If you use BayesRCO in your research, please cite our work:
- Mollandin et al. (2022) Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics, 23(365). https://doi.org/10.1186/s12859-022-04914-5
BayesRCO is a software for complex trait prediction with Bayesian hierarchical models using genome-wide genetic variation grouped into potentially overlapping annotation categories based on prior biological information (e.g., functional annotations, candidate gene lists, known causal variants).
BayesRCO includes implementations for three state-of-the-art Bayesian hierarchical models:
- BayesCpi: a two-class model, corresponding to null and non-null effects for genetic variants
- BayesR: a four-class model, corresponding to null, small, medium, and large effects for genetic variants
- BayesRC: a BayesR model incorporating disjoint prior categories for genetic variants.
In addition, BayesRCO includes two novel extensions of BayesRC to incorporate potentially overlapping prior categories for genetic variants:
- BayesRC+: a BayesR model where multi-categories are assumed to cumulatively impact variant estimates
- BayesRCpi: a BayesR model where the catégorization of multi-annotated variants is stochastically modeled.
The core code of BayesRCO is largely based off the Fortran implementation of bayesR (version 0.75).
A full user's guide with details on compilation, as well as a a full description of input data formats/parameters and example scripts, can be found here.
- BayesCpi: Habier, D. et al. (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics, 12(1):186. https://doi.org/10.1186/1471-2105-12-186
- BayesR: Moser, G. et al. (2015) Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genetics, 11(4): e1004969. https://doi.org/10.1371/journal.pgen.1004969
- BayesRC: MaxLeod, I. M. et al. (2016) Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics, 17:144. https://doi.org/10.1186/s12864-016-2443-6
- BayesRCpi and BayesRC+: Mollandin et al. (2022) Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics, 23(365). https://doi.org/10.1186/s12859-022-04914-5
- bayesR original source code: GitHub repo
This work was funded as part of the GENE-SWitCH project.
The GENE-SWitCH project has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No 817998. This publication reflects the views only of the author, and the European Union cannot be held responsible for any use which may be made of the information contained therein.
The BayesRCO package is free software; you can copy or redistribute it under the terms of the GNU GPL-3 License. This program is distributed in the hope that it will be useful, but without any warranty. See the GNU GPL-3 License for more details.