Genetic dissection of a major mouse obesity QTL (<i>Carfhg2</i>): integration of gene expression and causality modeling

Farber, Charles R.; Aten, Jason E.; Farber, Emily; Vera, Vincent de; Gularte, Rodrigo J.; Islas‐Trejo, Alma; Wen, Pengzi; Horvath, Steve; Lucero, Michael; Lusis, Aldons J.; Medrano, Juan F.

doi:10.1152/physiolgenomics.90245.2008

Cited by 23 publications

(19 citation statements)

References 18 publications

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“…One limitation of this approach is that it will not be successful in cases in which the responsible variant(s) does not affect expression or in which the change in expression is specific to a tissue not investigated. Despite this limitation, our work and that of others illustrates the power of eQTL analysis for gene discovery (25,47,48).…”

Section: Discussionmentioning

confidence: 81%

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Mesner¹,

Ray²,

Hsu³

et al. 2014

J. Clin. Invest.

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Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels. IntroductionOsteoporosis is a disease characterized by low bone mass, skeletal fragility, and increased risk of fracture (1). Of the traits intrinsic to bone that influence its strength, bone mineral density (BMD) is one of the strongest predictors of fractures (2). BMD is also highly heritable, with approximately 70% of its variation being attributable to genetic factors. As a result, developing a comprehensive understanding of the genes and pathways that regulate BMD promises to lead to novel therapies aimed at preventing and treating bone fragility.Genome-wide association studies (GWAS) have significantly expanded the list of known variants and genes that influence BMD (3). A recent meta-analysis of 17 BMD GWAS (Genetic Effects For Osteoporosis [GEFOS] Consortium) involving approximately 83,000 individuals identified 56 robustly significant associations (4). Interestingly, together, the associations explained less than 5% of the total phenotypic variance in BMD, suggesting that bone mass is highly polygenic and that most of the genes influencing BMD remain to be identified.One strategy that can help fill this knowledge gap is the use of gene discovery in the mouse to inform human BMD GWAS. Several recent studies (5-10) have demonstrated the effectiveness of this strategy. To date, dozens of quantitative trait loci (QTLs) (regions of the genome harboring genetic variation influencing a quantitative trait) affecting BMD have been identified in the mouse (11), and recently the pace of identifying QTL genes has rapidly progressed. This is due in part to the development of sys-

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Section: Discussionmentioning

confidence: 81%

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Mesner¹,

Ray²,

Hsu³

et al. 2014

J. Clin. Invest.

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“…An eQTL study is based on treating expression as a quantitative trait and associating it with genetic variation. eQTL studies have been tremendously successful, and have identified many loci involved in gene regulation (Ghazalpour et al 2008;Veyrieras et al 2008;Cookson et al 2009;Farber et al 2009;Hayes et al 2009;Jiménez-Gómez et al 2010;van Nas et al 2010;Steibel et al 2011;Gaffney et al 2012;Kabakchiev and Silverberg 2013;Francesconi and Lehner 2014). However, the traditional eQTL approach has some fundamental limitations.…”

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confidence: 99%

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

et al. 2016

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The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cisacting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases.

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“…eQTL can be cis-or trans-acting, reflecting local and distant regulation of gene expression, respectively. They have been thoroughly studied in humans, yeast, plants, rats, mice, and worms (Brem et al 2002;Schadt et al 2003;Li et al 2006;Petretto et al 2006;Keurentjes et al 2007;Farber et al 2009). But, as far as we are aware of, all eQTL studies only focused on one time point or age group, whereas most complex traits and diseases such as cancer are age-related.…”

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confidence: 99%

Genome-wide gene expression regulation as a function of genotype and age in C. elegans

Viñuela¹,

Snoek²,

Riksen³

et al. 2010

Genome Res.

133

196

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Gene expression becomes more variable with age, and it is widely assumed that this is due to a decrease in expression regulation. But currently there is no understanding how gene expression regulatory patterns progress with age. Here we explored genome-wide gene expression variation and regulatory loci (eQTL) in a population of developing and aging C. elegans recombinant inbred worms. We found almost 900 genes with an eQTL, of which almost half were found to have a genotype-by-age effect ( gxa eQTL). The total number of eQTL decreased with age, whereas the variation in expression increased. In developing worms, the number of genes with increased expression variation (1282) was similar to the ones with decreased expression variation (1328). In aging worms, the number of genes with increased variation (1772) was nearly five times higher than the number of genes with a decreased expression variation (373). The number of cis-acting eQTL in juveniles decreased by almost 50% in old worms, whereas the number of trans-acting loci decreased by~27%, indicating that cis-regulation becomes relatively less frequent than trans-regulation in aging worms. Of the 373 genes with decreased expression level variation in aging worms,~39% had an eQTL compared with~14% in developing worms. gxa eQTL were found for~21% of these genes in aging worms compared with only~6% in developing worms. We highlight three examples of linkages: in young worms (pgp-6), in old worms (daf-16), and throughout life (lips-16). Our findings demonstrate that eQTL patterns are strongly affected by age, and suggest that gene network integrity declines with age.

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Genetic dissection of a major mouse obesity QTL (Carfhg2): integration of gene expression and causality modeling

Cited by 23 publications

References 18 publications

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

Genome-wide gene expression regulation as a function of genotype and age in C. elegans

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