Hostname: page-component-76fb5796d-vvkck Total loading time: 0 Render date: 2024-04-27T02:47:21.642Z Has data issue: false hasContentIssue false
  • English
  • Français

A cautionary note on using Mendelian randomization to examine the Barker hypothesis and Developmental Origins of Health and Disease (DOHaD)

Published online by Cambridge University Press:  04 December 2020

Shannon D’Urso Open the ORCID record for Shannon D’Urso [Opens in a new window] ,
Geng Wang ,
Liang-Dar Hwang ,
Gunn-Helen Moen ,
Nicole M. Warrington  and
David M. Evans
Shannon D’Urso
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
Geng Wang
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
Liang-Dar Hwang
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
Gunn-Helen Moen
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
Nicole M. Warrington
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
David M. Evans*
Affiliation:
The University of Queensland Diamantina Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
*
Address for correspondence: David M. Evans, University of Queensland Diamantina Institute, Level 7, 37 Kent St, Translational Research Institute, Woolloongabba, QLD4102, Australia. Email: d.evans1@uq.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent studies have used Mendelian randomization (MR) to investigate the observational association between low birth weight (BW) and increased risk of cardiometabolic outcomes, specifically cardiovascular disease, glycemic traits, and type 2 diabetes (T2D), and inform on the validity of the Barker hypothesis. We used simulations to assess the validity of these previous MR studies, and to determine whether a better formulated model can be used in this context. Genetic and phenotypic data were simulated under a model of no direct causal effect of offspring BW on cardiometabolic outcomes and no effect of maternal genotype on offspring cardiometabolic risk through intrauterine mechanisms; where the observational relationship between BW and cardiometabolic risk was driven entirely by horizontal genetic pleiotropy in the offspring (i.e. offspring genetic variants affecting both BW and cardiometabolic disease simultaneously rather than a mechanism consistent with the Barker hypothesis). We investigated the performance of four commonly used MR analysis methods (weighted allele score MR (WAS-MR), inverse variance weighted MR (IVW-MR), weighted median MR (WM-MR), and MR-Egger) and a new approach, which tests the association between maternal genotypes related to offspring BW and offspring cardiometabolic risk after conditioning on offspring genotype at the same loci. We caution against using traditional MR analyses, which do not take into account the relationship between maternal and offspring genotypes, to assess the validity of the Barker hypothesis, as results are biased in favor of a causal relationship. In contrast, we recommend the aforementioned conditional analysis framework utilizing maternal and offspring genotypes as a valid test of not only the Barker hypothesis, but also to investigate hypotheses relating to the Developmental Origins of Health and Disease more broadly.

Keywords

Mendelian randomizationcausal inferencetype 2 diabetescardiometabolic diseaseconditional analysisBarker hypothesisDevelopmental Origins of Health and Disease
Type
Brief Reports
Information
Journal of Developmental Origins of Health and Disease , Volume 12 , Issue 5 , October 2021 , pp. 688 - 693
Check for updates
Copyright
© The Author(s), 2020. Published by Cambridge University Press in association with International Society for Developmental Origins of Health and Disease

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barker, DJ. The fetal and infant origins of adult disease. BMJ. 1990; 301(6761), 1111. doi: 10.1136/bmj.301.6761.1111 CrossRefGoogle ScholarPubMed
Hales, CN, Barker, DJ, Clark, PM, et al. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ. 1991; 303(6809), 1019–1022. doi: 10.1136/bmj.303.6809.1019 CrossRefGoogle ScholarPubMed
Hales, CN, Barker, DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia. 1992; 35(7), 595–601. doi: 10.1007/bf00400248 CrossRefGoogle ScholarPubMed
Dickinson, H, Moss, TJ, Gatford, KL, et al. A review of fundamental principles for animal models of DOHaD research: an Australian perspective. J Dev Origins Health Dis. 2016; 7(5), 449–472. doi: 10.1017/S2040174416000477 CrossRefGoogle Scholar
Suzuki, K. The developing world of DOHaD. J Dev Origins Health Dis. 2018; 9(3), 266–269. doi: 10.1017/S2040174417000691 CrossRefGoogle ScholarPubMed
Gage, SH, Munafò, MR, Davey Smith, G. Causal inference in developmental origins of health and disease (DOHaD) research. Annu Rev Psychol. 2016; 67(1), 567–585. doi: 10.1146/annurev-psych-122414-033352 CrossRefGoogle ScholarPubMed
Davey Smith, G, Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*. Int J Epidemiol. 2003; 32(1), 1–22. doi: 10.1093/ije/dyg070 CrossRefGoogle Scholar
Zanetti, D, Tikkanen, E, Gustafsson, S, Priest, JR, Burgess, S, Birthweight, Ingelsson E., Type 2 diabetes mellitus, and cardiovascular disease: addressing the Barker hypothesis with Mendelian randomization. Circ Genomic Precis Med. 2018; 11(6), e002054–e002054. doi: 10.1161/CIRCGEN.117.002054 CrossRefGoogle ScholarPubMed
Huang, T, Wang, T, Zheng, Y, et al. Association of birth weight with type 2 diabetes and glycemic traits: a Mendelian randomization study. JAMA Netw Open. 2019 ;2(9), e1910915. doi: 10.1001/jamanetworkopen.2019.10915 Google ScholarPubMed
Wang, T, Huang, T, Li, Y, et al. Low birthweight and risk of type 2 diabetes: a Mendelian randomisation study. Diabetologia. 2016; 59(9), 1920–1927. doi: 10.1007/s00125-016-4019-z CrossRefGoogle ScholarPubMed
Godfrey, KM, Barker, DJP. Fetal nutrition and adult disease. Am J Clin Nutr. 2000; 71(5), 1344S–1352S. doi: 10.1093/ajcn/71.5.1344s CrossRefGoogle ScholarPubMed
Warrington, NM, Freathy, RM, Neale, MC, Evans, DM. Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank. Int J Epidemiol. 2018; 47(4), 1229–1241. doi: 10.1093/ije/dyy015 CrossRefGoogle ScholarPubMed
Warrington, NM, Beaumont, RN, Horikoshi, M, et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet. 2019; 51(5), 804–814. doi: 10.1038/s41588-019-0403-1 CrossRefGoogle ScholarPubMed
Didelez, V, Sheehan, N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007; 16(4), 309–330. doi: 10.1177/0962280206077743 CrossRefGoogle ScholarPubMed
Bowden, J, Davey Smith, G, Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015; 44(2), 512–525. doi: 10.1093/ije/dyv080 CrossRefGoogle ScholarPubMed
Evans, DM, Moen, G-H, Hwang, L-D, Lawlor, DA, Warrington, NM. Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. Int J Epidemiol. 2019; 48(3), 861–875. doi: 10.1093/ije/dyz019 CrossRefGoogle ScholarPubMed
Tyrrell, J, Richmond, RC, Palmer, TM, et al. Genetic evidence for causal relationships between maternal obesity-related traits and birth weight. JAMA. 2016; 315(11), 1129–1140. doi: 10.1001/jama.2016.1975 CrossRefGoogle ScholarPubMed
Horikoshi, M, Beaumont, RN, Day, FR, et al. Genome-wide associations for birth weight and correlations with adult disease. Nature. 2016; 538(7624), 248–252. doi: 10.1038/nature19806 CrossRefGoogle ScholarPubMed
Hattersley, AT, Tooke, JE. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet. 1999; 353(9166), 1789–1792. doi: 10.1016/S0140–6736(98)07546–1 CrossRefGoogle ScholarPubMed
Palmer, TM, Lawlor, DA, Harbord, RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012; 21(3), 223–242. doi: 10.1177/0962280210394459 CrossRefGoogle ScholarPubMed
Burgess, S, Butterworth, A, Thompson, SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37(7), 658–665. doi: 10.1002/gepi.21758 CrossRefGoogle ScholarPubMed
Bowden, J, Davey Smith, G, Haycock, PC, Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016; 40(4), 304–314. doi: 10.1002/gepi.21965 CrossRefGoogle ScholarPubMed
Walker, VM, Davies, NM, Hemani, G, et al. Using the MR-base platform to investigate risk factors and drug targets for thousands of phenotypes. Wellcome open Res. 2019; 4, 113. doi: 10.12688/wellcomeopenres.15334.2 CrossRefGoogle ScholarPubMed
Moen, G-H, Brumpton, B, Willer, C, et al. Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort. Nat Commun. 2020; 11(1), 5404. doi: 10.1038/s41467-020-19257-zCrossRefGoogle Scholar
Freathy, RM. Can genetic evidence help us to understand the fetal origins of type 2 diabetes? Diabetologia. 2016; 59(9), 1850–1854. doi: 10.1007/s00125-016-4057-6 CrossRefGoogle ScholarPubMed
Moen, G-H, Hemani, G, Warrington, NM, Evans, DM. Calculating power to detect maternal and offspring genetic effects in genetic association studies. Behav Genet. 2019; 49(3), 327–339. doi: 10.1007/s10519-018-9944-9 CrossRefGoogle ScholarPubMed
Grotzinger, AD, Rhemtulla, M, de Vlaming, R, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019; 3(5), 513–525. doi: 10.1038/s41562-019-0566-x CrossRefGoogle ScholarPubMed
Zhu, Z, Zheng, Z, Zhang, F, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018; 9(1), 224. doi: 10.1038/s41467-017-02317-2 CrossRefGoogle ScholarPubMed
Hwang, L-D, Tubbs, JD, Luong, J, et al. Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs. PLoS Genet. 2020; 16(10), e1009154. doi: 10.1371/journal.pgen.1009154 CrossRefGoogle Scholar
Magnus, P, Birke, C, Vejrup, K, et al. Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int J Epidemiol. 2016; 45(2), 382–388. doi: 10.1093/ije/dyw029 CrossRefGoogle Scholar
Krokstad, S, Langhammer, A, Hveem, K, et al. Cohort profile: the HUNT study, Norway. Int J Epidemiol. 2013; 42(4), 968–977. doi: 10.1093/ije/dys095 CrossRefGoogle Scholar
Brumpton, B, Sanderson, E, Heilbron, K, et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun. 2020; 11(1), 3519. doi: 10.1038/s41467-020-17117-4 CrossRefGoogle ScholarPubMed
Supplementary material: File

D’Urso et al. supplementary material

D’Urso et al. supplementary material

Download D’Urso et al. supplementary material(File)
File 22.5 KB