A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

Ghulam Muhiuddin (), Sovan Samanta, Abdulrahman F. Aljohani and Abeer M. Alkhaibari
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Ghulam Muhiuddin: Department of Mathematics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia
Sovan Samanta: Department of Mathematics, Tamralipta Mahavidyalaya, West Bengal 721636, India
Abdulrahman F. Aljohani: Department of Mathematics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia
Abeer M. Alkhaibari: Department of Biology, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia

Mathematics, 2023, vol. 11, issue 14, 1-18

Abstract: Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases.

Keywords: graph theory; DNA sequencing; genetic diseases; centrality theory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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