Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Issue 9 (24th June 2019)

• Record Type: Journal Article
• Authors: 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
• Editors: Moult, John; Brenner, Steven E.
• Other Names: Karchin Rachel guestEditor.; Pal Lipika R. specialEditor.
• Is Part Of: Human mutation. Volume 40:Issue 9(2019)
• Page Start: 1314
• Page End: 1320
• Publication Date: 2019-06-24
• Subjects: exomes -- machine learning -- phenotype prediction -- prediction challenge -- venous thromboembolism; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.23825
• Languages: English
• ISSNs: 1059-7794

Abstract

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.