Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits

Ghazalpour, Anatole; Rau, Christoph; Farber, Charles R.; Bennett, Brian J.; Orozco, Luz D.; Nas, Atila van; Pan, Calvin; Allayee, Hooman; Beaven, Simon W.; Civelek, Mete; Davis, Richard C.; Drake, Thomas A.; Friedman, Rick A.; Furlotte, Nick; Hui, Simon T.; Jentsch, J. David; Kostem, Emrah; Kang, Hyun Min; Kang, Eun Yong; Joo, Jong Wha J.; Korshunov, Vyacheslav A.; Laughlin, Rick E.; Martin, Lisa; Ohmen, Jeffrey D.; Parks, Brian W.; Pellegrini, Matteo; Reue, Karen; Smith, Desmond J.; Tetradis, Sotirios; Wang, Jessica; Wang, Yibin; Weiss, James N.; Kirchgessner, Todd G.; Gargalovic, Peter S.; Eskin, Eleazar; Lusis, Aldons J.; LeBoeuf, Renée C.

doi:10.1007/s00335-012-9411-5

Cited by 131 publications

(144 citation statements)

References 41 publications

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“…1D; Ghazalpour et al 2012). The number of strains utilized can be selected based on experimental needs with recent studies using 100 strains to investigate behavioral traits (Park et al 2011), 96 strains to study bone mineral density , and 114 strains phenotyped for metabolic traits .…”

Section: Hybrid Mouse Diversity Panel (Hmdp)mentioning

confidence: 99%

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: Hybrid Mouse Diversity Panel (Hmdp)mentioning

confidence: 99%

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

Buchner

Nadeau

2015

Genome Res.

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“…Several recent studies (5)(6)(7)(8)(9)(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.…”

Section: Introductionmentioning

confidence: 99%

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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“…Furthermore, formal proof that a specific variant is responsible for a given trait has proven difficult. Buoyed by the prospects and successes of human association studies, several groups have proposed mouse GWAS (Bennett et al 2010;Ghazalpour et al 2012;Valdar et al 2006;Yalcin et al 2010;Churchill et al 2004;Flint and Eskin 2012;Mott and Flint 2013;Kirby et al 2010). For obvious reasons, mouse models have several advantages over human studies.…”

Section: Introductionmentioning

confidence: 99%

“…The environment can be more carefully controlled, measurements can be replicated in genetically identical animals, and the proportion of the variability explained by genetic variation is increased. Complex traits in mouse strains have been shown to have higher heritability and genetic loci often have stronger effects on the trait compared to humans (Lindblad-Toh et al 2000;Wiltshire et al 2003;Yalcin et al 2004) Furthermore, several recently developed strategies for mouse genetic studies, such as use of the hybrid mouse diversity panel (HMDP), provide much higher resolution for associated loci than traditional approaches to quantitative trait loci (QTL) mapping (Bennett et al 2010;Ghazalpour et al 2012). In genome-wide association studies in humans, the use of metaanalysis is becoming more and more popular because one can virtually collect tens of thousands of individuals that will provide power to identify associated variants with small effect sizes (Hinds et al 2013;Anttila et al 2013;Berndt et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study for Age-Related Hearing Loss (AHL) in the Mouse: A Meta-Analysis

Ohmen

Kang

et al. 2014

JARO

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Age-related hearing loss (AHL) is characterized by a symmetric sensorineural hearing loss primarily in high frequencies and individuals have different levels of susceptibility to AHL. Heritability studies have shown that the sources of this variance are both genetic and environmental, with approximately half of the variance attributable to hereditary factors as r e p o r t e d b y H u a g a n d T a n g ( E u r A r c h Otorhinolaryngol 267(8): [1179][1180][1181][1182][1183][1184][1185][1186][1187][1188][1189][1190][1191] 2010). Only a limited number of large-scale association studies for AHL have been undertaken in humans, to date. An alternate and complementary approach to these human studies is through the use of mouse models. Advantages of mouse models include that the environment can be more carefully controlled, measurements can be replicated in genetically identical animals, and the proportion of the variability explained by genetic variation is increased. Complex traits in mouse strains have been shown to have higher heritability and genetic loci often have stronger effects on the trait compared to humans.Motivated by these advantages, we have performed the first genome-wide association study of its kind in the mouse by combining several data sets in a meta-analysis to identify loci associated with age-related hearing loss. We identified five genome-wide significant loci (G10 −6). One of these loci confirmed a previously identified locus (ahl8) on distal chromosome 11 and greatly narrowed the candidate region. Specifically, the most significant associated SNP is located 450 kb upstream of Fscn2. These data confirm the utility of this approach and provide new high-resolution mapping information about variation within the mouse genome associated with hearing loss.

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Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits

Cited by 131 publications

References 41 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

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Genome-Wide Association Study for Age-Related Hearing Loss (AHL) in the Mouse: A Meta-Analysis

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