The flashfm approach for fine-mapping multiple quantitative traits
dc.contributor.author | Hernández, N | |
dc.contributor.author | Soenksen, J | |
dc.contributor.author | Newcombe, P | |
dc.contributor.author | Sandhu, M | |
dc.contributor.author | Barroso, I | |
dc.contributor.author | Wallace, C | |
dc.contributor.author | Asimit, JL | |
dc.date.accessioned | 2022-01-10T09:17:04Z | |
dc.date.issued | 2021-10-22 | |
dc.date.updated | 2022-01-10T08:52:10Z | |
dc.description.abstract | Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits. | en_GB |
dc.description.sponsorship | Medical Research Council (MRC) | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.description.sponsorship | National Institute for Health Research (NIHR) | en_GB |
dc.description.sponsorship | Research England | en_GB |
dc.format.extent | 6147- | |
dc.identifier.citation | Vol. 12 , article 6147 | en_GB |
dc.identifier.doi | https://doi.org/10.1038/s41467-021-26364-y | |
dc.identifier.grantnumber | MR/R021368/1 | en_GB |
dc.identifier.grantnumber | MC UU 00002/9 | en_GB |
dc.identifier.grantnumber | WT107881 | en_GB |
dc.identifier.grantnumber | BRC-1215-20014 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/128328 | |
dc.identifier | ORCID: 0000-0001-5800-4520 (Barroso, I) | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/34686674 | en_GB |
dc.relation.url | https://www.ebi.ac.uk/gwas/publications/31675503#study_panel | en_GB |
dc.relation.url | http://grch37.ensembl.org/Homo_sapiens/Tools/DataSlicer | en_GB |
dc.relation.url | https://jennasimit.github.io/flashfm/ | en_GB |
dc.rights | © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.subject | Bayes Theorem | en_GB |
dc.subject | Chromosome Mapping | en_GB |
dc.subject | Computer Simulation | en_GB |
dc.subject | Genome, Human | en_GB |
dc.subject | Genome-Wide Association Study | en_GB |
dc.subject | Humans | en_GB |
dc.subject | Linkage Disequilibrium | en_GB |
dc.subject | Models, Genetic | en_GB |
dc.subject | Phenotype | en_GB |
dc.subject | Polymorphism, Single Nucleotide | en_GB |
dc.subject | Quantitative Trait Loci | en_GB |
dc.title | The flashfm approach for fine-mapping multiple quantitative traits | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-01-10T09:17:04Z | |
dc.identifier.issn | 2041-1723 | |
exeter.article-number | 6147 | |
exeter.place-of-publication | England | |
dc.description | This is the final version. Available on open access from Nature Research via the DOI in this record | en_GB |
dc.description | Data availability: Detailed flashfm multi-trait fine-mapping results and FINEMAP single-trait fine-mapping results for the Ugandan cardiometabolic traits are provided in Supplementary Data 3 and 4, respectively; summary fine-mapping results are provided in Supplementary Data 2.pdf. The Uganda GWAS data used in this study are available in the GWAS Catalogue under PubMed ID 31675503 (https://www.ebi.ac.uk/gwas/publications/31675503#study_panel). The Ugandan genotype data are from the European Genome-phenome Archive (EGA) under accession numbers EGAS00001001558 /EGAD00010000965, EGAS00001000545 /EGAD00001001639. The phenotype data used in this study are not under restricted access and requests for access to data may be directed to segun.fatumo@mrcuganda.org. The CEU population 1000Geomes phase 3 haplotype data that were used in our simulations are available from http://grch37.ensembl.org/Homo_sapiens/Tools/DataSlicer. | en_GB |
dc.description | Code availability: Our proposed multi-trait fine-mapping method, Flexible and shared information fine-mapping (flashfm), is freely available as an R library at https://jennasimit.github.io/flashfm/ (DOI: 10.5281/zenodo.552291544). Single-trait fine-mapping was performed with FINEMAP 1.4 (http://www.christianbenner.com/), as well as our extended version of JAM (based on JAM from R2BGLiMS; https://github.com/pjnewcombe/R2BGLiMS) that is included in the flashfm package. Custom code for the analysis of the Ugandan data is available at https://github.com/nicolashernandezb/flashfm-analysis. The annotation tools we used are HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) and Ensembl Variant Effect Predictor (VEP) GRCh37 (https://grch37.ensembl.org/info/docs/tools/vep/index.html). We simulated genotype data with hapgen2 (http://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html). | en_GB |
dc.identifier.eissn | 2041-1723 | |
dc.identifier.journal | Nature Communications | en_GB |
dc.relation.ispartof | Nat Commun, 12(1) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2021-10-04 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-10-22 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-01-10T09:13:54Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-01-10T09:18:02Z | |
refterms.panel | A | en_GB |
refterms.dateFirstOnline | 2021-10-22 |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.