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Can we read the future from a tree?

Lässig M, Łuksza M - Elife (2014)

View Article: PubMed Central - PubMed

Affiliation: Michael Lässig is in the Institute for Theoretical Physics, University of Cologne, Cologne, Germany mlaessig@uni-koeln.de.

ABSTRACT

A new method uses genealogies based on sequence data to predict short-term evolutionary patterns.

Show MeSH
Fitness inference from genealogical trees.Lineages in these trees connect the individuals in a population sample and their evolutionary ancestors, which are the nodes of the tree. Evolutionarily successful lineages have descendants in the far future and are marked by thick lines; all other lineages are lost in the evolutionary process. (A) The relative numbers of mutations in successful and in lost lineages measure the predominant fitness effects in a population (orange dots: amino acid changes, blue dots: synonymous mutations). (B) The global statistics of nodes and branches measures the absolute rate of exponential population growth (indicated by the shaded area). (C) The local statistics of nodes and branches measures growth rate differences between clades. Neher and colleagues use this information to predict clade evolution.
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fig1: Fitness inference from genealogical trees.Lineages in these trees connect the individuals in a population sample and their evolutionary ancestors, which are the nodes of the tree. Evolutionarily successful lineages have descendants in the far future and are marked by thick lines; all other lineages are lost in the evolutionary process. (A) The relative numbers of mutations in successful and in lost lineages measure the predominant fitness effects in a population (orange dots: amino acid changes, blue dots: synonymous mutations). (B) The global statistics of nodes and branches measures the absolute rate of exponential population growth (indicated by the shaded area). (C) The local statistics of nodes and branches measures growth rate differences between clades. Neher and colleagues use this information to predict clade evolution.

Mentions: Inferring evolutionary patterns from genealogical trees has a long history. Geneticists use probabilistic methods to map mutations onto specific tree branches (Figure 1A). Counting how often these mutations appear in different lineages tells us which fitness effects are predominant in a population (McDonald and Kreitman, 1991; Strelkowa and Lässig, 2012). From the statistics of the genealogical tree itself, epidemiologists infer the growth rate of pathogen populations and use that information to predict the future course of an epidemic (Figure 1B, Stadler, 2010). Neher, Russell and Shraiman—who are at the Max Planck Institute for Developmental Biology, the University of Cambridge, and the University of California at Santa Barbara, respectively—extend this genealogy-based inference to genetic changes within a population (Figure 1C). This required developing new ways to extract information from genealogical trees: predictions must now be made for clades of genetically similar individuals, so we need a model that captures growth rate differences between different clades within one genealogical tree.Figure 1.Fitness inference from genealogical trees.


Can we read the future from a tree?

Lässig M, Łuksza M - Elife (2014)

Fitness inference from genealogical trees.Lineages in these trees connect the individuals in a population sample and their evolutionary ancestors, which are the nodes of the tree. Evolutionarily successful lineages have descendants in the far future and are marked by thick lines; all other lineages are lost in the evolutionary process. (A) The relative numbers of mutations in successful and in lost lineages measure the predominant fitness effects in a population (orange dots: amino acid changes, blue dots: synonymous mutations). (B) The global statistics of nodes and branches measures the absolute rate of exponential population growth (indicated by the shaded area). (C) The local statistics of nodes and branches measures growth rate differences between clades. Neher and colleagues use this information to predict clade evolution.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231209&req=5

fig1: Fitness inference from genealogical trees.Lineages in these trees connect the individuals in a population sample and their evolutionary ancestors, which are the nodes of the tree. Evolutionarily successful lineages have descendants in the far future and are marked by thick lines; all other lineages are lost in the evolutionary process. (A) The relative numbers of mutations in successful and in lost lineages measure the predominant fitness effects in a population (orange dots: amino acid changes, blue dots: synonymous mutations). (B) The global statistics of nodes and branches measures the absolute rate of exponential population growth (indicated by the shaded area). (C) The local statistics of nodes and branches measures growth rate differences between clades. Neher and colleagues use this information to predict clade evolution.
Mentions: Inferring evolutionary patterns from genealogical trees has a long history. Geneticists use probabilistic methods to map mutations onto specific tree branches (Figure 1A). Counting how often these mutations appear in different lineages tells us which fitness effects are predominant in a population (McDonald and Kreitman, 1991; Strelkowa and Lässig, 2012). From the statistics of the genealogical tree itself, epidemiologists infer the growth rate of pathogen populations and use that information to predict the future course of an epidemic (Figure 1B, Stadler, 2010). Neher, Russell and Shraiman—who are at the Max Planck Institute for Developmental Biology, the University of Cambridge, and the University of California at Santa Barbara, respectively—extend this genealogy-based inference to genetic changes within a population (Figure 1C). This required developing new ways to extract information from genealogical trees: predictions must now be made for clades of genetically similar individuals, so we need a model that captures growth rate differences between different clades within one genealogical tree.Figure 1.Fitness inference from genealogical trees.

View Article: PubMed Central - PubMed

Affiliation: Michael Lässig is in the Institute for Theoretical Physics, University of Cologne, Cologne, Germany mlaessig@uni-koeln.de.

ABSTRACT

A new method uses genealogies based on sequence data to predict short-term evolutionary patterns.

Show MeSH