Beaumont RN, Kotecha SJ, Wood AR, Knight BA, Sebert S, McCarthy MI, et al. (2020) Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies. PLoS Genet 16(12): e1009191. https://doi.org/10.1371/journal.pgen.1009191
Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies
|Author:||Beaumont, Robin N.1,2; Kotecha, Sarah J.2; Wood, Andrew R.1;|
1Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom
2Department of Child Health, School of Medicine, Cardiff University, Cardiff, United Kingdom
3Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulun yliopisto, Finland
4Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
5Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
6Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
7Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
8Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
9Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom
10Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102114499
Public Library of Science,
|Publish Date:|| 2021-02-11
Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced environment-related growth-restriction or overgrowth, respectively, and babies who are heritably small or large. However, the relative proportions within each group are unclear. We assessed the extent to which common genetic variants underlying variation in birth weight influence the probability of being SGA or LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 11,951 babies and 5,182 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model.
Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal = 0.75 (0.71,0.80) and 1.32 (1.26,1.39); ORmaternal = 0.81 (0.75,0.88) and 1.17 (1.09,1.25), respectively per 1 decile higher GS). We found evidence that the smallest 3% of babies had a higher BW GS, on average, than expected from their observed birth weight (assuming an additive polygenic model: Pfetal = 0.014, Pmaternal = 0.062). Higher maternal SBP GS was associated with higher odds of SGA P = 0.005.
We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1184 Genetics, developmental biology, physiology
RMF and RNB are funded by a Wellcome Trust and Royal Society Sir Henry Dale Fellowship (104150/Z/14/Z). ATH is supported by a NIHR Senior Investigator award and also a Wellcome Trust Senior Investigator award (098395/Z/12/Z). The funders had no role in the design of the study, the collection, analysis, or interpretation of the data; the writing of the manuscript, or the decision to submit the manuscript for publication. The views expressed in this paper are those of the authors and not necessarily those of any funder. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and Rachel M. Freathy will serve as guarantor for the contents of this paper. Genotyping of the EFSOCH study samples was funded by the Wellcome Trust and Royal Society grant 104150/Z/14/Z. NFBC1966 and 1986 have received core funding for data generation and curation from the Academy of Finland (project grants 104781, 120315, 129269, 1114194, 24300796, 85547, 285547 (EGEA)), University Hospital Oulu, Finland (75617), the EU FP5 EURO-BLCS, QLG1-CT-2000-01643, ERDF European Regional Development Fund Grant no. 539/2010 A31592 and the EU H2020--PHC-2014 DynaHEALTH action (grant no. 633595). The NFBCs are also funded by EU-H2020 LifeCycle Action (grant no. 733206), EU-H2020 EDCMET (grant no. 825762), EU-H2020 EUCAN Connect (grant no 824989), EU H2020-MSCA-ITN-2016 CAPICE Marie Sklodowska-Curie grant (grant no. 721567) and the Medical Research Council, UK (grants no. MR/M013138/1, MRC/BBSRC MR/S03658X/1 (JPI HDHL)).
|EU Grant Number:||
(633595) DYNAHEALTH - Understanding the dynamic determinants of glucose homeostasis and social capability to promote Healthy and active aging
(733206) LIFECYCLE - Early-life stressors and LifeCycle health
(824989) EUCAN-Connect - A federated FAIR platform enabling large-scale analysis of high-value cohort data connecting Europe and Canada in personalized health
|Academy of Finland Grant Number:||
129269 (Academy of Finland Funding decision)
285547 (Academy of Finland Funding decision)
© 2020 Beaumont et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.