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Abstract Sex differences appear in healthy and pathological conditions and may influence sex-specific therapeutic responses. Understanding such differences is a key activity for developing precision medicine strategies. This study investigates sex differences in gene expression across 40 human tissues by applying a Differential Causal Network (DCN) analysis using data from the Genotype-Tissue Expression project. We identified sex-based DCNs that highlight distinct molecular mechanisms influencing both health and disease in men and women. For example, in pancreas tissue, genes associated with immune system show significant differences in their regulatory patterns between sexes, demonstrating a possible different response to diseases such as diabetes mellitus and cancer. Our findings provide valuable information on the biological underpinnings of sex differences, offering potential pathways for the development of precision medicine strategies.
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Gene communities in co-expression networks across different tissues
With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest thatKRTAP3-1,KRTAP3-3, andKRTAP3-5share regulatory elements in skin and pancreas. Furthermore, we find thatCELA3AandCELA3Bshare associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.
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- PAR ID:
- 10493890
- Editor(s):
- Fu, Feng
- Publisher / Repository:
- Plos Computational Biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 19
- Issue:
- 11
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1011616
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1011616
