Maybe! Need to look into it :)

See you all next year for the 25th #GeneForum and some Estonian music! #plusskoor

And so many other excellent speakers like @srubinacci.bsky.social @stevesphd.bsky.social @hilarycmartin.bsky.social & many more who made it an awesome conference - now called mini-ASHG (by Hilary Martin) and we loved it 🥰

2nd day opened with a BEAUTIFUL talk by @nickywhiffin.bsky.social about ReNu variants playing such a huge role in developmental disorders despite the few bases..!

Huge thanks to Maris Alver for leading this important work, and for the rigorous and thoughtful data analysis together with Silva Kasela, Laura Birgit Luitva and Kristi Krebs!

3. Polygenic and pharmacogenomic predictors contributed independent and additive effects to dose variation.

This work underscores the potential of integrated genetic approaches to support more personalized and data-informed prescription of medicines.

2. GWAS picks known PGx associations independent of the underlying trait PGSs.
• Strong peaks for metoprolol (CYP2D6) and warfarin (VKORC1, CYP2C9)
• PGx genes enriched among GWAS top signals for statins.

🧬 Key insights:

1. PGSs for complex traits reveal both biological and healthcare-related influences on medication dosing
• High genetic predisposition -> higher dose
• BMI PGS linked with dose variation across several drug classes
• EA PGS linked to lower statin and higher antidepressant doses

Using prescription data from 212,000 @estbiobank.bsky.social participants, we examined how polygenic scores #PGSs and #pharmacogenomic variants relate to variation in medication dosing for metoprolol, warfarin, statins, antidepressants, and antipsychotics.

Take-home messages:
🔹Drug purchase data is reliable for deriving medication doses at scale.
🔹Genetic associations capture biological effects and the nature of EHR data.
🔹Both #PGx and polygenic liability should be integrated to advance #precisionmedicine.

Key findings:
🔹Polygenic liability to complex traits and PGx variation contribute to dose variability.
🔹PGS for BMI and the indicator trait associated with statins, metoprolol, and warfarin.
🔹GWAS confirmed known signals for metoprolol (CYP2D6), and warfarin (VKORC1, CYP2C9).