Skip to main content

Advertisement

Log in

Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2

  • Published:
Mammalian Genome Aims and scope Submit manuscript

Abstract

To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross, backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J (donor) strains. The results from these mapping populations were concordant and showed that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. Of the protein-coding genes, 7 have missense variants, including genes that may contribute to lean body weight, such as Angpt41, Slc52c3, and Rem1. Lean body mass was increased by the B6-derived variant relative to the 129-derived allele. Burly1 influenced lean body weight at all ages but not food intake or locomotor activity. However, congenic mice with the B6 allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs and to identify a short list of candidate genes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Almind K, Manieri M, Sivitz WI, Cinti S, Kahn CR (2007) Ectopic brown adipose tissue in muscle provides a mechanism for differences in risk of metabolic syndrome in mice. Proc Natl Acad Sci USA 104:2366–2371

    Article  CAS  Google Scholar 

  • Anonymous (2011) Mouse genomes project: query SNPs, indels or SVs. Wellcome Trust Sanger Institute, Hinxton

    Google Scholar 

  • Anonymous (2012) SIFT web server: predicting effects of amino acid substitutions on proteins. Nucl Acids Res 40:W542–W547

    Article  CAS  Google Scholar 

  • Anonymous (2015a) GWAS Catalog: the NHGRI-EBI Catalog of published genome-wide association studies. European Molecular Biology Laboratory

  • Anonymous (2015b) Mus musculus (laboratory mouse) genome view. National Center for Biotechnology Information

  • Attie AD, Churchill GA, Nadeau JH (2017) How mice are indispensable for understanding obesity and diabetes genetics. Curr Opin Endocrinol Diabetes Obes 24:83–91

    Article  PubMed  PubMed Central  Google Scholar 

  • Bachmanov AA, Li X, Reed DR, Ohmen JD, Li S, Chen Z, Tordoff MG, de Jong PJ, Wu C, West DB, Chatterjee A, Ross DA, Beauchamp GK (2001a) Positional cloning of the mouse saccharin preference (Sac) locus. Chem Senses 26:925–933

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bachmanov AA, Reed DR, Tordoff MG, Price RA, Beauchamp GK (2001b) Nutrient preference and diet-induced adiposity in C57BL/6ByJ and 129P3/J mice. Physiol Behav 72:603–613

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bachmanov AA, Reed DR, Beauchamp GK, Tordoff MG (2002) Food intake, water intake, and drinking spout side preference of 28 mouse strains. Behav Genet 32:435–443

    Article  PubMed  PubMed Central  Google Scholar 

  • Bashford JA, Chowdhury FA, Shaw CE (2017) Remarkable motor recovery after riboflavin therapy in adult-onset Brown-Vialetto-Van Laere syndrome. Pract Neurol 17:53–56

    Article  PubMed  Google Scholar 

  • Beck JA, Lloyd S, Hafezparast M, Lennon-Pierce M, Eppig JT, Festing MF, Fisher EM (2000) Genealogies of mouse inbred strains. Nat Genet 24:23–25

    Article  PubMed  CAS  Google Scholar 

  • Benson KF, Chada K (1994) Mini-mouse: phenotypic characterization of a transgenic insertional mutant allelic to pygmy. Genet Res 64:27–33

    Article  PubMed  CAS  Google Scholar 

  • Beqollari D, Romberg CF, Meza U, Papadopoulos S, Bannister RA (2014) Differential effects of RGK proteins on L-type channel function in adult mouse skeletal muscle. Biophys J 106:1950–1957

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Berndt SI, Gustafsson S, Magi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, Esko T, Fall T, Ferreira T, Gentilini D, Jackson AU, Luan J, Randall JC, Vedantam S, Willer CJ, Winkler TW, Wood AR, Workalemahu T, Hu YJ, Lee SH, Liang L, Lin DY, Min JL, Neale BM, Thorleifsson G, Yang J, Albrecht E, Amin N, Bragg-Gresham JL, Cadby G, den Heijer M, Eklund N, Fischer K, Goel A, Hottenga JJ, Huffman JE, Jarick I, Johansson A, Johnson T, Kanoni S, Kleber ME, Konig IR, Kristiansson K, Kutalik Z, Lamina C, Lecoeur C, Li G, Mangino M, McArdle WL, Medina-Gomez C, Muller-Nurasyid M, Ngwa JS, Nolte IM, Paternoster L, Pechlivanis S, Perola M, Peters MJ, Preuss M, Rose LM, Shi J, Shungin D, Smith AV, Strawbridge RJ, Surakka I, Teumer A, Trip MD, Tyrer J, Van Vliet-Ostaptchouk JV, Vandenput L, Waite LL, Zhao JH, Absher D, Asselbergs FW, Atalay M, Attwood AP, Balmforth AJ, Basart H, Beilby J, Bonnycastle LL, Brambilla P, Bruinenberg M, Campbell H, Chasman DI, Chines PS, Collins FS, Connell JM, Cookson WO, de Faire U, de Vegt F, Dei M, Dimitriou M, Edkins S, Estrada K, Evans DM, Farrall M, Ferrario MM, Ferrieres J, Franke L, Frau F, Gejman PV, Grallert H, Gronberg H, Gudnason V, Hall AS, Hall P, Hartikainen AL, Hayward C, Heard-Costa NL, Heath AC, Hebebrand J, Homuth G, Hu FB, Hunt SE, Hypponen E, Iribarren C, Jacobs KB, Jansson JO, Jula A, Kahonen M, Kathiresan S, Kee F, Khaw KT, Kivimaki M, Koenig W, Kraja AT, Kumari M, Kuulasmaa K, Kuusisto J, Laitinen JH, Lakka TA, Langenberg C, Launer LJ, Lind L, Lindstrom J, Liu J, Liuzzi A, Lokki ML, Lorentzon M, Madden PA, Magnusson PK, Manunta P, Marek D, Marz W, Mateo Leach I, McKnight B, Medland SE, Mihailov E, Milani L, Montgomery GW, Mooser V, Muhleisen TW, Munroe PB, Musk AW, Narisu N, Navis G, Nicholson G, Nohr EA, Ong KK, Oostra BA, Palmer CN, Palotie A, Peden JF, Pedersen N, Peters A, Polasek O, Pouta A, Pramstaller PP, Prokopenko I, Putter C, Radhakrishnan A, Raitakari O, Rendon A, Rivadeneira F, Rudan I, Saaristo TE, Sambrook JG, Sanders AR, Sanna S, Saramies J, Schipf S, Schreiber S, Schunkert H, Shin SY, Signorini S, Sinisalo J, Skrobek B, Soranzo N, Stancakova A, Stark K, Stephens JC, Stirrups K, Stolk RP, Stumvoll M, Swift AJ, Theodoraki EV, Thorand B, Tregouet DA, Tremoli E, Van der Klauw MM, van Meurs JB, Vermeulen SH, Viikari J, Virtamo J, Vitart V, Waeber G, Wang Z, Widen E, Wild SH, Willemsen G, Winkelmann BR, Witteman JC, Wolffenbuttel BH, Wong A, Wright AF, Zillikens MC, Amouyel P, Boehm BO, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Cupples LA, Cusi D, Dedoussis GV, Erdmann J, Eriksson JG, Franks PW, Froguel P, Gieger C, Gyllensten U, Hamsten A, Harris TB, Hengstenberg C, Hicks AA, Hingorani A, Hinney A, Hofman A, Hovingh KG, Hveem K, Illig T, Jarvelin MR, Jockel KH, Keinanen-Kiukaanniemi SM, Kiemeney LA, Kuh D, Laakso M, Lehtimaki T, Levinson DF, Martin NG, Metspalu A, Morris AD, Nieminen MS, Njolstad I, Ohlsson C, Oldehinkel AJ, Ouwehand WH, Palmer LJ, Penninx B, Power C, Province MA, Psaty BM, Qi L, Rauramaa R, Ridker PM, Ripatti S, Salomaa V, Samani NJ, Snieder H, Sorensen TI, Spector TD, Stefansson K, Tonjes A, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Vollenweider P, Wallaschofski H, Wareham NJ, Watkins H, Wichmann HE, Wilson JF, Abecasis GR, Assimes TL, Barroso I, Boehnke M, Borecki IB, Deloukas P, Fox CS, Frayling T, Groop LC, Haritunian T, Heid IM, Hunter D, Kaplan RC, Karpe F, Moffatt MF, Mohlke KL, O’Connell JR, Pawitan Y, Schadt EE, Schlessinger D, Steinthorsdottir V, Strachan DP, Thorsteinsdottir U, van Duijn CM, Visscher PM, Di Blasio AM, Hirschhorn JN, Lindgren CM, Morris AP, Meyre D, Scherag A, McCarthy MI, Speliotes EK, North KE, Loos RJ, Ingelsson E (2013) Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 45:501–512

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Brochu M, Mathieu ME, Karelis AD, Doucet E, Lavoie ME, Garrel D, Rabasa-Lhoret R (2008) Contribution of the lean body mass to insulin resistance in postmenopausal women with visceral obesity: a Monet study. Obesity (Silver Spring) 16:1085–1093

    Article  CAS  Google Scholar 

  • Brockmann GA, Tsaih SW, Neuschl C, Churchill GA, Li R (2009) Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both. Physiol Genom 36:114–126

    Article  CAS  Google Scholar 

  • Bultman SJ, Michaud EJ, Woychik RP (1992) Molecular characterization of the mouse agouti locus. Cell 71:1195–1204

    Article  PubMed  CAS  Google Scholar 

  • Cheverud JM, Lawson HA, Fawcett GL, Wang B, Pletscher LS, Fox AR, Maxwell TJ, Ehrich TH, Kenney-Hunt JP, Wolf JB, Semenkovich CF (2010) Diet-dependent genetic and genomic imprinting effects on obesity in mice. Obesity (Silver Spring) 19:160–170

    Google Scholar 

  • Cinti S (1999) The adipose organ. Editrice Kurtis, Milano

    Google Scholar 

  • Clayton JA, Collins FS (2014) Policy: NIH to balance sex in cell and animal studies. Nature 509:282–283

    Article  PubMed  PubMed Central  Google Scholar 

  • Clop A, Marcq F, Takeda H, Pirottin D, Tordoir X, Bibe B, Bouix J, Caiment F, Elsen JM, Eychenne F, Larzul C, Laville E, Meish F, Milenkovic D, Tobin J, Charlier C, Georges M (2006) A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nat Genet 38:813–818

    Article  PubMed  CAS  Google Scholar 

  • Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. L. Erlbaum Associates, Hillsdale

    Google Scholar 

  • Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, Butte NF (2012) Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS ONE 7:e51954

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Coordinators NR (2016) Database resources of the National Center for Biotechnology Information. Nucl Acids Res 44:D7–D19

    Google Scholar 

  • Crabtree NJ, Kibirige MS, Fordham JN, Banks LM, Muntoni F, Chinn D, Boivin CM, Shaw NJ (2004) The relationship between lean body mass and bone mineral content in paediatric health and disease. Bone 35:965–972

    Article  PubMed  CAS  Google Scholar 

  • Delignette-Muller M, Pouillot R, Denis J, Dutang C (2014) Fitdistrplus: help to fit of a parametric distribution to non-censored or censored data. R package

  • Diament AL, Farahani P, Chiu S, Fisler J, Warden CH (2004) A novel mouse Chromosome 2 congenic strain with obesity phenotypes. Mamm Genome 15:452–459

    Article  PubMed  CAS  Google Scholar 

  • Dietrich W, Katz H, Lincoln SE, Shin HS, Friedman J, Dracopoli NC, Lander ES (1992) A genetic map of the mouse suitable for typing intraspecific crosses. Genetics 131:423–447

    PubMed  PubMed Central  CAS  Google Scholar 

  • Dolgin E (2017) The most popular genes in the human genome. Nature 551:427–431

    Article  PubMed  Google Scholar 

  • Donahue LR, Beamer WG (1993) Growth hormone deficiency in ‘little’ mice results in aberrant body composition, reduced insulin-like growth factor-I and insulin-like growth factor-binding protein-3 (IGFBP-3), but does not affect IGFBP-2, -1 or -4. J Endocrinol 136:91–104

    Article  PubMed  CAS  Google Scholar 

  • Drinkwater NR, Gould MN (2012) The long path from QTL to gene. PLoS Genet 8:e1002975

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Eppig JT, Bult CJ, Kadin JA, Richardson JE, Blake JA, Anagnostopoulos A, Baldarelli RM, Baya M, Beal JS, Bello SM, Boddy WJ, Bradt DW, Burkart DL, Butler NE, Campbell J, Cassell MA, Corbani LE, Cousins SL, Dahmen DJ, Dene H, Diehl AD, Drabkin HJ, Frazer KS, Frost P, Glass LH, Goldsmith CW, Grant PL, Lennon-Pierce M, Lewis J, Lu I, Maltais LJ, McAndrews-Hill M, McClellan L, Miers DB, Miller LA, Ni L, Ormsby JE, Qi D, Reddy TB, Reed DJ, Richards-Smith B, Shaw DR, Sinclair R, Smith CL, Szauter P, Walker MB, Walton DO, Washburn LL, Witham IT, Zhu Y, Mouse Genome Database G (2005) The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology. Nucl Acids Res 33:D471–D475

    Google Scholar 

  • Farber CR, Medrano JF (2007a) Dissection of a genetically complex cluster of growth and obesity QTLs on mouse chromosome 2 using subcongenic intercrosses. Mamm Genome 18:635–645

    Article  PubMed  CAS  Google Scholar 

  • Farber CR, Medrano JF (2007b) 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  PubMed Central  Google Scholar 

  • Field Y, Boyle EA, Telis N, Gao Z, Gaulton KJ, Golan D, Yengo L, Rocheleau G, Froguel P, McCarthy MI, Pritchard JK (2016) Detection of human adaptation during the past 2000 years. Science 354:760–764

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Fukushima Y, Kurose S, Shinno H, Cao Thu H, Takao N, Tsutsumi H, Kimura Y (2016) Importance of lean muscle maintenance to improve insulin resistance by body weight reduction in female patients with obesity. Diabetes Metab J 40:147–153

    Article  PubMed  PubMed Central  Google Scholar 

  • Funk-Keenan J, Atchley W (2005) Maternal effects, genomic imprinting and evolution. In: Eisen EJ (ed) The mouse in animal genetics and breeding research. Imperial Press, London, pp 29–56

    Chapter  Google Scholar 

  • Gargiulo S, Gramanzini M, Megna R, Greco A, Albanese S, Manfredi C, Brunetti A (2014) Evaluation of growth patterns and body composition in C57Bl/6J mice using dual energy X-ray absorptiometry. Biomed Res Int. https://doi.org/10.1155/2014/253067

    Article  PubMed  PubMed Central  Google Scholar 

  • Gharib WH, Robinson-Rechavi M (2011) When orthologs diverge between human and mouse. Brief Bioinform 12:436–441

    Article  PubMed  PubMed Central  Google Scholar 

  • Grobet L, Martin LJ, Poncelet D, Pirottin D, Brouwers B, Riquet J, Schoeberlein A, Dunner S, Menissier F, Massabanda J, Fries R, Hanset R, Georges M (1997) A deletion in the bovine myostatin gene causes the double-muscled phenotype in cattle. Nat Genet 17:71–74

    Article  PubMed  CAS  Google Scholar 

  • Gularte-Merida R, Farber CR, Verdugo RA, Islas-Trejo A, Famula TR, Warden CH, Medrano JF (2015) Overlapping mouse subcongenic strains successfully separate two linked body fat QTL on distal MMU 2. BMC Genom 16:16

    Article  CAS  Google Scholar 

  • Guo YF, Zhang LS, Liu YJ, Hu HG, Li J, Tian Q, Yu P, Zhang F, Yang TL, Guo Y, Peng XL, Dai M, Chen W, Deng HW (2013) Suggestion of GLYAT gene underlying variation of bone size and body lean mass as revealed by a bivariate genome-wide association study. Hum Genet 132:189–199

    Article  PubMed  CAS  Google Scholar 

  • Gysel T, Calders P, Cambier D, Roman de Mettelinge T, Kaufman JM, Taes Y, Zmierczak HG, Goemaere S (2014) Association between insulin resistance, lean mass and muscle torque/force in proximal versus distal body parts in healthy young men. J Musculoskelet Neuronal Interact 14:41–49

    PubMed  CAS  Google Scholar 

  • Hai R, Zhang L, Pei Y, Zhao L, Ran S, Han Y, Zhu X, Shen H, Tian Q, Deng H (2012) Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche. Sci China Life Sci 55:516–520

    Article  PubMed  CAS  Google Scholar 

  • Halldorsdottir S, Carmody J, Boozer CN, Leduc CA, Leibel RL (2009) Reproducibility and accuracy of body composition assessments in mice by dual energy X-ray absorptiometry and time domain nuclear magnetic resonance. Int J Body Compos Res 7:147–154

    PubMed  PubMed Central  Google Scholar 

  • Hauge H, Patzke S, Aasheim HC (2007) Characterization of the FAM110 gene family. Genomics 90:14–27

    Article  PubMed  CAS  Google Scholar 

  • Hayakawa T, Yamasita H, Iwaki T (2001) A color atlas of sectional anatomy of the mouse. Braintree Scientific Inc, Braintree

    Google Scholar 

  • Hrbek T, de Brito RA, Wang B, Pletscher LS, Cheverud JM (2006) Genetic characterization of a new set of recombinant inbred lines (LGXSM) formed from the inter-cross of SM/J and LG/J inbred mouse strains. Mamm Genome 17:417–429

    Article  PubMed  CAS  Google Scholar 

  • Jones AS, Johnson MS, Nagy TR (2009) Validation of quantitative magnetic resonance for the determination of body composition of mice. Int J Body Compos Res 7:67–72

    PubMed  PubMed Central  CAS  Google Scholar 

  • Kambadur R, Sharma M, Smith TP, Bass JJ (1997) Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Res 7:910–916

    Article  PubMed  CAS  Google Scholar 

  • Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    Article  PubMed  PubMed Central  Google Scholar 

  • Keane TM, Goodstadt L, Danecek P, White MA, Wong K, Yalcin B, Heger A, Agam A, Slater G, Goodson M, Furlotte NA, Eskin E, Nellaker C, Whitley H, Cleak J, Janowitz D, Hernandez-Pliego P, Edwards A, Belgard TG, Oliver PL, McIntyre RE, Bhomra A, Nicod J, Gan X, Yuan W, van der Weyden L, Steward CA, Bala S, Stalker J, Mott R, Durbin R, Jackson IJ, Czechanski A, Guerra-Assuncao JA, Donahue LR, Reinholdt LG, Payseur BA, Ponting CP, Birney E, Flint J, Adams DJ (2011) Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477:289–294

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kemp JP, Sayers A, Smith GD, Tobias JH, Evans DM (2016) Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children. Int J Epidemiol 45:1560–1572

    Article  PubMed  PubMed Central  Google Scholar 

  • Kleyn PW, Fan W, Kovats SG, Lee JJ, Pulido JC, Wu Y, Berkemeier LR, Misumi DJ, Holmgren L, Charlat O, Woolf EA, Tayber O, Brody T, Shu P, Hawkins F, Kennedy B, Baldini L, Ebeling C, Alperin GD, Deeds J, Lakey ND, Culpepper J, Chen H, Glucksmann-Kuis MA, Carlson GA, Duyk GM, Moore KJ (1996) Identification and characterization of the mouse obesity gene tubby: a member of a novel gene family. Cell 85:281–290

    Article  PubMed  CAS  Google Scholar 

  • Kobayashi M, Ohno T, Ihara K, Murai A, Kumazawa M, Hoshino H, Iwanaga K, Iwai H, Hamana Y, Ito M, Ohno K, Horio F (2014) Searching for genomic region of high-fat diet-induced type 2 diabetes in mouse chromosome 2 by analysis of congenic strains. PLoS ONE 9:e96271

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Koscielny G, Yaikhom G, Iyer V, Meehan TF, Morgan H, Atienza-Herrero J, Blake A, Chen CK, Easty R, Di Fenza A, Fiegel T, Grifiths M, Horne A, Karp NA, Kurbatova N, Mason JC, Matthews P, Oakley DJ, Qazi A, Regnart J, Retha A, Santos LA, Sneddon DJ, Warren J, Westerberg H, Wilson RJ, Melvin DG, Smedley D, Brown SD, Flicek P, Skarnes WC, Mallon AM, Parkinson H (2014) The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucl Acids Res 42:D802–D809

    Article  CAS  Google Scholar 

  • Lawson HA, Cheverud JM, Wolf JB (2013) Genomic imprinting and parent-of-origin effects on complex traits. Nat Rev Genet 14:609–617

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Li S, Crenshaw EB 3rd, Rawson EJ, Simmons DM, Swanson LW, Rosenfeld MG (1990) Dwarf locus mutants lacking three pituitary cell types result from mutations in the POU-domain gene pit-1. Nature 347:528–533

    Article  PubMed  CAS  Google Scholar 

  • Lin SC, Lin CR, Gukovsky I, Lusis AJ, Sawchenko PE, Rosenfeld MG (1993) Molecular basis of the little mouse phenotype and implications for cell type-specific growth. Nature 364:208–213

    Article  PubMed  CAS  Google Scholar 

  • Lin C, Theodorides ML, McDaniel AH, Tordoff MG, Zhang Q, Li X, Bosak N, Bachmanov AA, Reed DR (2013) QTL analysis of dietary obesity in C57BL/6ByJ X 129P3/J F2 mice: diet- and sex-dependent effects. PloS ONE 8:e68776

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lin C, Fesi BD, Marquis M, Bosak NP, Theodorides ML, Avigdor M, McDaniel AH, Duke FF, Lysenko A, Khoshnevisan A, Gantick BR, Arayata CJ, Nelson TM, Bachmanov AA, Reed DR (2015) Body composition qtls identified in intercross populations are reproducible in consomic mouse strains. PLoS ONE 10:e0141494

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lin C, Fesi BD, Marquis M, Bosak NP, Lysenko A, Koshnevisan MA, Duke FF, Theodorides ML, Nelson TM, McDaniel AH, Avigdor M, Arayata CJ, Shaw L, Bachmanov AA, Reed DR (2017) Adiposity QTL Adip20 decomposes into at least four loci when dissected using congenic strains. PLoS ONE 12:e0188972

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lin C, Bosak N, Nelson T, Theodorides M, Smith Z, Kirkey M, Avigdor M, Gantick B, Khoshnevisan A, Lysenko A, Reed D, Bachmanov A (in preparation) Construction of reciprocal chromosome substitution strains from 129P3/J and C57BL/6ByJ mice

  • Lionikas A, Cheng R, Lim JE, Palmer AA, Blizard DA (2010) Fine-mapping of muscle weight QTL in LG/J and SM/J intercrosses. Physiol Genom 42:33–38

    Google Scholar 

  • Liu XG, Tan LJ, Lei SF, Liu YJ, Shen H, Wang L, Yan H, Guo YF, Xiong DH, Chen XD, Pan F, Yang TL, Zhang YP, Guo Y, Tang NL, Zhu XZ, Deng HY, Levy S, Recker RR, Papasian CJ, Deng HW (2009) Genome-wide association and replication studies identified TRHR as an important gene for lean body mass. Am J Hum Genet 84:418–423

    Article  PubMed  PubMed Central  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  PubMed  PubMed Central  CAS  Google Scholar 

  • McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F (2016) The ensembl variant effect predictor. Genome Biol 17:122

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • McMullan RC, Kelly SA, Hua K, Buckley BK, Faber JE, Pardo-Manuel de Villena F, Pomp D (2016) Long-term exercise in mice has sex-dependent benefits on body composition and metabolism during aging. Physiol Rep 4:21

    Article  Google Scholar 

  • McPherron AC, Lee SJ (1997) Double muscling in cattle due to mutations in the myostatin gene. Proc Natl Acad Sci USA 94:12457–12461

    Article  PubMed  CAS  Google Scholar 

  • McPherron AC, Lawler AM, Lee SJ (1997) Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature 387:83–90

    Article  PubMed  CAS  Google Scholar 

  • Mele M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R, Segre AV, Djebali S, Niarchou A, Wright FA, Lappalainen T, Calvo M, Getz G, Dermitzakis ET, Ardlie KG, Guigo R (2015) The human transcriptome across tissues and individuals. Science 348:660–665

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mollah MB, Ishikawa A (2011) Intersubspecific subcongenic mouse strain analysis reveals closely linked QTLs with opposite effects on body weight. Mamm Genome 22:282–289

    Article  Google Scholar 

  • Mosher DS, Quignon P, Bustamante CD, Sutter NB, Mellersh CS, Parker HG, Ostrander EA (2007) A mutation in the myostatin gene increases muscle mass and enhances racing performance in heterozygote dogs. PLoS Genet 3:e79

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Nagy TR, Clair AL (2000) Precision and accuracy of dual-energy X-ray absorptiometry for determining in vivo body composition of mice. Obes Res 8:392–398

    Article  PubMed  CAS  Google Scholar 

  • Nixon JP, Zhang M, Wang C, Kuskowski MA, Novak CM, Levine JA, Billington CJ, Kotz CM (2010) Evaluation of a quantitative magnetic resonance imaging system for whole body composition analysis in rodents. Obesity (Silver Spring) 18:1652–1659

    Article  Google Scholar 

  • Noben-Trauth K, Naggert JK, North MA, Nishina PM (1996) A candidate gene for the mouse mutation tubby. Nature 380:534–538

    Article  PubMed  CAS  Google Scholar 

  • Pei YF, Zhang L, Liu Y, Li J, Shen H, Liu YZ, Tian Q, He H, Wu S, Ran S, Han Y, Hai R, Lin Y, Zhu J, Zhu XZ, Papasian CJ, Deng HW (2014) Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet 23:820–830

    Article  PubMed  CAS  Google Scholar 

  • Peripato AC, De Brito RA, Matioli SR, Pletscher LS, Vaughn TT, Cheverud JM (2004) Epistasis affecting litter size in mice. J Evol Biol 17:593–602

    Article  PubMed  CAS  Google Scholar 

  • Rapp JP, Joe B (2012) Use of contiguous congenic strains in analyzing compound QTLs. Physiol Genom 44:117–120

    Article  CAS  Google Scholar 

  • Reed DR, Li X, McDaniel AH, Lu K, Li S, Tordoff MG, Price RA, Bachmanov AA (2003) Loci on chromosomes 2, 4, 9, and 16 for body weight, body length, and adiposity identified in a genome scan of an F2 intercross between the 129P3/J and C57BL/6ByJ mouse strains. Mamm Genome 14:302–313

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Reed DR, McDaniel AH, Li X, Tordoff MG, Bachmanov AA (2006) Quantitative trait loci for individual adipose depot weights in C57BL/6ByJ x 129P3/J F(2) mice. Mamm Genome 17:1065–1077

    Article  PubMed  PubMed Central  Google Scholar 

  • Reed DR, Bachmanov AA, Tordoff MG (2007) Forty mouse strain survey of body composition. Physiol Behav 91:593–600

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Reed DR, Lawler MP, Tordoff MG (2008) Reduced body weight is a common effect of gene knockout in mice. BMC Genet 9:4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Reed DR, Duke FF, Ellis HK, Rosazza MR, Lawler MP, Alarcon LK, Tordoff MG (2011) Body fat distribution and organ weights of 14 common strains and a 22-strain consomic panel of rats. Physiol Behav 103:523–529

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Revelle W (2018) Procedures for psychological, psychometric, and personality research, version 1.8.3. Northwestern University, Evanston, IL. https://CRAN.R-project.org/package=psych

    Google Scholar 

  • Reynolds D, Kunz T (2001) Standard methods for destructive body compositon analysis. In: Speakman J (ed) Body composition analysis of animals: a handbook of nondestructive methods. Cambridge University Press, Cambridge, pp 39–55

    Chapter  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Rocha JL, Eisen EJ, Van Vleck LD, Pomp D (2004b) A large-sample QTL study in mice: I. Growth. Mamm Genome 15:83–99

    Article  PubMed  CAS  Google Scholar 

  • Schuelke M, Wagner KR, Stolz LE, Hubner C, Riebel T, Komen W, Braun T, Tobin JF, Lee SJ (2004) Myostatin mutation associated with gross muscle hypertrophy in a child. N Engl J Med 350:2682–2688

    Article  PubMed  CAS  Google Scholar 

  • Shao H, Sinasac DS, Burrage LC, Hodges CA, Supelak PJ, Palmert MR, Moreno C, Cowley AW Jr, Jacob HJ, Nadeau JH (2010) Analyzing complex traits with congenic strains. Mamm Genome 21:276–286

    Article  PubMed  PubMed Central  Google Scholar 

  • Singer JB, Hill AE, Burrage LC, Olszens KR, Song J, Justice M, O’Brien WE, Conti DV, Witte JS, Lander ES, Nadeau JH (2004) Genetic dissection of complex traits with chromosome substitution strains of mice. Science 304:445–448

    Article  PubMed  CAS  Google Scholar 

  • Snell GD (1929) Dwarf, a new mendelian recessive character of the house mouse. Proc Natl Acad Sci USA 15:733–734

    Article  PubMed  CAS  Google Scholar 

  • Solberg Woods LC (2014) QTL mapping in outbred populations: successes and challenges. Physiol Genom 46:81–90

    Article  Google Scholar 

  • Speakman JR, Fletcher Q, Vaanholt L (2013) The ‘39 steps’: an algorithm for performing statistical analysis of data on energy intake and expenditure. Dis Model Mech 6:293–301

    Article  PubMed  PubMed Central  Google Scholar 

  • Spiezio SH, Amon LM, McMillen TS, Vick CM, Houston BA, Caldwell M, Ogimoto K, Morton GJ, Kirk EA, Schwartz MW, Nadeau JH, LeBoeuf RC (2014) Genetic determinants of atherosclerosis, obesity, and energy balance in consomic mice. Mamm Genome 25:549–563

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Su AI, Hogenesch JB (2007) Power-law-like distributions in biomedical publications and research funding. Genome Biol 8:404

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779

    Article  PubMed  PubMed Central  Google Scholar 

  • Szabo G, Dallmann G, Muller G, Patthy L, Soller M, Varga L (1998) A deletion in the myostatin gene causes the compact (Cmpt) hypermuscular mutation in mice. Mamm Genome 9:671–672

    Article  PubMed  CAS  Google Scholar 

  • Tartaglia LA, Dembski M, Weng X, Deng N, Culpepper J, Devos R, Richards GJ, Campfield LA, Clark FT, Deeds J, Muir C, Sanker S, Moriarty A, Moore KJ, Smutko JS, Mays GG, Wool EA, Monroe CA, Tepper RI (1995) Identification and expression cloning of a leptin receptor, OB-R. Cell 83:1263–1271

    Article  PubMed  CAS  Google Scholar 

  • Thompson DB, Aboulhouda S, Hysolli E, Smith CJ, Wang S, Castanon O, Church GM (2017) The future of multiplexed eukaryotic genome engineering. ACS Chem Biol 13:313–325

    Google Scholar 

  • Tordoff MG, Pilchak DM, Williams JA, McDaniel AH, Bachmanov AA (2002) The maintenance diets of C57BL/6J and 129X1/SvJ mice influence their taste solution preferences: implications for large-scale phenotyping projects. J Nutr 132:2288–2297

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tschop MH, Speakman JR, Arch JR, Auwerx J, Bruning JC, Chan L, Eckel RH, Farese RV Jr, Galgani JE, Hambly C, Herman MA, Horvath TL, Kahn BB, Kozma SC, Maratos-Flier E, Muller TD, Munzberg H, Pfluger PT, Plum L, Reitman ML, Rahmouni K, Shulman GI, Thomas G, Kahn CR, Ravussin E (2012) A guide to analysis of mouse energy metabolism. Nat Methods 9:57–63

    Article  CAS  Google Scholar 

  • Urano T, Shiraki M, Sasaki N, Ouchi Y, Inoue S (2014) Large-scale analysis reveals a functional single-nucleotide polymorphism in the 5′-flanking region of PRDM16 gene associated with lean body mass. Aging Cell 13:739–743

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Warden CH, Stone S, Chiu S, Diament AL, Corva P, Shattuck D, Riley R, Hunt SC, Easlick J, Fisler JS, Medrano JF (2004) Identification of a congenic mouse line with obesity and body length phenotypes. Mamm Genome 15:460–471

    Article  PubMed  CAS  Google Scholar 

  • White JK, Gerdin AK, Karp NA, Ryder E, Buljan M, Bussell JN, Salisbury J, Clare S, Ingham NJ, Podrini C, Houghton R, Estabel J, Bottomley JR, Melvin DG, Sunter D, Adams NC, Tannahill D, Logan DW, Macarthur DG, Flint J, Mahajan VB, Tsang SH, Smyth I, Watt FM, Skarnes WC, Dougan G, Adams DJ, Ramirez-Solis R, Bradley A, Steel KP (2013) Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell 154:452–464

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wilson LE, Harlid S, Xu Z, Sandler DP, Taylor JA (2017) An epigenome-wide study of body mass index and DNA methylation in blood using participants from the Sister Study cohort. Int J Obes (Lond) 41:194–199

    Article  CAS  Google Scholar 

  • Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, Chu AY, Estrada K, Luan J, Kutalik Z, Amin N, Buchkovich ML, Croteau-Chonka DC, Day FR, Duan Y, Fall T, Fehrmann R, Ferreira T, Jackson AU, Karjalainen J, Lo KS, Locke AE, Magi R, Mihailov E, Porcu E, Randall JC, Scherag A, Vinkhuyzen AA, Westra HJ, Winkler TW, Workalemahu T, Zhao JH, Absher D, Albrecht E, Anderson D, Baron J, Beekman M, Demirkan A, Ehret GB, Feenstra B, Feitosa MF, Fischer K, Fraser RM, Goel A, Gong J, Justice AE, Kanoni S, Kleber ME, Kristiansson K, Lim U, Lotay V, Lui JC, Mangino M, Mateo Leach I, Medina-Gomez C, Nalls MA, Nyholt DR, Palmer CD, Pasko D, Pechlivanis S, Prokopenko I, Ried JS, Ripke S, Shungin D, Stancakova A, Strawbridge RJ, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van Setten J, Van Vliet-Ostaptchouk JV, Wang Z, Yengo L, Zhang W, Afzal U, Arnlov J, Arscott GM, Bandinelli S, Barrett A, Bellis C, Bennett AJ, Berne C, Bluher M, Bolton JL, Bottcher Y, Boyd HA, Bruinenberg M, Buckley BM, Buyske S, Caspersen IH, Chines PS, Clarke R, Claudi-Boehm S, Cooper M, Daw EW, De Jong PA, Deelen J, Delgado G, Denny JC, Dhonukshe-Rutten R, Dimitriou M, Doney AS, Dorr M, Eklund N, Eury E, Folkersen L, Garcia ME, Geller F, Giedraitis V, Go AS, Grallert H, Grammer TB, Grassler J, Gronberg H, de Groot LC, Groves CJ, Haessler J, Hall P, Haller T, Hallmans G, Hannemann A, Hartman CA, Hassinen M, Hayward C, Heard-Costa NL, Helmer Q, Hemani G, Henders AK, Hillege HL, Hlatky MA, Hoffmann W, Hoffmann P, Holmen O, Houwing-Duistermaat JJ, Illig T, Isaacs A, James AL, Jeff J, Johansen B, Johansson A, Jolley J, Juliusdottir T, Junttila J, Kho AN, Kinnunen L, Klopp N, Kocher T, Kratzer W, Lichtner P, Lind L, Lindstrom J, Lobbens S, Lorentzon M, Lu Y, Lyssenko V, Magnusson PK, Mahajan A, Maillard M, McArdle WL, McKenzie CA, McLachlan S, McLaren PJ, Menni C, Merger S, Milani L, Moayyeri A, Monda KL, Morken MA, Muller G, Muller-Nurasyid M, Musk AW, Narisu N, Nauck M, Nolte IM, Nothen MM, Oozageer L, Pilz S, Rayner NW, Renstrom F, Robertson NR, Rose LM, Roussel R, Sanna S, Scharnagl H, Scholtens S, Schumacher FR, Schunkert H, Scott RA, Sehmi J, Seufferlein T, Shi J, Silventoinen K, Smit JH, Smith AV, Smolonska J, Stanton AV, Stirrups K, Stott DJ, Stringham HM, Sundstrom J, Swertz MA, Syvanen AC, Tayo BO, Thorleifsson G, Tyrer JP, van Dijk S, van Schoor NM, van der Velde N, van Heemst D, van Oort FV, Vermeulen SH, Verweij N, Vonk JM, Waite LL, Waldenberger M, Wennauer R, Wilkens LR, Willenborg C, Wilsgaard T, Wojczynski MK, Wong A, Wright AF, Zhang Q, Arveiler D, Bakker SJ, Beilby J, Bergman RN, Bergmann S, Biffar R, Blangero J, Boomsma DI, Bornstein SR, Bovet P, Brambilla P, Brown MJ, Campbell H, Caulfield MJ, Chakravarti A, Collins R, Collins FS, Crawford DC, Cupples LA, Danesh J, de Faire U, den Ruijter HM, Erbel R, Erdmann J, Eriksson JG, Farrall M, Ferrannini E, Ferrieres J, Ford I, Forouhi NG, Forrester T, Gansevoort RT, Gejman PV, Gieger C, Golay A, Gottesman O, Gudnason V, Gyllensten U, Haas DW, Hall AS, Harris TB, Hattersley AT, Heath AC, Hengstenberg C, Hicks AA, Hindorff LA, Hingorani AD, Hofman A, Hovingh GK, Humphries SE, Hunt SC, Hypponen E, Jacobs KB, Jarvelin MR, Jousilahti P, Jula AM, Kaprio J, Kastelein JJ, Kayser M, Kee F, Keinanen-Kiukaanniemi SM, Kiemeney LA, Kooner JS, Kooperberg C, Koskinen S, Kovacs P, Kraja AT, Kumari M, Kuusisto J, Lakka TA, Langenberg C, Le Marchand L, Lehtimaki T, Lupoli S, Madden PA, Mannisto S, Manunta P, Marette A, Matise TC, McKnight B, Meitinger T, Moll FL, Montgomery GW, Morris AD, Morris AP, Murray JC, Nelis M, Ohlsson C, Oldehinkel AJ, Ong KK, Ouwehand WH, Pasterkamp G, Peters A, Pramstaller PP, Price JF, Qi L, Raitakari OT, Rankinen T, Rao DC, Rice TK, Ritchie M, Rudan I, Salomaa V, Samani NJ, Saramies J, Sarzynski MA, Schwarz PE, Sebert S, Sever P, Shuldiner AR, Sinisalo J, Steinthorsdottir V, Stolk RP, Tardif JC, Tonjes A, Tremblay A, Tremoli E, Virtamo J, Vohl MC, Electronic Medical R, Genomics C, Consortium MI, Consortium P, LifeLines Cohort S, Amouyel P, Asselbergs FW, Assimes TL, Bochud M, Boehm BO, Boerwinkle E, Bottinger EP, Bouchard C, Cauchi S, Chambers JC, Chanock SJ, Cooper RS, de Bakker PI, Dedoussis G, Ferrucci L, Franks PW, Froguel P, Groop LC, Haiman CA, Hamsten A, Hayes MG, Hui J, Hunter DJ, Hveem K, Jukema JW, Kaplan RC, Kivimaki M, Kuh D, Laakso M, Liu Y, Martin NG, Marz W, Melbye M, Moebus S, Munroe PB, Njolstad I, Oostra BA, Palmer CN, Pedersen NL, Perola M, Perusse L, Peters U, Powell JE, Power C, Quertermous T, Rauramaa R, Reinmaa E, Ridker PM, Rivadeneira F, Rotter JI, Saaristo TE, Saleheen D, Schlessinger D, Slagboom PE, Snieder H, Spector TD, Strauch K, Stumvoll M, Tuomilehto J, Uusitupa M, van der Harst P, Volzke H, Walker M, Wareham NJ, Watkins H, Wichmann HE, Wilson JF, Zanen P, Deloukas P, Heid IM, Lindgren CM, Mohlke KL, Speliotes EK, Thorsteinsdottir U, Barroso I, Fox CS, North KE, Strachan DP, Beckmann JS, Berndt SI, Boehnke M, Borecki IB, McCarthy MI, Metspalu A, Stefansson K, Uitterlinden AG, van Duijn CM, Franke L, Willer CJ, Price AL, Lettre G, Loos RJ, Weedon MN, Ingelsson E, O’Connell JR, Abecasis GR, Chasman DI, Goddard ME, Visscher PM, Hirschhorn JN, Frayling TM (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46:1173–1186

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wuschke S, Dahm S, Schmidt C, Joost HG, Al-Hasani H (2006) A meta-analysis of quantitative trait loci associated with body weight and adiposity in mice. Int J Obes (Lond) 31:829

    Article  CAS  Google Scholar 

  • Yalcin B, Wong K, Agam A, Goodson M, Keane TM, Gan X, Nellaker C, Goodstadt L, Nicod J, Bhomra A, Hernandez-Pliego P, Whitley H, Cleak J, Dutton R, Janowitz D, Mott R, Adams DJ, Flint J (2011) Sequence-based characterization of structural variation in the mouse genome. Nature 477:326–329

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Yao Y, Yonezawa A, Yoshimatsu H, Masuda S, Katsura T, Inui K (2010) Identification and comparative functional characterization of a new human riboflavin transporter hRFT3 expressed in the brain. J Nutr 140:1220–1226

    Article  PubMed  CAS  Google Scholar 

  • Yuan R, Flurkey K, Meng Q, Astle MC, Harrison DE (2012) Genetic regulation of life span, metabolism, and body weight in Pohn, a new wild-derived mouse strain. J Gerontol A Biomed Sci Med Sci 68:27–35

    Google Scholar 

  • Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM (1994) Positional cloning of the mouse obese gene and its human homologue. Nature 372:425–432

    Article  PubMed  CAS  Google Scholar 

  • Zhao J, Xiao P, Guo Y, Liu YJ, Pei YF, Yang TL, Pan F, Chen Y, Shen H, Zhao LJ, Papasian CJ, Drees BM, Hamilton JJ, Deng HY, Recker RR, Deng HW (2008) Bivariate genome linkage analysis suggests pleiotropic effects on chromosomes 20p and 3p for body fat mass and lean mass. Genet Res (Camb) 90:259–268

    Article  CAS  Google Scholar 

  • Zillikens MCA, (2017) Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 8:80

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, Kutalik Z, Luan J, Malkin I, Ried JS, Smith AV, Thorleifsson G, Vandenput L, Hua Zhao J, Zhang W, Aghdassi A, Akesson K, Amin N, Baier LJ, Barroso I, Bennett DA, Bertram L, Biffar R, Bochud M, Boehnke M, Borecki IB, Buchman AS, Byberg L, Campbell H, Campos Obanda N, Cauley JA, Cawthon PM, Cederberg H, Chen Z, Cho NH, Jin Choi H, Claussnitzer M, Collins F, Cummings SR, De Jager PL, Demuth I, Dhonukshe-Rutten RAM, Diatchenko L, Eiriksdottir G, Enneman AW, Erdos M, Eriksson JG, Eriksson J, Estrada K, Evans DS, Feitosa MF, Fu M, Garcia M, Gieger C, Girke T, Glazer NL, Grallert H, Grewal J, Han BG, Hanson RL, Hayward C, Hofman A, Hoffman EP, Homuth G, Hsueh WC, Hubal MJ, Hubbard A, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson JO, Jordan JM, Jula A, Karlsson M, Khaw KT, Kilpelainen TO, Klopp N, Kloth JSL, Koistinen HA, Kraus WE, Kritchevsky S, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Launer LJ, Lee JY, Lerch MM, Lewis JR, Lind L, Lindgren C, Liu Y, Liu T, Liu Y, Ljunggren O, Lorentzon M, Luben RN, Maixner W, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellstrom D, Melov S, Michaelsson K, Mitchell BD, Morris AP, Mosekilde L, Newman A, Nielson CM, O’Connell JR, Oostra BA, Orwoll ES, Palotie A, Parker S, Peacock M, Perola M, Peters A, Polasek O, Prince RL, Raikkonen K, Ralston SH, Ripatti S, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schadt EE, Schipf S, Scott L, Sehmi J, Shen J, Soo Shin C, Sigurdsson G, Smith S, Soranzo N, Stancakova A, Steinhagen-Thiessen E, Streeten EA, Styrkarsdottir U, Swart KMA, Tan ST, Tarnopolsky MA, Thompson P, Thomson CA, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Tuomilehto J, van Schoor NM, Verma A, Vollenweider P, Volzke H, Wactawski-Wende J, Walker M, Weedon MN, Welch R, Wichmann HE, Widen E, Williams FMK, Wilson JF, Wright NC, Xie W, Yu L, Zhou Y, Chambers JC, Doring A, van Duijn CM, Econs MJ, Gudnason V, Kooner JS, Psaty BM, Spector TD, Stefansson K, Rivadeneira F, Uitterlinden AG, Wareham NJ, Ossowski V, Waterworth D, Loos RJF, Karasik D, Harris TB, Ohlsson C, Kiel DP (2017) Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 8:80

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the assistance with animal breeding of Rebecca James, Liang-Dar (Daniel) Hwang, Zakiyyah Smith, Matt Kirkey, Amy Colihan, and Laurie Pippett. We also acknowledge Richard Copeland and the consistent high-quality assistance of the animal care staff at the Monell Chemical Senses Center, and thank them for their service. Michael G. Tordoff and Gary K. Beauchamp commented on a draft of the manuscript. We thank two anonymous reviewers for their time spent providing constructive comments on this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Danielle R. Reed.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

335_2018_9746_MOESM1_ESM.tif

Supplementary Fig. S1 Correlations among three measures, body weight and lean body mass by DEXA and MR, in male mice of each mapping population. (a) Comparison of MR and DEXA results. (b) Comparison of body weight and lean body mass by MR (red data points) or DEXA (green data points). Despite the small sample size of two populations (B6-Chr2129 and C57BL/6ByJ), the three measures are highly correlated (r-values=0.62–0.95, p<0.00001). “B6.129-Burly1” refers to all congenic mice from Table 1. (TIF 105 KB)

Supplementary Table S1 (DOC 29 KB)

335_2018_9746_MOESM3_ESM.tif

Supplementary Fig. S2: Monthly lean body mass in male mice from two populations: mice with B6 background (a; n=319) and mice with 129 background (b; n=13). Monthly lean body mass increased significantly (*p<0.05) from 90 to 180 days for both strains. There is no significant difference (p=0.84) in lean body mass between 150 and 180 days for the 129 strain. (TIF 60 KB)

Supplementary Table S2 (XLSX 11 KB)

335_2018_9746_MOESM5_ESM.tif

Supplementary Fig. S3: The Burly1 locus region was isolated by comparing the 18 informative congenic strains. (a) Average lean body weight compared using a general linear model with body weight as a covariate, among all congenic mice grouped by genotype at each marker. The x-axis shows marker positions in Mb on chromosome 2 (mChr2); y-axis, –log10-transformed p-values. (b) We determined which congenic strains retained the Burly1 locus (i.e., were ’positive’) by comparing within each strain (shown at left) the average lean body weights of littermates with and without the donor fragment. Black bar indicates the donor region retained the Burly1 locus; gray bar, donor region did not retain the locus; blue bar, region contributed by the host strain. For the three strains labeled with the red $, there was no reliable genotype effect on lean body mass. Burly1-positive strains share a common region (red lines; 0.8 Mb from rs33197365 at 151.9 Mb to rs3700604 at 152.7 Mb) that Burly1-negative strains do not share. (c) Comparison of allele effect across strains. The allele effect direction matches that from the consomic mice, with the B6 allele increasing the trait. (TIF 351 KB)

Supplementary Table S3 (XLSX 12 KB)

335_2018_9746_MOESM7_ESM.tif

Supplementary Fig. S4: The Burly1 is a lean-body-mass-specific locus that has no effect on body fat mass in all congenic strains. (a) Average body fat weight compared using a general linear model with body weight as a covariate, among all congenic mice within each congenic strain grouped by genotype at each marker. The x-axis shows marker positions in Mb on chromosome 2 (mChr2); y-axis, –log10-transformed p-values. The blue bar shows the confidence interval of the fat locus, defined by a drop of 2 units of –log p-value. (b) The 0.8 Mb Burly1 region defined in the congenic strains, which is out of the fat locus region (blue bar in a). (c) A significant genotype effect on body fat mass was found only in strains C1 and C2 (red asterisks in b) that retain the two largest 129-derived donor fragments, and no genotype effect was found for the other 16 strains. Thus, the location of the fat locus differs from the Burly1 region. *p<0.05; post hoc tests. (TIF 336 KB)

Supplementary Table S4 (XLSX 11 KB)

335_2018_9746_MOESM9_ESM.tiff

Supplementary Fig. S5: Statistical comparison of Pearson correlation coefficients for body weight and each organ weight in congenic mice grouped by the Burly1 genotype (H=129/B6: N=169; A=B6/B6: N=198). (TIFF 1020 KB)

Supplementary Table S5 (XLSX 16 KB)

335_2018_9746_MOESM11_ESM.tif

Supplementary Fig. S6: No Burly1 genotype response to oral glucose tolerance tests in mice and their littermate controls from congenic strain C2.5. Data are mean ± SEM of genotype, and the significance of the genotype effect was evaluated by post hoc tests using p=0.05 as a significance level. (TIF 51 KB)

Supplementary Table S6 (XLSX 10 KB)

335_2018_9746_MOESM13_ESM.tif

Supplementary Fig. S7: Statistical category of variants within the Burly1 region based on their predicted variant effect. (TIF 115 KB)

Supplementary Table S7 (XLSX 24 KB)

Supplementary Table S8 (XLSX 11 KB)

Supplementary Table S9 (XLSX 54 KB)

Supplementary Table S10 (XLSX 17 KB)

Supplementary Table S11 (DOCX 15 KB)

Supplementary Table S12 (XLSX 11 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, C., Fesi, B.D., Marquis, M. et al. Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2. Mamm Genome 29, 325–343 (2018). https://doi.org/10.1007/s00335-018-9746-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00335-018-9746-7

Keywords

Navigation