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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

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


Part one

Part one

Codon-based models of molecular evolution


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


Types of single nucleotide mutation transitions vs transversions
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


Types of codon mutation synonymous vs non synonymous

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


Example ctt
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



Codeml
codeML evolution

  • 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


Part two

Part two evolution

An new method for estimating omega with recombination



Li and stephens 2003 approximation to the likelihood
Li and Stephens (2003) evolutionApproximation to the likelihood


Li and stephens 2003 approximation to the likelihood1
Li and Stephens (2003) evolutionApproximation to the likelihood

TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT

TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGCGAAATTCGTGATTTATTGCGCCGTTATCATCAT


Li and stephens 2003 approximation to the likelihood2
Li and Stephens (2003) evolutionApproximation to the likelihood

TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT

TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC



Estimating variable omega
Estimating variable omega evolution

  • 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 omega1
Estimating variable omega evolution

  • 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


Part three

Part three evolution

Does it work? Simulation studies and example data




Neutral dataset
Neutral dataset evolution

True omega

Posterior mean

Posterior HPD interval


Non neutral dataset
Non-neutral dataset evolution

True omega

Posterior mean

Posterior HPD interval


Hiv envelope gene slow non progressors vs rapid progressors
HIV envelope gene evolutionSlow Non-Progressors vs Rapid Progressors

Slow Non-Progressors

Rapid Progressors


Hiv envelope gene slow non progressors vs rapid progressors1
HIV envelope gene evolutionSlow Non-Progressors vs Rapid Progressors

Slow Non-Progressors

Rapid Progressors


Neisseria meningitidis porb3
Neisseria meningitidis evolution PorB3


Neisseria meningitidis porb31
Neisseria meningitidis evolution PorB3

95% HPD Upper0.0386

95% HPD Lower0.0187


Work in progress
Work in progress… evolution

  • Variable recombination rate

  • Model indels

  • Falsifiability test

  • Test for sensitivity to rate heterogeneity


Acknowledgements
Acknowledgements evolution

  • Gil McVean (Supervisor)

  • Martin Maiden (Supervisor)

  • Ziheng Yang

  • Rachel Urwin (meninge data)

  • Charlie Edwards (HIV data)


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