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On the need for a control line in selection experiments: A likelihood analysis

Abstract The question of whether selection experiments ought to include a control line, as opposed to investing all facilities in a single selected line, is addressed using a likelihood perspective. The consequences of using a control line are evaluated under two scenarios. In the first one, environ... Full description

Main Author: Jensen Just
Contributors: Guldbrandtsen Bernt | Author
Sorensen Daniel | Author
Contained in: Genetics Selection Evolution (01.01.2003)
Journal Title: Genetics Selection Evolution
Fulltext access: Fulltext access (direct link - free access) 10.1186/1297-9686-35-1-3
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Links: Additional Link (dx.doi.org)
Additional Link (doaj.org)
Additional Link (www.gsejournal.org)
Fulltext access (doaj.org)
Fulltext access (doaj.org)
ISSN: 0999-193X
DOI: 10.1186/1297-9686-35-1-3
Language: German
English
French
Physical Description: Online-Ressource
ID (e.g. DOI, URN): 10.1186/1297-9686-35-1-3
PPN (Catalogue-ID): DOAJ010336583
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520 |a Abstract The question of whether selection experiments ought to include a control line, as opposed to investing all facilities in a single selected line, is addressed using a likelihood perspective. The consequences of using a control line are evaluated under two scenarios. In the first one, environmental trend is modeled and inferred from the data. In this case, a control line is shown to be highly beneficial in terms of the efficiency of inferences about eheritability and response to selection. In the second scenario, environmental trend is not modeled. One can imagine that a previous analysis of the experimental data had lent support to this decision. It is shown that in this situation where a control line may seem superfluous, inclusion of a control line can result in minor gains in efficiency if a high selection intensity is practiced in the selected line. Further, if there is a loss, it is moderately small. The results are verified to hold under more complicated data structures via Monte Carlo simulation. For completeness, divergent selection designs are also reviewed, and inferences based on a conditional and full likelihood approach are contrasted. 
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