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Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

Abstract

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight–blood pressure association is attributable to genetic effects, and not to intrauterine programming.

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Fig. 1: SEM-adjusted fetal and maternal effects for the 193 lead SNPs that were identified in the GWASs of either own birth weight or offspring birth weight with a MAF greater than 5%.
Fig. 2: Genome-wide genetic correlation between birth weight and a range of traits and diseases in later life.
Fig. 3: Mendelian randomization to assess the causal effect of maternal intrauterine exposures on offspring birth weight (adapted from Lawlor et al.45).
Fig. 4: Mendelian randomization to assess the causal effect of intrauterine growth on offspring adult outcomes, using maternal intrauterine exposures that influence fetal growth.

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Data availability

The genotype and phenotype data are available on application from the UK Biobank (http://www.ukbiobank.ac.uk/). Individual cohorts participating in the EGG Consortium should be contacted directly as each cohort has different data access policies. GWAS summary statistics from this study are available via the EGG website (https://egg-consortium.org/).

Code availability

Custom-written code is available on request from N.M.W. (e-mail: n.warrington@uq.edu.au).

References

  1. Barker, D. J. et al. Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth. Diabetologia 36, 62–67 (1993).

    Article  CAS  PubMed  Google Scholar 

  2. Martin-Gronert, M. S. & Ozanne, S. E. Mechanisms underlying the developmental origins of disease. Rev. Endocr. Metab. Disord. 13, 85–92 (2012).

    Article  PubMed  Google Scholar 

  3. Lumey, L. H., Stein, A. D. & Susser, E. Prenatal famine and adult health. Annu. Rev. Public Health 32, 237–262 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Ben-Shlomo, Y. & Smith, G. D. Deprivation in infancy or in adult life: which is more important for mortality risk? Lancet 337, 530–534 (1991).

    Article  CAS  PubMed  Google Scholar 

  5. Horikoshi, M. et al. Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hattersley, A. T. & Tooke, J. E. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet 353, 1789–1792 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Beaumont, R. N. et al. Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Hum. Mol. Genet. 27, 742–756 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Horikoshi, M. et al. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat. Genet. 45, 76–82 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Freathy, R. M. et al. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat. Genet. 42, 430–435 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hattersley, A. T. et al. Mutations in the glucokinase gene of the fetus result in reduced birth weight. Nat. Genet. 19, 268–270 (1998).

    Article  CAS  PubMed  Google Scholar 

  11. Eaves, L. J., Pourcain, B. S., Smith, G. D., York, T. P. & Evans, D. M. Resolving the effects of maternal and offspring genotype on dyadic outcomes in genome wide complex trait analysis (“M-GCTA”). Behav. Genet. 44, 445–455 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Warrington, N. M., Freathy, R. M., Neale, M. C. & Evans, D. M. Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank. Int. J. Epidemiol. 47, 1229–1241 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 361–363 (2012).

    Article  Google Scholar 

  14. GTEx Consortium. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  Google Scholar 

  15. Peng, S. et al. Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases. Hum. Mol. Genet. 26, 3432–3441 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Way, G. P., Youngstrom, D. W., Hankenson, K. D., Greene, C. S. & Grant, S. F. Implicating candidate genes at GWAS signals by leveraging topologically associating domains. Eur. J. Hum. Genet. 25, 1286–1289 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhang, G. et al. Genetic associations with gestational duration and spontaneous preterm birth. N. Engl. J. Med. 377, 1156–1167 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).

    Article  PubMed  Google Scholar 

  22. Smith, G. D. et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med. 4, e352 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tyrrell, J. et al. Genetic evidence for causal relationships between maternal obesity-related traits and birth weight. J. Am. Med. Assoc. 315, 1129–1140 (2016).

    Article  CAS  Google Scholar 

  24. Pierce, B. L. & Burgess, S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am. J. Epidemiol. 178, 1177–1184 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Walton, A. & Hammond, J. The maternal effects on growth and conformation in shire horse–Shetland pony crosses. Proc. R. Soc. Lond. B 125, 311–335 (1938).

    Article  Google Scholar 

  26. Smith, D. W. et al. Shifting linear growth during infancy: illustration of genetic factors in growth from fetal life through infancy. J. Pediatr. 89, 225–230 (1976).

    Article  CAS  PubMed  Google Scholar 

  27. Sorensen, T. et al. Comparison of associations of maternal peri-pregnancy and paternal anthropometrics with child anthropometrics from birth through age 7 y assessed in the Danish National Birth Cohort. Am. J. Clin. Nutr. 104, 389–396 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Hypponen, E., Power, C. & Smith, G. D. Parental growth at different life stages and offspring birthweight: an intergenerational cohort study. Paediatr. Perinat. Epidemiol. 18, 168–177 (2004).

    Article  PubMed  Google Scholar 

  29. Knight, B. et al. Evidence of genetic regulation of fetal longitudinal growth. Early Hum. Dev. 81, 823–831 (2005).

    Article  PubMed  Google Scholar 

  30. Nahum, G. G. & Stanislaw, H. Relationship of paternal factors to birth weight. J. Reprod. Med. 48, 963–968 (2003).

    PubMed  Google Scholar 

  31. Griffiths, L. J., Dezateux, C. & Cole, T. J. Differential parental weight and height contributions to offspring birthweight and weight gain in infancy. Int. J. Epidemiol. 36, 104–107 (2007).

    Article  PubMed  Google Scholar 

  32. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhang, G. et al. Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. 12, e1001865 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Tyrrell, J. et al. Height, body mass index, and socioeconomic status: Mendelian randomisation study in UK Biobank. Br. Med. J. 352, i582 (2016).

    Article  Google Scholar 

  35. Li, X., Redline, S., Zhang, X., Williams, S. & Zhu, X. Height associated variants demonstrate assortative mating in human populations. Sci. Rep. 7, 15689 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pedersen J. Diabetes and Pregnancy: Blood Sugar of Newborn Infants. PhD thesis (Danish Science Press, 1952).

  37. Metzger, B. E. et al. Hyperglycemia and adverse pregnancy outcomes. N. Eng. J. Med. 358, 1991–2002 (2008).

    Article  Google Scholar 

  38. Crowther, C. A. et al. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N. Eng. J. Med. 352, 2477–2486 (2005).

    Article  CAS  Google Scholar 

  39. Jarvelin, M. R. et al. Early life factors and blood pressure at age 31 years in the 1966 northern Finland birth cohort. Hypertension 44, 838–846 (2004).

    Article  PubMed  Google Scholar 

  40. Tu, Y. K., West, R., Ellison, G. T. & Gilthorpe, M. S. Why evidence for the fetal origins of adult disease might be a statistical artifact: the “reversal paradox” for the relation between birth weight and blood pressure in later life. Am. J. Epidemiol. 161, 27–32 (2005).

    Article  PubMed  Google Scholar 

  41. Huxley, R., Neil, A. & Collins, R. Unravelling the fetal origins hypothesis: is there really an inverse association between birthweight and subsequent blood pressure? Lancet 360, 659–665 (2002).

    Article  PubMed  Google Scholar 

  42. Wang, T. et al. Low birthweight and risk of type 2 diabetes: a Mendelian randomisation study. Diabetologia 59, 1920–1927 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Freathy, R. M. Can genetic evidence help us to understand the fetal origins of type 2 diabetes? Diabetologia 59, 1850–1854 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zanetti, D. et al. Birthweight, type 2 diabetes mellitus, and cardiovascular disease: addressing the Barker hypothesis with Mendelian randomization. Circ. Genom. Precis. Med. 11, e002054 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lawlor, D. et al. Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: sources of bias and methods for assessing them. Wellcome Open Res. 2, 11 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Magi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kemp, J. P. et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat. Genet. 49, 1468–1475 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jones, S. E. et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat. Commun. 10, 343 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).

    Google Scholar 

  53. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mardia, K. V., Kent, J. T. & Bibby, J. M. Multivariate Analysis (Academic Press, 1979).

  56. Segrè, A. V. et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Frayling, T. M. et al. A common allele in FGF21 associated with sugar intake is associated with body shape, lower total body-fat percentage, and higher blood pressure. Cell Rep. 23, 327–336 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. & Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30, 543–552 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Delaneau, O., Zagury, J. F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  PubMed  Google Scholar 

  64. Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Full acknowledgements and supporting grant details can be found in the Supplementary Information.

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Contributions

The central analysis and writing team comprised N.M.W., R.N.B., M.H., F.R.D., K.K.O., M.I.M., J.R.B.P., D.M.E. and R.M.F. Statistical analysis was performed by N.M.W., R.N.B., M.H., F.R.D., Ø.H., C.Lau., J.B., S.P., K.H., B.F., A.R.W., A.Mah., J.T., N.R.R., N.W.R., Z.Q., G-H.M., M.Vau., M.N., T.M.S., M.H.Z., J.P.B., N.G., M.N.K., R.L.-G., F.G., T.S.A., L.P., R.R., V.H., J.-J.H., L.-P.L., A.C., S.M., D.L.C., Y.W., E.T., C.A.W., C.T.H., N.V.-T., P.K.J., J.N.P., I.N., R.M., N.P., E.M.v.L., R.J., V.L., R.C.R., A.E., S.J.B., W.A., J.A.M., K.L.L., C.A., G.Z., L.J.M., J.Heik., A.H.C.v.K., B.D.C.v.S., K.J.G., N.R.v.Z., C.M.-G., Z.K., S.D., H.M., E.V.R.A., M.Mur., S.B.-G., D.M.H., J.M.Mer., K.E.S., P.A.L., S.E.M., B.M.S., J.-F.C., K.Pan., F.S., D.T., I.P., M.A.T., H.Y., K.S.R., S.E.J., P.-R.L., A.Mur., M.N.W., E.Z., G.V.D., Y.-Y.T., M.G.H., K.L.M., J.F.F., D.M.S., N.J.T., A.P.M., D.A.L., J.R.B.P., D.M.E. and R.M.F. Genotyping was performed by F.R.D., Ø.H., T.M.S., M.H.Z., N.G., R.L.-G., L.P., J.-J.H., L.-P.L., J.W.H., X.E., L.M., L.B., C.S.M., C.Lan., J.L., R.A.S., J.H.Z., G.H., S.M.R., A.J.B., J.F.-T., C.M.-G., H.G.d.H., F.R.R., Z.K., P.M.-V., H.M., E.V.R.A., M.Bus., M.A., P.K., M.Stu., T.A.L., C.M.v.D., A.K., E.Z., S.-M.S., G.W.M., H.C., J.F.W., T.G.M.V., C.E.P., E.E.W., T.D.S., T.L., P.V., H.B., K.B., J.C.M., F.R., J.F.F., T.H., O.P., A.G.U., M.-R.J., W.L.L., G.D.S., N.J.T., N.J.W., H.H., S.F.A.G., T.M.F., D.A.L., P.R.N., K.K.O., M.I.M., J.R.B.P., D.M.E. and R.M.F. Sample collection and phenotyping were performed by F.R.D., B.F., C.J.M., J.C., J.P.B., M.N.K., R.L.-G., F.G., R.R., I.N., H.M.I., J.W.H., L.S.-M., C.R., B.H., C.L.R., M.Kog., L.C., M.-F.H., C.S.M., F.D.M., C.Lan, J.L., R.A.S., J.H.Z., S.M.R., C.M.-G., H.G.d.H., Z.K., P.M.-V., S.D., G.W., M.M.-N., M.Sta., C.E.F., C.T., C.E.M.v.B., M.Bus., D.M.H., A.L., B.A.K., M.Bar., J.S., R.K.V., S.M.W., B.L.C., A.T., K.F.M., A.-M.E., T.A.L., A.K., H.N., K.Pah., O.T.R., B.J., G.V.D., S.-M.S., G.W.M., J.F.W., T.G.M.V., M.Vri., J.-C.H., L.J.B., C.E.P., L.S.A., J.B.B., J.G.E., E.E.W., A.T.H., T.D.S., M.Käh., J.S.V., T.L., P.V., H.B., K.B., M.Mel., E.A.N., D.O.M.-K., J.F.F., V.W.V.J., C.Pis., A.A.V., M.-R.J., C.Pow., E.H., W.L.L., G.D.S., N.J.W., H.H., S.F.A.G., D.A.L., K.K.O., M.I.M. and J.R.B.P. The study designers and principal investigators included J.P.B., I.N., H.M.I., L.S.-M., X.E., B.H., J.M.Mur., M.Kog., L.C., M.-F.H., F.D.M., M.A., A.T., M.Stu., K.F.M., A.-M.E., T.A.L., C.M.v.D., W.K., A.K., H.N., K.Pah., O.T.R., B.J., E.Z., G.V.D., Y.-Y.T., S.-M.S., G.W.M., H.C., J.F.W., T.G.M.V., M.Vri., E.J.C.N.d.G., H.N.K., J.-C.H., L.J.B., C.E.P., J.Hein., L.S.A., J.B.B., K.L.M., J.G.E., E.E.W., A.T.H., T.D.S., M.Käh., J.S.V., T.L., D.I.B., S.S., P.V., T.I.A.S., H.B., K.B., J.C.M., M.Mel., E.A.N., D.O.M.-K., F.R., A.H., J.F.F., V.W.V.J., T.H., C.Pis., A.A.V., O.P., A.G.U., M.-R.J., C.Pow., E.H., W.L.L., N.J.T., A.P.M., N.J.W., H.H., S.F.A.G., T.M.F., D.A.L., P.R.N., S.J., K.K.O., M.I.M., J.R.B.P. and R.M.F.

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Correspondence to David M. Evans or Rachel M. Freathy.

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Competing interests

A.A.V. is an employee of AstraZeneca. S.F.A.G. has received support from GlaxoSmithKline for research that is not related to the study presented in this paper. D.A.L. has received support from Medtronic and Roche Diagnostics for biomarker research that is not related to the study presented in this paper. M.I.M. serves on advisory panels for Pfizer, Novo Nordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, has stock options in Zoe Global, and has received research funding from AbbVie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi–Aventis, Servier and Takeda.

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Warrington, N.M., Beaumont, R.N., Horikoshi, M. et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 51, 804–814 (2019). https://doi.org/10.1038/s41588-019-0403-1

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