Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Ghazalpour, Anatole; Doss, Sudheer; Zhang, Bin; Wang, Susanna; Plaisier, Christopher L.; Castellanos, Ruth; Brozell, Alec; Schadt, Eric E.; Drake, Thomas A.; Lusis, Aldons J.; Horvath, Steve

doi:10.1371/journal.pgen.0020130

Cited by 400 publications

(371 citation statements)

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“…Module-QTL have been identified for hepatic gene expression networks that were shown to be highly correlated, and to co-map with body weight in mice (GHAZALPOUR et al 2006;FULLER et al 2007). …”

Section: Discussionmentioning

confidence: 99%

“…Highly correlated transcripts are often associated with common physiological or biochemical processes GARGALOVIC et al 2006;GHAZALPOUR et al 2006;HORVATH et al 2006;KELLER et al 2008). We performed gene set enrichment analysis (NEWTON et al 2007) on each module to determine if any genes in the modules are associated with shared functional annotations.…”

Section: Co-expression Modules Highlight Biological Processes Relatedmentioning

confidence: 99%

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Genetic Drivers of Pancreatic Islet Function

et al. 2018

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The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45%-kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression quantitative trait loci. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse with those found in human populations and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.4

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Section: Discussionmentioning

confidence: 99%

Section: Co-expression Modules Highlight Biological Processes Relatedmentioning

confidence: 99%

Genetic Drivers of Pancreatic Islet Function

et al. 2018

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“…Many of them emphasize the advantages of weighted networks over classical (binary) ones (Ghazalpour et al, 2006;Zhang and Horvath, 2005). There are many approaches to assigning weights to edges in networks.…”

Section: Related Workmentioning

confidence: 99%

“…Edge weights in a network of air transport between cities can be assigned on the basis of the total number of passenger (or seats) on particular flights (Barrat et al, 2004a). The genes network may also benefit from assigning weights to edges based on a similar function of particular genes (Ghazalpour et al, 2006). It is quite common that more than one weighting approach based on reality exists.…”

Section: Related Workmentioning

confidence: 99%

Local dependency in networks

Kudźlka

Zehnalova

Horák

et al. 2015

International Journal of Applied Mathematics and Computer Science

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Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.

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“…As this result is based on only the first degree interactors, including information from a disease-related subnetwork or modules may further increase the coverage. Using as query genes four genes known to be associated with breast [84]. Although their network included only co-expression relationships, the approach can be potentially adapted for networks composed of other types of 'omics' data.…”

Section: Dynamic Network Modularity Links Genotypes To Complex Phenotmentioning

confidence: 99%