Empirically, GWAS suggest that there is considerable variability in the number and distribution of causal effects across complex traits, so some models are more appropriate for predicting a given complex trait than others. For example, an infinitesimal model that considers a large number of small effects performs best when predicting risk of schizophrenia, which is highly polygenic (Figure 2E). In contrast, autoimmune diseases typically have simpler genetic architectures that can be modeled well with linear mixed models; this approach models large effects primarily in the MHC region (28) as accurately estimated fixed effects, and a larger number of small, imprecise effect estimates as random effects.
