Piotr Jaholkowski
@jaholkowskipiotr.bsky.social
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MD PhD | Psychiatrist | Postdoc researcher in psychiatric genetics @SFFNORMENT
@uio.no
reposted by
Piotr Jaholkowski
Olav Smeland
2 months ago
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
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A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders
Nature Neuroscience - Smeland et al. demonstrate greater genetic overlap between neurological and psychiatric disorders than previously recognized, along with diverse neurobiological associations....
https://rdcu.be/ePmwD
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reposted by
Piotr Jaholkowski
Pravesh Parekh
8 months 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.
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reposted by
Piotr Jaholkowski
Pravesh Parekh
8 months 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
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reposted by
Piotr Jaholkowski
Pravesh Parekh
8 months 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.
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reposted by
Piotr Jaholkowski
Pravesh Parekh
8 months 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!
www.biorxiv.org/content/10.1...
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FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets
Linear mixed-effects (LME) models are commonly used for analyzing longitudinal data. However, most applications of LME models rely on random intercepts or simple, e.g., stationary, covariance. Here, w...
https://www.biorxiv.org/content/10.1101/2025.05.09.653146v1
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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...
about 1 year ago
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