Genetics of gene expression primer

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    Genetics of gene expression primer - Presentation Transcript

    1. Genetics of Gene Expression
        • Chris Cotsapas
        • [email_address]
    2. Outline
      • Background
        • Significance
        • Experimental design
        • Definitions
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight (?)‏
      • Last thoughts
    3. In the beginning...
      • King and Wilson, Science 1975
      • Chimp – human coding sequence differences don't match phenotypic distance
      • Gene regulation responsible for rest?
      • Basis for evo-devo, GGE
    4. Genetics of gene expression
      • Find genetic variation that (partially) controls levels of RNA
      • If RNA levels are different, maybe protein levels are also different
      • If protein concentration is different, maybe there is a phenotype?
    5. Experimental design
      • Protein measurements
        • 2D gels; quantitation hard; resolution?
      • RNA measurements
        • Expression chips ca. 1995 (Schena et al , Science)‏
    6. Experimental design - methods
      • Allelic discrimination
        • SBE
        • Pyrosequencing
        • SNP arrays
      • Whole-genome expression assays
      • Genotypes?
    7. Experimental design - samples
      • Pedigrees – multiplex/trios
        • HapMap
      • Population samples
      • Inbred strain panels
        • genotypes
      • New crosses
      • Inter-specific hybrids
    8. Definitions Cis Trans
    9. Outline
      • Background
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight (?)‏
      • Last thoughts
    10. First forays
      • Damerval et al 1994
      • Protein levels are different in 42/72 maize gene products
      • 2D electrophoresis, eyeball spot quantitation
      • Problems:
        • genome coverage
        • quantitation
        • post-translational modifications
    11. Cowles et al 2002
      • F1 mice
      • 69 genes in three tissues
      • 4 variant; 2 only in liver
    12. Yeast WG approaches
      • Brem et al Science 2002
      • Linkage in 40 offspring of lab x wild strain cross
      • 1528/6215 DE between parents
      • 570 map in cross
        • multiple QTLs
        • 32% of 570 have cis linkage
      • 262 not DE in parents also map
    13. Trans hotspots Brem et al Science 2002
    14. Yvert et al Nat Genet 2003
    15. Mammals I
      • F2 mice on atherogenic diet
      • Expression arrays; WG linkage
      Schadt et al Nature 2003
    16. Mammals II Chesler et al Nat Genet 2005 10% !!
    17. Mammals III
      • No major trans loci in humans
        • Cheung et al Nature 2003
        • Monks et al AJHG 2004
        • Stranger et al PLoS Genet 2005, Science 2007
        • Further scans (WT, Cheung, Schadt etc)‏
    18. What's the deal with trans ?
      • Artefacts
        • Normalization robustness
        • Replication?
        • Clusters of correlated genes
      • Power
        • trans often weak effects
        • Small sample sizes
    19. Outline
      • Background
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight (?)‏
      • Last thoughts
      • trans -acting factors may be tissue specific
        • TFs, signalling molecules, etc etc
      • cis regulatory sequences may be used by TS factors
      • So, shouldn't genetic effects on GE be tissue specific?
      Logic
    20. Examples
      • Chesler vs Bystrykh (Nat Genet 2005)‏
        • 39/101 (39%) forebrain cis in HSC
        • 297/1218 (24%) HSC cis in forebrain
      • Hubner (Nat Genet 2005)‏
        • 311/4297 (7%) shared between fat and kidney
      • Campbell et al (Gen Res 2008)‏
        • 12% of ~100 genes vary in at least one of three tissues
    21. Correlation networks persist across tissues
    22. Outline
      • Background
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight (?)‏
      • Last thoughts
    23. Slide courtesy Alkes Price
    24. Population differences could have non-genetic basis • Differences due to environment? (Idaghdour et al. 2008)‏ • Differences in cell line preparation? (Stranger et al. 2007)‏ • Differences due to batch effects? (Akey et al. 2007)‏ (Reviewed in Gilad et al. 2008)‏ Slide courtesy Alkes Price
    25. Gene expression experiment Does gene expression in 60 CEU + 60 YRI vary with ancestry? Does gene expression in 89 AA vary with % Eur ancestry? 60 CEU + 60 YRI from HapMap, 89 AA from Coriell HD100AA Gene expression measurements at 4,197 genes obtained using Affymetrix Focus array c Slide courtesy Alkes Price
    26. Gene expression differences in African Americans validate CEU-YRI differences c = 0.43 (± 0.02)‏ ( P -value < 10 -25 )‏ 12% ± 3% in cis Slide courtesy Alkes Price
    27. Outline
      • Background
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight
      • Last thoughts
    28. Does GGE matter?
      • Where is the phenotype?
        • Case/control differences?
      • Does it translate to protein?
      • Can we reconstruct networks/pathways?
    29. Insight
      • Mechanism?
      • trans – coregulated groups?
        • pathways
      • Model QTs
      • Molecular variation snapshots
        • What does it mean?
      • Ancillary information
    30. IRGM harbors a 20-kilobase deletion polymorphism immediately upstream IRGM Individuals Position on chr5 Slide courtesy Steve McCarroll McCarroll et al., Nature Genetics , Sept. 2008 rs13361189 (strongest Crohn’s-associated SNP)‏
    31. IRGM structural haplotypes have altered expression Slide adapted, courtesy Steve McCarroll McCarroll et al., Nature Genetics , Sept. 2008
    32. IRGM expression levels influence the efficacy of autophagy McCarroll et al., Nature Genetics , Sept. 2008 Slide adapted, courtesy Steve McCarroll
    33. Outline
      • Background
      • Basic findings
      • Tissue specificity
      • Population differences
      • Biological insight (?)‏
      • Last thoughts
    34. Success?
      • Baseline variation - tricky
      • Perturbations and phenotypes
        • mouse obesity crosses (Schadt et al )‏
        • Drug screening (Choi, Yelensky et al )‏
        • Crohn's disease (McCarroll et al )‏
    35. Stuff we haven't covered
      • Coregulated sets
      • Disease context
      • Annotation/pathway analysis
      • Modelling QTLs
      • Regression
      • WGAS
      • Multiple data sets/types
      • Post-transcriptional event variation
    36.  
    37.  

    + Chris CotsapasChris Cotsapas, 12 months ago

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