Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.

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. W...

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Autores principales:	Steve O'Hagan, Joshua Knowles, Douglas B Kell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0e1749a1484d4de8895b620e53dff3cc
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spelling	oai:doaj.org-article:0e1749a1484d4de8895b620e53dff3cc2021-11-18T08:08:02ZExploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.1932-620310.1371/journal.pone.0048862https://doaj.org/article/0e1749a1484d4de8895b620e53dff3cc2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23185279/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Comparatively 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).Steve O'HaganJoshua KnowlesDouglas B KellPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 11, p e48862 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Steve O'Hagan Joshua Knowles Douglas B Kell Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
description	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).
format	article
author	Steve O'Hagan Joshua Knowles Douglas B Kell
author_facet	Steve O'Hagan Joshua Knowles Douglas B Kell
author_sort	Steve O'Hagan
title	Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
title_short	Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
title_full	Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
title_fullStr	Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
title_full_unstemmed	Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
title_sort	exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/0e1749a1484d4de8895b620e53dff3cc
work_keys_str_mv	AT steveohagan exploitinggenomicknowledgeinoptimisingmolecularbreedingprogrammesalgorithmsfromevolutionarycomputing AT joshuaknowles exploitinggenomicknowledgeinoptimisingmolecularbreedingprogrammesalgorithmsfromevolutionarycomputing AT douglasbkell exploitinggenomicknowledgeinoptimisingmolecularbreedingprogrammesalgorithmsfromevolutionarycomputing
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Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.

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