Gene

Biology

Statistics

dNaM

Robustness (evolution)

Trait

Confounding

Data mining

Computer science

DNA methylation

Computational biology

Evolutionary biology

Software

Genetics

Bioinformatics

Mathematics

False discovery rate

Programming language

Quantitative trait locus

Omics

Human genetics

Gene expression

Genome biology

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.

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