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

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

Codon-based models of molecular evolution Part one Codon-based models of molecular evolution

Sampling usually occurs at this point i.e. post-selection Ancestral type Neutral mutant Inviable mutant Sampling usually occurs at this point i.e. post-selection Mutation Selection 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).

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

Types of codon mutation Synonymous vs. non-synonymous G A Leucine T G A Leucine Methionine 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

Example: CTT C T T T T T A T T G T T T C T T A T T G T T T C T T A T T Phe Non-synonymous transition wkm Ile Non-synonymous transversion wm Val Ser Tyr Cys Leu Synonymous transversion m km 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

Nielsen and Yang (1998) codon-based model of molecular evolution Mutation rate Synonymous transversion m Synonymous transition km Non-synonymous transversion wm Non-synonymous transition wkm Other Interpretation k Transition-transversion ratio w = dN/dS Relative viability of non-synonymous mutations

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

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

Inference with recombination

Li and Stephens (2003) Approximation to the likelihood

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

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

My modification to Li and Stephens(2003)

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

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

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

Posterior distribution for known and unknown genealogy

Posterior distribution for known and unknown genealogy

Neutral dataset True omega Posterior mean Posterior HPD interval

Non-neutral dataset True omega Posterior mean Posterior HPD interval

HIV envelope gene Slow Non-Progressors vs Rapid Progressors

HIV envelope gene Slow Non-Progressors vs Rapid Progressors

Neisseria meningitidis PorB3

Neisseria meningitidis PorB3 95% HPD Upper 0.0386 95% HPD Lower 0.0187

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

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