All of these features are implemented in a computationally fast manner, thereby allowing scalability to very large datasets as well as large number of outcome variables like voxel-wise or vertex-wise analyses.

FEMA-Long can perform longitudinal GWAS with SNP*time non-linear interaction to discover SNPs showing time-varying effects. The top part of the Miami plots show SNPs having time-dependent effect compared to longitudinal GWAS (bottom part). Last panel shows the effect of a few selected SNPs over time

FEMA-Long can model unstructured covariance such as time-varying heritability and genetic correlations which are super critical for longitudinal datasets. Here, using the MoBa dataset, we show time-varying random effects for length, weight, and BMI in the first year of life.