The Genotypic Imperative: Unraveling Disease-Permittivity in Functional Modules of Complex Diseases

In complex diseases, the interactions among genes are commonly elucidated through the lens of graphs. Amongst these genes, certain ones form bi-functional modules within the graph, contingent upon their (anti)correlation with a specific functional state, such as susceptibility to a genetic disorder...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2023-12, Vol.11 (24), p.4916
Main Authors: Kaba, Abdoul K, Vomo-Donfack, Kelly L, Morilla, Ian
Format: Article
Language:English
Subjects:
Anopheles
Asthma
Bell’s experiment
complex disease
Datasets
Disease
Disease susceptibility
Disease transmission
epistasis
Gene expression
Genes
Genetic aspects
Genomes
Genotype & phenotype
GWAS
Modules
non-linear gene correlations
Risk factors
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:In complex diseases, the interactions among genes are commonly elucidated through the lens of graphs. Amongst these genes, certain ones form bi-functional modules within the graph, contingent upon their (anti)correlation with a specific functional state, such as susceptibility to a genetic disorder of non-Mendelian traits. Consequently, a disease can be delineated by a finite number of these discernible modules. Within each module, there exist allelic variants that pose a genetic risk, thus qualifying as genetic risk factors. These factors precipitate a permissive state, which if all other modules also align in the same permissive state, can ultimately lead to the onset of the disease in an individual. To gain a deeper insight into the incidence of a disease, it becomes imperative to acquire a comprehensive understanding of the genetic transmission of these factors. In this work, we present a non-linear model for this transmission, drawing inspiration from the classic theory of the Bell experiment. This model aids in elucidating the variances observed in SNP interactions concerning the risk of disease.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11244916