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Population genetic differentiation of height and body mass index across Europe

Nature Genetics volume 47, pages 1357–1362 (2015)Cite this article

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Abstract

Across-nation differences in the mean values for complex traits are common1,2,3,4,5,6,7,8, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10−8; BMI, P < 5.95 × 10−4), and we find an among-population genetic correlation for tall and slender individuals (r = −0.80, 95% CI = −0.95, −0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

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Figure 1: Observed divergence and predicted genetic divergence in height and BMI across 14 European nations.
Figure 2: Predicted genetic differentiation compared to the expectation under genetic drift for height and BMI across 14 European nations.
Figure 3: Association between observed population means and predicted genetic population means for height and BMI across 14 European nations.

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Acknowledgements

We thank the reviewers for their very helpful and insightful comments that greatly improved the manuscript. We also thank B. Hill and O. Ovaskainen for useful discussions. The University of Queensland group is supported by the Australian National Health and Medical Research Council (NHMRC; grants 1078037, 1048853 and 1050218). J.E.P. is supported by Australian Research Council grant DE130100691. J.Y. is supported by a Charles and Sylvia Viertel Senior Medical Research Fellowship and by NHMRC grant 1052684. We thank our colleagues at the Centre for Neurogenetics and Statistical Genomics for comments and suggestions. We are grateful to the twins and their families for their generous participation in the full-sibling family data set, which includes data from many cohorts and received support from many funding bodies. TWINGENE was supported by the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254 and QLG2-CT-2002-01254), US National Institutes of Health (NIH) grant DK U01-066134, the Swedish Foundation for Strategic Research (SSF), and the Heart and Lung Foundation (20070481). For the Netherlands Twin Register (NTR), funding was obtained from the Netherlands Organization for Scientific Research (NWO; MagW/ZonMW grants 904-61-090, 985-10-002, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, Middelgroot-11-09-032 and Spinozapremie 56-464-14192), the Center for Medical Systems Biology (CSMB; NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL; 184.021.007), the VU University's Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA), the European Science Foundation (ESF; EU/QLRT-2001-01254), the European Community's Seventh Framework Programme (FP7/2007-2013) under the ENGAGE project grant agreement (HEALTH-F4-2007-201413), the European Research Council (ERC Advanced; 230374), the Rutgers University Cell and DNA Repository (National Institute for Mental Health (NIMH), U24-MH068457-06), the Avera Institute (Sioux Falls, South Dakota, USA) and the US NIH (R01-D0042157-01A, Grand Opportunity grants 1RC2-MH089951-01 and 1RC2-MH089995-01). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The TwinsUK study was funded by the Wellcome Trust and the European Community's Seventh Framework Programme (FP7/2007-2013) under the ENGAGE project grant agreement (HEALTH-F4-2007-201413). TwinsUK also receives support from the UK Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's and St Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. T.D.S. is the holder of an ERC Advanced Principal Investigator award. Genotyping for the TwinsUK study was performed by the Wellcome Trust Sanger Institute, with the support of the National Eye Institute via a US NIH/Center for Inherited Disease Research (CIDR) genotyping project. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University or NHLBI. Funding for SHARe Affymetrix genotyping was provided by NHLBI contract N02-HL-64278. Funding for SHARe Illumina genotyping was provided under an agreement between Illumina and Boston University. The QIMR researchers acknowledge funding from the Australian NHMRC (grants 241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688 and 552485) and the US NIH (grants AA07535, AA10248, AA014041, AA13320, AA13321, AA13326 and DA12854). We are grateful to M. Gill (Trinity College Dublin) and K. Nicodemus (University of Edinburgh) for access to the ISC–Trinity College Dublin cohort, which was supported by the Wellcome Trust and the Health Research Board, Ireland. Access to the Bulgarian cohort data was kindly facilitated by G. Kirov and V. Excott-Price. For the Danish cohort, the Danish Scientific Committees and the Danish Data Protection Agency approved the study and all the patients gave written informed consent before inclusion in the project. The National Institute on Aging (NIA) provided funding for the Health and Retirement Study (HRS; U01-AG09740). The HRS is performed at the Institute for Social Research at the University of Michigan. This manuscript was not prepared in collaboration with investigators of the HRS and does not necessarily reflect the opinions or views of the HRS, University of Michigan or NIA. The Netherlands genotype samples were part of Project MinE, which was supported by the ALS Foundation Netherlands. Research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013).

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Authors and Affiliations

  1. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Matthew R Robinson, Gibran Hemani, Konstantin Shakhbazov, Joseph E Powell, Anna Vinkhuyzen, Jian Yang & Peter M Visscher

  2. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

    Carolina Medina-Gomez & Fernando Rivadeneira

  3. Institute for Maternal and Child Health, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) 'Burlo Garofolo', Trieste, Italy

    Massimo Mezzavilla & Paolo Gasparini

  4. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Massimo Mezzavilla & Paolo Gasparini

  5. Estonian Genome Center, University of Tartu, Tartu, Estonia

    Tonu Esko, Andres Metspalu & Joel N Hirschhorn

  6. Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts, USA

    Tonu Esko, Tune H Pers, Sailaja Vedantam & Joel N Hirschhorn

  7. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Tonu Esko, Tune H Pers, Sailaja Vedantam & Joel N Hirschhorn

  8. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Tonu Esko, Tune H Pers, Daniel I Chasman & Joel N Hirschhorn

  9. University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia

    Joseph E Powell, Jian Yang & Peter M Visscher

  10. Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA

    Sonja I Berndt

  11. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    Stefan Gustafsson & Erik Ingelsson

  12. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Anne E Justice

  13. Division of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA

    Bratati Kahali & Kari E North

  14. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Bratati Kahali & Kari E North

  15. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Adam E Locke, Goncalo R Abecasis & Elizabeth K Speliotes

  16. Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

    Tune H Pers

  17. Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK

    Andrew R Wood & Timothy M Frayling

  18. Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands

    Wouter van Rheenen, Leonard H van den Berg & Jan H Veldink

  19. Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Ole A Andreassen

  20. Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Devices Copenhagen, Roskilde, Denmark

    Thomas M Werge

  21. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Thomas M Werge

  22. Lundbeck Foundation Initiative for Integrative Psychiatric Research, (iPSYCH), Aarhus, Denmark

    Thomas M Werge

  23. Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands

    Dorret I Boomsma, Eco J C de Geus & Jouke Jan Hottenga

  24. EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

    Dorret I Boomsma, Eco J C de Geus & Jouke Jan Hottenga

  25. Department of Biological Psychology, VU University Amsterdam, Amsterdam, the Netherlands

    Dorret I Boomsma, Eco J C de Geus & Jouke Jan Hottenga

  26. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Daniel I Chasman

  27. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Erik Ingelsson

  28. Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK

    Ruth J F Loos

  29. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Ruth J F Loos

  30. Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Ruth J F Loos

  31. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Ruth J F Loos

  32. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Patrik K E Magnusson & Nancy L Pedersen

  33. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Nicholas G Martin & Grant W Montgomery

  34. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Kari E North

  35. Department of Twin Research and Genetic Epidemiology, King's College London, St. Thomas' Hospital, London, UK

    Timothy D Spector

  36. Biosciences Research Division, Department of Primary Industries, Melbourne, Victoria, Australia

    Michael E Goddard

  37. Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Victoria, Australia

    Michael E Goddard

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  25. Goncalo R Abecasis
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  42. Jian Yang
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Contributions

Conception and design of the study: M.R.R., M.E.G., J.Y. and P.M.V. Data analysis: M.R.R., with additional contributions from G.H., C.M.-G., M.M., K.S., T.E., J.E.P., A.V., S.I.B., S.G., A.E.J., B.K., A.E.L., T.H.P., S.V., A.R.W. and W.v.R. Study oversight, sample collection and management: J.H.V., L.H.v.d.B., O.A.A., P.G., A.M., F.R., T.M.W., G.R.A., D.I.B., D.I.C., E.J.C.d.G., T.M.F., J.N.H., J.J.H., E.I., R.J.F.L., P.K.E.M., N.G.M., G.W.M., K.E.N., N.L.P., T.D.S. and E.K.S. Manuscript writing: M.R.R. and P.M.V., with contributions from all authors on the final version.

Corresponding authors

Correspondence to Matthew R Robinson or Peter M Visscher.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Overview of the study design.

Supplementary Figure 2 Projection of the prediction samples onto the first two HapMap principal components.

For the non-ascertained set of independent (LD r2 < 0.1), common, HapMap 3 loci that we used for prediction, we projected our prediction samples onto the first two principal components from HapMap.

Supplementary Figure 3 Simulation study comparing population and within-family association testing.

Using real genotype data, causal variants were allocated to 5,000 independent loci at random across the genome, with effects sampled from a normal distribution. Effect sizes were estimated in a genome-wide association study without controlling for population stratification (GWAS; yellow), in a GWAS that controlled for the first 20 principal components (green) and in a sibling pair within-family design (blue). The effect sizes were then used to predict the phenotype in an independent set of sibling pair data, and a recently derived approach was used to test for population stratification bias in the effect size estimates (Online Methods and ref. 33). Variance attributable to a Cg or a Ce term is indicative of population stratification bias, which can be observed in the GWAS scenario that did not appropriately control for population stratification.

Supplementary Figure 4 Simulation study comparing population and within-family association.

Using real genotype data, causal variants were allocated to 5,000 independent loci at random across the genome, with their effects sampled from a normal distribution. Fifty simulation replicates were conducted. Effect sizes were estimated in a genome-wide association study without controlling for population stratification (GWAS) and in a sibling pair within-family design. The effect sizes from the top 100 independent SNPs identified by the GWAS, the top 500 independent SNPs identified by the GWAS and genome-wide independent loci were then used for prediction in an independent sample. Individuals from the prediction sample were projected onto the first principal component of the discovery sample, and then two groups were selected based on the upper and lower quartiles of the distribution of the projected principal component. The mean difference in the predictor is shown when the predictor was created using effect sizes estimated in a GWAS that did not control for population stratification, using effect sizes estimated in a within-family design, using the true simulated effect sizes and when the within-family effect sizes were randomly allocated to loci under our null model. A predictor created from within-family estimates of effect size yields similar estimates to the true simulated values and the null model, and in no simulation were our predictions significantly different from our null model. This result was irrespective of whether there was ascertainment of loci from a discovery GWAS containing population stratification bias, as when we selected the top 100 or top 500 loci from the GWAS, re-estimated the effects in a within-family analysis and created a predictor we found no evidence of differentiation from our null model. A predictor created from GWAS estimates of effect size that are biased by population stratification yields variable estimates of the prediction difference between the 2 groups, and in 10 of the 50 simulations our predictions differed significantly from our null model.

Supplementary Figure 5 Simulation study comparing population and within-family association testing showing potential ascertainment bias.

Using real genotype data, causal variants were allocated to 5,000 independent loci at random across the genome, with their effects sampled from a normal distribution. Fifty simulation replicates were conducted. Genotype-environment correlation was induced through a phenotypic mean difference of 0.5 s.d. along the first principal component of the discovery sample. Effect sizes were estimated in a genome-wide association study without controlling for population stratification (GWAS) and in a sibling pair within-family design. The effect sizes from the top 100 independent SNPs identified by the GWAS, the top 500 independent SNPs identified by the GWAS and genome-wide independent loci were then used for prediction in an independent sample. Individuals from the prediction sample were projected onto the first principal component of the discovery sample, and two groups were then selected based on the upper and lower quartiles of the distribution of the projected principal component. The mean difference in the predictor is shown when the predictor was created using effect sizes estimated in a GWAS that did not control for population stratification, using effect sizes estimated in a within-family design, using the true simulated effect sizes and when the within-family effect sizes were randomly allocated to loci under our null model. Ascertainment bias is evident here, where if the top 100 or 500 loci from a GWAS containing population stratification were selected to create a predictor then, irrespective of whether biased SNP effect estimates from the GWAS or unbiased SNP effect estimates from the within-family analysis were used, a significant deviation from the null model is observed. This is because the loci selected from the GWAS were those where the genotype-environment correlation was the strongest. In this simulation scenario, there is no selection on the phenotype and, thus, when there is no ascertainment of loci (when genome-wide SNPs are used to create the predictor), no prediction differences from the true values or the null model were evident.

Supplementary Figure 6 Proportion of population-level variance in a genetic predictor comprised of different sets of SNPs for height and BMI.

Genetic predictors were created from independent (pairwise LD correlation < 0.1, >1 Mb apart), common HapMap 3 loci selected at different significance thresholds (P < 5 × 10–8, P < 5 × 10–6, P < 5 × 10–4, P < 0.005, P < 0.05, P < 0.1) from large-scale meta-analyses. All refers to genome-wide independent (pairwise LD correlation < 0.1, >1 Mb apart), common HapMap 3 loci that were either selected preferentially on the basis of their within-family association with either trait or selected at random.

Supplementary Figure 7 Increased prediction accuracy results in increased population-level variance.

We selected SNPs from large-scale meta-analyses, re-estimated their effects in a within-family design that is unbiased of population stratification and then assessed prediction accuracy in an independent sample for (a) height and (b) body mass index. We find that the population-level variance is better captured when a greater amount of phenotypic variation is explained by the predictor.

Supplementary Figure 8 Genome-wide pattern of population genetic differentiation for height and body mass index.

Supplementary Figure 9 The characteristics of a SNP and its contribution to the genome-wide pattern of population differentiation for height.

Relationship between the contribution of a SNP to the genome-wide pattern of population differentiation (c2 value) and (a) allele frequency, (b) phenotypic variance explained and (c) meta-analysis P value.

Supplementary Figure 10 The characteristics of a SNP and its contribution to the genome-wide pattern of population differentiation for body mass index.

Relationship between the contribution of a SNP to the genome-wide pattern of population differentiation (c2 value) and (a) allele frequency, (b) phenotypic variance explained and (c) meta-analysis P value.

Supplementary Figure 11 Simulation study of the general approach.

(a) Simulated distribution of allele frequency differentiation, θ, at 10,000 loci plotted against allele frequency. (b) The simulated association between θ and the additive genetic variance contributed by each locus, which assumes that the most differentiated loci are those that contribute most to the additive genetic variance. (c) Profile scores were calculated from different sets of simulated loci, where each set explained differing amounts of the total variance. Population-level variance is shown for each set of loci, demonstrating that, even when the loci cumulatively explain only 20% of the total variance, 50% of the population-level genetic variance is captured. (d) Error variance for each locus was simulated from a normal distribution with variance equal to a percentage of the total variance in profile score. Increasing amounts of error variance were added to the profile score to approximate the effects of adding an increasing number of false positive loci. Population-level variance is shown at different error variances, demonstrating that including a large number of false positives decreases the population-level effects.

Supplementary Figure 12 Simulation study of the error variance induced by the addition of null SNPs.

The error variance for each locus was simulated from a normal distribution as a percentage of the total variance in profile score. Increasing amounts of error variance were added to the profile score to approximate the effects of adding an increasing number of false positive loci. The 95% confidence intervals are shown for the population mean profile score of 11 simulated populations across increasing amounts of incorporated error variance: (a) 0%, (b) 1%, (c) 2.5%, (d) 5%, (e) 7.5% and (f) 10%. This pattern reflects the fact that including a large number of false positives decreases the amount of population-level variance estimated.

Supplementary Figure 13 Overlap in the annotation of differentiated loci to genes for height and BMI.

Five hundred SNP loci were selected that are expected to contribute most to the pattern of population genetic differentiation for height and body mass index. These SNP loci were annotated to genes, and the overlap was estimated. The annotation was then repeated by randomly selecting 500 loci from the top 10,000 SNP loci 100 times. The 95% confidence interval of the percentage of overlapping genes across the 100 sampling steps was 8.4–19.4%. In the expected top 500 loci contributing to population genetic variation of each trait, the percentage overlap was 19.8%.

Supplementary Figure 14 Population genetic differentiation across six Italian villages.

Predicted population genetic differentiation for BMI and height on a small scale across six northern Italian villages. There was no significant differentiation from the null model for height (c2 = 0.23, P = 0.985) and for BMI (c2 = 6.27, P = 0.817) at any set of SNPs, and we present the results across the genome. The villages are Sauris (sa), Resia (re), Illegio (il), Erto (er), Clausetto (cl) and San Martino del Corso (sm) in the Friuli-Venezia Giulia region in northeastern Italy. On this small scale, we find no evidence for population differentiation for either phenotype.

Supplementary Figure 15 Population genetic differentiation in the Human Genetic Diversity Panel for independent genome-wide SNPs ascertained on within-family effect sizes.

(a) Height and (b) BMI. P values give the deviation of the predicted means from the null expectation. The proportion of population-level variance was 17.5% (95% CI = 9.6, 27.9) for height and 13.1% (95% CI = 7.2, 21.3) for BMI. Worldwide, there was no evidence of any population-level genetic correlation (0.007, 95% CI = –0.051, 0.064).

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Robinson, M., Hemani, G., Medina-Gomez, C. et al. Population genetic differentiation of height and body mass index across Europe. Nat Genet 47, 1357–1362 (2015). https://doi.org/10.1038/ng.3401

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