There is now substantial empirical evidence demonstrating that the majority of complex traits and diseases in humans are influenced by hundreds if not thousands of genetic loci of small effect scattered across the genome as was first predicted a century ago (East, 1916; Fisher, 1918). The advent of high throughput micro-array genotyping and now next generation sequencing technologies has meant that genome-wide data can be leveraged to ask fundamental questions concerning the underlying genetic architecture of common complex traits and diseases including the degree to which genetic variation affecting complex phenotypes is tagged by SNPs on genome-wide arrays (Lee et al., 2011; Yang et al., 2010, 2011), the degree to which this variation represents different functional categories and/or biological pathways (Finucane et al., 2015; Gusev et al., 2014), and the extent to which genetic aetiologies are shared across different phenotypes (Bulik-Sullivan et al., 2015b; Lee et al., 2012). To date most of these types of analyses have been performed using genetic restricted maximum likelihood analysis (GREML) as implemented in software packages such as GCTA and LDAK (Lee et al., 2011;
