Until recently, there have been few undisputed genetic associations to non-mendelian forms of common human diseases, but for many diseases, the advent of genome-wide association (GWAS) technology has recently transformed this position1. The attainment of highly significant associations though GWAS reflects in some cases, the availability of large sample sizes2, for others, for example the HLA locus in rheumatoid arthritis and T1D, the existence of at least some common alleles with a greater than average effect size3. These conditions may not readily be satisfied for most complex disorders, for example psychotic disorders, where the extremely large sample sizes used for some disorders2 are difficult to obtain since diagnosis is laborious and expensive. Moreover, the necessary use of phenotypes entirely defined by symptoms will very likely increase aetiological heterogeneity, and thus the observed correlations between genotypes and phenotypes. Therefore far from having a few common risk genes with higher than expected effect sizes, the observed effect sizes in psychosis might be even smaller than typical for other complex disorders. Moreover, even for those disorders where successes have been legion, the majority of genetic risk remains un-attributed2,4. There is therefore a pressing need for alternative methods for extracting information from GWAS datasets.
