In statistical genetics, genetic association and genomic prediction become more successful with a highly heritable trait. Identifying highly heritable components of a complex disease can thus advance scientific understanding of the disease and potentially lead to effective prevention and treatments. Using Matlab and existing large-scale genome datasets, we evaluate a restricted maximum likelihood approach to identify highly heritable components of a complex disease as a function of multiple clinical variables.