  Identifying shared genetic risk factors for multiple measured traits has been
of great interest in studying complex disorders. Marlow's (2003) method for
detecting shared gene effects on complex traits has been highly influential in
the literature of neurodevelopmental disorders as well as other disorders
including obesity and asthma. Although its method has been widely applied and
has been recommended as potentially powerful, the validity and power of this
method have not been examined either theoretically or by simulation. This paper
establishes the validity and quantifies and explains the power of the method.
We show the method has correct type 1 error rates regardless of the number of
traits in the model, and confirm power increases compared to standard
univariate methods across different genetic models. We discover the main source
of these power gains is correlations among traits induced by a common major
gene effect component. We compare the use of the complete pleiotropy model, as
assumed by Marlow, to the use of a more general model allowing additional
correlation parameters, and find that even when the true model includes those
parameters, the complete pleiotropy model is more powerful as long as traits
are moderately correlated by a major gene component. We implement this method
and a power calculator in software that can assist in designing studies by
using pilot data to calculate required sample sizes and choose traits for
further linkage studies. We apply the software to data on reading disability in
the Russian language.
