| Title: | Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning |
| Journal: | American Journal of Human Genetics |
| Published: | 21 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38908374/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2024.06.003 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 11819
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Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.</p>
8 Keywords
- Biological Specimen Banks
- Diabetes Mellitus, Type 1
- Genome-Wide Association Study
- Humans
- Machine Learning
- Multifactorial Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
24 Authors
- Remo Monti
- Lisa Eick
- Georgi Hudjashov
- Kristi Läll
- Stavroula Kanoni
- Brooke N. Wolford
- Benjamin Wingfield
- Oliver Pain
- Sophie Wharrie
- Bradley Jermy
- Aoife McMahon
- Tuomo Hartonen
- Henrike Heyne
- Nina Mars
- Samuel Lambert
- Genes and Health Research Team
- Kristian Hveem
- Michael Inouye
- David A. van Heel
- Reedik Mägi
- Pekka Marttinen
- Samuli Ripatti
- Andrea Ganna
- Christoph Lippert
1 Application
| Application ID | Title |
|---|---|
| 78537 | International consortium for integrative genomics prediction (INTERVENE) |
Enabling scientific discoveries that improve human health