A scalable variational inference approach for increased mixed-model association power
Centre for Human Genetics · University of Oxford · +1 more institution
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…
Citation impact
- FWCI
- 68.73
- Percentile
- 100%
- References
- 80
Authors
4Topics & keywords
- Biobank
- Inference
- Scalability
- Computer science
- Robustness (evolution)
- Data mining
- Machine learning
- Artificial intelligence
Funding
- WTWellcome TrustAwards: 108861/Z/15/Z, /Z/15/Z, 204826
- ISInternational Seafood Sustainability Foundation
- NINational Institute for Health and Care Research
- UOUniversity of OxfordAward: 204826/Z/16/Z
- UOUniversity of California, Los Angeles
- MRMedical Research CouncilAward: EP/L016044/1
- EAEngineering and Physical Sciences Research CouncilAward: EP/L016044/1