Molecular Evolution with an emphasis on substitution rates

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Molecular Evolutionwith an emphasis on
substitution rates

• Gavin JD Smith
• State Key Laboratory of Emerging Infectious
  Diseases
• Department of Microbiology
• The University of Hong Kong
• Bioinformatic and Comparative Genome Analysis
  CourseHKU-Pasteur Research Centre - Hong
  KongAugust 17 - August 29, 2009

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Why?

• Understanding the selective pressures that have
  shaped genetic variation is a central goal in the
  study of evolutionary biology Pond et al. 2007

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Dynamics of evolution

• The diversity exhibited by a population reflects
  the organisms natural history
• The genetic diversity of a population is a
  combination of
• - biological properties (e.g. mutation rates,
  generation time)
• - evolutionary forces (e.g. molecular adaptation,
  genetic drift)
• Three principal mechanisms are responsible for
  viral genetic variation
• - mutation
• - selection
• - recombination

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Mutations

• As nonsynonymous (ß) mutations directly alter
  proteins ( potentially their function) they are
  more likely to affect organism fitness than
  synonymous (a) mutations that leave the amino
  acid sequence unchanged

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Mutations

• Mutations that result in amino acid changes are
  non-synonymous
• Mutations that do not result in amino acid
  changes are silent or synonymous

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Selective pressures

• Selective pressure on coding sequences can be
  calculated by comparison of the relative rates of
  a ß mutations
• The ratio ? ß/a (also referred to as dN/dS or
  KA/KS) is a standard measure of selective pressure

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Selective pressures

• ? 1 indicates neutral evolution, ? lt 1 negative
  (or purifying) selection, ? gt 1 positive (or
  diversifying) selection
• To infer selective pressures it is necessary to
  be able to accurately estimate nonsynonymous
  synonymous rates
• this is where models come in (discussed later)

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Evolutionary rates and Selection

• Mutations have evolutionary consequences ONLY if
  they are successfully transmitted to the next
  generation
• MUTATION RATE
• Number of nucleotide alterations per round of
  replication
• SUBSTITUTION (or EVOLUTION) RATE
• Number of nucleotide alterations fixed in a
  population per unit of time
• The rate of evolution of a virus reflects the
  relative proportion of advantageous, neutral or
  deleterious evolutionary forces exerted on it

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Selective pressures

• Under negative selection less fit nonsynonymous
  subst. accumulate more slowly than synonymous
  subst.
• Alternatively expressed, negative selection
  exerts pressure to remove deleterious subst. from
  a population
• Positive selection acts to fix more fit or
  advantageous subst. in a population

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Evolutionary models

• Necessary for accurate rate estimation
• Current models either take the nucleotide or the
  codon as the unit of evolution
• The structure of the genetic code determines that
  realistic models of evolution should consider
  triplets of nucleotides (i.e. codons) to be the
  basic unit of evolution

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Nucleotide based models

• Nucleotide substitution models
• each nucleotide position of an alignment is
  treated independently
• Codon position substitution models
• partitions nucleotide data so that codon
  positions 1, 2 3 may have different parameters
• SRD06 model has two categories 12 3

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Codon based models

• A model of DNA sequence evolution applicable to
  coding regions
• Uses the codon, as opposed to the nucleotide, as
  the unit of evolution
• Accounts for dependencies among nucleotides
  within a codon
• Most commonly used are GY94 (Goldman Yang) and
  MG94 (Muse Gaut)

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Nucleotide substitution modelsas an example
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Models of nucleotide evolution

• Several probabilistic models of evolution have
  been developed to convert observed nucleotide
  distances into measures of actual evolutionary
  distances
• The relative complexity of these models is a
  function of the extent of the biological,
  biochemical ad evolutionary assumptions (i.e.
  parameters) they incorporate
• Substitutions are usually described as
  probabilities of mutational events,
  mathematically modeled by matrices of relative
  rates

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Jukes-Cantor (JC)

• First proposed model
• It assumes that the four bases have equal
  frequencies and all substitutions are equally
  likely

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Kimuras 2 parameter

• Transitions are generally more frequent than
  transversions
• K2P model assumes that the rate of transitions
  per site (a) differs from the rate of
  transversions per site (ß)

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Felsenstein (1981)

• If some substitutions are more common in one
  sequence than others, some substitutions may be
  more frequent than others
• F81 model allows the frequency (p) of the four
  nucleotides to be different

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Hasegawa, Kishino and Yano

• The HKY85 model allows rates of transitions and
  transversions to differ and base frequencies to
  vary

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General Time Reversible

• The GTR/REV model allows each possible
  substitution to have its own probability
• Substitutions are reversible (i.e. substitutions
  from i to j has the same probability as a
  substitution from j to i)

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After Whelan et al. 2001
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Rate heterogeneity

• Different regions of RNA/DNA may have different
  probabilities of change, and variable rates of
  substitution can have considerable impact on
  sequence divergence
• Typically, a gamma distribution is used to
  describe heterogeneity in nucleotide substitution
  rate across sequences
• The range of rate variation among sites is
  dictated by the shape parameter a of the
  distribution

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Beware of recombination!!

• Many phylogenetic methods implicitly assume that
  all sites in a sequence share a common
  evolutionary history
• However, recombination can violate this
  assumption by allowing sites to move freely
  between different genetic backgrounds
• This may cause different sections of an alignment
  to lead to contradictory estimates of the tree
  and subsequently confuse model inferences

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Global vs. Local ? models

• Global fits a single model to a given alignment
  tree (i.e. all branches are equal)
• Local can a unique set of substitution rates to
  every branch in a tree

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Acknowledgements

• HKU Vijaykrishna Dhanasekaran Justin Bahl for
  help with preparing the presentation practical
  component
• Estimating selection pressures on alignments of
  coding sequences Analyses using HyPhy. Edited by
  Sergei L. Kosakovsky Pond, Art F.Y. Poon, and
  Simon D.W. Frost

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