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DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT

DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT. ESTIMATING THE dN/dS RATIO FOR GENE SEQUENCES IN THE PRESENCE OF RECOMBINATION. Danny Wilson 12 th October 2004. Menu. Codon-based models of molecular evolution An new method for estimating omega with recombination

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DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT

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  1. DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT ESTIMATING THE dN/dS RATIO FOR GENE SEQUENCES IN THE PRESENCE OF RECOMBINATION Danny Wilson 12th October 2004

  2. Menu • Codon-based models of molecular evolution • An new method for estimating omega with recombination • Does it work? Simulation studies and example data

  3. Part one Codon-based models of molecular evolution

  4. Selection Mutation Ancestral type Neutral mutant Inviable mutant Underlying rates of non-synonymous mutation are usually confounded with selection against inviable mutants.Thus it is convenient to model functional constraint as mutational bias.(Or rather, make no attempt to disentangle the two). Sampling usuallyoccurs at this pointi.e. post-selection

  5. Types of single nucleotide mutationTransitions vs. transversions For any base there are always 2 possible transversions and 1 possible transition. A G Purine Transitions Transversions T C Pyramidine Transitions

  6. T T G T T G Leucine Leucine T T A Leucine A T G Methionine Types of codon mutationSynonymous vs. non-synonymous Synonymous Non-synonymous Leucine pH 5.98 6-fold degeneracy in the genetic code Methionine pH 5.74 Single unique codon ATG CH3-S-(CH2)2-CH(NH2)-COOH (CH3)2-CH-CH2-CH(NH2)-COOH

  7. Example: CTT C T T T T T A T T Leucine G T T T C T T A T T G T T T C T T A T T G

  8. Nielsen and Yang (1998) codon-based model of molecular evolution

  9. codeML • Pros • Viable method for detecting mode of selection on a codon sequence • Cons • Categorizes possible values for omega into a small number of discrete intervals • Results can be misleading in the presence of recombination

  10. Part two An new method for estimating omega with recombination

  11. Inference with recombination

  12. Li and Stephens (2003)Approximation to the likelihood

  13. Li and Stephens (2003)Approximation to the likelihood TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC TTTGATACCGTTGCCGAAGGTTTGGGCGAAATTCGTGATTTATTGCGCCGTTATCATCAT

  14. Li and Stephens (2003)Approximation to the likelihood TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC

  15. My modification to Li and Stephens(2003) t

  16. Estimating variable omega • The problem • A constant omega model is prone to averaging positive and negative omegas in a gene • Allowing every site its own omega leaves little information for inference • The solution • A change-point model where windows of adjacent sites share the same omega

  17. Estimating variable omega • MCMC moves: • Change omega for a single block • Extend a block 5’ or 3’ • Split an existing block • Merge adjacent blocks w1 w2 w3 w4 w5

  18. Part three Does it work? Simulation studies and example data

  19. Posterior distribution for known and unknown genealogy

  20. Posterior distribution for known and unknown genealogy

  21. Neutral dataset True omega Posterior mean Posterior HPD interval

  22. Non-neutral dataset True omega Posterior mean Posterior HPD interval

  23. HIV envelope geneSlow Non-Progressors vs Rapid Progressors Slow Non-Progressors Rapid Progressors

  24. HIV envelope geneSlow Non-Progressors vs Rapid Progressors Slow Non-Progressors Rapid Progressors

  25. Neisseria meningitidis PorB3

  26. Neisseria meningitidis PorB3 95% HPD Upper0.0386 95% HPD Lower0.0187

  27. Work in progress… • Variable recombination rate • Model indels • Falsifiability test • Test for sensitivity to rate heterogeneity

  28. Acknowledgements • Gil McVean (Supervisor) • Martin Maiden (Supervisor) • Ziheng Yang • Rachel Urwin (meninge data) • Charlie Edwards (HIV data)

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