ArticleHuman genomics2025
The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank.
Article in Human genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Sex-specific transcriptome similarity networks elucidate comorbidity relationships.Communications medicine · 2025Article
- Disease network analysis to reveal comorbidity patterns in hospitalized patients with COPD using large-scale administrative health data.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundDisease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.
resultsUsing UK Biobank summary statistics, we built male- and female-specific DDNs for 103 diseases. This revealed that male and female diseasomes have similar topology and central diseases (e.g., hypertensive, chronic respiratory, and thyroid-based disorders), yet some phenotypes exhibit sex-specific influence in cross-phenotype associations. Multiple sclerosis and osteoarthritis are central only in the female DDN, while cardiometabolic diseases and skin cancer are more prominent in the male DDN. Edge comparison indicated similar shared genetics between the two graphs relative to a random model of disease association, though notable discrepancies in embedding distances and clustering patterns imply a more expansive genetic influence on multimorbidity risk for females than males. Analysis of pleiotropic contributions of two sexually-dimorphic single-nucleotide polymorphisms related to thyroid disorders further validated a distinct genetic architecture across sexes that influences associations, confirmed through examination of corresponding gene expression profiles from the GTEx Portal.
conclusionsOur analysis affirms the presence of GxS interactions in cross-phenotype associations, emphasizing the need to investigate the role of sex in disease onset and its importance in biomedical discovery and precision medicine research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.