Diversifying selection and functional constraint
This presentation is the property of its rightful owner.
Sponsored Links
1 / 29

DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT PowerPoint PPT Presentation


  • 73 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

DIVERSIFYING SELECTION AND FUNCTIONAL CONSTRAINT

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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


Diversifying selection and functional constraint

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


Diversifying selection and functional constraint

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


Nielsen and yang 1998 codon based model of molecular evolution

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


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


Inference with recombination

Inference with recombination


Li and stephens 2003 approximation to the likelihood

Li and Stephens (2003)Approximation to the likelihood


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


My modification to li and stephens 2003

My modification to Li and Stephens(2003)

t


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


Posterior distribution for known and unknown genealogy

Posterior distribution for known and unknown genealogy


Posterior distribution for known and unknown genealogy1

Posterior distribution for known and unknown genealogy


Neutral dataset

Neutral dataset

True omega

Posterior mean

Posterior HPD interval


Non neutral dataset

Non-neutral dataset

True omega

Posterior mean

Posterior HPD interval


Hiv envelope gene slow non progressors vs rapid progressors

HIV envelope geneSlow Non-Progressors vs Rapid Progressors

Slow Non-Progressors

Rapid Progressors


Hiv envelope gene slow non progressors vs rapid progressors1

HIV envelope geneSlow Non-Progressors vs Rapid Progressors

Slow Non-Progressors

Rapid Progressors


Neisseria meningitidis porb3

Neisseria meningitidis PorB3


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)


  • Login