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... - ThreadSky

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parekhpravesh.bsky.social • 33 days ago

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!
https://www.biorxiv.org/content/10.1101/2025.05.09.653146v1

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parekhpravesh.bsky.social•33 days ago

Massive thank you to my amazing co-authors including @nadineparker.bsky.social @neuromusicnerd.bsky.social @jaholkowskipiotr.bsky.social @cmakowski.bsky.social @nichols.bsky.social and others who are not on BlueSky. Wouldn't have been possible without their help, support, kindness, and patience :)

parekhpravesh.bsky.social•33 days ago

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.

parekhpravesh.bsky.social•33 days ago

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

parekhpravesh.bsky.social•33 days ago

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.

parekhpravesh.bsky.social•33 days ago

FEMA-Long is available for use here: https://github.com/cmig-research-group/cmig_tools

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