ArticleFrontiers in genetics2022
A statistical boosting framework for polygenic risk scores based on large-scale genotype data.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 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
- An introduction to polygenic scores - methodological basics and recent advances.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- Detecting gene-environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Polygenic modeling of genetic effects on both phenotypic mean and variance: distributional regression for BMI, blood and urine biomarkers in the UK Biobank.Frontiers in bioinformatics · 2026Article
- Exploring the Link Between C-Reactive Protein Change and Stroke Risk: Insights From a Prospective Cohort Study and Genetic Evidence.Journal of the American Heart Association · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- A polygenic score method boosted by non-additive models.Nature communications · 2024Article
- Generalizability of polygenic prediction models: how is the RBMC medical genomics · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise
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