A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

Langfelder, Peter; Castellani, Lawrence W.; Zhou, Zhiqiang; Paul, Eric; Davis, Richard C.; Schadt, Eric E.; Lusis, Aldons J.; Horvath, Steve; Mehrabian, Margarete

doi:10.1016/j.bbalip.2011.07.014

Cited by 28 publications

(26 citation statements)

References 72 publications

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“…Although differential expression analyses have unquestionable utility, the inherent natural organisation of the transcriptome remains largely unexplored. Conversely, co-expression analyses that consider the gene-wise relationships in gene expression data have cast new light on previously unappreciated patterns of transcriptional organisation with regards to processes and functions such as lipid metabolism [13], cancer [14], human brain development and neuropathology [15–17], and embryonic development [18]. Gene co-expression analyses identify groups of genes where expression levels are highly correlated across samples.…”

Section: Introductionmentioning

confidence: 99%

Placental transcriptome co-expression analysis reveals conserved regulatory programs across gestation

et al. 2017

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BackgroundMammalian development in utero is absolutely dependent on proper placental development, which is ultimately regulated by the placental genome. The regulation of the placental genome can be directly studied by exploring the underlying organisation of the placental transcriptome through a systematic analysis of gene-wise co-expression relationships.ResultsIn this study, we performed a comprehensive analysis of human placental co-expression using RNA sequencing and intergrated multiple transcriptome datasets spanning human gestation. We identified modules of co-expressed genes that are preserved across human gestation, and also identifed modules conserved in the mouse indicating conserved molecular networks involved in placental development and gene expression patterns more specific to late gestation. Analysis of co-expressed gene flanking sequences indicated that conserved co-expression modules in the placenta are regulated by a core set of transcription factors, including ZNF423 and EBF1. Additionally, we identified a gene co-expression module enriched for genes implicated in the pregnancy pathology preeclampsia. By using an independnet transcriptome dataset, we show that these co-expressed genes are differentially expressed in preeclampsia.ConclusionsThis study represents a comprehensive characterisation of placental co-expression and provides insight into potential transcriptional regulators that govern conserved molecular programs fundamental to placental development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3384-9) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Placental transcriptome co-expression analysis reveals conserved regulatory programs across gestation

et al. 2017

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“…We used weighted gene correlation network analysis (WGCNA) , a widely used method that finds modules of highly correlated genes, relates these modules to one another, and tests the influence of sample phenotypes on gene expression correlations. WGCNA has been widely used to identify co-expressed gene networks in various human brain regions (Oldham et al, 2008), animals (Fuller et al, 2007;Langfelder et al, 2012), and in human phenotypes, including schizophrenia (Torkamani et al, 2010), autism (Voineagu et al, 2011), cancer (Clarke et al, 2013), aggressive behavior (Malki et al, 2014), BD (Chen et al, 2013a), and psoriasis (Li et al, 2014). However, aside from one study of a few gene networks (Hong et al, 2013), WGCNA has not yet been applied to the complete brain transcriptome in BD as revealed by RNA-seq.…”

Section: Introductionmentioning

confidence: 99%

An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder

Akula

Wendland

Choi

et al. 2015

Neuropsychopharmacol

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Genome-wide association studies (GWAS) have identified several common variants associated with bipolar disorder (BD), but the biological meaning of these findings remains unclear. Integrative genomics-the integration of GWAS signals with gene expression data-may illuminate genes and gene networks that have key roles in the pathogenesis of BD. We applied weighted gene co-expression network analysis (WGCNA), which exploits patterns of co-expression among genes, to brain transcriptome data obtained by sequencing of poly-A RNA derived from postmortem dorsolateral prefrontal cortex from people with BD, along with age-and sex-matched controls. WGCNA identified 33 gene modules. Many of the modules corresponded closely to those previously reported in human cortex. Three modules were associated with BD, enriched for genes differentially expressed in BD, and also enriched for signals in prior GWAS of BD. Functional analysis of genes within these modules revealed significant enrichment of several functionally related sets of genes, especially those involved in the postsynaptic density (PSD). These results provide convergent support for the hypothesis that dysregulation of genes involved in the PSD is a key factor in the pathogenesis of BD. If replicated in larger samples, these findings could point toward new therapeutic targets for BD.

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“…In addition, we took advantage of the availability of the expression level of the transcripts in the F2 mice to 1 ) improve the QTL gene identifi cation by leveraging expression QTL (eQTL) and correlation, and 2 ) apply the weighted gene co-expression network analysis (WGCNA). WGCNA allowed us to identify gene modules of tightly connected and correlated genes that are themselves correlated with HDL-cholesterol ( 11,12 ). The identifi cation of the underlying genetics of these modules adds a new dimension in the identifi cation of genes regulating HDL-cholesterol at the genome-wide level and helped identify several candidate genes for the chromosome 11 HDL QTL through the combined use of bioinformatics and systems genetics.…”

Section: Qtl Analysismentioning

confidence: 99%

“…Thresholds for signifi cant ( P < 0.05) and suggestive ( P < 0.63) LOD scores were based on 1,000 permutations of the observed data for the autosomes, 17,940 other complex traits. However, the addition of systems genetic approaches allows for identifi cation of a group of genes that infl uences a complex trait as a group, not individually, and that usually represents a functional and biological process that infl uences the trait ( 11,12 ).…”

Section: Qtl Analysismentioning

confidence: 99%

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ × SM/J intercross

Leduc

Blair

Verdugo

et al. 2012

Journal of Lipid Research

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A higher incidence of coronary artery disease (CAD) is associated with a lower level of HDL-cholesterol ( 1, 2 ), which is determined by multiple environmental and genetic factors. In the past few years, new therapeutic approaches have focused on fi nding ways to increase HDL ( 3, 4 ). To date, however, only a few environmental interventions, such as diet and exercise, have been successful in raising HDL level ( 5 ), whereas drug therapy development is still ongoing and has targeted only a limited number of proteins such as CETP ( 3, 4 ). In the search for a potential drug target to raise HDL and decrease CAD, the identifi cation of new genes involved in HDL determination is essential.The genetic basis of HDL-cholesterol variation has been widely studied in various animal models. In humans, recent genome-wide association studies (GWAS) have identifi ed known HDL genes such as LIPG and CETP in addition to new genes such as GALNT2 ( 6 ). These known genes, however, explain only a small proportion of the total variation, indicating that additional genes are yet to be discovered. Mouse models are a powerful strategy for identifying such genes. A large overlap exists among species for quantitative trait loci (QTL) and genes involved in lipid metabolism ( 7 ). Many study methodologies are similar between human and mouse, but mouse studies offer advantages by capitalizing on genomics tools that are not available and practical for studies of human populations. To date, our laboratory and those of others have identifi ed over 35 mouse HDL QTL (as reviewed in Wang et al., Refs. 7,8 ). In addition, we and others have developed bioinformatics tools to narrow the QTL interval to just a few candidate genes ( 9, 10 ). Application of these tools has led to the discovery of single genes underlying QTL for HDL and

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A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models

Cited by 28 publications

References 72 publications

Placental transcriptome co-expression analysis reveals conserved regulatory programs across gestation

Placental transcriptome co-expression analysis reveals conserved regulatory programs across gestation

An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ × SM/J intercross

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