Improving estimation in genetic models using prior information

Espen Moen Eilertsen


Statistical models used to investigate research questions in behavioral genetics often require large amounts
of data. This paper introduces some key concepts of Bayesian analysis and illustrates how these methods
can aid model estimation when the data does not provide enough information to reliably answer research
questions. The use of informative prior distributions is discussed as a method of incorporating information
from other sources than the data at hand. The procedure is illustrated with an ACE model decomposition of
the variance of antisocial personality disorder. The data originates from the Norwegian Twin Registry, and
includes adult twins assessed with the Structured Interview for DSM Personality (SIDP-IV). Inclusion of
prior information lead to a shift with respect to conclusions about the presence of shared environmental
effects compared to a traditional analysis. Small and medium sized studies should consider use of prior
information to aid estimation of population parameters.

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