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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

slide2
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

slide4

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
  • 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

An new method for estimating omega with recombination

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

TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT

TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGCGAAATTCGTGATTTATTGCGCCGTTATCATCAT

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

TTTGATACTGTTGCCGAAGGTTTGGGCGAAATTCGCGATTTATTGCGCCGTTATCATCAT

TTTGATACCGTTGCCGAAGGTTTGGGTGAAATTCGCGATTTATTGCGCCGTTACCACCGC

TTTGATACCGTTGCCGAAGGTTTGGGTAAAATTCGCGATTTATTGCGCCGTTACCACCGC

estimating variable omega
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 omega1
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

part three

Part three

Does it work? Simulation studies and example data

neutral dataset
Neutral dataset

True omega

Posterior mean

Posterior HPD interval

non neutral dataset
Non-neutral dataset

True omega

Posterior mean

Posterior HPD interval

neisseria meningitidis porb31
Neisseria meningitidis PorB3

95% HPD Upper0.0386

95% HPD Lower0.0187

work in progress
Work in progress…
  • Variable recombination rate
  • Model indels
  • Falsifiability test
  • Test for sensitivity to rate heterogeneity
acknowledgements
Acknowledgements
  • Gil McVean (Supervisor)
  • Martin Maiden (Supervisor)
  • Ziheng Yang
  • Rachel Urwin (meninge data)
  • Charlie Edwards (HIV data)
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