Bioinformatics approaches for studying molecular sex differences in complex diseases

Abstract Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of mol...

Full description

Saved in:
Bibliographic Details
Published in:Briefings in bioinformatics 2024-09, Vol.25 (6)
Main Authors: Loo, Rebecca Ting Jiin, Soudy, Mohamed, Nasta, Francesco, Macchi, Mirco, Glaab, Enrico
Format: Article
Language:English
Subjects:
Bioinformatics
Biomarkers
Comorbidity
Computational Biology - methods
Disease
Female
Gender differences
Gene Regulatory Networks
Humans
Male
Network analysis
Review
Sex Characteristics
Sex differences
Sex linkage
Statistical analysis
Statistical methods
Surveys
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae499