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Short introduction to systems genetics

Steve Munger
September 25, 2015

Short introduction to systems genetics

Talk given at Tufts Genetics Graduate Program Retreat 9/25/2015

Steve Munger

September 25, 2015
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  1. Harnessing natural genetic diversity to
    understand normal development,
    disorder, and disease.
    “Systems Genetics”
    Steven Munger
    The Jackson Laboratory
    @stevemunger
    Slides available at
    https://speakerdeck.com/stevemunger

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  2. Our genetic differences make us unique.
    •  Eye color, height, behavior
    •  Genetic predisposition to
    disease
    •  Drug efficacy/toxicity

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  3. Is the mouse a good model of human biology?

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  4. Why didn’t I see this adverse effect in my mouse model?

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  5. Photo: Brynn Voy
    Embracing genetic diversity
    Founder Strains of the Collaborative Cross/ Diversity Outcross
    Morgan  &  Welsh,  Mamm.Gen.,  2015  

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  6. Collaborative Cross
    Outbreeding
    Diversity Outbred
    Inbreeding
    The Mouse Diversity Outcross:
    A designed discovery population

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  7. 50
    40
    30
    20
    10
    Body weight (gm)
    7/11/2014 7/31/2014 8/20/2014
    date
    50
    40
    30
    20
    10
    Body weight (gm)
    7/11/2014 7/31/2014 8/20/2014
    date
    female DO mice male DO mice
    DO mice are genetically and phenotypically diverse
    Alan Attie &
    Mark Keller
    Female DO mice Male DO mice

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  8. Diversity Outbred mice exhibit phenotypes far exceeding the
    range observed in the founder strains.

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  9. b
    MS
    pping
    Inbreeding
    Outbreeding
    Founder Strains
    Collaborative Cross (CC)
    Diversity Outcross (DO)
    A/J
    C57BL/6J
    129S1/SvImJ
    NOD/ShiLtJ
    NZO/H1LtJ
    CAST/EiJ
    PWK/PhJ
    WSB/EiJ
    Discovery è Predictions è Validation
    Diversity Outbred (DO)

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  10. a b
    192 DO Livers
    Proteins
    Peptides
    Transcripts
    Short Reads
    eQTL pQTL
    RNA-Seq MS/MS
    eQTL Mapping pQTL Mapping
    Compare
    1
    ?
    How  does  gene6c  varia6on  influence  protein  abundance?  
    Collaboration w/ Gary Churchill (JAX), Steve Gygi, Joel Chick (HMS)

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  11. Mapping regions of the genome (QTL) that influence
    transcript (eQTL) and protein (pQTL) abundance
    Linear  Model  1:    
    y  ~  covariates  
     
    Linear  Model  2:  
     y  ~  covariates  +  Q  
     
    Gene1  exp  ~  Sex  +  Diet  +  SNP1.geno  
     
    Marker  regression  for  each  of  
    64,000  SNPs    

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  12. Protein Location
    pQTL Location
    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 X
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    X


























    a
    200 No pQTL
    Local pQTL
    Distant pQTL
    c e
    d
    b
    Total
    eQTL
    pQTL
    3477
    2289 1403
    N=8050
    Which  gene6c  variants  affect  transcript  abundance?  
    Which  affect  protein  abundance?  How  do  they  compare?  
    Transcriptome   Proteome  

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  13. Protein Location
    pQTL Location
    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 X
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    X


























    a
    c e
    d
    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.84
    −3
    −2
    −1
    0
    1
    2
    Protein
    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.69
    −3
    −2
    −1
    0
    1
    2
    Protein
    Acss3 La
    Glul Pp
    Local eQTL, local pQTL N
    Distant eQTL, distant pQTL L
    b
    eQTL
    pQTL
    The  Liver  pQTL  Landscape  
    pQTL Loc
    1 2 3 4 5 6 7 8 9
    1
    2
    3
    4
    b
    Total
    Local
    Distant
    eQTL
    pQTL
    3477
    2322
    1155
    2289
    338 1390
    1951 13
    1403
    N=8050
    f
    Local regulation

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  14. Protein Location
    pQTL Location
    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 X
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    X


























    a
    150
    200
    ency
    No pQTL
    Local pQTL
    Distant pQTL
    c
    30
    40
    50
    | Transcript
    Local pQTL
    20
    25
    30
    Distant pQTL
    | Transcript
    e
    d
    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.84






























































































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    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.04
    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.69


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    −2 −1 0 1 2
    −3
    −2
    −1
    0
    1
    2
    Protein
    Transcript
    r = 0.22
    Acss3 Lars2
    Glul Ppie
    Local eQTL, local pQTL No eQTL, local pQTL
    Distant eQTL, distant pQTL Local eQTL, distant pQTL
    b
    Total
    eQTL
    pQTL
    3477
    2289 1403
    N=8050
    The  Liver  pQTL  Landscape  

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  15. Models  of  Protein  Expression  Regula6on  
    (N  =  1390)  
    (N  =  338)  
    (N  =  2322)  
    (N  =  13)  
    (N  =  1951)  

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