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Hidden Markov Models in Population Genomics.

Julien Y Dutheil1

  • 1Department of Evolutionary Genetics, Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, August-Thienemann-Straße 2, 24306, Plön, Germany. dutheil@evolbio.mpg.de.

Methods in Molecular Biology (Clifton, N.J.)
|February 23, 2017
PubMed
Summary
This summary is machine-generated.

Population genomics has shifted to analyzing complete genomes, moving from limited loci to comprehensive data. New methods model spatial sequence evolution along genomes, enabling efficient inference for eukaryotic species.

Keywords:
Coalescence theoryPopulation genomicsRecombination

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Population genomics has evolved significantly with advancements in sequencing technologies.
  • Data sets have transitioned from analyzing a few genomic loci in limited individuals to comprehensive collections of complete genomes.
  • Genomic data set sizes are rapidly increasing, with haplotype numbers now comparable to or exceeding traditional data sets.

Purpose of the Study:

  • To introduce recent methodological developments in population genomics.
  • To enable the modeling of evolutionary history for samples of multiple individual genomes.
  • To address the complexities of spatial sequence evolution in genomic data.

Main Methods:

  • Modeling evolutionary processes spatially along genomes, reflecting the non-independent nature of loci.
  • Utilizing approximations that incorporate Markovian properties for efficient inference.
  • Developing models that account for meiotic recombination.

Main Results:

  • New models allow for the analysis of evolutionary history across entire genomes.
  • Spatial modeling captures the complexities of sequence evolution more accurately than temporal models.
  • Efficient inference is achievable due to Markovian properties in spatial models.

Conclusions:

  • Recent developments facilitate the modeling of evolutionary history for samples of several individual genomes.
  • These spatial models are currently applicable to eukaryotic species due to their assumption of meiotic recombination.
  • The shift to spatial modeling represents a significant methodological change in population genomics.