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Understanding and using quantitative genetic variation

Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work....

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Detalles Bibliográficos
Autor principal: Hill, William G.
Formato: Texto
Lenguaje:English
Publicado: The Royal Society 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2842708/
https://www.ncbi.nlm.nih.gov/pubmed/20008387
http://dx.doi.org/10.1098/rstb.2009.0203
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author Hill, William G.
author_facet Hill, William G.
author_sort Hill, William G.
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description Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet.
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spelling pubmed-28427082010-03-23 Understanding and using quantitative genetic variation Hill, William G. Philos Trans R Soc Lond B Biol Sci Articles Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet. The Royal Society 2010-01-12 /pmc/articles/PMC2842708/ /pubmed/20008387 http://dx.doi.org/10.1098/rstb.2009.0203 Text en © 2010 The Royal Society http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Hill, William G.
Understanding and using quantitative genetic variation
title Understanding and using quantitative genetic variation
title_full Understanding and using quantitative genetic variation
title_fullStr Understanding and using quantitative genetic variation
title_full_unstemmed Understanding and using quantitative genetic variation
title_short Understanding and using quantitative genetic variation
title_sort understanding and using quantitative genetic variation
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2842708/
https://www.ncbi.nlm.nih.gov/pubmed/20008387
http://dx.doi.org/10.1098/rstb.2009.0203
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