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Role of Knowledge Graphs in Analyzing Epidemics and Health Disasters
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
Various researchers are working on comorbidities and their prediction. He et al. use multiscale data to predict the existing comorbidities in a patient; Biswas et al. create a knowledge graph using three ontologies about genes, humans, and diseases and then apply knowledge graph completion to predict multimorbidity. It is a process to identify the missing data and then to predict the missing parts. Other factors can be involved like gene-gene interaction, genetic mutations, etc.
Attention Deficit Hyperactivity Disorder
Published in Cathy Laver-Bradbury, Margaret J.J. Thompson, Christopher Gale, Christine M. Hooper, Child and Adolescent Mental Health, 2021
Margaret J.J. Thompson, Anan El Masry, Samuele Cortese, Wai Chen
Twin studies indicated that monozygous twins were more likely to have ADHD with a hereditary variance of around 60% (Greven et al., 2011). Categorical viewpoint with a molecular viewpoint of genetic causation, but although single genes have been implicated, the odds ratios are low.Dimensional viewpoint that suggests that the condition is caused by the quantitative extreme of the same genetic factors that are responsible for variation in the normal range, as well as the disorder range, known as quantitative trait loci (QTL) hypothesis (Plomin, Owen and McGuffin 1994; Chen and Taylor, 2006). The resulting trait is distributed quantitatively as a continuous dimension rather than qualitatively different as a discrete disorder.Gene–gene interaction (epitaxis is important).Gene–environment effect on the dimension of hyperactivity in all children, including children at the lower end of the expression.
Genetics of Obesity: Overview and Research Directions
Published in Claude Bouchard, The Genetics of Obesity, 2020
Among the issues that have not been considered at all so far, gene-gene interaction effects must be mentioned. It seems to us that the whole field of the genetic basis of human obesities and of the genetic susceptibility to metabolic derangements in the presence of an obese state is one in which gene-gene interactions are ubiquitous. There is no doubt that the topic will attract a lot of attention over the next decade. It will likely be a complicated area of research, however, in the sense that large sample sizes will be needed if several genes are to be considered in the same analysis, new and innovative experimental designs will have to be developed, and the panel of the most important genes to investigate will probably emerge only when strong data become available from association, linkage, QTL, positional cloning, or transgenic studies.
The role of miRNAs in regulation of platelet activity and related diseases - a bioinformatic analysis
Published in Platelets, 2022
Zofia Wicik, Pamela Czajka, Ceren Eyileten, Alex Fitas, Marta Wolska, Daniel Jakubik, Dirk von Lewinski, Harald Sourij, Jolanta M. Siller-Matula, Marek Postula
The most affected platelet-related pathways identified in our study included the signaling by interleukins, diseases associated with GFRs, MAPK family cascades signaling, and platelet activation and aggregation. GFRs, produced by multiple cell types including platelets, are known to be involved in the response to viruses, wound healing, and heparin-binding [84,85]. Interesting finding of our study is that only platelet-related targets, not miRNAs, seem to significantly influence ESR mediated signaling, extra-nuclear estrogen signaling, and endometriosis-related phenotypes. We also observed ESR mediated signaling as central for top platelet-related genes, pointing out its potential role in regulating platelet activity. The function and impact on platelets of the ESR1 gene pointed out in our study, is yet not known. The oral administration of estrogen during Menopausal Hormonal Therapy increase thrombotic events with a high risk of venous thromboembolism [86,87]. Higher risk seems to be associated with the route of administration leading to the hepatic first-pass, therefore increasing some circulating blood coagulation factors [88]. This hypothesis seems convergent with our analysis, showing that alteration of this signaling seems secondary and may occur on the gene-gene interaction level.
Differential expression of lncRNAs in hypertension-induced pericytes
Published in Scandinavian Cardiovascular Journal, 2021
Qingbin Wu, Xiaochen Yuan, Honggang Zhang, Ruijuan Xiu
The GO database [8] was used to help us define concepts for describing gene function, and relationships among molecular function, cellular compartmentalization, and biological processes. We first performed GO enrichment analyses within differential expression genes, and the most significantly up-regulated and down-regulated genes were selected to further construct the entire gene-gene interaction network. Similarly, to facilitate investigations of the precise interactions and relationships between lncRNAs and genes, we built up co-expression networks based upon differential expression, and added the information for the respective expression of lncRNAs. Therefore, our process revealed the primary elements in the network and predicted the potential functions of these unknown lncRNAs based upon the resultant network interactions (Figure 1). Our findings indicated that up-regulated lncRNAs mainly included ENSRNOT00000081316, and ENSRNOT00000038660, ENSRNOT00000075105, and that down-regulated lncRNAs mainly included ENSRNOT00000091124, ENSRNOT00000072431, ENSRNOT00000082815, and ENSRNOT00000076989. Our findings also implied that all these lncRNAs were significantly involved in the interactions we assessed and thus acted as potent regulators. Additional details for important dynamics of the assessed pathways in the network are provided in Supplementary Table 2.
Case-only analysis of gene–gene interactions in inflammatory bowel disease
Published in Scandinavian Journal of Gastroenterology, 2020
Milda Aleknonytė-Resch, Sandra Freitag-Wolf, Stefan Schreiber, Michael Krawczak, Astrid Dempfle
Originally, the term ‘epistasis’ was used to refer to the ability of one or more genotypes of a gene, say A, to mask the phenotypic effects of another gene, B [5]. Over time, however, epistasis has become more or less synonymous of gene–gene interaction in general [6], where it is important to distinguish between biological and statistical interaction. The former is usually postulated when the gene products in question share some common role in the disease etiology, i.e., if they either interact physically with one another or if they impede upon one and the same, disease-relevant biological pathway. Statistical interaction, on the other hand, is defined as the lack of additivity of the genotype-associated disease risk difference, measured on a particular scale (usually linear, log or logit). Notably, absence of statistical interaction on one scale implies the presence of interaction on all other scales, i.e., there is no such thing as a lack of statistical gene–gene interaction. Statistical interaction can also be interpreted as ‘effect modification’ in that the risk difference associated with a given genotype of gene A, scaled correspondingly (i.e., risk difference, relative risk, or odds ratio), depends upon the genotype of gene B. While certain types of biological interaction result in statistical interaction on a certain scale, the presence of statistical interaction does not necessarily imply the concurrent presence of any meaningful biological interaction [7].