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Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus


Both obesity and being underweight have been associated with increased mortality1,2. Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m2 in adults and ≤ −2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive3,4,5, feeding and eating disorder and/or anorexia nervosa6,7. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported8,9. We previously showed that hemizygosity of a 600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities10. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.

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Figure 1: Effect of the chromosome 16p11.2 duplication on BMI and head circumference.
Figure 2: Transcript levels for genes within and near to the 16p11.2 rearrangements.


  1. 1

    Berrington de Gonzalez, A. et al. Body-mass index and mortality among 1.46 million white adults. N. Engl. J. Med. 363, 2211–2219 (2010)

    CAS  Article  Google Scholar 

  2. 2

    Flegal, K. M., Graubard, B. I., Williamson, D. F. & Gail, M. H. Cause-specific excess deaths associated with underweight, overweight, and obesity. J. Am. Med. Assoc. 298, 2028–2037 (2007)

    CAS  Article  Google Scholar 

  3. 3

    Olsen, E. M. et al. Failure to thrive: the prevalence and concurrence of anthropometric criteria in a general infant population. Arch. Dis. Child. 92, 109–114 (2007)

    CAS  Article  Google Scholar 

  4. 4

    Corbett, S. S. & Drewett, R. F. To what extent is failure to thrive in infancy associated with poorer cognitive development? A review and meta-analysis. J. Child Psychol. Psychiatry 45, 641–654 (2004)

    CAS  Article  Google Scholar 

  5. 5

    Rudolf, M. C. & Logan, S. What is the long term outcome for children who fail to thrive? A systematic review. Arch. Dis. Child. 90, 925–931 (2005)

    CAS  Article  Google Scholar 

  6. 6

    Bravender, T. et al. Classification of eating disturbance in children and adolescents: proposed changes for the DSM-V. Eur. Eat. Disord. Rev. 18, 79–89 (2010)

    CAS  Article  Google Scholar 

  7. 7

    American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR 39–134 and 583–596 (Am. Psychiatric Assoc., 2000)

    Google Scholar 

  8. 8

    Scherag, S., Hebebrand, J. & Hinney, A. Eating disorders: the current status of molecular genetic research. Eur. Child Adolesc. Psychiatry 19, 211–226 (2010)

    Article  Google Scholar 

  9. 9

    Wang, K. et al. A genome-wide association study on common SNPs and rare CNVs in anorexia nervosa. Mol. Psychiatry 10.1038/mp.2010.107 (2010)

  10. 10

    Walters, R. G. et al. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 463, 671–675 (2010)

    CAS  Article  ADS  Google Scholar 

  11. 11

    Marshall, C. R. et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008)

    CAS  Article  Google Scholar 

  12. 12

    McCarthy, S. E. et al. Microduplications of 16p11.2 are associated with schizophrenia. Nature Genet. 41, 1223–1227 (2009)

    CAS  Article  Google Scholar 

  13. 13

    Weiss, L. A. et al. Association between microdeletion and microduplication at 16p11.2 and autism. N. Engl. J. Med. 358, 667–675 (2008)

    CAS  Article  Google Scholar 

  14. 14

    Crespi, B., Stead, P. & Elliot, M. Evolution in health and medicine Sackler colloquium: comparative genomics of autism and schizophrenia. Proc. Natl Acad. Sci. USA 107 (Suppl 1). 1736–1741 (2010)

    CAS  Article  ADS  Google Scholar 

  15. 15

    Bochukova, E. G. et al. Large, rare chromosomal deletions associated with severe early-onset obesity. Nature 463, 666–670 (2010)

    CAS  Article  ADS  Google Scholar 

  16. 16

    Firmann, M. et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc. Disord. 8, 6 (2008)

    Article  Google Scholar 

  17. 17

    Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nature Genet. 41, 35–46 (2009)

    CAS  Article  Google Scholar 

  18. 18

    Nelis, M. et al. Genetic structure of Europeans: a view from the north-east. PLoS ONE 4, e5472 (2009)

    Article  ADS  Google Scholar 

  19. 19

    Girirajan, S. & Eichler, E. E. Phenotypic variability and genetic susceptibility to genomic disorders. Hum. Mol. Genet. 19, R176–R187 (2010)

    CAS  Article  Google Scholar 

  20. 20

    Pramyothin, P. & Khaodhiar, L. Metabolic syndrome with the atypical antipsychotics. Curr. Opin. Endocrinol. Diabetes Obes. 17, 460–466 (2010)

    CAS  Article  Google Scholar 

  21. 21

    Shinawi, M. et al. Recurrent reciprocal 16p11.2 rearrangements associated with global developmental delay, behavioral problems, dysmorphism, epilepsy, and abnormal head size. J. Med. Genet. 5, 332–341 (2009)

    Google Scholar 

  22. 22

    Merla, G. et al. Submicroscopic deletion in patients with Williams–Beuren syndrome influences expression levels of the nonhemizygous flanking genes. Am. J. Hum. Genet. 79, 332–341 (2006)

    CAS  Article  Google Scholar 

  23. 23

    Stranger, B. E. et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007)

    CAS  Article  ADS  Google Scholar 

  24. 24

    Henrichsen, C. N. et al. Segmental copy number variation shapes tissue transcriptomes. Nature Genet. 41, 424–429 (2009)

    CAS  Article  Google Scholar 

  25. 25

    Ricard, G. et al. Phenotypic consequences of copy number variation: insights from Smith–Magenis and Potocki–Lupski syndrome mouse models. PLoS Biol. 8, e1000543 (2010)

    Article  Google Scholar 

  26. 26

    Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genet. 41, 25–34 (2009)

    CAS  Article  Google Scholar 

  27. 27

    Courchesne, E., Carper, R. & Akshoomoff, N. Evidence of brain overgrowth in the first year of life in autism. J. Am. Med. Assoc. 290, 337–344 (2003)

    Article  Google Scholar 

  28. 28

    Baxter, P. S., Rigby, A. S., Rotsaert, M. H. & Wright, I. Acquired microcephaly: causes, patterns, motor and IQ effects, and associated growth changes. Pediatrics 124, 590–595 (2009)

    Article  Google Scholar 

  29. 29

    Li, Y., Dai, Q., Jackson, J. C. & Zhang, J. Overweight is associated with decreased cognitive functioning among school-age children and adolescents. Obesity (Silver Spring) 16, 1809–1815 (2008)

    Article  Google Scholar 

  30. 30

    de Onis, M., Blossner, M., Borghi, E., Frongillo, E. A. & Morris, R. Estimates of global prevalence of childhood underweight in 1990 and 2015. J. Am. Med. Assoc. 291, 2600–2606 (2004)

    CAS  Article  Google Scholar 

  31. 31

    Coin, L. J. et al. cnvHap: an integrative population and haplotype-based multiplatform model of SNPs and CNVs. Nature Methods 7, 541–546 (2010)

    CAS  Article  Google Scholar 

  32. 32

    Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004)

    Article  Google Scholar 

  33. 33

    Venkatraman, E. S. & Olshen, A. B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007)

    CAS  Article  Google Scholar 

  34. 34

    Colella, S. et al. QuantiSNP: an objective Bayes Hidden-Markov model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res. 35, 2013–2025 (2007)

    CAS  Article  Google Scholar 

  35. 35

    Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007)

    CAS  Article  Google Scholar 

  36. 36

    Korn, J. M. et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nature Genet. 40, 1253–1260 (2008)

    CAS  Article  Google Scholar 

  37. 37

    Marioni, J. C. et al. Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization. Genome Biol. 8, R228 (2007)

    Article  Google Scholar 

  38. 38

    Diskin, S. J. et al. Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms. Nucleic Acids Res. 36, e126 (2008)

    Article  Google Scholar 

  39. 39

    Bengtsson, H., Irizarry, R., Carvalho, B. & Speed, T. P. Estimation and assessment of raw copy numbers at the single locus level. Bioinformatics 24, 759–767 (2008)

    CAS  Article  Google Scholar 

  40. 40

    Bengtsson, H., Ray, A., Spellman, P. & Speed, T. P. A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods. Bioinformatics 25, 861–867 (2009)

    CAS  Article  Google Scholar 

  41. 41

    Huang, J. et al. Whole genome DNA copy number changes identified by high density oligonucleotide arrays. Hum. Genomics 1, 287–299 (2004)

    CAS  Article  Google Scholar 

  42. 42

    Wang, B. et al. Abraxas and RAP80 form a BRCA1 protein complex required for the DNA damage response. Science 316, 1194–1198 (2007)

    CAS  Article  ADS  Google Scholar 

  43. 43

    Schouten, J. P. et al. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res. 30, e57 (2002)

    Article  Google Scholar 

  44. 44

    Bunyan, D. J. et al. Dosage analysis of cancer predisposition genes by multiplex ligation-dependent probe amplification. Br. J. Cancer 91, 1155–1159 (2004)

    CAS  Article  Google Scholar 

  45. 45

    Fernández, L. et al. Comparative study of three diagnostic approaches (FISH, STRs and MLPA) in 30 patients with 22q11.2 deletion syndrome. Clin. Genet. 68, 373–378 (2005)

    Article  Google Scholar 

  46. 46

    Slater, H. R. et al. Rapid, high throughput prenatal detection of aneuploidy using a novel quantitative method (MLPA). J. Med. Genet. 40, 907–912 (2003)

    CAS  Article  Google Scholar 

  47. 47

    Mei, Z. et al. Validity of body mass index compared with other body-composition screening indexes for the assessment of body fatness in children and adolescents. Am. J. Clin. Nutr. 75, 978–985 (2002)

    CAS  Article  Google Scholar 

  48. 48

    Physical. status: the use and interpretation of anthropometry. Report of a WHO expert committee. World Health Organ. Tech. Rep. Ser. 854, 1–452 (1995)

  49. 49

    Kuczmarski, R. J. et al. CDC growth charts: United States. Adv. Data 314, 1–27 (2000)

    Google Scholar 

  50. 50

    Sempé, M., Pedron, G. & Roy-Pernot, M. P. Auxologie, Méthode et Séquences. (Théraplix, 1979)

    Google Scholar 

  51. 51

    Rolland-Cachera, M. F. et al. Body mass index variations: centiles from birth to 87 years. Eur. J. Clin. Nutr. 45, 13–21 (1991)

    CAS  PubMed  Google Scholar 

  52. 52

    Prader, A., Largo, R. H., Molinari, L. & Issler, C. Physical growth of Swiss children from birth to 20 years of age. First Zurich longitudinal study of growth and development. Helv. Paediatr. Acta. Suppl. 521–125 (1989)

  53. 53

    Fredriks, M. Growth Diagrams, 1997. Fourth Dutch Nation-wide Survey. 233–242 (Bohn Stafleu Van Loghum, 1997)

    Google Scholar 

  54. 54

    de Onis, M. et al. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. 85, 660–667 (2007)

    Article  Google Scholar 

  55. 55

    de Onis, M., Garza, C., Onyango, A. W. & Borghi, E. Comparison of the WHO child growth standards and the CDC 2000 growth charts. J. Nutr. 137, 144–148 (2007)

    CAS  Article  Google Scholar 

  56. 56

    Mei, Z., Ogden, C. L., Flegal, K. M. & Grummer-Strawn, L. M. Comparison of the prevalence of shortness, underweight, and overweight among US children aged 0 to 59 months by using the CDC 2000 and the WHO 2006 growth charts. J. Pediatr. 153, 622–628 (2008)

    Article  Google Scholar 

  57. 57

    Molina, J. et al. Abnormal social behaviors and altered gene expression rates in a mouse model for Potocki–Lupski syndrome. Hum. Mol. Genet. 17, 2486–2495 (2008)

    CAS  Article  Google Scholar 

  58. 58

    Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25, 402–408 (2001)

    CAS  Article  Google Scholar 

  59. 59

    Hellemans, J., Mortier, G., De Paepe, A., Speleman, F. & Vandesompele, J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 8, R19 (2007)

    Article  Google Scholar 

  60. 60

    Vandesompele, J. et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3, 1–11 (2002)

    Article  Google Scholar 

  61. 61

    Morris, J. A. & Gardner, M. J. Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates. Br. Med. J. (Clin. Res. Ed.) 296, 1313–1316 (1988)

    CAS  Article  Google Scholar 

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We thank the Vital-IT high-performance computing centre of the Swiss Institute of Bioinformatics. S.J. is recipient of a bourse de relève académique de la Faculté de Biologie et Médecine de l’Université de Lausanne. This work was supported by the Leenaards Foundation Prize (S.J., D.M. and A.Reymond), the Jérôme Lejeune Foundation (A.Reymond), the Telethon Action Suisse Foundation (A.Reymond), the Swiss National Science Foundation (A.Reymond, J.S.B., S.B. and S.E.A.), a Swiss National Science Foundation Sinergia grant (S.J., D.M., S.B., J.S.B. and A.Reymond), the European Commission anEUploidy Integrated Project grant 037627 (A.Reymond, S.B., X.E., H.G.B. and S.E.A.), the Ludwig Institute for Cancer Research (A.V.), the Swiss Institute of Bioinformatics (S.B. and Z.K.), an Imperial College Department of Medicine PhD studentship (J.S.E.-S.M), the Comprehensive Biomedical Research Centre, Imperial College Healthcare NHS Trust, and the National Institute for Health Research (P.E.), the Wellcome Trust and the Medical Research Council (A.I.F.B. and P.F.), the Instituto de Salud Carlos III (ISCIII)-FIS, the German Mental Retardation Network funded through a grant of the German Federal Ministry of Education and Research (NGFNplus 01GS08160) (A.Reis), European Union-FEDER (PI081714, PS09/01778) (F.F.A., M.G. and X.E.), SAF2008-02278 (C.R.), the Belgian National Fund for Scientific Research, Flanders (N.V.A. and R.F.K.), the Dutch Organisation for Health Research and Development (ZON-MW grant 917-86-319) and Hersenstichting Nederland (B.B.A.d.V.), grant 81000346 from the Chinese National Natural Science Foundation (Y.G.Y.), the Simons Foundation Autism Research Initiative, Autism Speaks and NIH grant GM061354 (J.F.G.), and Oesterreichische Nationalbank (OENB) grant 13059 (A.K.-B.). Y.S. holds a Young Investigator Award from the Children’s Tumor Foundation and a Catalyst Award from Harvard Medical School. B.L.W. holds a Fudan Scholar Research Award from Fudan University, a grant from Chinese National ‘973’ project on Population and Health (2010CB529601) and a grant from the Science and Technology Council of Shanghai (09JC1402400). E.R.S. and S.L., recipients of the Michael Smith Foundation for Health Research Scholar award, acknowledge the CIHR MOP 74502 operational grant. The Estonian Genome Center of the University of Tartu (EGCUT) received support from the EU Centre of Excellence in Genomics and FP7 grants 201413 and 245536, and from Estonian Government SF0180142s08, SF0180026s09 and SF0180027s10 (A.M., K.M. and A.K.). D.S. thanks the Direction Générale de l’Organisation des Soins from the French Ministry of Health for their support in the development of several array-CGH platforms, and the Centres Labellisés Anomalies du Development in France. The Helmholtz Zentrum Munich and the State of Bavaria financed the KORA study, also supported by the German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823), the German Federal Ministry of Education and Research (BMBF), and the Munich Center of Health Sciences (MC Health, LMUinnovativ). CIBEROBN and CIBERESP are initiatives of ISCIII (Spain). S.W.S. holds the GlaxoSmithKline-Canadian Institutes of Health Chair in Genetics and Genomics at the University of Toronto and the Hospital for Sick Children, and is supported by Genome Canada and the McLaughlin Centre. Funding for deCODE came in part from NIH grant MH071425 (K.S.), EU grant HEALTH-2007-2.2.1-10-223423 (Project PsychCNV) and EU grant IMI-JU-NewMeds. NFBC1966 received financial support from the Academy of Finland (project grants 104781, 120315, 129269, 1114194, Center of Excellence in Complex Disease Genetics and SALVE), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617), the European Commission (EURO-BLCS, Framework 5 award QLG1-CT-2000-01643), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01), NIH/NIMH (5R01MH63706:02), ENGAGE project and grant agreement HEALTH-F4-2007-201413, and the Medical Research Council, UK (G0500539, G0600705, PrevMetSyn/SALVE). The DNA extractions, sample quality controls, biobank up-keeping and aliquotting was performed in the National Public Health Institute, Biomedicum Helsinki, Finland and supported financially by the Academy of Finland and Biocentrum Helsinki. We thank M. Hass, Z. Jaros, M. Jussila, M. Koiranen, P. Rantakallio, M. C. Rudolf, V. Soo, O. Tornwall, S. Vaara, T. Ylitalo and the French DHOS national CGH network for their help, as well as all participating patients and clinicians. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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S.J., A.Reymond, P.F. and J.S.B. wrote the manuscript with contributions from F.Z., L.H., R.G.W., N.D.B., Z.K., A.I.F.B. and A.V. L.H., A.V. and A.Reymond produced and analyzed the expression data. Z.K., A.V., R.G.W. and N.D.B. conducted the statistical analyses, guided by S.J., A.Reymond, P.F. and J.S.B. S.J., A.Reymond, F.Z., L.H., D.M., Y.S., G.T., M.B., S.B., D.C., N.d.L., B.B.A.d.V., B.A.F., F.F.A., M.G., A.G., J.H., A.K., C.L.C., K.M., O.S.P. D.S., M.M.V.H., S.V.G., A.T.V.-v.S., F.W., B.-L.W., Y.Y., J.A., X.E., J.F.G., A.M., S.W.S., K.S., U.T., A.I.F.B., J.S.B., P.F. and all other authors phenotyped and/or genotyped patients and/or individuals of the general population. S.J., A.Reymond and J.S.B. designed the study. All authors commented on and approved the manuscript.

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Correspondence to Jacques S. Beckmann.

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Jacquemont, S., Reymond, A., Zufferey, F. et al. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 478, 97–102 (2011).

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