| Title: | A scalable variational inference approach for increased mixed-model association power |
| Journal: | Nature Genetics |
| Published: | 9 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39789286/ |
| DOI: | https://doi.org/10.1038/s41588-024-02044-7 |
Publication 14545
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Abstract
The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.</p>
9 Keywords
- Algorithms
- Biological Specimen Banks
- Genome-Wide Association Study
- Humans
- Machine Learning
- Models, Genetic
- Polymorphism, Single Nucleotide
- Software
- United Kingdom
4 Authors
- Hrushikesh Loya
- Georgios Kalantzis
- Fergus Cooper
- Pier Francesco Palamara
1 Application
| Application ID | Title |
|---|---|
| 43206 | Statistical methods for large-scale genomic analysis |
Enabling scientific discoveries that improve human health