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

Tim Wiltshire

School of PharmacyUniversity of North Carolina

Chapel Hill

How do we efficiently annotate the function of all the genes in the mammalian genome?

Goal: “Genome-wide functional genomics”

What do we know about gene function?

40234 entries in Entrez Gene

19709 genes (49%) have zero linked references

31672 genes (78%) have five or fewer linked references

Fraction of all Citations Accounted for by Highly-Cited Genes

TP53TNFAPOEMTHFRHLA-DRB1IL6ACETGFB1EGFRVEGFA

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.

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

New RI initiatives - A new set of comprehensive RI strains

Outbred strains – most closely model human populations

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

Genetic diversity through mating

Both methods require months or years to define candidate region

Nature Genetics 36:1133, 2004

Mammalian Genome 13:175, 2002

129S1/SvImJ NOD/LtJ

A/J NZO/HlLtJ

C57BL/6J PWK/PhJ

CAST/EiJ WSB/EiJ

Parental Strains

Randomization of Variation through Meiosis

CAST WSBC57BL6 PWKA/J 129S1 NZONOD

Representative CC genome

The CC has many Independent IterationsHigh Statistical Power

X

Infinitely Reproducible

CC Population ~ Human PopulationCC Population ~ Human Population

SNPs Insertion/deletions

20 x 106 1 x 106

50 x 106 4 x 106

Human

CC

CAST/EiJ WSB/EiJC57BL6/J PWK/PhJA/J 129S1/SvIm NZO/HlLtNOD/Lt

Captures 90% of the variation present in the mouse!Captures 90% of the variation present in the mouse!

The variation is randomly distributed across the genome The variation is randomly distributed across the genome (there are no blind spots)(there are no blind spots)

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.

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

macrophages

New RI initiatives - A new set of comprehensive RI strains

Outbred strains – most closely model human populations

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Inter-strain phenotypic variance

Hamilton, Frankel (Cell, 2001)Hamilton, Frankel (Cell, 2001)

Clinical Phenotypes

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«Open Field Center Time

Tail Suspension Immobility »

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PERA/EiJ 1 0

C58/J 0.8 0.41

C57L/J 0.7 0.47

C57BL/10J 0.6 0.503

C57BL/6J 0.361 0.487

BALB/cJ 0.25 0.444

C3H/HeJ 0.222 0.428

WSB/EiJ 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

BTBR T+ tf/J 0.04 0.2

AKR/J 0 0

I/LnJ 0 0

NZB/BlNJ 0 0

SM/J 0 0

Quantitative Traits

Susceptibility to developing gallstones

C C C C C C C G C C G C G C G C G C G G C C C G G C G C C G GA A A A A A A A A A T A A A A A A A A A A A A T A A A A A A AG G G G G G G G G G A G G G G G G G G G G G G A G G G G G G GA A A A A A A A A A T A A A A A A A A A A A A T A A A A A A AG G G G G G G G G G A G G G G G G G G G G G G A G G G G G G GT T T T T T T T T T T T T T G T G T G G T T T T T T T T T T TG G G G G G G G G G A G G G G G G G G G G G G A G G G G G G GT T T T T T T T T T T T T T G T G T G G T T T T T T T T T T TT T T T T T T T T T C T T T T T T T T T T T T C T T T T T T TT T T T T T T T T T T T T T G T G T G G T T T T T T T T T T TT T T T T T T T T T C T T T T T T T T T T T T C T T T T T T TC C C C C C C C C C C C T C C C C C C C C C C C C C C C C C CT T T T T T T T T T C T T T T T T T T T T T T C T T T T T T TC C C C C C C C C C C C T C C C C C C C C C C C C C C C C C CT T T T T T T T T T A T T T T T T T T T T T T A T T T T T T TC C C C C C C C C C C C T C C C C C C C C C C C C C C C C C CT T T T T T T T T T A T T T T T T T T T T T T A T T T T T T TC C C C C C C C C C T C T C C C C C C C C C C T C C C C C C CC C C C C C C C C C C C T C C C C C C C C C C C C C C C C C CT T T T T T T T T T A T T T T T T T T T T T T A T T T T T T TC C C C C C C C C C T C T C C C C C C C C C C T C C C C C C CC C C C C C C C C C T C T C C C C C C C C C C T C C C C C C CG G G G G G G G G G A G G G G G G G G G G G G A G G G G G G GA A A G A A A G G A G A G A G A G A G G A G A G G A G A A G G

Haplotype Association Mapping

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

Chr Pos 129S1/SvImJ A/J AKR/J BALB/cByJ BTBR_T+_tf/J BUB/BnJ C3H/HeJ1 171297027 T C C T C C T1 171297120 G G A G A G G1 171297250 C T T C T T C1 171297364 T C C T C C T1 171297418 G G G G G G G1 171297467 C C T C T C C1 171297468 C C C C C C C

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

CTG

ANOVA analysis: Identify associations between shared haplotypes and phenotypes

129S1/SvImJ 120.7 A/J 67.3BALB/cByJ 105.4 AKR/J 84.6C3H/HeJ 120.1 BTBR_T+_tf/J 110.2FVB/NJ 116.5 BUB/BnJ 67.8NZB/BlNJ 165.5 C57BL/6J 71.7NZW/LacJ 130.7 C57BLKS/J 78.6

C57L/J 80CAST/EiJ 67.1CBA/J 85.4CZECHII/EiJ 81.3DBA/2J 63.4I/LnJ 93.4JF1/Ms 88.8MA/MyJ 122.9MOLF/EiJ 81.6MSM/Ms 103.2NOD/LtJ 103PL/J 97RIIIS/J 48.8SEA/GnJ 82SJL/J 76SM/J 94.7SWR/J 91.2

126.4833 84.34783

TCG

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

IH Groups at ApoA2 Locus

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CTG TCG129S1/SvImJ 120.7 AKR/J 84.6BALB/cByJ 105.4 BTBR_T+_tf/J 110.2C3H/HeJ 120.1 BUB/BnJ 67.8FVB/NJ 116.5 C57BL/6J 71.7NZB/BlNJ 165.5 C57BLKS/J 78.6NZW/LacJ 130.7 C57L/J 80

CAST/EiJ 67.1CBA/J 85.4CZECHII/EiJ 81.3DBA/2J 63.4I/LnJ 93.4JF1/Ms 88.8MA/MyJ 122.9MOLF/EiJ 81.6MSM/Ms 103.2NOD/LtJ 103PL/J 97RIIIS/J 48.8SEA/GnJ 82SJL/J 76SM/J 94.7SWR/J 91.2

126.4833 85.12273

Inferred Haplotype Groups at ApoA2 locus

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

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Identification of clinical QTL and expression difference for open field behavior

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Grm7

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.

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

Whole organism phenotypesgene expressionbiomarkers

identification of biological networks

Anxiety and

Depression

Gene expression analysis

Biomarker analysis

Haplotype association mapping

Clinical phenotypes

What phenotypes can be used?

Gene Expression as a PhenotypeMendelian or complex?

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«Glutamate transporter (Slc1a1)hippocampus

Catechol-o-methyltransferase (Comt) »hippocampus

Using gene expression differences between strains to identify gene networks

Probe X

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Cis - local regulationcis-QTL band

trans-QTL band

Visualizing eQTL Results

Trans - non-local regulation through diffusable factors

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

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

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Cis - local regulationcis-QTL band

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Visualizing eQTL Results

Trans - non-local regulation through diffusable factors

functionalenrichment

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Gene Ontology KEGG pathway

functionalenrichment

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Gene Ontology KEGG pathway

Schema of trans-band analysis

Trans-regulator candidates functionalenrichment

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Gene Ontology KEGG pathway

GeneID -logP15502 5.3074559 4.66107652 4.4219357 4.40212862 4.3073074 4.30107652 4.2314828 4.12108946 4.09

>transband at chr=3, pos=46,624,006

Biological hypothesis

Putative Regulator

putative targets

Enrichment Analysis

Rank Name logp Description1 C1qa 3.17 complement component 1, q subcomponent, alpha polypeptide2 Gdap10 3.12 ganglioside-induced differentiation-associated-protein 103 1500011K16Rik 3.09 RIKEN cDNA 1500011K16 gene4 4633402C03Rik 3.07 gnf1m29444_at5 Cradd 3.03 CASP2 and RIPK1 domain containing adaptor with death domain6 Onecut1 3.03 one cut domain, family member 17 Npm3 3.01 nucleoplasmin 38 Ccdc22 2.99 DNA segment, Chr X, Immunex 40, expressed9 Gtpbp4 2.95 GTP binding protein 4

10 Rarres1 2.93 retinoic acid receptor responder (tazarotene induced) 111 Bad 2.92 Bcl-associated death promoter12 Gab1 2.89 growth factor receptor bound protein 2-associated protein 113 Mtap 2.87 methylthioadenosine phosphorylase14 Apcs 2.84 serum amyloid P-component15 Pex6 2.80 peroxisomal biogenesis factor 616 Chd8 2.78 chromodomain helicase DNA binding protein 817 Bnip2 2.77 BCL2/adenovirus E1B 19kDa-interacting protein 1, NIP218 AA407659 2.71 expressed sequence AA40765919 Ankfy1 2.71 ankyrin repeat and FYVE domain containing 120 Bap1 2.68 Brca1 associated protein 121 Hs3st3b1 2.68 heparan sulfate (glucosamine) 3-O-sulfotransferase 3B122 A430005L14Rik 2.67 RIKEN cDNA A430005L14 gene23 Akt1 2.65 thymoma viral proto-oncogene 124 Myh9 2.63 myosin, heavy polypeptide 9, non-muscle25 Casp3 2.63 caspase 3, apoptosis related cysteine protease

… …

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

Chr 19, 52.7 MB

Interactions between Gsk3b with trans-band targets

*Gray-genes are from trans-band targets

Five candidate regulators from transband in adipose tissue (GO: Integrin signaling)Name Description LOCUSLINK_ACCS4932425I24Rik RIKEN cDNA 4932425I24 gene 320214Cox17 cytochrome c oxidase, subunit XVII assembly protein homolog (yeast) 12856Gsk3b glycogen synthase kinase 3 beta 56637Nr1i2 nuclear receptor subfamily 1, group I, member 2 18171Popdc2 popeye domain containing 2 64082

Known ns-SNP

Known drug targetEnzastaurin

-/A Frame-shifting variation

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Integration of phenotype and expression data

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

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.

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 Associated Mapping case study

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 pre-treatment baseline.

Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humansAlison 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

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.

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Cellular Genetics

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

What phenotypes to measure?

Infectability with lentiviral vectors

High content imagingGene expression profiling

a.

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Purify MEFs from 30 different strains

Seed in 96 wells and grow inor 1% serum for 72hrs

At end of each timepoint, stain cellswith JC-1 and measure flourescence

with facs

Technical replicates for 1% FBS 24hr

Interday replicates for 1% FBS 24hr

Heritability:64.7%

Interday replicates for 1% FBS 72hr

Strain distribution pattern of mitochondrial membrane potential across 30 different strains

Scatter Plot

cumulative position0 500000000 1000000000 1500000000 2000000000 2500000000

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cumulative position2141500000 2142000000 2142500000 2143000000 2143500000 2144000000 2144500000 2145000000

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Chromosome 15: Gene name: Fbxl7

Genome scan for mitochondrial membrane potential

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days

Percentage Increase of Mitochondria Superoxide over ctrl siRNA

siRNA knockdown of Fbxl7

P-ampk (Thr 175) P-p53 (Ser15)

total p53

Ctrl siRNA 3

tubulintubulin

total ampka

Ctrl siRNA 3

p21

Ctrl siRNA 3

Effect of huFbxl7 knockdown in cancer cell lines

GM1600(gliobastoma)

LnCAP(prostate)

Colo741(colorectal)

Hs587t(mammary)

mRNA knockdown 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

3.7 uM Vinblastine-10.41 uM Vinblastine-1 33.3 uM Vinblastine-1

Hoescht G21-0.41 uM Vinblastine-1

Hoescht G19-33.3 uM Vinblastine-1

Hoescht G20-3.7 uM Vinblastine-1

Mito Red G21-0.41 uM Vinblastine-1

Mito Red G20-3.70 uM Vinblastine-1

Mito Red G19-33.3 uM Vinblastine-1

DNA Content, Nuclear Count & Size

Mitochondrial Membrane Potential Changes (Intensity)

CY5 G19-33.3 uM Vinblastine-1

CY5 G20-3.7 uM Vinblastine-1

CY5 G21-0.41 uM Vinblastine-1

FITC G20-3.7 uM Vinblastine-1

FITC G21-0.41 uM Vinblastine-1

FITC G19-33.3 uM Vinblastine-1

Cell Morphology & Permeability

Cytochrome C Localization and Release

0

10

20

30

40

50

60

70

80

-5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5

Mill

ions

log[Docetaxel] (mM)

RFU

LP/J

C57BL/6J

C57L/J

CBA/J

MRL/MpJ

NON/ShiLtJ

SEA/GnJ

BUB/BnJ

C57BR/cdJ

CZECHII/EiJ

WSB/EiJ

NOD/ShiLtJ

RIIIS/J

SWR/J

AKR/J

LG/J

I/LnJ

NOR/LtJ

BTBRT+tf/J

SJL/J

DBA/2J

0

10

20

30

40

50

60

70

80

90

-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Mil

lio

ns

log[Acetaminophen] (mM)

RF

U

LP/J

C57BL/6J

MRL/MpJ

SEA/GnJ

C57BR/cdJ

CZECHII/EiJ

WSB/EiJ

NOD/ShiLtJ

RIIIS/J

AKR/J

CE/J

NZO/HILtJ

LG/J

I/LnJ

NOR/LtJ

SM/J

BALBc/ByJ

BTBRT+tf/J

PL/J

SJL/J

129S1/SvImJ

A/J

DBA/2J

MEF cell viability studies

Alomar blue analysis Whole well measurement

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

GNFSerge BatalovAndrew SuChunlei WuJeff JanesDave DelanoStephen Su

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

collaborators

Genome-wide Distribution of Variation

PP