Abstract

Objective:

Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population.

Subjects:

Using eligible participants from the Population Architecture using Genomics and Epidemiology consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models.

Results:

We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 107). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue.

Conclusion:

Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , . Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer 2004; 4: 579–591.

  2. 2.

    . Obesity as a medical problem. Nature 2000; 404: 635–643.

  3. 3.

    , , , , . Obesity and lipids. Curr Cardiol Rep 2005; 7: 465–470.

  4. 4.

    , . Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci 2012; 1271: 37–43.

  5. 5.

    , . The medical care costs of obesity: an instrumental variables approach. J Health Econ 2012; 31: 219–230.

  6. 6.

    , , , , , et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med 2009; 6: e1000058.

  7. 7.

    , , , , , et al. A potential decline in life expectancy in the United States in the 21st century. N Engl J Med 2005; 352: 1138–1145.

  8. 8.

    , , . Summary health statistics for U.S. adults: national health interview survey 2012. Vital Health Stat 10 2014; 1–161.

  9. 9.

    , , , , , et al. Genetic influences on growth traits of BMI: a longitudinal study of adult twins. Obesity (Silver Spring) 2008; 16: 847–852.

  10. 10.

    , , . Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 1997; 27: 325–351.

  11. 11.

    , , . A twin study of human obesity. JAMA 1986; 256: 51–54.

  12. 12.

    , , , , , et al. Ten putative contributors to the obesity epidemic. Crit Rev Food Sci Nutr 2009; 49: 868–913.

  13. 13.

    , , , , , et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 2013; 45: 501–512.

  14. 14.

    , , , , , et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937–948.

  15. 15.

    , , , , , et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009; 41: 25–34.

  16. 16.

    , , , , , et al. Fine Mapping and Identification of BMI Loci in African Americans. Am J Hum Genet 2013; 93: 661–671.

  17. 17.

    , , . Multiethnic genetic association studies improve power for locus discovery. PLoS One 2010; 5: e12600.

  18. 18.

    , . The role of local ancestry adjustment in association studies using admixed populations. Genet Epidemiol 2014; 38: 502–515.

  19. 19.

    , , , , , et al. Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals. Hum Mol Genet 2013; 22: 184–201.

  20. 20.

    , , , , , et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet 2013; 45: 690–696.

  21. 21.

    , , , , , et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat Genet 2012; 44: 302–306.

  22. 22.

    , , , , , et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet 2012; 44: 307–311.

  23. 23.

    , , , , , et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet 2014; 23: 5492–5504.

  24. 24.

    , , , , , et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015; 518: 197–206.

  25. 25.

    , , , , , et al. The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study. Am J Epidemiol 2011; 174: 849–859.

  26. 26.

    , , , , , et al. Assessing the accuracy of observer-reported ancestry in a biorepository linked to electronic medical records. Genet Med 2010; 12: 648–650.

  27. 27.

    , , , , . Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank. PLoS One 2014; 9: e99161.

  28. 28.

    , . The bias in self-reported obesity from 1976 to 2005: a Canada-US comparison. Obesity (Silver Spring) 2010; 18: 354–361.

  29. 29.

    , , , , , et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 2012; 8: e1002793.

  30. 30.

    , , , , , et al. Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative. Genet Epidemiol 2012; 36: 107–117.

  31. 31.

    , , , , , et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65.

  32. 32.

    , , , . Genotype imputation. Annu Rev Genomics Hum Genet 2009; 10: 387–406.

  33. 33.

    , , , , . MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816–834.

  34. 34.

    , , , , . Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 2012; 44: 955–959.

  35. 35.

    , , , , , et al. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci. Hum Genet 2017; 136: 771–800.

  36. 36.

    , , , , , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

  37. 37.

    , , , , , et al. Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet 2014; 95: 675–688.

  38. 38.

    , , . Population structure and eigenanalysis. PLoS Genet 2006; 2: e190.

  39. 39.

    , , , , , . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909.

  40. 40.

    , , , , , et al. Evaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study. PLoS One 2012; 7: e35651.

  41. 41.

    , , , , , et al. Genetic risk factors for BMI and obesity in an ethnically diverse population: results from the population architecture using genomics and epidemiology (PAGE) study. Obesity (Silver Spring) 2013; 21: 835–846.

  42. 42.

    , , . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

  43. 43.

    , , , , , et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun 2017; 8: 14977.

  44. 44.

    , , , , , et al. Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults. PLoS Genet 2017; 13: e1006528.

  45. 45.

    , . HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 2012; 40: D930–D934.

  46. 46.

    , , , , , et al. Genome-wide association study of body mass index in 23 000 individuals with and without asthma. Clin Exp Allergy 2013; 43: 463–474.

  47. 47.

    , , , , , et al. Genome-wide association study of height-adjusted BMI in childhood identifies functional variant in ADCY3. Obesity (Silver Spring) 2014; 22: 2252–2259.

  48. 48.

    , , , , , et al. A genome-wide association study of body mass index across early life and childhood. Int J Epidemiol 2015; 44: 700–712.

  49. 49.

    , , , , , et al. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLoS Genet 2015; 11: e1005378.

  50. 50.

    , , , , , et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet 2012; 8: e1002607.

  51. 51.

    , , , , , et al. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet 2009; 5: e1000508.

  52. 52.

    , , , , , et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010; 42: 949–960.

  53. 53.

    , , , , , et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 2009; 41: 18–24.

  54. 54.

    , , , , , et al. Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum Mol Genet 2013; 22: 3597–3607.

  55. 55.

    , , , , , et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat Genet 2010; 42: 1077–1085.

  56. 56.

    , , . Genomics for the world. Nature 2011; 475: 163–165.

  57. 57.

    , , , , , . Genome-wide association studies in diverse populations. Nat Rev Genet 2010; 11: 356–366.

  58. 58.

    . Impact of limited population diversity of genome-wide association studies. Genet Med 2010; 12: 81–84.

Download references

Acknowledgements

KKN was supported by a National Cancer Institute training grant: Cancer Prevention Training in Nutrition, Exercise and Genetics (R25CA094880). LFR was supported by the Cardiovascular Disease Epidemiology Training Grant from the National Heart, Lung and Blood Institute (T32HL007055) and the American Heart Association (AHA) predoctoral grant (13PRE16100015). The Population Architecture Using Genomics and Epidemiology (PAGE-I) program is funded by the National Human Genome Research Institute (NHGRI), supported by U01HG004803 (CALiCo), U01HG004798 (EAGLE), U01HG004802 (MEC), U01HG004790 (WHI) and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. The Population Architecture Using Genomics and Epidemiology (PAGE-II) program is funded by the National Human Genome Research Institute (NHGRI), with co-funding from the National Institute on Minority Health and Health Disparities (NIMHD), supported by U01HG007416 (CALiCo), U01HG007417 (ISMMS), U01HG007397 (MEC), U01HG007376 (WHI) and U01HG007419 (Coordinating Center). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The complete list of PAGE members can be found at PAGE website (http://www.pagestudy.org). The data and materials included in this report result from a collaboration between the following studies: The ‘Epidemiologic Architecture for Genes Linked to Environment (EAGLE)’ is funded through the NHGRI PAGE program (U01HG004798-01 and its NHGRI ARRA supplement). The data set(s) used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. The Vanderbilt University Center for Human Genetics Research, Computational Genomics Core provided computational and/or analytical support for this work. The Multiethnic Cohort study (MEC) characterization of epidemiological architecture is funded through NHGRI (HG004802, and HG007397) and the NHGRI PAGE program (U01HG007397, U01HG004802 and its NHGRI ARRA supplement). The MEC study is funded through the National Cancer Institute (CA164973, R37CA54281, R01 CA 063464, P01CA33619, U01CA136792 and U01CA98758). The data sets used for the analyses described in this manuscript were obtained from dbGaP under accession phs000220. Funding support for the ‘Epidemiology of putative genetic variants: The Women’s Health Initiative’ study is provided through the NHGRI PAGE program (U01HG004790 and its NHGRI ARRA supplement). Funding support for the ‘Exonic variants and their relation to complex traits in minorities of the WHI’ study is provided through the NHGRI PAGE program (U01HG007376, U01HG004790). The WHI program is funded by the National Heart, Lung, and Blood Institute; NIH; and U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32 and 44221. WHI PAGE-II is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HSN268201100003C, HHSN268201100004C and HHSN271201100004C. We thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. The data sets used for the analyses described in this manuscript were obtained from dbGaP under accession phs000227. A full listing of WHI investigators can be found at: http://www.whiscience.org/publications/WHI_investigators_shortlist.pdf. Funding support for the Genetic Epidemiology of Causal Variants Across the Life Course (CALiCo) program was provided through the NHGRI PAGE program (U01HG007416, U01HG004803 and its NHGRI ARRA supplement). The following CALiCo studies contributed to this manuscript and are funded by the following agencies: The Atherosclerosis Risk in Communities Study (ARIC) is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Coronary Artery Risk Development in Young Adults (CARDIA) study is supported by the following National Institutes of Health, National Heart, Lung and Blood Institute contracts: N01-HC-95095; N01-HC-48047; N01-HC-48048; N01-HC-48049; N01-HC-48050; N01-HC-45134; N01-HC-05187; and N01-HC-45205. CARDIA is conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201300025C & HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C) and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. The Hispanic Community Health Study/Study of Latinos (SOL) was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). Additional support was provided by 1R01DK101855-01 and 13GRNT16490017. The following Institutes/Centers/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the Office of Dietary Supplements. The Cardiovascular Health Study (CHS) is supported by contracts HHSN268201200036C, HHSN268200800007C, N01 HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grants HL080295 and HL087652 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/PI.htm. CHS GWAS DNA handling and genotyping at Cedars-Sinai Medical Center was supported in part by the National Center for Research Resources, grant UL1RR033176, and is now at the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124; in addition, the National Institute of Diabetes and Digestive and Kidney Diseases grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The Strong Heart Study (SHS) is supported by NHLBI grants U01 HL65520, U01 HL41642, U01 HL41652, U01 HL41654, U01 HL65521 and R01 HL109301. The data sets used for the analyses described in this manuscript were obtained from dbGaP under accession phs000223 (ARIC), phs000236, (CARDIA), phs000301 (CHS), phs000555 (HCHS/SOL). The opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Indian Health Service. GenNet is one of four networks in the Family Blood Pressure Program, established in 1995 and supported by a series of agreements with the NIH National Heart, Lung and Blood Institute. Samples and data from The Charles Bronfman Institute for Personalized Medicine (IPM) BioMe Biobank used in this study were provided by The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai (New York). Phenotype data collection was supported by The Andrea and Charles Bronfman Philanthropies. Funding support for the Population Architecture Using Genomics and Epidemiology (PAGE) IPM BioMe Biobank study was provided through the National Human Genome Research Institute (U01HG007417). The data sets used for the analyses described in this manuscript were obtained from dbGaP under accession phs000925. The Hypertension Genetic Epidemiology Network (HyperGEN) study was supported by National Heart, Lung and Blood Institute contracts HL086694 and HL055673. Assistance with phenotype harmonization, SNP selection and annotation, data cleaning, data management, integration and dissemination, and general study coordination was provided by the PAGE Coordinating Center (U01HG007419, U01HG004801-01 and its NHGRI ARRA supplement). The National Institutes of Mental Health also contributes to the support for the Coordinating Center. We gratefully acknowledge Dr Ben Voight for sharing the Metabochip SNP LD and minor allele frequency statistics estimated in the Malmö Diet and Cancer Study. The PAGE consortium thanks the staff and participants of all PAGE studies for their important contributions.

Author information

Affiliations

  1. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • J Gong
    • , K K Nishimura
    • , J Haessler
    • , S Bien
    • , C S Carlson
    • , C Kooperberg
    •  & U Peters
  2. Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • L Fernandez-Rhodes
    • , M Graff
    • , S Yoneyama
    •  & K E North
  3. Cancer Research Center, University of Hawaii, Honolulu, HI, USA

    • U Lim
    • , L R Wilkens
    • , L Park
    •  & L Le Marchand
  4. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Y Lu
    • , E P Bottinger
    •  & R J F Loos
  5. The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Y Lu
    •  & R J F Loos
  6. Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA

    • Y Lu
  7. Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA

    • M Gross
  8. Health Science Center, University of Texas, Austin, TX, USA

    • M Fornage
  9. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    • C R Isasi
  10. Department of Biostatistics, University of Washington, Seattle, WA, USA

    • P Buzkova
  11. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    • M Daviglus
  12. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • D-Y Lin
    •  & R Tao
  13. Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA

    • R Goodloe
    • , E Farber-Eger
    • , J Boston
    •  & D C Crawford
  14. Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA

    • W S Bush
  15. Sarah Cannon Research Institute, Nashville, TN, USA

    • H H Dilks
  16. Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • G Ehret
    •  & K-D H Nguyen
  17. Division of Cardiology, Geneva University Hospital, Geneva, Switzerland

    • G Ehret
  18. Department of Biostatistics, Washington University, St Louis, MO, USA

    • C C Gu
  19. Department of Medicine, University of Alabama, Birmingham, AL, USA

    • C E Lewis
  20. Preventive Medicine and Epidemiology, Loyola University, Chicago, IL, USA

    • R Cooper
  21. Department of Human Genetics, University of Utah, Salt Lake City, UT, USA

    • M Leppert
  22. Department of Epidemiology, University of Alabama, Birmingham, AL, USA

    • M R Irvin
  23. Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • C A Haiman
    • , K R Monroe
    •  & D O Stram
  24. Cancer Prevention Institute of California, Fremont, CA, USA

    • I Cheng
  25. Department of Internal Medicine, Ohio State Medical Center, Columbus, OH, USA

    • R Jackson
  26. Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    • L Kuller
  27. Wake Forest University School of Medicine, Winston-Salem, NC, USA

    • D Houston
  28. Department of Genetics, Rutgers University, Piscataway, NJ, USA

    • S Buyske
    •  & T C Matise
  29. Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, USA

    • S Buyske
  30. Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    • L A Hindorff
  31. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • R J F Loos

Authors

  1. Search for J Gong in:

  2. Search for K K Nishimura in:

  3. Search for L Fernandez-Rhodes in:

  4. Search for J Haessler in:

  5. Search for S Bien in:

  6. Search for M Graff in:

  7. Search for U Lim in:

  8. Search for Y Lu in:

  9. Search for M Gross in:

  10. Search for M Fornage in:

  11. Search for S Yoneyama in:

  12. Search for C R Isasi in:

  13. Search for P Buzkova in:

  14. Search for M Daviglus in:

  15. Search for D-Y Lin in:

  16. Search for R Tao in:

  17. Search for R Goodloe in:

  18. Search for W S Bush in:

  19. Search for E Farber-Eger in:

  20. Search for J Boston in:

  21. Search for H H Dilks in:

  22. Search for G Ehret in:

  23. Search for C C Gu in:

  24. Search for C E Lewis in:

  25. Search for K-D H Nguyen in:

  26. Search for R Cooper in:

  27. Search for M Leppert in:

  28. Search for M R Irvin in:

  29. Search for E P Bottinger in:

  30. Search for L R Wilkens in:

  31. Search for C A Haiman in:

  32. Search for L Park in:

  33. Search for K R Monroe in:

  34. Search for I Cheng in:

  35. Search for D O Stram in:

  36. Search for C S Carlson in:

  37. Search for R Jackson in:

  38. Search for L Kuller in:

  39. Search for D Houston in:

  40. Search for C Kooperberg in:

  41. Search for S Buyske in:

  42. Search for L A Hindorff in:

  43. Search for D C Crawford in:

  44. Search for R J F Loos in:

  45. Search for L Le Marchand in:

  46. Search for T C Matise in:

  47. Search for K E North in:

  48. Search for U Peters in:

Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to U Peters.

Supplementary information

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/ijo.2017.304

Supplementary Information accompanies this paper on International Journal of Obesity website (http://www.nature.com/ijo)