ArticleNature communications2024
A polygenic score method boosted by non-additive models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects.bioRxiv : the preprint server for biology · 2026Article
- A pre-train and fine-tune framework for adaptive boosting of pre-trained polygenic risk scores.Nature communications · 2026Article
- The role of polygenic risk in Alzheimer's disease prediction for African Americans.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Observational
- Investigations on transferability of polygenic risk scores depending on demography and dominance coefficients.HGG advances · 2026Article
- An introduction to polygenic scores - methodological basics and recent advances.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- Deviations from additivity in APOE4-mediated late-onset Alzheimer's disease risk across races and ethnicities.Human genetics · 2026Article
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
- Predicting dementia in people with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction.medRxiv : the preprint server for health sciences · 2025Article
- ApplyPolygenicScore: An R package for applying polygenic risk score models.Genetics in medicine open · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
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