Evidence map›Paper›PMID 39789286›Full record

ArticleNature genetics2025

A scalable variational inference approach for increased mixed-model association power.

Hrushikesh Loya, Georgios Kalantzis, Fergus Cooper, Pier Francesco Palamara

Abstract read
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hrushikesh LoyaDepartment of Statistics, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-4587-6378
Georgios KalantzisDepartment of Statistics, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-0729-1125
Fergus CooperDoctoral Training Centre, University of Oxford, Oxford, UK.
Pier Francesco PalamaraDepartment of Statistics, University of Oxford, Oxford, UK. palamara@stats.ox.ac.uk.ORCID http://orcid.org/0000-0002-7999-1972

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 850869RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/L016044/1Wellcome TrustWellcome Trust (Wellcome) 108861/Z/15/Z
6 · The paper itself

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.

Indexed as

Genome-Wide Association StudyModels, GeneticAlgorithmsBiological Specimen BanksHumansMachine LearningPolymorphism, Single NucleotideSoftwareUnited Kingdom

Identifiers

PMID39789286
PMCPMC11821521

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.