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

Leduc, Magalie S.; Blair, Rachael Hageman; Verdugo, Ricardo A.; Tsaih, Shirng‐Wern; Walsh, Kenneth A.; Churchill, Gary A.; Paigen, Beverly

doi:10.1194/jlr.m025833

Cited by 14 publications

(18 citation statements)

References 52 publications

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“…Other groups have used the WGCNA approach to identify module-QTL in liver that are associated with HDL-cholesterol (LEDUC et al 2012), or adiposity and hepatic steatosis (DAVIS et al 2012), and in heart for cardiac left ventricular mass (SCOTT-BOYER et al 2014). Our study is the first to apply the WGCNA module-QTL approach to pancreatic islets.…”

Section: Discussionmentioning

confidence: 99%

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%

Genetic Drivers of Pancreatic Islet Function

et al. 2018

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“…The one with the best correlation contained all eight c3-eQTLs near Lvm1 , and was linked itself to a mQTL whose profile matched that of Lvm1 . Although previous studies have reported on modules linking to a QTL overlapping with the interval of a phenotypic QTL ( 15 , 16 ), these studies did not explain why a genetic determinant could lead to coordinate changes in the expression of genes in the module. In the present study, 27 gene co-expression modules showed linkage to one main mQTL; among them, 21 modules had their mQTL on the same chromosome that contributed more genes to the module.…”

Section: Discussionmentioning

confidence: 88%

“…However, such genes usually represent only a small fraction of genes in the module, and their identification is not sufficient to identify how genetic determinants may lead to coordinate changes in the expression of all genes in the module. Alternatively, genetic mapping of “eigengenes” (which represent the first principal component of all expression profiles in modules) has shown that entire modules could be linked to QTLs and that some of such “module-QTLs (mQTLs)” may have profiles matching that of phenotypic QTLs ( 15 , 16 ). Although such findings suggest that the same genetic determinants may link to both a phenotype and the expression levels of genes within the associated module, the nature of such variants remains to be elucidated.…”

Section: Introductionmentioning

confidence: 99%

Dual Linkage of a Locus to Left Ventricular Mass and a Cardiac Gene Co-Expression Network Driven by a Chromosome Domain

Scott-Boyer

Praktiknjo

Llamas

et al. 2014

Front. Cardiovasc. Med.

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We have previously reported Lvm1 as a quantitative trait locus (QTL) on chromosome 13 that links to cardiac left ventricular mass (LVM) in a panel of AxB/BxA mouse recombinant inbred strains (RIS). When performing a gene expression QTL (eQTL) analysis, we detected 33 cis-eQTLs that correlated with LVM. Among the latter, a group of eight cis-eQTLs clustered in a genomic region smaller than 6 Mb and surrounding the Lvm1 peak on chr13. Co-variant analysis indicated that all eight genes correlated with the phenotype in a causal rather than a reactive fashion, a finding that (despite its functional interest) did not provide grounds to prioritize any of these candidate genes. As a complementary approach, we performed weighted gene co-expression network analysis, which allowed us to detect 49 modules of highly connected genes. The module that correlated best with LVM: (1) showed linkage to a module QTL whose boundaries matched closely those of the phenotypic Lvm1 QTL on chr13; (2) harbored a disproportionately high proportion of genes originating from a small genomic region on chromosome 13 (including the 8 previously detected cis-eQTL genes); (3) contained genes that, beyond their individual level of expression, correlated with LVM as a function of their inter-connectivity; and (4) showed increased abundance of polymorphic insertion–deletion elements in the same region. Taken together, these data suggest that a domain on chromosome 13 constitutes the biologic principle responsible for the organization and linkage of the gene co-expression module, and indicate a mechanism whereby genetic variants within chromosome domains may associate to phenotypic changes via coordinate changes in the expression of several genes. One other possible implication of these findings is that candidate genes to consider as contributors to a particular phenotype should extend further than those that are closest to the QTL peak.

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“…4 and was originally described as influencing locomotion in the inner area of the open field test (OF), a putative experimental index of anxiety. Since then, several studies have mapped different QTLs in both rats and mice for numerous phenotypes including bone inflammation [10], blood glucose levels [11], locomotion [12], alcohol preference [13], blood corticosterone levels [14], novelty/stress-induced locomotor activation [15], HDL-cholesterol level [16], radiation susceptibility [17], brain inflammation [18], stress response [19] and circadian rhythms [20]. Differently from knockout and transgenic approaches, this strategy is based on natural variations in genes and gene products that physiologically modulate the trait of interest, in a manner that may ultimately facilitate translation from animal models to human clinical conditions.…”

Section: Introductionmentioning

confidence: 99%

The Long Way from Complex Phenotypes to Genes: The Story of Rat Chromosome 4 and Its Behavioral Effects

Medeiros¹,

Corrêa²,

Corvino³

et al. 2014

WJNS

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Quantitative trait loci (QTLs) mapping has been performed during the past decades in an attempt to identify genes, gene products and mechanisms underlying numerous quantitative traits. It's a strategy based on natural variations in genes and gene products, which facilitates translation from animal models to human clinical conditions. Our team has shown that the inbred rat strains Lewis (LEW) and Spontaneously Hypertensive Rats (SHR) differ with respect to several emotionalityrelated behaviors, one of which (inner locomotion in the open field) was strongly influenced by a QTL (Anxrr16) on rat chromosome 4. Since then, several other studies not only corroborated the initial description of Anxrr16, but also extrapolated its effects to a broader context (rats from both sexes and regardless of the estrous cycle phase) and suggested that this same region influences other emotionality-related behaviors as well as alcohol intake. Other QTLs affecting neurobiological traits were also found on rat chromosome 4 and several candidate genes have been pointed out as possibly influencing those phenotypes. Altogether, these studies suggest that rat chromosome 4 constitutes an interesting target for the study of the molecular bases of anxiety and other traits related to emotional reactivity.

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

Cited by 14 publications

References 52 publications

Genetic Drivers of Pancreatic Islet Function

Genetic Drivers of Pancreatic Islet Function

Dual Linkage of a Locus to Left Ventricular Mass and a Cardiac Gene Co-Expression Network Driven by a Chromosome Domain

The Long Way from Complex Phenotypes to Genes: The Story of Rat Chromosome 4 and Its Behavioral Effects

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