Prediction of human population responses to toxic compounds by a collaborative competition

Open access
Author
Eduati, Federica
Mangravite, Lara M.
Wang, Tao
Tang, Hao
Bare, J.C.
Huang, Ruili
Norman, Thea
Kellen, Mike
Menden, Michael P.
Yang, Jichen
Zhan, Xiaowei
Zhong, Rui
Xiao, Guanghua
Xia, Menghang
Abdo, Nour
Kosyk, Oksana
The NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration
Friend, Stephen
Dearry, Allen
Simeonov, Anton
Tice, Raymond R.
Rusyn, Ivan
Wright, Fred A.
Stolovitzky, Gustavo
Xie, Yang
Saez-Rodriguez, Julio
Date
2015-09Type
- Journal Article
Abstract
The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal Show more
Permanent link
https://doi.org/10.3929/ethz-b-000109369Publication status
publishedJournal / series
Nature BiotechnologyVolume
Pages / Article No.
Publisher
Nature Publishing GroupSubject
Health occupations; High-throughput screening; Predictive medicine; Risk factorsOrganisational unit
09486 - Borgwardt, Karsten M. / Borgwardt, Karsten M.
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