Genetic association testing with multivariate survival phenotypes under interval censoring
Abstract
Set-based genetic association tests provide a powerful framework for detecting genetic effects on complex traits by jointly analyzing multiple genetic variants. Although set-based methods have been developed for interval-censored survival outcomes, existing approaches primarily focus on a single survival phenotype and therefore do not fully use information from multiple correlated outcomes. In this paper, we develop two Weighted V Tests for Multivariate Interval-Censored Data (WV-M-IC), extending the weighted V-statistic framework (Wu et al., 2021) to the joint analysis of multiple correlated interval-censored survival outcomes. The performance of these methods is evaluated through simulation studies, showing that the proposed approaches can provide power gains compared with single-outcome analyses. We apply the proposed methods to the ZOE 2.0 study to investigate dental caries progression in children.
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BibTeX
@misc{lee2026genetic,
title = {{Genetic association testing with multivariate survival phenotypes under interval censoring}},
author = {Juhee Lee and Kun Xia and Jianrui Zhang and Gongjun Xu and Qing Lu and Chenxi Li},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00456},
eprint = {2609.00456},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00456},
}
RIS
TY - UNPB
TI - Genetic association testing with multivariate survival phenotypes under interval censoring
AU - Juhee Lee
AU - Kun Xia
AU - Jianrui Zhang
AU - Gongjun Xu
AU - Qing Lu
AU - Chenxi Li
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.00456
UR - https://arxiv.org/abs/2609.00456
AB - Set-based genetic association tests provide a powerful framework for detecting genetic effects on complex traits by jointly analyzing multiple genetic variants. Although set-based methods have been developed for interval-censored survival outcomes, existing approaches primarily focus on a single survival phenotype and therefore do not fully use information from multiple correlated outcomes. In this paper, we develop two Weighted V Tests for Multivariate Interval-Censored Data (WV-M-IC), extending the weighted V-statistic framework (Wu et al., 2021) to the joint analysis of multiple correlated interval-censored survival outcomes. The performance of these methods is evaluated through simulation studies, showing that the proposed approaches can provide power gains compared with single-outcome analyses. We apply the proposed methods to the ZOE 2.0 study to investigate dental caries progression in children.
ER -