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A Quantitative Overview to Gene Expression Profiling in Animal Genetics

A Quantitative Overview to Gene Expression Profiling in Animal Genetics. Networks Dynamics. Gene Networks @ Work using Digital Organisms. Reverter & Dalrymple, 2005 BioInfoSummer, ANU, Canberra. Armidale Animal Breeding Summer Course, UNE, Feb. 2006.

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A Quantitative Overview to Gene Expression Profiling in Animal Genetics

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  1. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Networks Dynamics Gene Networks @ Work using Digital Organisms Reverter & Dalrymple, 2005 BioInfoSummer, ANU, Canberra Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  2. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Fleece Rot Resistance Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  3. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Fleece Rot Resistance Log2 Intensities Gene x Flock (RANDOM) Residual (RANDOM) Gene x Dye (RANDOM) Gene x Variety (RANDOM) Comparison Group Array|Block|Dye (FIXED) Main Gene Effect (RANDOM) Gene x Array|Block (RANDOM) DE Genes Control of FDR The proportion of the Total Variation accounted for by the G x Variety Interaction anticipates the proportion of DE Genes CLAIM Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  4. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Fleece Rot Resistance Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  5. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Fleece Rot Resistance 297 DE Clones 102 DE Genes Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  6. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  7. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  8. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene RES SUS Mean 34.4 44.2 Std 11.9 18.2 Min. 15 11 Max. 53 70 Clust. 0.34 0.44 Tot.Conn 1,755 2,255 Corr > |.90| 439 600 Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  9. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Offending Correlations Offending Connections Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  10. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Susceptible Resistant Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  11. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Susceptible Resistant Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  12. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Setting the Scene Susceptible Resistant 24 Most Offending Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  13. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Introduction • Building a gene network is a challenge. • Understanding how the essential genes function within a network is an even bigger challenge. • Evidence of changes in network topology due to a number of factors. • Given a network, postulating a hypothesis could be tricky (Type III Error). • Biologically testing a network could be impossible. Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  14. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Introduction APPROACHES None Heaps • Complex Systems • Expression Profiling • Stoichiometric Analysis Data Requirements Expert Knowledge Heaps None Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  15. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Digital Organisms • Digital Organisms are an elegant way to decipher the gradual evolutionary process found in complex structures that retain features related to earlier ancestral evolutionary steps. • Digital organisms have been shown to provide an increased understanding of fundamental problems including: • Why complex organisms have more robust fitness than simple ones (Lenski et al., 1999); • The relative contributions of replication and mutation rates to survival (Wilke et al., 2001  “Survival of the Flattest”); • The evolutionary effect of productivity on species richness (Chow et al., 2004). • Digital organisms can be understood as model systems to study the kinds of gene interactions that may result in phenotypic variation. • It is anticipated that this knowledge will, in turn, improve our ability to understand common diseases (Moore and Williams, 2005). Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  16. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Digital Organisms PARAMETERS • Descendants: • Connections: • Genetic Load: • Phenotype: Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  17. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Digital Organisms Normalised Mean Expressions Master Correlations 3 4 2 1 5 6 Master Genetic Load = 50.0 Master Phenotype = 45.4 Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  18. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Evolutionary Process Master Founders Descendants (constant population size) Repeat at nauseum Extreme Extreme • What global changes are required to generate an extreme phenotype? • What are the minimal changes to generate a massive change in a target gene? • What extreme changes in phenotype can be seen after knocking out a given gene? Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  19. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Evolutionary Process Start MaxRuns = 1,000 MaxG = 20,000 GL tolerance = 5% Define Master GRN Run = 1 YES Run = Run + 1 Finish Run > Max Runs NO Generate Foundation Organisms G = 1 YES Store Details of Extreme Organism G = G + 1 Progeny become parents G > Max G NO Generate Progeny Update Extreme Organisms Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  20. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Largest Founder Smallest Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  21. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Examples • Simulated (Luscombe et al. 04, Nature 431:308) 1 2 Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  22. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Examples • Simulated (Luscombe et al. 04, Nature 431:308) 1 2 Master Genetic Load = 97.5 Master Phenotype = 85.5 QUESTION • What global changes are required to generate an extreme phenotype? Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  23. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Largest Founder Smallest Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  24. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Examples • Simulated (Luscombe et al. 04, Nature 431:308) QUESTION Master Genetic Load = 86.7 Master Phenotype = 84.6 • What 2 genes need to be regulated by > 20% to generate an extreme change to gene No. 7? Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  25. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Examples Largest Smallest Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  26. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Examples ENDOGENOUS NETWORK • What 2 genes need to be regulated to by > 20% to generate an extreme change to gene No. 7? Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  27. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Real Example • MYOG (Reverter et al. 05, Bioinformatics 21:1112 Blais et al. 05, Genes & Development 19:553) Master Genetic Load = 186.9 Master Phenotype = 279.9 MYOG …to be knocked out Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  28. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Real Example • What negligible and extreme changes can be expected after knocking out MYOG? Biological Inconsistency Biological Challenge Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  29. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Real Example • What negligible and extreme changes can be expected after knocking out MYOG? Armidale Animal Breeding Summer Course, UNE, Feb. 2006

  30. A Quantitative Overview to Gene Expression Profiling in Animal Genetics Network Dynamics Digital Organisms Final Remarks • Note similarities with Genetic Algorithms. • Potentially naïve computation of Phenotype • …to be improved with WGS studies: • Further improvements from population growth rate. • Aggravating vs Buffering effect of connections. • Initial results warrant further research Armidale Animal Breeding Summer Course, UNE, Feb. 2006

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