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Genetical Genomics

Genetical Genomics. Manjunatha N Jagalur ECE 697S. In last class……. Mendelian Traits Allele detection Markers Interval Mapping QTL analysis. Interval mapping. Today…. Using expression as phenotype Biological significance Work by Brem et al Work by Schadt et al QTG model

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Genetical Genomics

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  1. Genetical Genomics Manjunatha N Jagalur ECE 697S

  2. In last class…… • Mendelian Traits • Allele detection • Markers • Interval Mapping • QTL analysis

  3. Interval mapping

  4. Today….. • Using expression as phenotype • Biological significance • Work by Brem et al • Work by Schadt et al • QTG model • Using graphical models

  5. What if we use gene expression as phenotype? • QTL may contain alleles responsible for expression of that gene • Allele may be in coding/non-coding region • Allele might be involved in gene expression regulation

  6. Biological Significance Modulator Transcription mRNA Promoter Regions Translation Protein Protein Folding Target

  7. Polymorphism sites Modulator Target

  8. Trans-acting 1 Modulator Target Expression(Target)=F(Expression(Modulator)) Expression(Target)~=F(Genotype(Modulator_Promoter))

  9. Trans-acting 2 Modulator Target Expression(Target)=F(Expression(Modulator), Genotype(Modulator))

  10. Cis-acting 1 Modulator Target Expression(Target)=F(Expression(Modulator)),Genotype(Target_Promoter))

  11. Work by Brem et al • Organism of interest: Yeast (Saccharomyces cerevisiae) • Parents • Baker’s Yeast • Wild strain from a California vineyard (RM) • Expression was measured for 6215 genes • Genotyped 3312 markers across 16 chromosomes • Samples size 32 • Later it was increased to ~=120

  12. Observations • 1528 genes showed differential expression at P<0.005 • 1165 differed by < twofold • 363 differed by > twofold • 147 >fourfold • 62 > eightfold • Linkages to some known traits were tested

  13. Observations

  14. Observations

  15. Observations

  16. Work by Schadt et al • Organism of interest: Mouse (Mus Musculus) • Parents • C57BL/6J • DBA/2J • Expression was measured for 23574 genes • Evenly placed markers • Sample size: 111

  17. Observations • 7861 genes were found to be differentially expressed (with P=0.05) in the parental strains or 10% of F2 mice • Of these, found QTLs for 2123 genes (Threshold =4.3, P<0.00005) • Overall 4339 genes had a QTL

  18. Observations

  19. Observations

  20. Summary of results • There are quite a few mendelian like genes • But for most of the genes control is not so simple • In many cases QTL may contain genes which are not the direct regulators • Most of the analysis is bioinformatic • Need sophisticated models for computational analysis

  21. Motivation for my work • Interval mapping is a very simple model • May not capture all the interactions • Need a better model • Should be general enough to account for all kinds of interaction • Should be enough so that we can build it using small number of samples • Should be extendable to non-mendelian scenario

  22. Transcription Model Genotype(Modulator_Promoter) Genotype(Modulator) Expression(Modulator) Genotype(Target_Promoter) Expression(Target)

  23. Proposed Model for Trans-acting 2 Et=a*Em+b*Em*Gm+c*Gm+d Where • Et is expression of the target • Em is expression of the modulator • Gm is genotype of the modulator • a,b,c,d are parameters to fit

  24. Proposed Model for Cis-acting Et=a*Em+b*Em*Gpt+c* Gpt +d Where • Et is expression of the target • Em is expression of the modulator • Gpt is genotype of the target promoter • a,b,c,d are parameters to fit

  25. Process • For any two genes G1 & G2 • Fit the data to any of the models (trans or cis) • Score=log(P(data|model))-log(P(data)) • Test if this score is significant • If yes then implicate putative regulator • Scores are not continuous over genome

  26. Datasets • All the following results are on Yeast dataset by Kruglyak Lab • 113 samples • ~3000 markers • 6000+ gene expressions

  27. Example

  28. Results 1

  29. Results 2

  30. Results 3

  31. Verifying Brem’s regulators

  32. Approximate Network Construction

  33. Future Work • Using Bayesian Network • Testing some hypothesis by experimentally verifying • Using other available data • Chip-CHIP data • More expression data • Protein-protein interaction data

  34. Summary • Gene regulation • Using interval mapping to find regulating allele • Using interval mapping to construct gene regulatory network • Using more general models

  35. Thanks to…. • My advisor David Kulp • Gary Churchill for his comments and getting us Schadt et al dataset • Rachel Brem for her comments and getting us yeast dataset

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