Using Ancestral Information to Inform Analyses of Complex Data Sets

Over the last decade, improvements in sequencing technologies coupled with active development of association mapping methods have made it possible to link genotypes and quantitative traits in humans. Despite substantial progress in the ability to generate and analyze large data sets, however, genotype-phenotype associations are often difficult to find, even in studies with large numbers of individuals and genetic markers. This is due, in part, to the fact that effects of individual loci can be small and/or dependent on genetic variation at other loci or the environment. 

Tree-based mapping, which uses the evolutionary relatedness of sampled individuals to gain information during association mapping, has the potential to significantly improve our ability to detect loci impacting human traits. However, current tree-based methods are too computationally intensive and inflexible to be of practical use. Here, we compare tree-based methods with more classical approaches for association mapping and discuss how the limitations of these newer methods might be addressed. Ultimately, these advances have the potential to advance our understanding of the molecular mechanisms underlying complex diseases.