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Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
Author(s) -
McInnes Gregory,
Daneshjou Roxana,
Katsonis Panagiostis,
Lichtarge Olivier,
Srinivasan Rajgopal,
Rana Sadhna,
Radivojac Predrag,
Mooney Sean D.,
Pagel Kymberleigh A.,
Stamboulian Moses,
Jiang Yuxiang,
Capriotti Emidio,
Wang Yanran,
Bromberg Yana,
Bovo Samuele,
Savojardo Castrense,
Martelli Pier Luigi,
Casadio Rita,
Pal Lipika R.,
Moult John,
Brenner Steven E.,
Altman Russ
Publication year - 2019
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23825
Subject(s) - exome sequencing , biology , exome , confounding , venous thromboembolism , risk assessment , warfarin , bioinformatics , computational biology , genetics , medicine , mutation , gene , computer science , computer security , thrombosis , atrial fibrillation
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.