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Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

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  • Steve O'Hagan
  • Joshua Knowles
  • Douglas B Kell

Abstract

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information).

Suggested Citation

  • Steve O'Hagan & Joshua Knowles & Douglas B Kell, 2012. "Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-14, November.
  • Handle: RePEc:plo:pone00:0048862
    DOI: 10.1371/journal.pone.0048862
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    References listed on IDEAS

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    1. Shimon Bershtein & Michal Segal & Roy Bekerman & Nobuhiko Tokuriki & Dan S. Tawfik, 2006. "Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein," Nature, Nature, vol. 444(7121), pages 929-932, December.
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    1. Alexander V Spirov & Ekaterina M Myasnikova, 2022. "Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-42, January.
    2. Shiori Yabe & Masanori Yamasaki & Kaworu Ebana & Takeshi Hayashi & Hiroyoshi Iwata, 2016. "Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-21, April.

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