Using gene expression differences between strains to identify gene networks Probe X Significance Threshold

ChrX

- LogP

Probe Z Probe Y Probe X

Chr1 1

2

3

4

5

6

7

8

9 10 11 12 13 14 1516 171819 X

Chr

Visualizing eQTL Results

trans-QTL band

cis-QTL band

 Cis - local regulation  Trans - non-local regulation through diffusable factors

Catechol-O-Methyltransferase (COMT) cis-QTL in Nucleus Accumbens

•

3000

Haplotype mapping of expression data for COMT probeset expression in nucleus accumbens

2500

Intensity

2000

1500

1000

500

0

1

2 Haplotype Group

Visualizing eQTL Results

trans-QTL band

cis-QTL band

 Cis - local regulation  Trans - non-local regulation through diffusable factors

Schema of trans-band analysis

>transband at chr=3, pos=46,624,006 GeneID 15502 74559 107652 19357 212862 73074 107652 14828 108946

functional enrichment

Trans-regulator candidates

other knowledge: expression, literature, known interactions, etc

Gene Ontology Biological hypothesis Putative Regulator

putative targets

-logP 5.30 4.66 4.42 4.40 4.30 4.30 4.23 4.12 4.09

KEGG pathway

Enrichment Analysis

Chr 19, 52.7 MB  Transband occurrence of “apoptosis”: 5/25 = 20%  Background occurrence of “apoptosis”: 100/6247 = 1.6%  “Enrichment” = 12.5x  Significance by hypergeometric distribution: p < 10-4

Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

…

Name logp Description C1qa 3.17 complement component 1, q subcomponent, alpha polypeptide Gdap10 3.12 ganglioside-induced differentiation-associated-protein 10 1500011K16Rik 3.09 RIKEN cDNA 1500011K16 gene 4633402C03Rik 3.07 gnf1m29444_at Cradd 3.03 CASP2 and RIPK1 domain containing adaptor with death domain Onecut1 3.03 one cut domain, family member 1 Npm3 3.01 nucleoplasmin 3 Ccdc22 2.99 DNA segment, Chr X, Immunex 40, expressed Gtpbp4 2.95 GTP binding protein 4 Rarres1 2.93 retinoic acid receptor responder (tazarotene induced) 1 Bad 2.92 Bcl-associated death promoter Gab1 2.89 growth factor receptor bound protein 2-associated protein 1 Mtap 2.87 methylthioadenosine phosphorylase Apcs 2.84 serum amyloid P-component Pex6 2.80 peroxisomal biogenesis factor 6 Chd8 2.78 chromodomain helicase DNA binding protein 8 Bnip2 2.77 BCL2/adenovirus E1B 19kDa-interacting protein 1, NIP2 AA407659 2.71 expressed sequence AA407659 Ankfy1 2.71 ankyrin repeat and FYVE domain containing 1 Bap1 2.68 Brca1 associated protein 1 Hs3st3b1 2.68 heparan sulfate (glucosamine) 3-O-sulfotransferase 3B1 A430005L14Rik 2.67 RIKEN cDNA A430005L14 gene Akt1 2.65 thymoma viral proto-oncogene 1 Myh9 2.63 myosin, heavy polypeptide 9, non-muscle Casp3 2.63 caspase 3, apoptosis related cysteine protease

…

Five candidate regulators from transband in adipose tissue (GO: Integrin signaling) Name 4932425I24Rik Cox17 Gsk3b Nr1i2 Popdc2

Description RIKEN cDNA 4932425I24 gene cytochrome c oxidase, subunit XVII assembly protein homolog (yeast) glycogen synthase kinase 3 beta nuclear receptor subfamily 1, group I, member 2 popeye domain containing 2

LOCUSLINK_ACCS 320214 12856 56637 18171 64082

Interactions between Gsk3b with trans-band targets

Known ns-SNP

-/A

Frame-shifting variation

Known drug target Enzastaurin

*Gray-genes are from trans-band targets

Intensity Level

30

0

250

150

100

Pct Time Center

35

A/J CZECHII/EiJ 129S1/SvImJ NZO/HLLTJ LP/J BALB/cByJ KK/HlJ SEA/GnJ DBA/2J PWD/Ph LG/J RIIIS/J SM/J CE/J MRL/MPJ NZB/BlNJ CBA/J I/LnJ SJL/J PL/J AKR/J C3H/HeJ BUB/BnJ SWR/J DDY NON/LtJ MA/MyJ P/J WSB/EiJ BTBR_T+_tf/ FVB/NJ PERA/EiJ C58/J C57BL/6J NOR/LTJ C57BR/cdJ NOD/LtJ NZW/LacJ

Percent Time in Center

Integration of phenotype and expression data

Female Male

25

20

15

10

5

Hap Group 1 Hap Group 2

30

200

**

Nucleus Accumbens

*

0 Amygdala Hippocampus Prefrontal Cortex

25

20

15

10

50

5

0 1

Haplotype Group 2

In Silico Pharmacogenetics: Warfarin Metabolism Guo et al. Nat Biotechnol. 2006 May; 24(5): 531–536.

The log-transformation of the measured combined amount of 7-hydroxywarfarin (7-OH) and its glucuronidated metabolite (M8) as a % of the total amount of drug and metabolites for each of 13 inbred strains.

Haplotype-based genetic analysis of warfarin metabolites. A representative set of haplotype blocks having the highest correlation with this data set. For each predicted block, the chromosomal location, number of SNPs within a block, its gene symbol and an indicator of gene expression in liver are shown. The haplotype for each strain is represented by a colored block, and is presented in the same order as the phenotypic data in the top panel. The calculated p-value measures the probability that strain groupings within an individual block would have the same degree of association with the phenotypic data by random chance. In the gene expression column, a green square indicates the gene is expressed in liver tissue, while a gray square indicates that it is unknown.

Haplotype Associated Mapping case study Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans Alison H. Harrill, Paul B. Watkins, Stephen Su, Pamela K. Ross, David E. Harbourt, Ioannis M. Stylianou, Gary A. Boorman, Mark W. Russo, Richard S. Sackler, Steven C. Harris, , Philip C. Smith , Raymond Tennant, Molly Bogue, Kenneth Paigen, Christopher Harris, Tanupriya Contractor, Timothy Wiltshire, Ivan Rusyn and David W. Threadgill

Fig. 1. Serum ALT measured in human volunteers taking daily oral doses of APAP (4g/day). (A) Lines represent per subject daily serum ALT (U/L) values 14 days prior to clinic admission and throughout the 14-day duration of the study. Subjects were considered responders if peak serum ALT reached greater than 1.5-fold higher than the average of their baseline values (average of values obtained for days -14 and 1-3; N = 22). ALT elevations were observed following the start of treatment on day 4 and continued to fall beyond treatment cessation on day 11. (B) Daily ALT (U/L) values of non-responder volunteers receiving APAP treatment were not significantly different from those receiving placebo (N = 9). (C) The peak ALT fold change (over baseline) reached over the course of treatment per subject number is plotted for both non-responder (white bars) and responder (black bars) individuals. Horizontal line represents a 1.5-fold increase over the subject’s pretreatment baseline. Genome Research 2009

(A) Representative APAP-treated mice of strains CAST/EiJ, SM/J, C57BL/6J, DBA/2J, and B6C3F1/J showing varying levels of centrilobular necrosis. (B) A percent necrosis score (mean ± S.E.) of H&E stained liver sections. (D) Serum ALT levels (mean ± S.E.) in mice sacrificed 24 hours after dosing

Whole-genome association analysis and targeted sequencing determined that polymorphisms in Ly86, Cd44, Cd59a, and Capn8 correlate strongly with liver injury. Variation in the orthologous human gene, CD44, is associated with susceptibility to acetaminophen in two independent cohorts.

Infectability with lentiviral vectors

Cellular Genetics

% GFP Positives

35

Develop cell-based assay system for MEFs from 30 strains. What cell types?

30 25 20 15 10 5

Gene expression profiling High content imaging 100.00%

10.00%

1000 1.00% 100 0.10%

10

1

2

4

8

16

32

64

0.01% 128

1 256

Frequency

10000

Max / min expression fold-change

Cumulative frequency .

100000

HeLa

C3H/HeJ

CBA/J

AKR/J

A/J

What phenotypes to measure?

DBA/2J

C57BL/6J

0

a.

Strain distribution pattern of mitochondrial membrane potential across 30 different strains Interday replicates for 1% FBS 24hr

Purify MEFs from 30 different strains Seed in 96 wells and grow in or 1% serum for 72hrs At end of each timepoint, stain cells with JC-1 and measure flourescence with facs

Interday replicates for 1% FBS 72hr

c.

Heritability: 64.7%

Technical replicates for 1% FBS 24hr

Genome scan for mitochondrial membrane potential

P = 1.02E-08

nmol O2/min/1x10^6 cells

d.

Scatter Plot

Scatter Plot

4

4

3

3

2

2

1

1

0

0

500000000

1000000000

1500000000

2000000000

2500000000

2141500000

cumulative position

2142000000

2142500000

2143000000

2143500000

2144000000

2144500000

2145000000

cumulative position

Chromosome 15: Gene name: Fbxl7 siRNA knockdown of Fbxl7 70000

Ctrl

60000 50000

siRNA 3

P-ampkα (Thr 175) total ampka

30000

total p53

20000 10000

tubulin

tubulin

0

ctrl

siRNA 1

siRNA 2

siRNA 3

siRNA 4

Growth Curve of siFbxl7 treated MEFs

Percentage Increase of Mitochondria Superoxide over ctrl siRNA

4.E+04

140 120

3.E+04

100

ctrl

2.E+04

80

siRNA 3

s a re c in %

60

1.E+04

40 20 0

0

0.E+00

0

1

2

3

Days

4

5

6

1

2

7

days

Ctrl

P-p53 (Ser15)

40000

# e lC a to

0

3

4

5

siRNA 3

Ctrl

p21

siRNA 3

Effect of huFbxl7 knockdown in cancer cell lines

mRNA knockdown

Hs587t (mammary)

GM1600 (gliobastoma)

LnCAP (prostate)

Colo741 (colorectal)

cell proliferation

mito. membrane potential

MEF Cytotoxicity Assay • • •

32 Inbred MEF Cell Lines 100 Compounds; 9 concentrations, 4 multiplexed assays Data capture BD Pathway 435 high content imaging system

0.41 uM Vinblastine-1

3.7 uM Vinblastine-1

33.3 uM Vinblastine-1

DNA Content, Nuclear Count & Size

Hoescht G21-0.41 uM Vinblastine-1

Hoescht G20-3.7 uM Vinblastine-1

Hoescht G19-33.3 uM Vinblastine-1

Mitochondrial Membrane Potential Changes (Intensity)

Mito Red G21-0.41 uM Vinblastine-1

Mito Red G20-3.70 uM Vinblastine-1

Mito Red G19-33.3 uM Vinblastine-1

Cell Morphology & Permeability

FITC G21-0.41 uM Vinblastine-1

FITC G20-3.7 uM Vinblastine-1

FITC G19-33.3 uM Vinblastine-1

Cytochrome C Localization and Release

CY5 G21-0.41 uM Vinblastine-1

CY5 G20-3.7 uM Vinblastine-1

CY5 G19-33.3 uM Vinblastine-1

Millions

MEF cell viability studies

90 LP/J

80

C57BL/6J MRL/MpJ SEA/GnJ

70

C57BR/cdJ CZECHII/EiJ

Alomar blue analysis Whole well measurement

WSB/EiJ

60

NOD/ShiLtJ

RFU

RIIIS/J AKR/J

50

CE/J NZO/HILtJ LG/J

40

I/LnJ NOR/LtJ SM/J

30

BALBc/ByJ BTBRT+tf/J PL/J

20

SJL/J 129S1/SvImJ A/J

Millions

10

DBA/2J

0 -0.1

80

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

log[Acetaminophen] (mM)

70

LP/J C57BL/6J C57L/J

60

CBA/J MRL/MpJ NON/ShiLtJ SEA/GnJ

50

BUB/BnJ

RFU

C57BR/cdJ CZECHII/EiJ

40

WSB/EiJ NOD/ShiLtJ RIIIS/J SWR/J

30

AKR/J LG/J I/LnJ

20

NOR/LtJ BTBRT+tf/J SJL/J DBA/2J

10 0 -5

-4.5

-4

-3.5

-3

-2.5

log[Docetaxel] (mM)

-2

-1.5

-1

-0.5

Strain specific phenotypic differences

Summary Inbred strains can provide genetic variation that models human variation. The use of a mouse model allows for control of environmental variation. All phenotypes measured show variability across inbred mouse strains. Whole organism studies can be used to model disease status. Cellular genetics can be used for cell function, toxicogenomics, pharmacogenetics.

Future directions Improve the haplotype map across the inbred strains Screening drugs and toxicants in cell-based assays

Acknowledgements GNF Serge Batalov Andrew Su Chunlei Wu Jeff Janes Dave Delano Stephen Su

collaborators Joe Bass (Northwestern U.) Bev Paigen (JAX) Mat Pletcher (Pfizer) Lisa Tarantino (UNC) Russell Thomas (Hamner Inst)

Genome-wide Distribution of Variation

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