ArticleBMC bioinformatics2022
Accounting for overlapping annotations in genomic prediction models of complex traits.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.Nature communications · 2026Article
- Mammary gland multi-omics data reveals new genetic insights into milk production traits in dairy cattle.PLoS genetics · 2025Article
- Evaluation of genomic selection models using whole genome sequence data and functional annotation in Belgian Blue cattle.Genetics, selection, evolution : GSE · 2025Article
- Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.The plant genome · 2025Article
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIt is now widespread in livestock and plant breeding to use genotyping data to predict phenotypes with genomic prediction models. In parallel, genomic annotations related to a variety of traits are increasing in number and granularity, providing valuable insight into potentially important positions in the genome. The BayesRC model integrates this prior biological information by factorizing the genome according to disjoint annotation categories, in some cases enabling improved prediction of heritable traits. However, BayesRC is not adapted to cases where markers may have multiple annotations.
resultsWe propose two novel Bayesian approaches to account for multi-annotated markers through a cumulative (BayesRC+) or preferential (BayesRC[Formula: see text]) model of the contribution of multiple annotation categories. We illustrate their performance on simulated data with various genetic architectures and types of annotations. We also explore their use on data from a backcross population of growing pigs in conjunction with annotations constructed using the PigQTLdb. In both simulated and real data, we observed a modest improvement in prediction quality with our models when used with informative annotations. In addition, our results show that BayesRC+ successfully prioritizes multi-annotated markers according to their posterior variance, while BayesRC[Formula: see text] provides a useful interpretation of informative annotations for multi-annotated markers. Finally, we explore several strategies for constructing annotations from a public database, highlighting the importance of careful consideration of this step.
conclusionWhen used with annotations that are relevant to the trait under study, BayesRC[Formula: see text] and BayesRC+ allow for improved prediction and prioritization of multi-annotated markers, and can provide useful biological insight into the genetic architecture of traits.
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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.