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

• Published in: Genome Biology Vol. 20; no. 1; p. 107
• Main Authors: Zhang, Futao, Chen, Wenhan, Zhu, Zhihong, Zhang, Qian, Nabais, Marta F, Qi, Ting, Deary, Ian J, Wray, Naomi R, Visscher, Peter M, McRae, Allan F, Yang, Jian
• Format: Journal Article
• Language: English
• Published: England BioMed Central 28-05-2019; BMC
• Subjects: Age; Aged; Computer Simulation; Data analysis; Deoxyribonucleic acid; DNA; DNA Methylation; DNA probes; Gene expression; Genetic Techniques; Genotype & phenotype; Humans; Linear Models; Metabolomics - methods; Method; Phenotype; Software; Studies
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-019-1718-z

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.