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

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

Posted 17 Oct 2018
bioRxiv DOI: 10.1101/445163 (published DOI: 10.1186/s13059-019-1718-z)

The rapid increase of omic data in the past decades has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we proposed 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 the effects of unobserved confounders as well as the correlations between distal probes induced by the confounders. We demonstrated by simulations that MOMENT showed 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 analysis including the estimation of variance in a trait captured by all measures of multiple omic profiles, omic-data-based quantitative trait locus (xQTL) analysis, and meta-analysis of xQTL data.

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