1. Quantitative genetic modeling and inference in the presence of nonignorable missing data.
- Author
-
Steinsland I, Larsen CT, Roulin A, and Jensen H
- Subjects
- Animals, Genetic Variation, Phenotype, Population genetics, Sample Size, Selection, Genetic, Genetics, Population methods, Models, Genetic, Models, Statistical, Quantitative Trait, Heritable, Strigiformes genetics
- 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., (© 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.)
- Published
- 2014
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