ArticleHuman mutation2019
Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.
Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Updated benchmarking of variant effect predictors using deep mutational scanning.Molecular systems biology · 2023Article
- A Clinical-Genetic Risk Score for Predicting Cancer-Associated Venous Thromboembolism: A Development and Validation Study Involving Two Independent Prospective Cohorts.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- A validated clinical-genetic score for assessing the risk of thrombosis in patients with COVID-19 receiving thromboprophylaxis.Haematologica · 2022Article
- New approaches to predict the effect of co-occurring variants on protein characteristics.American journal of human genetics · 2021Article
- CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.Human mutation · 2019Article
- Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation.Human mutation · 2019Article
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Authors and funding
22 authors at 12 institutions in 3 countries.
Funding
Abstract
Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.
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Registered trials
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