We apply an analysis based upon mixed-models to the Genetic Analysis Workshop 15, Problem 3 simulated data. Such models are commonly used to mitigate the tendency for population structure, or cryptic relatedness, to inflate the false-positive rate of test statistics. They also allow for explicit modeling of varying degrees of relatedness in samples in which some individuals are related by (possibly unknown) pedigree, whereas others are not. Furthermore, the implementation of the method we describe here is quick enough to be used effectively on genome-wide data. We present an analysis of the data for Genetic Analysis Workshop 15, Problem 3, in which we show that these methods can effectively find signals in this data. Somewhat disappointingly, the false-positive rate does not appear to be reduced, but this is largely because the method used to simulate the data appears not to have encompassed effects, such as population stratification, that might have led to inflation of p-values.