Ville Karhunen, Ilkka Launonen, Marjo-Riitta Järvelin, Sylvain Sebert, Mikko J Sillanpää, Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants, Bioinformatics, Volume 39, Issue 7, July 2023, btad396, https://doi.org/10.1093/bioinformatics/btad396
Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants
|Author:||Karhunen, Ville1,2; Launonen, lkka1; Järvelin, Marjo-Riitta2,3,4;|
1Research Unit of Mathematical Sciences, University of Oulu, Oulu, P.O.Box 8000, FI-90014, Finland
2Research Unit of Population Health, University of Oulu, Oulu, Finland
3Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
4Department of Life Sciences, College of Health and Life Sciences, Brunel University, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230908121884
Oxford University Press,
|Publish Date:|| 2023-09-08
Motivation: Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns.
Results: We present “FiniMOM” (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1184 Genetics, developmental biology, physiology
113 Computer and information sciences
This work was funded by the University of Oulu & the Academy of Finland Profi 5 Project 326291 [funding for mathematics and AI: data insight for high-dimensional dynamics to V.K., I.L., and M.J.S.] and European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 848158 [EarlyCause to V.K., S.S., and M.-R.J.].
|EU Grant Number:||
(848158) EarlyCause - Causative mechanisms & integrative models linking early-life-stress to psycho-cardio-metabolic multi-morbidity
NFBC1966 and NFBC1986 genotype data are available by application via http://oulu.fi/nfbc/. HAPNEST synthetic genotype data are available at: https://www.ebi.ac.uk/biostudies/studies/S-BSST936. IL18 GWAS summary statistics used in the applied example are available at: https://doi.org/10.5523/bris.3g3i5smgghp0s2uvm1doflkx9x (discovery) and https://doi.org/10.5281/zenodo.2615265 (replication).
© The Author(s) 2023. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.