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Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci

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Abstract

We previously identified a Chr 15 quantitative trait locus (QTL) Fob3b in lines of mice selected for high (Fat line) and low (Lean line) body fat content that represent a unique model of polygenic obesity. Here we genetically dissected the Fob3b interval by analyzing the phenotypes of eight overlapping congenic lines and four F2 congenic intercrosses and prioritized candidates by bioinformatics approaches. Analyses revealed that the Fob3b QTL consists of at least two separate linked QTLs Fob3b1 and Fob3b2. They exhibit additive inheritance and are linked in coupling with alleles originating from the Lean line, decreasing obesity-related traits. In further analyses, we focused on Fob3b1 because it had a larger effect on obesity-related traits than Fob3b2, e.g., the difference between homozygotes for adiposity index (ADI) percentage was 1.22 and 0.77% for Fob3b1 and Fob3b2, respectively. A set of bioinformatics tools was used to narrow down positional candidates from 85 to 4 high-priority Fob3b1 candidates. A previous single Fob3b QTL was therefore resolved into another two closely linked QTLs, confirming the fractal nature of QTLs mapped at low resolution. The interval of the original Fob3b QTL was narrowed from 22.39 to 4.98 Mbp for Fob3b1 and to 7.68 Mbp for Fob3b2, which excluded the previously assigned candidate squalene epoxidase (Sqle) as the causal gene because it maps proximal to refined Fob3b1 and Fob3b2 intervals. A high-resolution map along with prioritization of Fob3b1 candidates by bioinformatics represents an important step forward to final identification of the Chr 15 obesity QTL.

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References

  • Beigneux AP, Davies BSJ, Gin P, Weinstein MM, Farber E et al (2007) Glycosylphosphatidylinositol-anchored high-density lipoprotein-binding protein 1 plays a critical role in the lipolytic processing of chylomicrons. Cell Metab 5:279–291

    Article  CAS  PubMed  Google Scholar 

  • Bunger L, Hill WG (1999) Inbred lines of mice derived from long-term divergent selection on fat content and body weight. Mamm Genome 10:645–648

    Article  CAS  PubMed  Google Scholar 

  • Burgess-Herbert SL, Cox A, Tsaih SW, Paigen B (2008) Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci. Genetics 180:2227–2235

    Article  PubMed  Google Scholar 

  • Casas E, Shackleford SD, Keele JW, Stone RT, Kappes SM et al (2000) Quantitative trait loci affecting growth and carcass composition of cattle segregating alternate forms of myostatin. J Anim Sci 78:560–569

    CAS  PubMed  Google Scholar 

  • Casas E, Shackelford SD, Keele JW, Koohmaraie M, Smith TPL et al (2003) Detection of quantitative trait loci for growth and carcass composition in cattle. J Anim Sci 81:2976–2983

    CAS  PubMed  Google Scholar 

  • Chen HC (2006) Enhancing energy and glucose metabolism by disrupting triglyceride synthesis: lessons from mice lacking DGAT1. Nutr Metab 3:10

    Article  Google Scholar 

  • Chen HC, Smith SJ, Ladha Z, Jensen DR, Ferreira LD et al (2002) Increased insulin and leptin sensitivity in mice lacking acyl CoA: diacylglycerol acyltransferase 1. J Clin Invest 109:1049–1055

    CAS  PubMed  Google Scholar 

  • Chiu S, Kim K, Haus KA, Espinal GM, Millon LV et al (2007) Identification of positional candidate genes for body weight and adiposity in subcongenic mice. Physiol Genomics 31:75–85

    Article  CAS  PubMed  Google Scholar 

  • Christians JK, Keightley PD (2004) Fine mapping of a murine growth locus to a 1.4-cM region and resolution of linked QTL. Mamm Genome 15:482–491

    Article  CAS  PubMed  Google Scholar 

  • Christians JK, Hoeflich A, Keightley PD (2006) PAPPA2, an enzyme that cleaves an insulin-like growth-factor-binding protein, is a candidate gene for a quantitative trait locus affecting body size in mice. Genetics 173:1547–1553

    Article  CAS  PubMed  Google Scholar 

  • DiPetrillo K, Wang XS, Stylianou IM, Paigen B (2005) Bioinformatics toolbox for narrowing rodent quantitative trait loci. Trends Genet 21:683–692

    Article  CAS  PubMed  Google Scholar 

  • Dokmanovic-Chouinard M, Chung WK, Chevre JC, Watson E, Yonan J et al (2008) Positional cloning of “Lisch-like”, a candidate modifier of susceptibility to type 2 diabetes in mice. PLoS Genet 4(7):e1000137

    Article  PubMed  Google Scholar 

  • Farber CR, Medrano JF (2007) Fine mapping reveals sex bias in quantitative trait loci affecting growth, skeletal size and obesity-related traits on mouse chromosomes 2 and 11. Genetics 175:349–360

    Article  PubMed  Google Scholar 

  • Fawcett GL, Jarvis JP, Roseman CC, Wang B, Wolf JB et al (2009) Fine-mapping of obesity-related quantitative trait loci in an F(9/10) advanced intercross line. Obesity (Silver Spring) [Epub ahead of print]

  • Flint J, Mackay TFC (2009) Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res 19:723–733

    Article  CAS  PubMed  Google Scholar 

  • Flint J, Mott R (2001) Finding the molecular basis of quantitative traits: Successes and pitfalls. Nat Rev Genet 2:437–445

    Article  CAS  PubMed  Google Scholar 

  • Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New York

    Google Scholar 

  • Graham D, McBride MW, Brain NJ, Dominiczak AF (2005) Congenic/consomic models of hypertension. Methods Mol Med 108:3–15

    PubMed  Google Scholar 

  • Grubb SC, Maddatu TP, Bult CJ, Bogue MA (2009) Mouse phenome database. Nucleic Acids Res 37:D720–D730

    Article  CAS  PubMed  Google Scholar 

  • Hasegawa J, Osatomi K, Wu RF, Uyeda K (1999) A novel factor finding to the glucose response elements of liver pyruvate kinase and fatty acid synthase genes. J Biol Chem 274:1100–1107

    Article  CAS  PubMed  Google Scholar 

  • Horvat S, Bunger L, Falconer VM, Mackay P, Law A et al (2000) Mapping of obesity QTLs in a cross between mouse lines divergently selected on fat content. Mamm Genome 11:2–7

    Article  CAS  PubMed  Google Scholar 

  • Hu ZL, Fritz ER, Reecy JM (2007) AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond. Nucleic Acids Res 35:D604–D609

    Article  CAS  PubMed  Google Scholar 

  • Jerez-Timaure NC, Eisen EJ, Pomp D (2005) Fine mapping of a QTL region with large effects on growth and fatness on mouse chromosome 2. Physiol Genomics 21:411–422

    Article  CAS  PubMed  Google Scholar 

  • Keightley PD, Morris KH, Ishikawa A, Falconer VM, Oliver F (1998) Test of candidate gene quantitative trait locus association applied to fatness in mice. Heredity 81:630–637

    Article  PubMed  Google Scholar 

  • Laird PW, Zijderveld A, Linders K, Rudnicki MA, Jaenisch R et al (1991) Simplified mammalian DNA isolation procedure. Nucleic Acids Res 19:4293–4293

    Article  CAS  PubMed  Google Scholar 

  • Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:325–337

    Article  Google Scholar 

  • Lyons PA, Armitage N, Lord CJ, Phillips MS, Todd JA et al (2001) Mapping by genetic interaction—high-resolution congenic mapping of the type 1 diabetes loci Idd10 and Idd18 in the NOD mouse. Diabetes 50:2633–2637

    Article  CAS  PubMed  Google Scholar 

  • Maes HHM, Neale MC, Eaves LJ (1997) Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27:325–351

    Article  CAS  PubMed  Google Scholar 

  • Matsuda D, Tomoda H (2007) DGAT inhibitors for obesity. Curr Opin Invest Drugs 8:836–841

    CAS  Google Scholar 

  • McDaniel AH, Li X, Tordoff MG, Bachmanov AA, Reed DR (2006) A locus on mouse Chromosome 9 (Adip5) affects the relative weight of the gonadal but not retroperitoneal adipose depot. Mamm Genome 17:1078–1092

    Article  CAS  PubMed  Google Scholar 

  • Morton NM, Densmore V, Wamil M, Ramage L, Nichol K et al (2005) A polygenic model of the metabolic syndrome with reduced circulating and intra-adipose glucocorticoid action. Diabetes 54:3371–3378

    Article  CAS  PubMed  Google Scholar 

  • Mott R, Flint J (2008) Prospects for complex trait analysis in the mouse. Mamm Genome 19:306–308

    Article  PubMed  Google Scholar 

  • Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6(1):7-11. http://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf. Accessed 22 Feb 2010

  • Pomp D, Nehrenberg D, Estrada-Smith D (2008) Complex genetics of obesity in mouse models. Annu Rev Nutr 28:331–345

    Article  CAS  PubMed  Google Scholar 

  • R Development Core Team (2008) R: a language and environment for statistical computing. R version 2.8.1. R Foundation for Statistical Computing, Vienna, ISBN 3-900051-07-0. http://www.r-project.org

  • Rance KA, Hambly C, Dalgleish G, Fustin JM, Bunger L et al (2007) Quantitative trait loci for regional adiposity in mouse lines divergently selected for food intake. Obesity (Silver Spring) 15:2994–3004

    Article  Google Scholar 

  • Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G et al (2006) The human obesity gene map: the 2005 update. Obesity 14:529–644

    Article  PubMed  Google Scholar 

  • Rocha JL, Eisen EJ, Van Vleck LD, Pomp D (2004) A large-sample QTL study in mice: II. Body composition. Mamm Genome 15:100–113

    Article  CAS  PubMed  Google Scholar 

  • Rogner UC, Avner P (2008) Sourcebook of models for biomedical research. Humana Press, Totowa

    Google Scholar 

  • Scherneck S, Nestler M, Vogel H, Bluher M, Block MD et al (2009) Positional cloning of zinc finger domain transcription factor Zfp69, a candidate gene for obesity-associated diabetes contributed by mouse locus Nidd/SJL. PLoS Genet 5(7):e1000541

    Article  PubMed  Google Scholar 

  • Sharp GL, Hill WG, Robertson A (1984) Effects of selection on growth, body-composition and food-intake in mice. 1. Responses in selected traits. Genet Res 43:75–92

    Article  CAS  PubMed  Google Scholar 

  • Silver LM (1995) Mouse genetics: concepts and applications. Oxford University Press, Oxford

    Google Scholar 

  • Simoncic M, Rezen T, Juvan P, Fievet C, Staels B et al (2008) Transcriptome analysis revealed association of some P450 genes with obesity in a polygenic obese mouse model. Acta Chim Slovenica 55:101–110

    CAS  Google Scholar 

  • Smith SJ, Cases S, Jensen DR, Chen HC, Sande E et al (2000) Obesity resistance and multiple mechanisms of triglyceride synthesis in mice lacking Dgat. Nat Genet 25:87–90

    Article  CAS  PubMed  Google Scholar 

  • Smith PL, Myers JT, Rogers CE, Zhou L, Petryniak B et al (2002) Conditional control of selectin ligand expression and global fucosylation events in mice with a targeted mutation at the FX locus. J Cell Biol 158:801–815

    Article  CAS  PubMed  Google Scholar 

  • Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 64(4):583–639

    Article  Google Scholar 

  • Stanford WL, Haque S, Alexander R, Liu XM, Latour AM et al (1997) Altered proliferative response by T lymphocytes of Ly-6A (Sca-1) null mice. J Exp Med 186:705–717

    Article  CAS  PubMed  Google Scholar 

  • Sturtz S, Ligges U, Gelman A (2005) R2WinBUGS: a package for running WinBUGS from R. J Stat Softw 12:1–16

    Google Scholar 

  • Stylianou IM, Christians JK, Keightley PD, Bunger L, Clinton M et al (2004) Genetic complexity of an obesity QTL (Fob3) revealed by detailed genetic mapping. Mamm Genome 15:472–481

    Article  CAS  PubMed  Google Scholar 

  • Stylianou IM, Clinton M, Keightley PD, Pritchard C, Tymowska-Lalanne Z et al (2005) Microarray gene expression analysis of the Fob3b obesity QTL identifies positional candidate gene Sqle and perturbed cholesterol and glycolysis. Physiol Genomics 20:224–232

    CAS  PubMed  Google Scholar 

  • Thaller G, Kuhn C, Winter A, Ewald G, Bellmann O et al (2003) DGAT1, a new positional and functional candidate gene for intramuscular fat deposition in cattle. Anim Genet 34:354–357

    Article  CAS  PubMed  Google Scholar 

  • Warden CH, Stone S, Chiu S, Diament AL, Corva P et al (2004) Identification of a congenic mouse line with obesity and body length phenotypes. Mamm Genome 15:460–471

    Article  CAS  PubMed  Google Scholar 

  • Willer CJ, Speliotes EK, Loos RJF, Li SX, Lindgren CM et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34

    Article  CAS  PubMed  Google Scholar 

  • Wuschke S, Dahm S, Schmidt C, Joost HG, Al-Hasani H (2007) A meta-analysis of quantitative trait loci associated with body weight and adiposity in mice. Int J Obes (Lond) 31:829–841

    CAS  Google Scholar 

  • Yalcin B, Willis-Owen SAG, Fullerton J, Meesaq A, Deacon RM et al (2004) Genetic dissection of a behavioral quantitative trait locus shows that Rgs2 modulates anxiety in mice. Nat Genet 36:1197–1202

    Article  CAS  PubMed  Google Scholar 

  • Yen CLE, Stone SJ, Koliwad S, Harris C, Farese RV (2008) DGAT enzymes and triacylglycerol biosynthesis. J Lipid Res 49:2283–2301

    Article  CAS  PubMed  Google Scholar 

  • Young SG, Davies BSJ, Fong LG, Gin P, Weinstein MM et al (2007) GPIHBP1: an endothelial cell molecule important for the lipolytic processing of chylomicrons. Curr Opin Lipidol 18:389–396

    Article  CAS  PubMed  Google Scholar 

  • Zhang SM, Lou YG, Amstein TM, Anyango M, Mohibullah N et al (2005) Fine mapping of a major locus on Chromosome 10 for exploratory and fear-like behavior in mice. Mamm Genome 16:306–318

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This project was supported by the Slovenian Research Agency (ARRS) young investigator grant and program grant P4-0220. Sincere thanks to all members of Beverly Paigen’s lab at the Jackson Laboratory for help with the bioinformatics. We thank Dr. McWhir for critical review of the manuscript and Ana Zanjkovic for excellent technical assistance with the mouse colony. Finally, we are grateful to two anonymous reviewers for meticulous review and constructive criticism that helped us improve the article.

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Authors and Affiliations

  1. Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, 1230, Domžale, Slovenia

    Zala Prevoršek, Gregor Gorjanc & Simon Horvat

  2. The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA

    Beverly Paigen

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  1. Zala Prevoršek
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  2. Gregor Gorjanc
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Correspondence to Simon Horvat.

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Prevoršek, Z., Gorjanc, G., Paigen, B. et al. Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci. Mamm Genome 21, 172–185 (2010). https://doi.org/10.1007/s00335-010-9252-z

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