A general framework for powerful confounder adjustment in omics association studies.

ABSTRACT:

Motivation

Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.

Results

This study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control

SUBMITTER: Roy A 

PROVIDER: S-EPMC10539716 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature