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Nathan Bell

@nateyates.bsky.social

PhD candidate studying complex trait genetics at @vuamsterdam.bsky.social

Nathan Bell @nateyates.bsky.social · 14/10/2025

Huge thanks to my co-authors and mentors - Douglas Wightman, Christiaan de Leeuw, and @daniposthu.bsky.social — for their guidance and collaboration, and to the REALMENT consortium for supporting this work. 🧵 6/6

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Nathan Bell @nateyates.bsky.social · 14/10/2025

Take home: Additive PGSs remain the most robust default for most complex traits. ML/DL can help when traits are: • highly heritable • low in polygenicity • driven by strong dominance deviations Full paper + code: github.com/nybell/non-a... 🧵 5/6

github.com

GitHub - nybell/non-add-paper: Repository with code and data for non additive PGS paper

Repository with code and data for non additive PGS paper - nybell/non-add-paper

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Nathan Bell @nateyates.bsky.social · 14/10/2025

In the UK Biobank (10 traits), ML/DL models outperformed additive PGSs for traits known to show dominance - including lipoprotein(a), alkaline phosphatase, and ApoB - but not for height (no dominance). 🧵 4/6

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Nathan Bell @nateyates.bsky.social · 14/10/2025

Across most scenarios, additive PGSs were remarkably robust - even when up to 20% of SNP-h² came from dominance SNPs. Performance dropped mainly for traits with: • high SNP-h² • low polygenicity • strong dominance deviations 🧵 3/6

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Nathan Bell @nateyates.bsky.social · 14/10/2025

Most PGS methods assume additivity - each allele contributes linearly to risk - but real traits can show dominance deviations. We simulated phenotypes varying in: • SNP heritability (SNP h²) • % heritability from dominance • polygenicity • dominance deviation strength 🧵 2/6

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Nathan Bell @nateyates.bsky.social · 14/10/2025

When do machine learning models actually outperform standard polygenic scores? 🤔 In our new preprint, we benchmark how non-additive genetic effects (i.e, dominance deviations) shape polygenic prediction across simulated and UK Biobank traits. 👉 www.medrxiv.org/content/10.1... 🧵 1/6

medrxiv.org

Benchmarking non-additive genetic effects on polygenic prediction and machine learning-based approaches

Polygenic scores (PGSs) are widely used to translate genome-wide association study (GWAS) findings into tools for genetic risk prediction. Most current approaches assume additive effects, yet the cont...

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