QUANTITATIVE GENETIC MODELING AND INFERENCE IN THE PRESENCE OF NONIGNORABLE MISSING DATA |
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Authors: | Ingelin Steinsland Camilla Thorrud Larsen Henrik Jensen |
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Affiliation: | 1. Department of Mathematical Sciences, NTNU, , 7491 Trondheim, Norway;2. Department of Electric Power Engineering, NTNU, , 7491 Trondheim, Norway |
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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. |
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Keywords: | Animal model missing not at random sex‐linked inheritance shared parameter model Tyto alba |
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