Given the difficulties of SNP-by-SNP approaches, it is important to consider other potential uses of genome-wide data that could include efforts to characterize genetic similarity between each unique pair of unrelated observations in a study. Like quantitative genetic models that rely on assumed genetic relationships among related individuals, new methods rely on measured genetic similarity among unrelated individuals to decompose phenotypic variance into genetic and environmental components. Yang and colleagues (2011a) developed a set of statistical tools in the GCTA package (described in detail later in the article) that implements the aforementioned methods in a very simple and intuitive set of models. This method has produced heritability estimates for height and other phenotypes that are comparable to values obtained from pair-based models of twins and siblings (Yang et al. 2010). Importantly, their statistical approach also highlights the centrality of genetic similarity as a critical source of information for scientific inquiry rather than emphasizing the discovery of specific genetic loci, per se.
