Fig. 1. Modeling strategy.

a Disentangling autoencoder learns ancestry and phenotype-specific representation separately using autoencoder architecture and unique contrastive latent loss. b The disentangled phenotype-specific representation is then used for the phenotype prediction. A separate linear prediction model is trained on the obtained representation for the phenotype prediction. c To increase prediction power, we use the ensemble modeling approach, where the parameters can be obtained either from grid-search or gradient-based search.