Genetic determinants of atherosclerosis, obesity, and energy balance in consomic mice

Spiezio, Sabrina H.; Amon, Lynn M.; McMillen, Timothy S.; Vick, Cynthia M.; Houston, Barbara; Caldwell, Mark; Ogimoto, Kayoko; Morton, Gregory J.; Kirk, Elizabeth A.; Schwartz, Michael W.; Nadeau, Joseph H.; LeBoeuf, Renée C.

doi:10.1007/s00335-014-9530-2

Cited by 12 publications

(34 citation statements)

References 67 publications

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“…2; Shao et al 2010;Yazbek et al 2011;DeSantis et al 2013;Kato et al 2014;Winawer et al 2014;Zhu and Matin 2014). The CSSs and congenic strains derived from CSSs have been used to map QTLs and identify causal genetic variants for traits such as body weight, glucose homeostasis, anxiety, hearing loss, bone morphology, blood clotting, liver fibrosis, energy expenditure, seizure susceptibility, among others (Singer et al 2004;Singer 2005;Winawer et al 2007;Gregorová et al 2008;Sa et al 2008;Shao et al 2008;Boell et al 2011;DeSantis et al 2013;Spiezio et al 2014;Street et al 2014). …”

Section: Chromosome Substitution Strains (Csss)mentioning

confidence: 99%

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Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Buchner

Nadeau

2015

Genome Res.

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Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine contextdependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

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Section: Chromosome Substitution Strains (Csss)mentioning

confidence: 99%

“…As a result, CSSs can detect weaker and hence more QTLs than other resources given similar sample sizes. A striking example involves results from two recent studies (Logan et al 2013;Spiezio et al 2014). One study focused on behavioral traits in DO mice and the other on metabolic traits in CSSs.…”

Section: Mapped Qtlsmentioning

confidence: 99%

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Buchner

Nadeau

2015

Genome Res.

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“…Previously, this diet was used as a metabolic challenge to evoke broad phenotypic responses in 43 inbred mouse strains, and to genetically map several metabolic phenotypes, including obesity, gut microbial composition, glucose homeostasis, dyslipidemia, hepatic steatosis, atherosclerosis and energy balance (SVENSON et al 2007;PARKS et al 2013;SPIEZIO et al 2014;SINASAC et al 2016). A recent study by Threadgill and colleagues compared the metabolic effects of the Western-style diet to a control diet, and three additional diets (Mediterranean, Japanese, and Maasai/ketogenic) in four mouse strains; A/J, C57BL/6J, FVB/NJ and NOD/ShiltJ (BARRINGTON et al 2018).…”

Section: Introductionmentioning

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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“…50,51 Genome surveys using a panel of chromosome substitution strains (CSSs) in which each C57BL/6J chromosome has been individually replaced with the corresponding A/J chromosome 52 showed that sequence variants on many chromosomes control susceptibility, with the effects of each acting in a highly non-additive manner. 53,54 For the majority of traits, the cumulative phenotypic effect (sum of the signed deviation from C57BL/6J) across all strains in the CSS panel was found to greatly exceed 100%, with an average cumulative effect of $800% across 90 blood, bone, and metabolic traits. 53 Similar results for 54 traits in a panel of rat CSSs argue that pervasive and strong non-additive effects may be a general genetic phenomenon.…”

Section: Modifiers Of Complex Traitsmentioning

confidence: 99%

“…The A/J allele of Obrq2 significantly decreases weight gain induced with a high-fat diet, but the magnitude of this effect depends on the genetic source (A/J or C57BL/6J) of multiple additional QTLs on the same chromosome. [53][54][55] In some cases, nearly complete protection from diet-induced obesity is found. Protection is lost, however, with various small changes in the strain origin of closely flanking chromosome segments.…”

Section: Modifiers Of Complex Traitsmentioning

confidence: 99%

From Peas to Disease: Modifier Genes, Network Resilience, and the Genetics of Health

Riordan

Nadeau

2017

The American Journal of Human Genetics

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Phenotypes are rarely consistent across genetic backgrounds and environments, but instead vary in many ways depending on allelic variants, unlinked genes, epigenetic factors, and environmental exposures. In the extreme, individuals carrying the same causal DNA sequence variant but on different backgrounds can be classified as having distinct conditions. Similarly, some individuals that carry disease alleles are nevertheless healthy despite affected family members in the same environment. These genetic background effects often result from the action of so-called ''modifier genes'' that modulate the phenotypic manifestation of target genes in an epistatic manner. While complicating the prospects for gene discovery and the feasibility of mechanistic studies, such effects are opportunities to gain a deeper understanding of gene interaction networks that provide organismal form and function as well as resilience to perturbation. Here, we review the principles of modifier genetics and assess progress in studies of modifier genes and their targets in both simple and complex traits. We propose that modifier effects emerge from gene interaction networks whose structure and function vary with genetic background and argue that these effects can be exploited as safe and effective ways to prevent, stabilize, and reverse disease and dysfunction.

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Genetic determinants of atherosclerosis, obesity, and energy balance in consomic mice

Cited by 12 publications

References 67 publications

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Genetic Drivers of Pancreatic Islet Function

From Peas to Disease: Modifier Genes, Network Resilience, and the Genetics of Health

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