Tweet
Tweet

Slide 1

Slide 1 text

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

Slide 2

Slide 2 text

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

Slide 3

Slide 3 text

Is the mouse a good model of human biology?

Slide 4

Slide 4 text

No content

Slide 5

Slide 5 text

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

Slide 6

Slide 6 text

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

Slide 7

Slide 7 text

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

Slide 8

Slide 8 text

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

Slide 9

Slide 9 text

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

Slide 10

Slide 10 text

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)

Slide 11

Slide 11 text

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)

Slide 12

Slide 12 text

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    

Slide 13

Slide 13 text

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  

Slide 14

Slide 14 text

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

Slide 15

Slide 15 text

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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −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  

Slide 16

Slide 16 text

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

Slide 17

Slide 17 text

No content

Slide 18

Slide 18 text

No content

Slide 19

Slide 19 text

No content

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

No content

Slide 22

Slide 22 text

No content

Slide 23

Slide 23 text

No content

Slide 24

Slide 24 text

No content

Slide 25

Slide 25 text

No content

Slide 26

Slide 26 text

No content

Slide 27

Slide 27 text

No content