Joint analysis using a mixed model approach is attractive because all participants are included irrespective of ancestry. Ideally, mixed model approaches control for population stratification by modelling distant relatedness between individuals due to ancestry (Sul et al., 2018; Wojcik et al., 2019). Several implementations exist and some are listed in Supplementary Methods Section IV and Supplementary Table S4. Mixed models may yield greater statistical power, both through increased sample size and by controlling for the variance explained by the genetic relatedness between individuals (i.e., a random effects component; (Loh et al., 2018)). However, there is evidence that basic mixed models may not fully control for population structure in diverse cohorts, especially if there is an environmental component to phenotypic associations with ancestry beyond the modelled genetic relatedness (Conomos et al., 2018; Heckerman et al., 2016; Zhang and Pan, 2015). Non-genetic factors such as environmental exposures may be correlated with ancestry due to a shared local environment (familial or community effects) or due to the relationship between ancestry and socio-cultural factors such as race and ethnicity. More methodological development is needed before mixed models or other strategies for joint GWAS of a diverse cohort can be confidently recommended as robust.
