Genetic control of liver transcript and protein abundance

I presented this talk at the 2014 International Mammalian Genome Conference. I describe our work mapping transcript and protein abundance QTL in the Diversity Outbred mouse population.

Transcript

1. Gene$c  control  of  transcript  and   protein  abundance  in  the  liver.   Steve  Munger   @stevemunger    #IMGC14     Gary  Churchill  Group   The  Jackson  Laboratory   Bar  Harbor,  Maine  USA     Interna$onal  Mammalian  Genome  Conference   October  29,  2014      
2. 18M 18M 4M 4M 4M 4M 7M More  gene$c  diversity  =     More  phenotypic  diversity  =     More  analy$cal  complexity   C57BL/6J Brynn  Voy  
3. The  Diversity  Outbred  (DO)  heterogeneous  stock.   Collaborative Cross Funnel Diversity Outbred … G2:F4-F12 mice from 144 different funnels Random Outbreeding
4. High  Fat   Chow   Diversity  Outbred  Mice     Genera$ons  G4-­‐G12   Phenotyping   Livers   Sac  @  26  weeks  
5. Resources  from  Svenson  850  DO  Project   Animal Information Diet Genotype Sex Generation (4-12) Litter No. Born Date Coat Color Other Coat Color Feature Chow or HF Urinalysis Urinary Glu 1 ACR1 Urinary Glu2 ACR2 Change in ACR Electrocardiogram HR HRV PQ PR QRS QTc RR ST QTc dispers ion mean SR amplitude mean R amplitude pNN50 (>6ms) rMSSD Body Composition at two ages: (1) 12 weeks; (2) 21 weeks Length 1 Weight 1 % Fat 1 LTM1 BMD1 BMC1 B Area1 T Area1 RST1 TTM1 Length 2 Weight 2 % Fat 2 LTM2 BMD2 BMC2 B Area2 T Area2 RST2 TTM2 Change in Weight Change in % Fat Organ Weights Heart Spleen Kidney L Kidney R Weekly Body Weights; Growth Curve BW 4 BW 5 BW 6 BW 7 BW 8 BW 9 BW 10 BW 11 BW 12 BW 13 BW 14 BW 15 BW 16 BW 17 BW 18 BW 19 BW 20 BW 21 BW 22 BW 23 BW 24 BW 25 BW 26 Liver Harvest RNAseq Metabol- omics Protein Abundance Other Tissue Harvest Kidney/R RNAseq Muscle (gastrocnemius) RNAseq Pellets: Microbiome Gen 8 Ltr 2 (n=100) 18w Gen 9 Ltr 1 (n=100) 18w, 26w N  =  146*  traits   Genome Dynamics Center for *Other  calculated  traits  can  be  derived   Karen  Svenson  and  Lisa  Somes   15  August  2012   650  Diversity  Outbred  animals  have  been  phenotyped  for  clinically  relevant  traits.  QTL  analysis  is  underway.   Plasma chemistries at two ages: (1) 8 weeks; (2) 19 weeks CHOL1 HDLD1 TG1 Glu1 NEFA1 Ca1 Phos1 GLDH1 BUN1 Change in CHOL2 HDLD2 TG2 Glu2 NEFA2 Ca2 Phos2 GLDH2 BUN2 Chol TG NEFA Glu Hematological parameters at two ages: (1) 10 weeks; (2) 22 weeks WBC1 RBC1 mHGB1 HCT1 MCV1 MCH1 MCHC 1 CHCM1 RDW1 HDW1 PLT1 MPV1 NEUT1 LYM1 MONO1 EOS1 LUC1 BASO1 Retic1 cHGB1 WBC2 RBC2 mHGB2 HCT2 MCV2 MCH2 MCHC 2 CHCM2 RDW2 HDW2 PLT2 MPV2 NEUT2 LYM2 MONO2 EOS2 LUC2 BASO2 Retic2 cHGB2 Insulin (17w) Leptin (17w)
6. ~  30  million  SE  100bp  reads   Yfg   1.  Align  reads  to  transcriptome.   Yfg   Yfg   Yfg   Mouse  1   Mouse  2   Mouse  3   x  453  mice   RSEM  (Li  and  Dewey  2010)   2.  Es$mate  gene  and  isoform  expression.   3.  Map  expression  QTL   RNA-­‐seq  -­‐>  Gene  Expression  -­‐>  eQTL  
7. Munger  et  al.  2014   Gac  et  al.  2014   Construc$ng  individualized  diploid  transcriptomes  for     RNA-­‐seq  alignment  with  Seqnature.  
8. Coming  soon  –     EMASE  (N.  Raghupathy)   POPULASE  (KB  Choi)   Seqnature   Munger  et  al.  2014   Gac  et  al.  2014  
9. How  does  individualized  alignment  affect  eQTL?   Yfg   Yfg   Yfg   Mouse  1   Mouse  2   Mouse  3   x  453mice    Es$mate  gene  and  isoform  expression.   3.  Map  expression  QTL  
10. Alignment  to  individualized  transcriptomes  corrects   (removes)  many  spurious  liver  eQTL.   Tweet  this  please:  One  read  misalignment  can  cause  two   spurious  eQTL.     Rps12-ps2 Aligned to NCBIm37 Aligned to DO IRGs Munger  et  al.  2014  
11. Hebp1 Aligned to NCBIM37 Aligned to DO IRGs Alignment  to  individualized  transcriptomes   reveals  significant  local  eQTLs  for  2,000+  genes.   Munger  et  al.  2014  
12. Munger  et  al.  2014   Are  these  unmasked  local  eQTLs  real?   CC/DO  Founder  Strain  samples  
13. The  founder  origin  of  each  allele  is  tagged  and  provides  direct   es$mates  of  allele  specific  expression.   The  local  eQTL  for  Gm12976  is  cis-­‐ac$ng.   DO  samples  N=453   N=906  allele-­‐specific  es$mates.  
14. Liver  Transcriptome  Map   21,316  genes  expressed   above  threshold   11,932  (56%)  have  eQTL     10,243  (86%)  are  local   1,689  are  distant  
15. Local  eQTL  map  close  to  the  controlled  transcript.  
16. Mul$ple  causal  variants  appear  to   underlie  some  cis-­‐eQTL  (~10%).     eQTL  Founder  Coefficients  from  Gene  model   Allele-­‐level  Es$mates   Cis  eQTL  
17. Predic$ng  candidate  causal  variants   from  DO  founder  sequences.  
18. Associa$on  mapping  with  imputed   Founder  SNPs.     DOQTL  –  Dan  Gac  
19. eQTL  with  common  allele  paperns   resist  fine-­‐mapping.  
20. But…  in  some  cases  a  single  variant  emerges.  
21. What  about  some  of  those  highly  significant  trans-­‐eQTL?  
22. Predic$ng  regulators  underlying  trans-­‐eQTL.   Lrtm1   Lrtm1  -­‐  leucine-­‐rich  repeats  and  transmembrane  domains  1   cis-­‐eQTL   trans-­‐eQTL   331  genes  within  +/-­‐  5Mb  of  the  trans-­‐eQTL  peak  SNP  
23. Condi$on  Lrtm1  eQTL  scan  on  local  genotype.   Lrtm1  
24. Regress  out  the  expression  of  every  candidate  gene  in   the  region.   Lrtm1   Condi$oning  the  Lrtm1  eQTL  scan  on  the  expression  of  Igsf23     knocks  down  the  Chromosome  7  peak  from  15  -­‐>  6  LOD.  
25. Founder  paperns  at  Chr  7  suggest  that   Igsf23  represses  Lrtm1  expression.   Expression  of  Lrtm1   129,  NOD,  PWK  Low   is  controlled  by  Igsf23     129,  NOD,  PWK  High   Testable  Hypothesis:      IGSF23   Lrtm1  
26. What  about  some  of  those  other  significant  trans-­‐eQTL?  
27. Using  eQTL  to  iden$fy  strain-­‐specific  genome  integra$on   events  (and  inform  de  novo  gene  annotaJon  of  CC/DO   founder  strain  genomes).   Mad2l1  
28. Copy  of  Mad2l1  found  only  in  A/J  and  129S1  
29. Hot  off  the  Press  MS/MS  -­‐  Gene$c   Control  of  Protein  Abundance   Subset  of  194  DO  livers   Collabora$on  with   Steven  Gygi,  Harvard   Joel  Chick  
30. Liver  Proteome  Map   8,500  proteins  expressed  in   >100  samples.   1,671  (20%)  have  pQTL   1,198  local  pQTL   1,092  also  have  local  eQTL   419  distant  pQTL   Almost  no  overlap  with  eQTL  
31. n  =  1,092  genes   Min  =  -­‐0.56   Median  =  0.52   Max  =  0.93  
33. Acss3   Transcript     Abundance   QTL  (eQTL)   Protein     Abundance   QTL  (pQTL)  
34. Acss3   Chromosome  10   eQTL   pQTL  
36. Akr1c18  
37. Akr1c18   eQTL   pQTL   Chromosome  13  
38. Paox  
39. Paox   Transcript     Abundance   QTL  (eQTL)   Protein     Abundance   QTL  (pQTL)  
40. Paox   eQTL   pQTL   Chromosome  7  
42. Transcript     Abundance   QTL  (eQTL)   Protein     Abundance   QTL  (pQTL)   Akr1e1    
43. Akr1e1   Chromosome  4   eQTL   pQTL  
44. Drug  metabolism  pathways  are  enriched  for  genes   with  significant  liver  pQTL.   Tamoxifen  
45. Go  back  to  the  Collabora$ve  Cross  strains   CC  Strain   Cyp3a13   Cyp3a16   Cyp2d10   Cyp2d22   Fmo1   Fmo5   PredicFon   CC001   ++   +   -­‐   +++   -­‐   +   Highest   CC002   -­‐   +   +   -­‐   -­‐   +   Medium   CC003   -­‐   -­‐   -­‐   +   +   +   Medium   CC004   -­‐   +   -­‐-­‐-­‐   +   -­‐-­‐   -­‐   Lowest   CC005   +   -­‐-­‐   ++   -­‐   +   -­‐   Medium   CC006   +   -­‐   -­‐   -­‐   -­‐   +   Low   CC007   +   -­‐   +   -­‐   +   +   High   Pathway-­‐centered  predic$on   Toy  Example   Test!  
46. Looking  Ahead   •  For  5-­‐10k  genes,  we  can  predict  allele  and  gene  expression   with  high  accuracy  in  any  mouse  derived  from  one  or  more   of  the  CC/DO  founder  strains.   •  For  500-­‐1k  genes  with  concordant  e/pQTL,  we  can  predict   protein  abundance  in  any  mouse  derived  from  the  CC/DO   founder  strains.     –  We  can  predict  the  gene$c  variants  underlying  these  e/pQTL.   •  Use  the  mouse  to  find  blood/urine/stool  biomarkers  of   $ssue  transcript/protein  abundance.  Variants  >   Endophenotypes  >  Disorder   •  Expression  Gene$cs-­‐guided  Model  Development.   •  By  applying  genome  edi$ng  (CRISPR/Cas)  to  next   genera$on  mapping  popula$ons,  we  can  fix  known  risk   alleles  to  amplify  and  iden$fy  novel  low-­‐effect  variants.      
47. Acknowledgements  
48. Thank  you!   •  DOQTL  -­‐  hpp://cgd.jax.org/apps/doqtl/DOQTL.shtml   •  Seqnature  -­‐  hpp://cgd.jax.org/tools/Seqnature.shtml   •  EMASE  –  Coming  Soon!   •  eQTL  Viewer  -­‐  hpp://cgd.jax.org/apps/eqtlviewer-­‐beta/   •  AceWeb  (DO  founder  expression  viewer)   –  hpp://cgd.jax.org/ace/rnaseq/   •  Sanger  Mouse  Genomes  Project  (Keane  and  others)   –  hpp://www.sanger.ac.uk/sanger/Mouse_SnpViewer/rel-­‐1303   •  r/qtlcharts  (K  Broman)  -­‐  hpp://kbroman.github.io/qtlcharts/