Quantitative genetic modeling and inference in the presence of nonignorable missing data.

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Version: Final published version
Serval ID
serval:BIB_2F19E6AD992C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Quantitative genetic modeling and inference in the presence of nonignorable missing data.
Journal
Evolution
Author(s)
Steinsland I., Larsen C.T., Roulin A., Jensen H.
ISSN
1558-5646 (Electronic)
ISSN-L
0014-3820
Publication state
Published
Issued date
2014
Peer-reviewed
Oui
Volume
68
Number
6
Pages
1735-1747
Language
english
Abstract
Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.
Keywords
Animal model, missing not at random, sex-linked inheritance, shared parameter model, Tyto alba
Pubmed
Web of science
Open Access
Yes
Create date
10/02/2014 22:21
Last modification date
20/08/2019 13:13
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