BICEP: an extension to indels and copy number variants for rare variant prioritisation in pedigree analysis

Abstract Summary BICEP is a Bayesian inference model that evaluates how likely a rare variant is to be causal for a genomic trait in pedigree-based analyses. The original prior model in BICEP was designed for single nucleotide variants only. Here, we have developed an extension of the prior models for more comprehensive genomic analysis to include indels and copy number variants. We benchmark the performance of these new priors and show comparable performance accuracy with the existing single nucleotide variant prior model. For copy number variants we evaluate four different input predictors to the models and recommend the best performing ones as the default. Availability and implementation the updated prior models have been implemented in the current version of BICEP available from: https://github.com/cathaloruaidh/BICEP .

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Publication Details

Journal
BMC Bioinformatics
Published
2026-11-06
DOI
https://doi.org/10.1186/s12859-026-06676-w
Citations
1
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
2.33

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article

BICEP: an extension to indels and copy number variants for rare variant prioritisation in pedigree analysis

Aiden Peter Corvin, Cathal Ormond, Elizabeth A. Heron, Niamh Ryan
1 citations
BMC Bioinformatics
Genomics and Rare Diseases
2.33
article

BICEP: an extension to indels and copy number variants for rare variant prioritisation in pedigree analysis

Aiden Peter Corvin, Cathal Ormond, Elizabeth A. Heron, Niamh Ryan
article en
1 citations

Abstract

Abstract Summary BICEP is a Bayesian inference model that evaluates how likely a rare variant is to be causal for a genomic trait in pedigree-based analyses. The original prior model in BICEP was designed for single nucleotide variants only. Here, we have developed an extension of the prior models for more comprehensive genomic analysis to include indels and copy number variants. We benchmark the performance of these new priors and show comparable performance accuracy with the existing single nucleotide variant prior model. For copy number variants we evaluate four different input predictors to the models and recommend the best performing ones as the default. Availability and implementation the updated prior models have been implemented in the current version of BICEP available from: https://github.com/cathaloruaidh/BICEP .

BMC Bioinformatics
Trinity College Dublin (IE)
National Institutes of Health, National Institute of Mental Health
Good health and well-being
Openalex Percentile: Top 16%
Genomics and Rare Diseases
2.33
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