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OSCA: a tool for omic-data-based complex trait analysis.

ABSTRACT: The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package called OSCA together with a number of other implementations for omic-data-based analyses.

SUBMITTER: Zhang F

PROVIDER: S-EPMC6537380 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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OSCA: a tool for omic-data-based complex trait analysis.

Zhang Futao F Chen Wenhan W Zhu Zhihong Z Zhang Qian Q Nabais Marta F MF Qi Ting T Deary Ian J IJ Wray Naomi R NR Visscher Peter M PM McRae Allan F AF Yang Jian J

Genome biology 20190528 1

The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more ...[more]

PMID: 31138268

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