OSCA: A tool for omic-data-based complex trait analysis

Futao Zhang, Wenhan Chen, Zhihong Zhu, Qian Zhang, Marta F. Nabais, Ting Qi, Ian Deary, Naomi R Wray, Peter M. Visscher, Allan F. Mcrae, Jian Yang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)1-13
JournalGenome Biology
Volume20
Issue number107
DOIs
Publication statusPublished - 28 May 2019

Fingerprint

Dive into the research topics of 'OSCA: A tool for omic-data-based complex trait analysis'. Together they form a unique fingerprint.

Cite this