Walters, K. orcid.org/0000-0002-5718-5734, Cox, A. and Yaacob, H. (2021) The utility of the Laplace effect size prior distribution in Bayesian fine‐mapping studies. Genetic Epidemiology, 45 (4). pp. 386-401. ISSN 0741-0395
Abstract
The Gaussian distribution is usually the default causal single‐nucleotide polymorphism (SNP) effect size prior in Bayesian population‐based fine‐mapping association studies, but a recent study showed that the heavier‐tailed Laplace prior distribution provided a better fit to breast cancer top hits identified in genome‐wide association studies. We investigate the utility of the Laplace prior as an effect size prior in univariate fine‐mapping studies. We consider ranking SNPs using Bayes factors and other summaries of the effect size posterior distribution, the effect of prior choice on credible set size based on the posterior probability of causality, and on the noteworthiness of SNPs in univariate analyses. Across a wide range of fine‐mapping scenarios the Laplace prior generally leads to larger 90% credible sets than the Gaussian prior. These larger credible sets for the Laplace prior are due to relatively high prior mass around zero which can yield many noncausal SNPs with relatively large Bayes factors. If using conventional credible sets, the Gaussian prior generally yields a better trade off between including the causal SNP with high probability and keeping the set size reasonable. Interestingly when using the less well utilised measure of noteworthiness, the Laplace prior performs well, leading to causal SNPs being declared noteworthy with high probability, whilst generally declaring fewer than 5% of noncausal SNPs as being noteworthy. In contrast, the Gaussian prior leads to the causal SNP being declared noteworthy with very low probability.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Wiley Periodicals LLC. This is an author-produced version of a paper subsequently published in Genetic Epidemiology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | bayesian; credible set; fine mapping; gaussian prior; Laplace prior |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Jan 2021 17:00 |
Last Modified: | 09 Feb 2022 17:05 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/gepi.22375 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170033 |