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Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow
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Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Dec 14, 2015

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Page 1: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Using mouse genetics to understand human disease

Mark Daly

Whitehead/PfizerComputational BiologyFellow

Page 2: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

What we do

• Genetics: the study of the inheritance of biological phenotype– Mendel recognized discrete units of inheritance– Theories rediscovered and disputed ca. 1900– Experiments on mouse coat color proved

Mendel correct and generalizable to mammals– We now recognize this inheritance as being

carried by variation in DNA

Page 3: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Why mice?

• Mammals, much better biological model

• Easy to breed, feed, and house

• Can acclimatize to human touch

• Most important: we can experiment in many ways not possible in humans

What do theywant with me?

Page 4: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mice are close to humans

Page 5: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Kerstin Lindblad-TohWhitehead/MIT Center for Genome Research

Page 6: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse sequence reveals great similarity with the human genome

Extremely high conservation: 560,000 “anchors”

Mouse-Human Comparisonboth genomes 2.5-3 billion bp long> 99% of genes have homologs> 95% of genome “syntenic”

Page 7: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Genomes are rearranged copiesof each other

Roughly 50% of bases change in the evolutionary time from mouse to human

Page 8: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse sequence reveals great similarity with the human genome

Extremely high conservation: 560,000 “anchors”

Anchors (hundreds of bases with >90% identity)represent areas of evolutionary selection…

…but only 30-40% of the highly conservedsegments correspond to exons of genes!!!

Page 9: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

What we can do

• Directed matings• Inbred lines and crosses

• Knockouts• Transgenics• Mutagenesis• Nuclear transfer

• Control exposure to pathogens, drugs, diet, etc.

YIKES!!!

Page 10: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Example: diabetes related miceavailable from The Jackson Labs

• Type I diabetes (3)• Type II diabetes (3)• Hyperglycemic (27)• Hyperinsulinemic (25)• Hypoglycemic (1)• Hypoinsulinemic (5)• Insulin resistant (30)

• Impaired insulin processing (7)

• Impaired wound healing (13)

Page 11: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Inbreeding

• Repeated brother-sister mating leads to completely homozygous genome – no variation!

Page 12: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Experimental Crosses

• Breed two distinct inbred lines

• Offspring (F1) are all genetically identical – they each have one copy of each chromosome from each parent

• Further crosses involving F1 lead to mice with unique combinations of the two original strains

Page 13: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Experimental Cross

Page 14: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Experimental Cross: backcross

• F1 bred back to one of the parents

• Backcross (F1 x RED) offspring:

50% red-red

50% red-blue

Page 15: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Experimental Cross: F2 intercross

• One F1 bred to another F1

• F2 intercross (F1xF1) offspring:

25% red-red

50% red-blue

25% blue-blue

F2

Page 16: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Trait mapping

100 200 300

F2

Page 17: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Trait mapping

Blue trees = tall, Red trees = short

In the F2 generation, short trees tend to carry “red” chromosomes where theheight genes are located, taller trees tend to carry “blue” chromosomes

QTL mapping use statistical methods to find these regions

Page 18: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

How do we distinguish chromosomes from different strains?

• Polymorphic DNA markers such as Single Nucleotide Polymorphisms (SNPs) can be used to distinguish the parental origin of offspring chromosomes

ATTCGACGTATTGGCACTTACAGGATTCGATGTATTGGCACTTACAGG

SNP

Page 19: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Example: susceptibility to Tb

• C3H mice extremely susceptible to Tb

• B6 mice resistant

• F1, F2 show intermediate levels of susceptibility

B6

C3H

0 100 200 3000

50

100

Days post infection

% s

urvi

val

Page 20: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

One gene location already known

• Previous work identified chromosome 1 as carrying a major susceptibility factor

• Congenic C3H animals carrying a B6 chromosome 1 segment were bred

0 50 100 150 2000

50

100

Survival Time

% s

urv

iva

l

C3H

B6

C3H.B6-sst1

Page 21: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Congenic and consomic mice

• Derived strains of mice in which the homozygous genome of one mouse strain has a chromosome or part of a chromosome substituted from another strain

Chr 1

Chr 2

Chr 3

Chr 4

Etc.

C3H B6 C3H.B6_chr1

Page 22: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Tb mapping cross

F2 intercross:

C3H.B6-sst1 - MTB-susceptible, carrying B6 chr 1 resistance

B6 - MTB-resistant

Trait – survival following MTB infection

B6

F1

x

x

n = 368F2…

C3H.B6-sst1

Page 23: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Results: 3 new gene locations identified!

Page 24: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Gene identified on chromosome 12

0 100 200 300

0

50

100

bb

bh

hh

Chi square

df

P value

18.99

2

P<0.0001

days post infection

% s

urv

ival

0 25 50 75 100 125 150

0

50

100

C57Bl/6J

B6-Igh6

B6-IL12-/-

Chi square

df

P value

30.02

2

P<0.0001

Days after infection

% s

urv

iva

l

0 25 50 75 100 125 150

0

50

100

BALB/cBJ

BALB/c-mMT-/-

Days after infection

%

surv

ival

Chi square

df

P value

20.17

1

P<0.0001

A. B. C.

At the end of chr 12 – miceinheriting two C3H copies survive significantly longerthan those with one or twoB6 copies

Mice engineered to bemissing a critical component of the immune system located in this region arelikewise more susceptible,validating that particulargene as involved in Tbsusceptibility

Page 25: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.
Page 26: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.
Page 27: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse History

• Modern “house mice” emerged from Asia into the fertile crescent as agriculture was born

Page 28: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse history

Page 29: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Recent mouse history

W.E. Castle C.C. Little

Fancy mouse breeding - Asia, Europe(last few centuries)

Retired schoolteacher Abbie Lathropcollects and breeds these mice

Granby, MA – 1900

Castle, Little and others form most commonly usedinbred strains

from Lathrop stock(1908 on)

Page 30: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse history

Page 31: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Mouse history

• Asian musculus and European domesticus mice dominate the world but have evolved separately over ~ 1 Million years

• Mixing in Abbie Lathrop’s schoolhouse created all our commonly used mice from these two distinct founder groups

Page 32: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.
Page 33: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Genetic Background of the inbred lab mice

musc musc

musc

musc

domest

domestdomest

domestdomest

C57BL/6

C3H

DBA

Avg segment size ~ 2 Mb{cast

Page 34: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Comparing two inbred strains – frequency of differences in 50 kb segments

{

<1 SNP/10 kb

{~40 SNP/10 kb

Page 35: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Finding the genes responsible for biomedical phenotypes

C3H (susceptible)

B6 (resistant)

20 Mb

Traditionally: positional cloning is painful(e.g., generating thousands of mice for fine mapping, breeding congenics) –

As a result, countless significant QTLs have been identified in mappingcrosses but only a small handful have thusfar resulted in identificationof which gene is responsible – the critical information that will advanceresearch into prevention and treatment!

Page 36: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Using DNA patterns to find genes

C3H (susc.)

B6 (res.)

Critical Region

20 Mb

Page 37: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Using DNA patterns to find genes

C3H (susc.)

B6 (res.)

DBA (susc.)

Critical Region

20 Mb

Page 38: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Example: mapping of albinism

Critical region

Page 39: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

First genomic region mapped129S1 T A * C C C * C G G T A C G A G G G

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

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

BALB_c T A * C C C G C G G T A C G A G G G

C3H A G T T T A A T G G T A C G A G G G

C57B6 A G T T T A A T C T A G T A C C C A

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

DBA2 A G T T T A A T C T A G T A C C C A

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

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

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

NZB * A C C C C * C C T * G T A C C C A

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

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

Chr 4 35.7 37.6 37.9 39.4 (Mb)

Page 40: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Future Genetic Studies

Pathways Expression

Mapping

Model Systems

Page 41: Using mouse genetics to understand human disease Mark Daly Whitehead/Pfizer Computational Biology Fellow.

Thanks to

(Whitehead Institute)Claire WadeAndrew Kirby

(MIT Genome Center)EJ KulbokasMike ZodyEric LanderKerstin Lindblad-Toh

Funding:Whitehead InstitutePfizer, Inc.National Human Genome Research Institute