Excited to share our cross-disorder GWAS analysis of neurological and psychiatric disorders (~1 M cases), now out in @natneuro.nature.com! We show more extensive genetic pleiotropy than previously recognized, supporting a more unified view of these disorders
rdcu.be/ePmwD

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.

Introducing FEMA-Long for high-dimensional large-scale mixed-effects modelling! Includes modelling unstructured covariance, non-linear effects using splines, time-dependent effects with spline interactions, and longitudinal GWAS with time-dependent genetic effects!
www.biorxiv.org/content/10.1...

New paper alert! Our paper 'Charting the shared genetic architecture of Alzheimer's disease, cognition, and educational attainment, and associations with brain development' is out in Neurobiology of Disease!
@SFFNORMENT

www.sciencedirect.com/science/arti...