ACDC: a general approach for detecting phenotype or exposure associated co-expression

Katelyn Queen; My-Nhi Nguyen; Frank D Gilliland; Sung Chun; Benjamin A Raby; Joshua Millstein

doi:10.3389/fmed.2023.1118824

ACDC: a general approach for detecting phenotype or exposure associated co-expression

Front Med (Lausanne). 2023 May 19:10:1118824. doi: 10.3389/fmed.2023.1118824. eCollection 2023.

Authors

Katelyn Queen¹, My-Nhi Nguyen¹, Frank D Gilliland¹, Sung Chun², Benjamin A Raby^{2

3

4}, Joshua Millstein¹

Affiliations

¹ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
² Division of Pulmonary Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
³ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
⁴ Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.

Abstract

Background: Existing module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses.

Methods: Here, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types.

Results: We report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control.

Conclusion: ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.

Keywords: asthma; asthma control; differential co-expression; gene expression; inflammation.

Abstract

Grants and funding