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Hughes AM, Torvik FA, van Bergen E, Hannigan LJ, Corfield EC, Andreassen OA, Ystrom E, Ask H, Smith GD, Davies NM, Havdahl A. Parental education and children's depression, anxiety, and ADHD traits, a within-family study in MoBa. NPJ SCIENCE OF LEARNING 2024; 9:46. [PMID: 39025869 PMCID: PMC11258307 DOI: 10.1038/s41539-024-00260-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 07/05/2024] [Indexed: 07/20/2024]
Abstract
Children born to parents with fewer years of education are more likely to have depression, anxiety, and attention-deficit hyperactivity disorder (ADHD), but it is unclear to what extent these associations are causal. We estimated the effect of parents' educational attainment on children's depressive, anxiety, and ADHD traits at age 8 years, in a sample of 40,879 Norwegian children born in 1998-2009 and their parents. We used within-family Mendelian randomization, which employs genetic variants as instrumental variables, and controlled for direct genetic effects by adjusting for children's polygenic indexes. We found little evidence that mothers' or fathers' educational attainment independently affected children's depressive, anxiety, or ADHD traits. However, children's own polygenic scores for educational attainment were independently and negatively associated with these traits. Results suggest that differences in these traits according to parents' education may reflect direct genetic effects more than genetic nurture. Consequences of social disadvantage for children's mental health may however be more visible in samples with more socioeconomic variation, or contexts with larger socioeconomic disparities than present-day Norway. Further research is required in populations with more educational and economic inequality and in other age groups.
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Affiliation(s)
- Amanda M Hughes
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK.
| | - Fartein Ask Torvik
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Laurie J Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Elizabeth C Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Eivind Ystrom
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Helga Ask
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
| | - Neil M Davies
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Science, University College London, London, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alexandra Havdahl
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, UK
- Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
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Ronald A, Gui A. The potential and translational application of infant genetic research. Nat Genet 2024; 56:1346-1354. [PMID: 38977854 DOI: 10.1038/s41588-024-01822-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/10/2024] [Indexed: 07/10/2024]
Abstract
In the current genomic revolution, the infancy life stage is the most neglected. Although clinical genetics recognizes the value of early identification in infancy of rare genetic causes of disorders and delay, common genetic variation is almost completely ignored in research on infant behavioral and neurodevelopmental traits. In this Perspective, we argue for a much-needed surge in research on common genetic variation influencing infant neurodevelopment and behavior, findings that would be relevant for all children. We now see convincing evidence from different research designs to suggest that developmental milestones, skills and behaviors of infants are heritable and thus are suitable candidates for gene-discovery research. We highlight the resources available to the field, including genotyped infant cohorts, and we outline, with recommendations, special considerations needed for infant data. Therefore, infant genetic research has the potential to impact basic science and to affect educational policy, public health and clinical practice.
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Affiliation(s)
- Angelica Ronald
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK.
| | - Anna Gui
- Department of Psychology, University of Essex, Essex, UK
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Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. Mendelian randomization: causal inference leveraging genetic data. Psychol Med 2024; 54:1461-1474. [PMID: 38639006 DOI: 10.1017/s0033291724000321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
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Affiliation(s)
- Lane G Chen
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
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4
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Power GM, Sanderson E, Pagoni P, Fraser A, Morris T, Prince C, Frayling TM, Heron J, Richardson TG, Richmond R, Tyrrell J, Warrington N, Davey Smith G, Howe LD, Tilling KM. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review. Eur J Epidemiol 2024; 39:501-520. [PMID: 37938447 PMCID: PMC7616129 DOI: 10.1007/s10654-023-01032-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 11/09/2023]
Abstract
Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.
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Affiliation(s)
- Grace M Power
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Panagiota Pagoni
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Claire Prince
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jon Heron
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Nicole Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, University of Queensland, Woolloongabba, Queensland, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- NIHR Bristol Biomedical Research Centre Bristol, University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate M Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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5
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Zhou Q, Jin X, Li H, Wang Q, Tao M, Wang J, Cao Y. Cholesterol and low-density lipoprotein as a cause of psoriasis: Results from bidirectional Mendelian randomization. J Eur Acad Dermatol Venereol 2024; 38:710-718. [PMID: 38031463 DOI: 10.1111/jdv.19670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 10/06/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Psoriasis is an inflammatory disease that affects many people. However, the causal effect of lipid metabolism on psoriasis has not yet been verified. This study aimed to identify the genetic relationship between serum lipid levels and psoriasis. METHODS Bidirectional two-sample Mendelian randomization (MR) was used to analyse the causal relationship between cholesterol and psoriasis. The outcome of the forward causality test was psoriasis. In the analysis of reverse causality, psoriasis was exposed, and 79 single-nucleotide polymorphisms were detected in the genome-wide association study (GWASs) database from the IEU GWASs Project. MR-Egger regression, inverse variance-weighted, weighted median, weighted mode and simple mode were used for the MR analyses. RESULTS The level of triglyceride, lipase member N, chylomicrons, extremely large very low-density lipoprotein (VLDL) particles, high-density lipoprotein (HDL) cholesterol levels, cholesterol esters in large HDL, cholesterol esters in medium HDL and cholesterol esters in medium VLDL have not affected the development of psoriasis. However, total cholesterol, total free cholesterol, low-density lipoprotein (LDL) cholesterol levels, cholesterol esters in large VLDL and cholesterol esters in medium LDL were unidirectional causal effects on psoriasis. CONCLUSION Bidirectional two-sample MR analysis indicated that high levels of total cholesterol, total free cholesterol, LDL cholesterol, cholesterol esters in large VLDL and cholesterol esters in medium LDL are genetic risk factors for psoriasis.
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Affiliation(s)
- Qiujun Zhou
- Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang, China
- First Clinical Medicine College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiaoliang Jin
- First Clinical Medicine College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Hui Li
- First Clinical Medicine College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Qionglin Wang
- First Clinical Medicine College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Maocan Tao
- The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
| | - Jinhui Wang
- Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang, China
| | - Yi Cao
- The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
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Zhu Y, Zhang H, Qi J, Liu Y, Yan Y, Wang T, Zeng P. Evaluating causal influence of maternal educational attainment on offspring birthweight via observational study and Mendelian randomization analyses. SSM Popul Health 2024; 25:101587. [PMID: 38229657 PMCID: PMC10790093 DOI: 10.1016/j.ssmph.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/25/2023] [Accepted: 12/16/2023] [Indexed: 01/18/2024] Open
Abstract
Background Although extensive discussions on the influence of maternal educational attainment on offspring birthweight, the conclusion remains controversial, and it is challenging to comprehensively assess the causal association between them. Methods To estimate effect of maternal educational attainment on the birthweight of first child, we first conducted an individual-level analysis with UK Biobank participants of white ancestry (n = 208,162). We then implemented Mendelian randomization (MR) methods including inverse variance weighted (IVW) MR and multivariable MR to assess the causal relation between maternal education and maternal-specific birthweight. Finally, using the UK Biobank parent-offspring trio data (n = 618), we performed a polygenic score based MR to simultaneously adjust for confounding effects of fetal-specific birthweight and paternal educational attainment. We also conducted simulations for power evaluation and sensitivity analyses for horizontal pleiotropy of instruments. Results We observed that birthweight of first child was positively influenced by maternal education, with 7 years of maternal education as the reference, adjusted effect = 44.8 (95%CIs 38.0-51.6, P = 6.15 × 10-38), 54.9 (95%CIs 47.6-62.2, P = 4.21 × 10-128), and 89.4 (95%CIs 82.1-96.7, P = 4.28 × 10-34) for 10, 15 and 20 years of maternal educational attainment, respectively. A causal relation between maternal education and offspring birthweight was revealed by IVW MR (estimated effect = 0.074 for one standard deviation increase in maternal education years, 95%CIs 0.054-0.093, P = 2.56 × 10-13) and by complementary MR methods. This connection was not substantially affected by paternal education or horizontal pleiotropy. Further, we found a positive but insignificant causal association (adjusted effect = 24.0, 95%CIs -150.1-198.1, P = 0.787) between maternal education and offspring birthweight after simultaneously controlling for fetal genome and paternal education; this null causality was largely due to limited power of small sample sizes of parent-offspring trios. Conclusion This study offers supportive evidence for a causal association between maternal education and offspring birthweight, highlighting the significance of enhancing maternal education to prevent low birthweight.
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Affiliation(s)
- Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Hao Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Jike Qi
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yuxin Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yu Yan
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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7
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Diemer EW. The importance of translating genetic partitioning into causal language. Int J Epidemiol 2024; 53:dyae036. [PMID: 38441195 DOI: 10.1093/ije/dyae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/20/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Elizabeth W Diemer
- CAUSALab, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
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8
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Leinonen JT, Pirinen M, Tukiainen T. Disentangling the link between maternal influences on birth weight and disease risk in 36,211 genotyped mother-child pairs. Commun Biol 2024; 7:175. [PMID: 38347176 PMCID: PMC10861556 DOI: 10.1038/s42003-024-05872-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
Epidemiological studies have robustly linked lower birth weight to later-life disease risks. These observations may reflect the adverse impact of intrauterine growth restriction on a child's health. However, causal evidence supporting such a mechanism in humans is largely lacking. Using Mendelian Randomization and 36,211 genotyped mother-child pairs from the FinnGen study, we assessed the relationship between intrauterine growth and five common health outcomes (coronary heart disease (CHD), hypertension, statin use, type 2 diabetes and cancer). We proxied intrauterine growth with polygenic scores for maternal effects on birth weight and took into account the transmission of genetic variants between a mother and a child in the analyses. We find limited evidence for contribution of normal variation in maternally influenced intrauterine growth on later-life disease. Instead, we find support for genetic pleiotropy in the fetal genome linking birth weight to CHD and hypertension. Our study illustrates the opportunities that data from genotyped parent-child pairs from a population-based biobank provides for addressing causality of maternal influences.
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Affiliation(s)
- Jaakko T Leinonen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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Wang G, Warrington NM, Evans DM. Partitioning genetic effects on birthweight at classical human leukocyte antigen loci into maternal and fetal components, using structural equation modelling. Int J Epidemiol 2024; 53:dyad142. [PMID: 37831898 PMCID: PMC10859143 DOI: 10.1093/ije/dyad142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Single nucleotide polymorphisms in the human leukocyte antigen (HLA) region in both maternal and fetal genomes have been robustly associated with birthweight (BW) in previous genetic association studies. However, no study to date has partitioned the association between BW and classical HLA alleles into maternal and fetal components. METHODS We used structural equation modelling (SEM) to estimate the maternal and fetal effects of classical HLA alleles on BW. Our SEM leverages the data structure of the UK Biobank (UKB), which includes ∼270 000 participants' own BW and/or the BW of their firstborn child. RESULTS We show via simulation that our model yields asymptotically unbiased estimates of the maternal and fetal allelic effects on BW and appropriate type I error rates, in contrast to simple regression models. Asymptotic power calculations show that we have sufficient power to detect moderate-sized maternal or fetal allelic effects of common HLA alleles on BW in the UKB. Applying our SEM to imputed classical HLA alleles and own and offspring BW from the UKB replicated the previously reported association at the HLA-C locus and revealed strong evidence for maternal (HLA-A*03:01, B*35:01, B*39:06, P <0.001) and fetal allelic effects (HLA-B*39:06, P <0.001) of non-HLA-C alleles on BW. CONCLUSIONS Our model yields asymptotically unbiased estimates, appropriate type I error rates and appreciable power to estimate maternal and fetal effects on BW. These novel allelic associations between BW and classical HLA alleles provide insight into the immunogenetics of fetal growth in utero.
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Affiliation(s)
- Geng Wang
- Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Nicole M Warrington
- Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - David M Evans
- Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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10
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D’Urso S, Moen GH, Hwang LD, Hannigan LJ, Corfield EC, Ask H, Johannson S, Njølstad PR, Beaumont RN, Freathy RM, Evans DM, Havdahl A. Intrauterine Growth and Offspring Neurodevelopmental Traits: A Mendelian Randomization Analysis of the Norwegian Mother, Father and Child Cohort Study (MoBa). JAMA Psychiatry 2024; 81:144-156. [PMID: 37878341 PMCID: PMC10600722 DOI: 10.1001/jamapsychiatry.2023.3872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/18/2023] [Indexed: 10/26/2023]
Abstract
Importance Conventional epidemiological analyses have suggested that lower birth weight is associated with later neurodevelopmental difficulties; however, it is unclear whether this association is causal. Objective To investigate the relationship between intrauterine growth and offspring neurodevelopmental difficulties. Design, Setting, and Participants MoBa is a population-based pregnancy cohort that recruited pregnant women from June 1999 to December 2008 included approximately 114 500 children, 95 200 mothers, and 75 200 fathers. Observational associations between birth weight and neurodevelopmental difficulties were assessed with a conventional epidemiological approach. Mendelian randomization analyses were performed to investigate the potential causal association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties conditional on offspring allele scores. Exposures Birth weight and maternal allele scores for birth weight (derived from genetic variants robustly associated with birth weight) were the exposures in the observational and mendelian randomization analyses, respectively. Main Outcomes and Measures Clinically relevant maternal ratings of offspring neurodevelopmental difficulties at 6 months, 18 months, 3 years, 5 years, and 8 years of age assessing language and motor difficulties, inattention and hyperactivity-impulsivity, social communication difficulties, and repetitive behaviors. Results The conventional epidemiological sample included up to 46 970 offspring, whereas the mendelian randomization sample included up to 44 134 offspring (median offspring birth year, 2005 [range, 1999-2009]; mean [SD] maternal age at birth, 30.1 [4.5] years; mean [SD] paternal age at birth, 32.5 [5.1] years). The conventional epidemiological analyses found evidence that birth weight was negatively associated with several domains at multiple offspring ages (outcome of autism-related trait scores: Social Communication Questionnaire [SCQ]-full at 3 years, β = -0.046 [95% CI, -0.057 to -0.034]; SCQ-Restricted and Repetitive Behaviors subscale at 3 years, β = -0.049 [95% CI, -0.060 to -0.038]; attention-deficit/hyperactivity disorder [ADHD] trait scores: Child Behavior Checklist [CBCL]-ADHD subscale at 18 months, β = -0.035 [95% CI, -0.045 to -0.024]; CBCL-ADHD at 3 years, β = -0.032 [95% CI, -0.043 to -0.021]; CBCL-ADHD at 5 years, β = -0.050 [95% CI, -0.064 to -0.037]; Rating Scale for Disruptive Behavior Disorders [RS-DBD]-ADHD at 8 years, β = -0.036 [95% CI, -0.049 to -0.023]; RS-DBD-Inattention at 8 years, β = -0.037 [95% CI, -0.050 to -0.024]; RS-DBD-Hyperactive-Impulsive Behavior at 8 years, β = -0.027 [95% CI, -0.040 to -0.014]; Conners Parent Rating Scale-Revised [Short Form] at 5 years, β = -0.041 [95% CI, -0.054 to -0.028]; motor scores: Ages and Stages Questionnaire-Motor Difficulty [ASQ-MOTOR] at 18 months, β = -0.025 [95% CI, -0.035 to -0.015]; ASQ-MOTOR at 3 years, β = -0.029 [95% CI, -0.040 to -0.018]; and Child Development Inventory-Gross and Fine Motor Skills at 5 years, β = -0.028 [95% CI, -0.042 to -0.015]). Mendelian randomization analyses did not find any evidence for an association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties. Conclusions and Relevance This study found that the maternal intrauterine environment, as proxied by maternal birth weight genetic variants, is unlikely to be a major determinant of offspring neurodevelopmental outcomes.
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Affiliation(s)
- Shannon D’Urso
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 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, United Kingdom
- Frazer Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Laurie J. Hannigan
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Elizabeth C. Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Helga Ask
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Stefan Johannson
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Pål Rasmus Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section for Endocrinology and Metabolism, Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Robin N. Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, United Kingdom
| | - Rachel M. Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, United Kingdom
| | - David M. Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, Queensland, Australia
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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11
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Borges MC, Clayton GL, Freathy RM, Felix JF, Fernández-Sanlés A, Soares AG, Kilpi F, Yang Q, McEachan RRC, Richmond RC, Liu X, Skotte L, Irizar A, Hattersley AT, Bodinier B, Scholtens DM, Nohr EA, Bond TA, Hayes MG, West J, Tyrrell J, Wright J, Bouchard L, Murcia M, Bustamante M, Chadeau-Hyam M, Jarvelin MR, Vrijheid M, Perron P, Magnus P, Gaillard R, Jaddoe VWV, Lowe WL, Feenstra B, Hivert MF, Sørensen TIA, Håberg SE, Serbert S, Magnus M, Lawlor DA. Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes. BMC Med 2024; 22:32. [PMID: 38281920 PMCID: PMC10823651 DOI: 10.1186/s12916-023-03167-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 11/09/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.
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Affiliation(s)
- Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alba Fernández-Sanlés
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ana Gonçalves Soares
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Fanny Kilpi
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Xueping Liu
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Amaia Irizar
- Department of Preventive Medicine and Public Health, University of the Basque Country, Leioa, Spain
- BIODONOSTIA Health Research Institute, Paseo Dr. Beguiristain, 20014, San Sebastian, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Barbara Bodinier
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ellen A Nohr
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Tom A Bond
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Mario Murcia
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
| | - Mariona Bustamante
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Marc Chadeau-Hyam
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | | | - Martine Vrijheid
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Patrice Perron
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CR-CHUS), Sherbrooke, Québec, Canada
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - William L Lowe
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Thorkild I A Sørensen
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Diseases, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sylvain Serbert
- Center For Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Maria Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
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12
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Liu D, Gan Y, Zhang Y, Cui L, Tao T, Zhang J, Zhao J. Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study. Front Endocrinol (Lausanne) 2023; 14:1140499. [PMID: 37351103 PMCID: PMC10282929 DOI: 10.3389/fendo.2023.1140499] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
Associations between lower birth weight and higher polycystic ovary syndrome (PCOS) risk have been reported in previous observational studies, however, the causal relationship is still unknown. Based on decomposed fetal and maternal genetic effects on birth weight (n = 406,063), we conducted a two-sample Mendelian randomization (MR) analysis to assess potential causal relationships between fetal genome predicted birth weight and PCOS risk using a large-scale genome-wide association study (GWAS) including 4,138 PCOS cases and 20,129 controls. To further eliminate the maternally transmitted or non-transmitted effects on fetal growth, we performed a secondary MR analysis by utilizing genetic instruments after excluding maternally transmitted or non-transmitted variants, which were identified in another birth weight GWAS (n = 63,365 parent-offspring trios from Icelandic birth register). Linkage disequilibrium score regression (LDSR) analysis was conducted to estimate the genetic correlation. We found little evidence to support a causal effect of fetal genome determined birth weight on the risk of developing PCOS (primary MR analysis, OR: 0.86, 95% CI: 0.52 to 1.43; secondary MR analysis, OR: 0.86, 95% CI: 0.54 to 1.39). In addition, a marginally significant genetic correlation (rg = -0.14, se = 0.07) between birth weight and PCOS was revealed via LDSR analysis. Our findings indicated that observed associations between birth weight and future PCOS risk are more likely to be attributable to genetic pleiotropy driven by the fetal genome rather than a causal mechanism.
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Affiliation(s)
- Dong Liu
- Ministry of Education and Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuexin Gan
- Ministry of Education and Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Linlin Cui
- Center for Reproductive Medicine, The Second Hospital, Cheeloo College of Medicine, National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Tao Tao
- Department of Endocrinology and Metabolism, Renji Hospital, School of Medicine, Shanghai JiaoTong University, Shanghai, China
| | - Jun Zhang
- Ministry of Education and Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Zhao
- Ministry of Education and Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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13
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Decina CS, Hopkins R, Bowden J, Shields BM, Lawlor DA, Warrington NM, Evans DM, Freathy RM, Beaumont RN. Investigating a possible causal relationship between maternal serum urate concentrations and offspring birthweight: a Mendelian randomization study. Int J Epidemiol 2023; 52:178-189. [PMID: 36191079 PMCID: PMC9908052 DOI: 10.1093/ije/dyac186] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 09/14/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Higher urate levels are associated with higher systolic blood pressure (SBP) in adults, and in pregnancy with lower offspring birthweight. Mendelian randomization (MR) analyses suggest a causal effect of higher urate on higher SBP and of higher maternal SBP on lower offspring birthweight. If urate causally reduces birthweight, it might confound the effect of SBP on birthweight. We therefore tested for a causal effect of maternal urate on offspring birthweight. METHODS We tested the association between maternal urate levels and offspring birthweight using multivariable linear regression in the Exeter Family Study of Childhood Health (EFSOCH; n = 872) and UK Biobank (UKB; n = 133 187). We conducted two-sample MR to test for a causal effect of maternal urate [114 single-nucleotide polymorphisms (SNPs); n = 288 649 European ancestry] on offspring birthweight (n = 406 063 European ancestry; maternal SNP effect estimates adjusted for fetal effects). We assessed a causal relationship between urate and SBP using one-sample MR in UKB women (n = 199 768). RESULTS Higher maternal urate was associated with lower offspring birthweight with similar confounder-adjusted magnitudes in EFSOCH [22 g lower birthweight per 1-SD higher urate (95% CI: -50, 6); P = 0.13] and UKB [-28 g (95% CI: -31, -25); P = 1.8 × 10-75]. The MR causal effect estimate was directionally consistent, but smaller [-11 g (95% CI: -25, 3); PIVW = 0.11]. In women, higher urate was causally associated with higher SBP [1.7 mmHg higher SBP per 1-SD higher urate (95% CI: 1.4, 2.1); P = 7.8 × 10-22], consistent with that previously published in women and men. CONCLUSION The marked attenuation of the MR result of maternal urate on offspring birthweight compared with the multivariable regression result suggests previous observational associations may be confounded. The 95% CIs of the MR result included the null but suggest a possible small effect on birthweight. Maternal urate levels are unlikely to be an important contributor to offspring birthweight.
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Affiliation(s)
- Caitlin S Decina
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Rhian Hopkins
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Jack Bowden
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Beverly M Shields
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 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
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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14
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Brito Nunes C, Huang P, Wang G, Lundberg M, D'Urso S, Wootton RE, Borges MC, Lawlor DA, Warrington NM, Evans DM, Hwang LD, Moen GH. Mendelian randomization study of maternal coffee consumption and its influence on birthweight, stillbirth, miscarriage, gestational age and pre-term birth. Int J Epidemiol 2023; 52:165-177. [PMID: 35679582 PMCID: PMC9908064 DOI: 10.1093/ije/dyac121] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Coffee consumption has been associated with several adverse pregnancy outcomes, although data from randomized-controlled trials are lacking. We investigate whether there is a causal relationship between coffee consumption and miscarriage, stillbirth, birthweight, gestational age and pre-term birth using Mendelian randomization (MR). METHODS A two-sample MR study was performed using summary results data from a genome-wide association meta-analysis of coffee consumption (N = 91 462) from the Coffee and Caffeine Genetics Consortium. Outcomes included self-reported miscarriage (N = 49 996 cases and 174 109 controls from a large meta-analysis); the number of stillbirths [N = 60 453 from UK Biobank (UKBB)]; gestational age and pre-term birth (N = 43 568 from the 23andMe, Inc cohort) and birthweight (N = 297 356 reporting own birthweight and N = 210 248 reporting offspring's birthweight from UKBB and the Early Growth Genetics Consortium). Additionally, a one-sample genetic risk score (GRS) analysis of coffee consumption in UKBB women (N up to 194 196) and the Avon Longitudinal Study of Parents and Children (N up to 6845 mothers and 4510 children) and its relationship with offspring outcomes was performed. RESULTS Both the two-sample MR and one-sample GRS analyses showed no change in risk of sporadic miscarriages, stillbirths, pre-term birth or effect on gestational age connected to coffee consumption. Although both analyses showed an association between increased coffee consumption and higher birthweight, the magnitude of the effect was inconsistent. CONCLUSION Our results suggest that coffee consumption during pregnancy might not itself contribute to adverse outcomes such as stillbirth, sporadic miscarriages and pre-term birth or lower gestational age or birthweight of the offspring.
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Affiliation(s)
- Caroline Brito Nunes
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Peiyuan Huang
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geng Wang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - Mischa Lundberg
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - Shannon D'Urso
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Robyn E Wootton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Maria Carolina Borges
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicole M Warrington
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Gunn-Helen Moen
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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15
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Zhao J, Stewart ID, Baird D, Mason D, Wright J, Zheng J, Gaunt TR, Evans DM, Freathy RM, Langenberg C, Warrington NM, Lawlor DA, Borges MC. Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study. EBioMedicine 2023; 88:104441. [PMID: 36696816 PMCID: PMC9879767 DOI: 10.1016/j.ebiom.2023.104441] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/28/2022] [Accepted: 01/06/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear. METHODS We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomisation (MR) and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight in UK Biobank and Early Growth Genetics Consortium, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms robustly associated with 19 amino acids (p < 4.9 × 10-10) were used as genetic instrumental variables (IV). Wald ratio and inverse variance weighted methods were used in MR main analysis. A series of sensitivity analyses were performed to explore IV assumption violations. FINDINGS Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per standard deviation increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (-59 g, 95% CI: -106, -11) and phenylalanine (-25 g, 95% CI: -47, -4) lower offspring birthweight. These findings are supported by sensitivity analyses. INTERPRETATION Our findings strengthen evidence for key roles of maternal circulating amino acids during pregnancy in healthy fetal growth. FUNDING A full list of funding bodies that contributed to this study can be found under Acknowledgments.
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Affiliation(s)
- Jian Zhao
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
| | | | - Denis Baird
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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16
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Moen GH, Nivard M, Bhatta L, Warrington NM, Willer C, Åsvold BO, Brumpton B, Evans DM. Using Genomic Structural Equation Modeling to Partition the Genetic Covariance Between Birthweight and Cardiometabolic Risk Factors into Maternal and Offspring Components in the Norwegian HUNT Study. Behav Genet 2023; 53:40-52. [PMID: 36322199 PMCID: PMC9823066 DOI: 10.1007/s10519-022-10116-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.
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Affiliation(s)
- Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. .,Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia. .,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. .,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK. .,The University of Queensland Diamantina Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia.
| | - Michel Nivard
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands ,grid.16872.3a0000 0004 0435 165XAmsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Laxmi Bhatta
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nicole M Warrington
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia
| | - Cristen Willer
- grid.214458.e0000000086837370Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA ,grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI US ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, Ann Arbor, USA
| | - Bjørn Olav Åsvold
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben Brumpton
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - David M. Evans
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia ,grid.5337.20000 0004 1936 7603Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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17
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Balbona JV, Kim Y, Keller MC. The estimation of environmental and genetic parental influences. Dev Psychopathol 2022; 34:1-11. [PMID: 36524242 PMCID: PMC10272284 DOI: 10.1017/s0954579422000761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Parents share half of their genes with their children, but they also share background social factors and actively help shape their child's environment - making it difficult to disentangle genetic and environmental causes of parent-offspring similarity. While adoption and extended twin family designs have been extremely useful for distinguishing genetic and nongenetic parental influences, these designs entail stringent assumptions about phenotypic similarity between relatives and require samples that are difficult to collect and therefore are typically small and not publicly shared. Here, we describe these traditional designs, as well as modern approaches that use large, publicly available genome-wide data sets to estimate parental effects. We focus in particular on an approach we recently developed, structural equation modeling (SEM)-polygenic score (PGS), that instantiates the logic of modern PGS-based methods within the flexible SEM framework used in traditional designs. Genetically informative designs such as SEM-PGS rely on different and, in some cases, less rigid assumptions than traditional approaches; thus, they allow researchers to capitalize on new data sources and answer questions that could not previously be investigated. We believe that SEM-PGS and similar approaches can lead to improved insight into how nature and nurture combine to create the incredible diversity underlying human behavior.
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Affiliation(s)
- Jared V. Balbona
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO 80303, USA
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Matthew C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80303, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO 80303, USA
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18
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Martin L, Zhang Y, First O, Mustieles V, Dodson R, Rosa G, Coburn-Sanderson A, Adams CD, Messerlian C. Lifestyle interventions to reduce endocrine-disrupting phthalate and phenol exposures among reproductive age men and women: A review and future steps. ENVIRONMENT INTERNATIONAL 2022; 170:107576. [PMID: 36283156 PMCID: PMC9890927 DOI: 10.1016/j.envint.2022.107576] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/08/2022] [Accepted: 10/08/2022] [Indexed: 05/04/2023]
Abstract
Non-persistent endocrine-disrupting chemicals (EDCs), including phthalates and phenols, are ubiquitous in both the environment and human body. A growing body of epidemiologic studies have identified concerning links between EDCs and adverse reproductive and developmental health effects. Despite consistent evidence, risk assessments and policy interventions often arrive late. This presents an urgent need to identify evidence-based interventions for implementation at both clinical and community levels to reduce EDC exposure, especially in susceptible populations. The reproductive life cycle (menarche to menopause for females and after pubertal onset for males) includes some of the most vulnerable periods to environmental exposures, such as the preconception and perinatal stages, representing a key window of opportunity to intervene and prevent unfavorable health outcomes. This review aims to synthesize and assess behavioral, dietary, and residential EDC-driven interventions to develop recommendations for subsequent, larger-scale studies that address knowledge-gaps in current interventions during the reproductive life cycle. We selected 21 primary interventions for evaluation, in addition to four supplemental interventions. Among these, accessible (web-based) educational resources, targeted replacement of (known) toxic products, and personalization of the intervention through meetings and support groups, were the most promising strategies for reducing EDC concentrations. However, we document a paucity of interventions to prevent phthalate and phenol exposures during the reproductive years, especially among men. Accordingly, we recommend additional, larger clinical and community-based intervention studies to reduce EDC exposure. Specifically, future intervention studies should focus on short-term, mid-, and long-term exposure reduction to phthalates and phenols. The latter, especially, is required for the development of clinical and public health guidelines to promote reproductive and developmental health globally.
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Affiliation(s)
- Leah Martin
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yu Zhang
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Olivia First
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Vicente Mustieles
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Gabriela Rosa
- Massachusetts General Hospital Fertility Center, Department of Obstetrics and Gynecology, Boston, MA, USA
| | - Ayanna Coburn-Sanderson
- Massachusetts General Hospital Fertility Center, Department of Obstetrics and Gynecology, Boston, MA, USA
| | - Charleen D Adams
- Massachusetts General Hospital Fertility Center, Department of Obstetrics and Gynecology, Boston, MA, USA
| | - Carmen Messerlian
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Massachusetts General Hospital Fertility Center, Department of Obstetrics and Gynecology, Boston, MA, USA.
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19
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Ding Z, Pang L, Chai H, Li F, Wu M. The causal association between maternal smoking around birth on childhood asthma: A Mendelian randomization study. Front Public Health 2022; 10:1059195. [PMID: 36408054 PMCID: PMC9670139 DOI: 10.3389/fpubh.2022.1059195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022] Open
Abstract
To explore the causal relationship between maternal smoking around birth and childhood asthma using Mendelian randomization (MR). Using the data from large-scale genome-wide association studies, we selected independent genetic loci closely related to maternal smoking around birth and maternal diseases as instrumental variables and used MR methods. In this study, we considered the inverse variance weighted method (MR-IVW), weighted median method, and MR-Egger regression. We investigated the causal relationship between maternal smoking around birth and maternal diseases in childhood asthma using the odds ratio (OR) as an evaluation index. Multivariable MR (MVMR) included maternal history of Alzheimer's disease, illnesses of the mother: high blood pressure and illnesses of the mother: heart diseaseas covariates to address potential confounding. Sensitivity analyses were evaluated for weak instrument bias and pleiotropic effects. It was shown with the MR-IVW results that maternal smoking around birth increased the risk of childhood asthma by 1.5% (OR = 1.0150, 95% CI: 1.0018-1.0283). After the multivariable MR method was used to correct for relevant covariates, the association effect between maternal smoking around birth and childhood asthma was still statistically significant (P < 0.05). Maternal smoking around birth increases the risk of childhood asthma.
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Affiliation(s)
- Zijun Ding
- Department of Neonatology, Shanxi Children's Hospital, Taiyuan, China
| | - Lei Pang
- Department of Urology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital), Taiyuan, China,*Correspondence: Lei Pang
| | - Hongqiang Chai
- Department of Urology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital), Taiyuan, China
| | - Fei Li
- Department of Urology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital), Taiyuan, China
| | - Ming Wu
- Department of Urology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital), Taiyuan, China
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20
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Richmond RC, Sanderson E. Methods and practical considerations for performing Mendelian randomization. Int J Epidemiol 2022. [DOI: 10.1093/ije/dyac166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, Bristol University , Bristol, UK E-mail:
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Bristol University , Bristol, UK E-mail:
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21
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Hwang LD, Moen GH, Evans DM. Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes. eLife 2022; 11:e73671. [PMID: 35822614 PMCID: PMC9323003 DOI: 10.7554/elife.73671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Maternal genetic effects can be defined as the effect of a mother's genotype on the phenotype of her offspring, independent of the offspring's genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.
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Affiliation(s)
- Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
- The University of Queensland Diamantina Institute, The University of QueenslandBrisbaneAustralia
- Institute for Clinical Medicine, Faculty of Medicine, University of OsloOsloNorway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and TechnologyTrondheimNorway
- Population Health Science, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - David M Evans
- Institute for Molecular Bioscience, The University of QueenslandBrisbaneAustralia
- The University of Queensland Diamantina Institute, The University of QueenslandBrisbaneAustralia
- MRC Integrative Epidemiology Unit, University of BristolBristolUnited Kingdom
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22
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Barry CJS, Lawlor DA, Shapland CY, Sanderson E, Borges MC. Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight. Metabolites 2022; 12:537. [PMID: 35736469 PMCID: PMC9231269 DOI: 10.3390/metabo12060537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/06/2022] [Accepted: 06/07/2022] [Indexed: 12/27/2022] Open
Abstract
Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.
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Affiliation(s)
- Ciarrah-Jane Shannon Barry
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
- NIHR Bristol Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Chin Yang Shapland
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
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23
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Gan Y, Lu D, Yan C, Zhang J, Zhao J. Maternal Polycystic Ovary Syndrome and Offspring Birth Weight: A Mendelian Randomization Study. J Clin Endocrinol Metab 2022; 107:1020-1029. [PMID: 34849988 DOI: 10.1210/clinem/dgab843] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Indexed: 02/05/2023]
Abstract
CONTEXT Observational associations between maternal polycystic ovary syndrome (PCOS) and offspring birth weight (BW) have been inconsistent and the causal relationship is still uncertain. OBJECTIVE We conducted a 2-sample Mendelian randomization (MR) study to estimate the causal effect of maternal PCOS on offspring BW. METHODS We constructed genetic instruments for PCOS with 14 single nucleotide polymorphisms (SNPs) which were identified in a genome-wide association study (GWAS) meta-analysis including 10 074 PCOS cases and 103 164 controls of European ancestry from 7 cohorts. The genetic associations of these SNPs with the offspring BW were extracted from summary statistics estimated by the Early Growth Genetics consortium (n = 406 063 European ancestry individuals) using the weighted linear model, an approximation method of structural equation model, which separated maternal genetic effects from fetal genetic effects. We used a 2-sample MR design to examine the causal relationship between maternal PCOS and offspring BW. Sensitivity analyses were conducted to assess the robustness of the MR results. RESULTS We found little evidence for a causal effect of maternal PCOS on offspring BW (-6.1 g, 95% CI -16.8 g, 4.6 g). Broadly consistent results were found in the sensitivity analyses. CONCLUSION Despite the large scale of this study, our results suggested little causal effect of maternal PCOS on offspring BW. MR studies with a larger sample size of women with PCOS or more genetic instruments that would increase the variation of PCOS explained are needed in the future.
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Affiliation(s)
- Yuexin Gan
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Donghao Lu
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Chonghuai Yan
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jun Zhang
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jian Zhao
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
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24
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Bianchi ME, Restrepo JM. Low Birthweight as a Risk Factor for Non-communicable Diseases in Adults. Front Med (Lausanne) 2022; 8:793990. [PMID: 35071274 PMCID: PMC8770864 DOI: 10.3389/fmed.2021.793990] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/08/2021] [Indexed: 11/18/2022] Open
Abstract
According to studies undertaken over the past 40 years, low birthweight (LBW) is not only a significant predictor of perinatal death and morbidity, but also increases the risk of chronic non-communicable diseases (NCDs) in adulthood. The purpose of this paper is to summarize the research on LBW as a risk factor for NCDs in adults. The Barker hypothesis was based on the finding that adults with an LBW or an unhealthy intrauterine environment, as well as a rapid catch-up, die due to NCDs. Over the last few decades, terminology such as thrifty genes, fetal programming, developmental origins of health and disease (DOHaD), and epigenetic factors have been coined. The most common NCDs include cardiovascular disease, diabetes mellitus type 2 (DMT2), hypertension (HT), dyslipidemia, proteinuria, and chronic kidney disease (CKD). Studies in mothers who experienced famine and those that solely reported birth weight as a risk factor for mortality support the concept. Although the etiology of NCD is unknown, Barry Brenner explained the notion of a low glomerular number (nGlom) in LBW children, followed by the progression to hyperfiltration as the physiopathologic etiology of HT and CKD in adults based on Guyton's renal physiology work. Autopsies of several ethnic groups have revealed anatomopathologic evidence in fetuses and adult kidneys. Because of the renal reserve, demonstrating renal function in proportion to renal volume in vivo is more difficult in adults. The greatest impact of these theories can be seen in pediatrics and obstetrics practice.
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Affiliation(s)
- Maria Eugenia Bianchi
- Laboratory Physiology, Department Basic Sciences, Institute School of Medicine, National Northeast University, Corrientes, Argentina
| | - Jaime M Restrepo
- Department of Pediatrics, Pediatric Nephrology Service, Icesi University, Valle del Lili, Cali, Colombia.,Fundación Valle del Lili, Cali, Colombia
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25
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Abstract
Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.
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Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol BS1 3NU, United Kingdom
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26
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Wang G, Bhatta L, Moen GH, Hwang LD, Kemp JP, Bond TA, Åsvold BO, Brumpton B, Evans DM, Warrington NM. Investigating a Potential Causal Relationship Between Maternal Blood Pressure During Pregnancy and Future Offspring Cardiometabolic Health. Hypertension 2022; 79:170-177. [PMID: 34784738 PMCID: PMC8654122 DOI: 10.1161/hypertensionaha.121.17701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/20/2021] [Indexed: 12/20/2022]
Abstract
Observational epidemiological studies have reported that higher maternal blood pressure (BP) during pregnancy is associated with increased future risk of offspring cardiometabolic disease. However, it is unclear whether this association represents a causal relationship through intrauterine mechanisms. We used a Mendelian randomization (MR) framework to examine the relationship between unweighted maternal genetic scores for systolic BP and diastolic BP and a range of cardiometabolic risk factors in the offspring of up to 29 708 genotyped mother-offspring pairs from the UKB study (UK Biobank) and the HUNT study (Trøndelag Health). We conducted similar analyses in up to 21 423 father-offspring pairs from the same cohorts. We confirmed that the BP-associated genetic variants from the general population sample also had similar effects on maternal BP during pregnancy in independent cohorts. We did not detect any association between maternal (or paternal) unweighted genetic scores and cardiometabolic offspring outcomes in the meta-analysis of UKB and HUNT after adjusting for offspring genotypes at the same loci. We find little evidence to support the notion that maternal BP is a major causal risk factor for adverse offspring cardiometabolic outcomes in later life.
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Affiliation(s)
- Geng Wang
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
| | - Gunn-Helen Moen
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), 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 (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway (G.-H.M.)
- Population Health Sciences, Bristol Medical School (G.-H.M., T.A.B.), University of Bristol, United Kingdom
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
| | - John P. Kemp
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Tom A. Bond
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Population Health Sciences, Bristol Medical School (G.-H.M., T.A.B.), University of Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Bjørn Olav Åsvold
- Department of Endocrinology, Clinic of Medicine (B.O.A.), St Olavs Hospital, Trondheim University Hospital, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway (B.O.A., B.B.)
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (L.B., G.-H.M., B.O.A., B.B., N.M.W.)
- Clinic of Medicine (B.B.), St Olavs Hospital, Trondheim University Hospital, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway (B.O.A., B.B.)
| | - David M. Evans
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
| | - Nicole M. Warrington
- The University of Queensland Diamantina Institute (G.W., G.-H.M., L.-D.H., J.P.K., T.A.B., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Institute of Molecular Bioscience (L.-D.H., J.P.K., D.M.E., N.M.W.), The University of Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit (J.P.K., T.A.B., D.M.E., N.M.W.), University of Bristol, United Kingdom
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27
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He B, Kwok MK, Chan II, Schooling CM. Maternal respiratory health and intrauterine exposure-driven birthweight: a two-sample Mendelian randomization study. Int J Epidemiol 2021; 51:958-963. [PMID: 34931235 DOI: 10.1093/ije/dyab263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/11/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Observationally, poorer maternal respiratory health is associated with poorer birth outcomes, possibly confounded by socioeconomic position and other maternal attributes. We used multivariable Mendelian randomization (MR) to obtain unconfounded estimates of effect of maternal lung function on birthweight, independent of maternal height. METHODS Single nucleotide polymorphisms (SNPs) for forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) in women were obtained from publicly available summary statistics from the UK Biobank. SNPs for asthma were obtained from the Trans-National Asthma Genetic consortium. SNPs for height in women were obtained from the Genetic Investigation of Anthropometric Traits consortium and the genetic estimates were obtained the UK Biobank. The genetic associations with maternally-driven birthweight were obtained from the Early Growth Genetics consortium. Multivariable MR estimates were obtained using inverse variance weighting with multivariable MR-Egger as sensitivity analysis. RESULTS Maternal lung capacity, as indicated by FVC, was positively associated with maternally-driven birthweight (0.08 per standard deviation, 95% confidence interval 0.01 to 0.15) independent of maternal height, whereas no clear such associations were shown for maternal airway function, indicated by FEV1 and peak expiratory flow, or for asthma, on maternally-driven birthweight. Similar findings were shown using MR-Egger. CONCLUSIONS These findings suggest that maternal lung function, especially lung capacity independent of maternal height, is directly associated with maternally-driven birthweight, and highlights the importance of maternal respiratory health in fetal growth.
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Affiliation(s)
- Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Man Ki Kwok
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Io Ieong Chan
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China.,School of Public Health and Health Policy, City University of New York, New York, USA
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28
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Jones HJ, Borges MC, Carnegie R, Mongan D, Rogers PJ, Lewis SJ, Thompson AD, Zammit S. Associations between plasma fatty acid concentrations and schizophrenia: a two-sample Mendelian randomisation study. Lancet Psychiatry 2021; 8:1062-1070. [PMID: 34735824 DOI: 10.1016/s2215-0366(21)00286-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/21/2021] [Accepted: 07/22/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Although studies suggest that concentrations of omega-3 and omega-6 fatty acids are lower in individuals with schizophrenia, evidence for beneficial effects of fatty acid supplementation is scarce. Therefore, in this study, we aimed to determine whether omega-3 and omega-6 fatty acid concentrations are causally related to schizophrenia. METHODS We did a two-sample Mendelian randomisation study, using deidentified summary-level data that were publicly available. Exposure-outcome relationships were evaluated using the inverse variance weighted two-sample Mendelian randomisation method using results from genome-wide association studies (GWASs) of fatty acid concentrations and schizophrenia. GWAS results were available for European (fatty acids) and European and Asian (schizophrenia) ancestry samples. Overall age and gender information were not calculable from the summary-level GWAS results. Weighted median, weighted mode, and Mendelian randomisation Egger regression methods were used as sensitivity analyses. To address underlying mechanisms, further analyses were done using single instruments within the FADS gene cluster and ELOVL2 gene locus. FADS gene cluster and ELOVL2 gene causal effects on schizophrenia were calculated by dividing the single nucleotide polymorphism (SNP)-schizophrenia effect estimate by the SNP-fatty acid effect estimate with standard errors derived using the first term from a delta method expansion for the ratio estimate. Multivariable Mendelian randomisation was used to estimate direct effects of omega-3 fatty acids on schizophrenia, independent of omega-6 fatty acids, lipoproteins (ie, HDL and LDL), and triglycerides. FINDINGS Mendelian randomisation analyses indicated that long-chain omega-3 and long-chain omega-6 fatty acid concentrations were associated with a lower risk of schizophrenia (eg, inverse variance weighted odds ratio [OR] 0·83 [95% CI 0·75-0·92] for docosahexaenoic acid). By contrast, there was weak evidence that short-chain omega-3 and short-chain omega-6 fatty acids were associated with an increased risk of schizophrenia (eg, inverse variance weighted OR 1·07 [95% CI 0·98-1·18] for α-linolenic acid). Effects were consistent across the sensitivity analyses and the FADS single-SNP analyses, suggesting that long-chain omega-3 and long-chain omega-6 fatty acid concentrations were associated with lower risk of schizophrenia (eg, OR 0·74 [95% CI 0·58-0·96] for docosahexaenoic acid) whereas short-chain omega-3 and short-chain omega-6 fatty acid concentrations were associated with an increased risk of schizophrenia (eg, OR 1·08 [95% CI 1·02-1·15] for α-linolenic acid). By contrast, estimates from the ELOVL2 single-SNP analyses were more imprecise and compatible with both risk-increasing and protective effects for each of the fatty acid measures. Multivariable Mendelian randomisation indicated that the protective effect of docosahexaenoic acid on schizophrenia persisted after conditioning on other lipids, although evidence was slightly weaker (multivariable inverse variance weighted OR 0·84 [95% CI 0·71-1·01]). INTERPRETATION Our results are compatible with the protective effects of long-chain omega-3 and long-chain omega-6 fatty acids on schizophrenia, suggesting that people with schizophrenia might have difficulty converting short-chain polyunsaturated fatty acids to long-chain polyunsaturated fatty acids. Further studies are required to determine whether long-chain polyunsaturated fatty acid supplementation or diet enrichment might help prevent onset of schizophrenia. FUNDING National Institute for Health Research Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol.
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Affiliation(s)
- Hannah J Jones
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK.
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rebecca Carnegie
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Peter J Rogers
- Nutrition and Behaviour Unit, School of Psychological Science, University of Bristol, Bristol, UK; National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Sarah J Lewis
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Bristol Dental School, University of Bristol, Bristol, UK
| | - Andrew D Thompson
- Division of Mental Health and Wellbeing, University of Warwick, Coventry, UK; Orygen, Centre of Youth Mental Health, Melbourne, Australia
| | - Stanley Zammit
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK; MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff University, Cardiff, UK
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29
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Howe LJ, Battram T, Morris TT, Hartwig FP, Hemani G, Davies NM, Smith GD. Assortative mating and within-spouse pair comparisons. PLoS Genet 2021; 17:e1009883. [PMID: 34735433 PMCID: PMC8594845 DOI: 10.1371/journal.pgen.1009883] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 11/16/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.
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Affiliation(s)
- Laurence J. Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Thomas Battram
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tim T. Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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30
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Warrington NM, Hwang LD, Nivard MG, Evans DM. Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data. Nat Commun 2021; 12:5420. [PMID: 34521848 PMCID: PMC8440517 DOI: 10.1038/s41467-021-25723-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 08/26/2021] [Indexed: 01/12/2023] Open
Abstract
Estimation of direct and indirect (i.e. parental and/or sibling) genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies.
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Affiliation(s)
- Nicole M Warrington
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia.
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia.
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Liang-Dar Hwang
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | - Michel G Nivard
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, VU University, Amsterdam, The Netherlands
| | - David M Evans
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
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31
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The role of genetics in fetal programming of adult cardiometabolic disease. J Dev Orig Health Dis 2021; 13:292-299. [PMID: 34176548 DOI: 10.1017/s2040174421000350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Disturbances affecting early development have broad repercussions on the individual's health during infancy and adulthood. Multiple observational studies throughout the years have shown that alterations of fetal growth are associated with increased cardiometabolic disease risks. However, the genetic component of this association only started to be investigated in the last 40 years, when single genes with distinct effects were investigated. Birth weight (BW), commonly reported as the outcome of developmental growth, has been estimated to be 20% to 60% heritable. Through Genome-Wide Association (GWA) meta-analyses, 190 different loci have been identified being associated with BW, and while many of these loci designate genes involved in glucose and lipid metabolism, with clear ties to fetal development, the role of others is not yet understood. In addition, due to its influence over the intrauterine environment, the maternal genotype also plays an important part in the determination of offspring BW, with the same loci having independent effects of different magnitude or even direction. There is still much to uncover regarding the genetic determinants of BW and the interactions between maternal, offspring, and even paternal genotype. To fully understand these, diverse and novel cohorts from multiple ancestries collecting extensive neonatal phenotype will be needed. This review compiles, chronologically, the main findings in the investigation of the genetics of BW.
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32
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Moen GH, Beaumont RN, Grarup N, Sommer C, Shields BM, Lawlor DA, Freathy RM, Evans DM, Warrington NM. Investigating the causal effect of maternal vitamin B12 and folate levels on offspring birthweight. Int J Epidemiol 2021; 50:179-189. [PMID: 33347560 PMCID: PMC7938507 DOI: 10.1093/ije/dyaa256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2020] [Indexed: 11/29/2022] Open
Abstract
Background Lower maternal serum vitamin B12 (B12) and folate levels have been associated with lower offspring birthweight, in observational studies. The aim of this study was to investigate whether this relationship is causal. Methods We performed two-sample Mendelian randomization (MR) using summary data on associations between genotype-B12 (10 genetic variants) or genotype-folate (four genetic variants) levels from: a genome-wide association study of 45 576 individuals (sample 1); and both maternal- and fetal-specific genetic effects on offspring birthweight from the latest Early Growth Genetics consortium meta-analysis with 297 356 individuals reporting their own birthweight and 210 248 women reporting their offspring's birthweight (sample 2). We used the inverse variance weighted method, and sensitivity analyses to account for pleiotropy, in addition to excluding a potentially pleiotropic variant in the FUT2 gene for B12 levels. Results We did not find evidence for a causal effect of maternal or fetal B12 levels on offspring birthweight. The results were consistent across the different methods. We found a positive causal effect of maternal folate levels on offspring birthweight [0.146 (0.065, 0.227), which corresponds to an increase in birthweight of 71 g per 1 standard deviation higher folate]. We found some evidence for a small inverse effect of fetal folate levels on their own birthweight [−0.051 (−0.100, −0.003)]. Conclusions Our results are consistent with evidence from randomized controlled trials that higher maternal folate levels increase offspring birthweight. We did not find evidence for a causal effect of B12 levels on offspring birthweight, suggesting previous observational studies may have been confounded.
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Affiliation(s)
- Gunn-Helen Moen
- Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, QLD, Australia.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christine Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Beverley M Shields
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Bristol National Institute of Health Research Biomedical Research Centre, Bristol, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - David M Evans
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, QLD, Australia.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, QLD, Australia.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
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33
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Beck JJ, Pool R, van de Weijer M, Chen X, Krapohl E, Gordon SD, Nygaard M, Debrabant B, Palviainen T, van der Zee MD, Baselmans B, Finnicum CT, Yi L, Lundström S, van Beijsterveldt T, Christiansen L, Heikkilä K, Kittelsrud J, Loukola A, Ollikainen M, Christensen K, Martin NG, Plomin R, Nivard M, Bartels M, Dolan C, Willemsen G, de Geus E, Almqvist C, Magnusson PKE, Mbarek H, Ehli EA, Boomsma DI, Hottenga JJ. Genetic Meta-Analysis of Twin Birth Weight Shows High Genetic Correlation with Singleton Birth Weight. Hum Mol Genet 2021; 30:1894-1905. [PMID: 33955455 PMCID: PMC8444448 DOI: 10.1093/hmg/ddab121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/19/2021] [Accepted: 04/20/2021] [Indexed: 01/09/2023] Open
Abstract
Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardio-metabolic disorders, autoimmune diseases, and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson's r = 0.66, 95% confidence interval [CI]: 0.47-0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95% CI: 0.66-1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.
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Affiliation(s)
- Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota, United States of America
| | - René Pool
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Margot van de Weijer
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Xu Chen
- Department of Medical Epidemiology and Biostatististics, Karolinska Institutet, Stockholm, Sweden
| | - Eva Krapohl
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Scott D Gordon
- Genetic Epidemiology Laboratory, QIMR Berghofer, Brisbane, Queensland, Australia
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Birgit Debrabant
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Teemu Palviainen
- University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Bart Baselmans
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Casey T Finnicum
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota, United States of America
| | - Lu Yi
- Department of Medical Epidemiology and Biostatististics, Karolinska Institutet, Stockholm, Sweden
| | - Sebastian Lundström
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Toos van Beijsterveldt
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lene Christiansen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Kauko Heikkilä
- University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Julie Kittelsrud
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota, United States of America
| | - Anu Loukola
- University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Miina Ollikainen
- University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Nicholas G Martin
- Genetic Epidemiology Laboratory, QIMR Berghofer, Brisbane, Queensland, Australia
| | - Robert Plomin
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Michel Nivard
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Conor Dolan
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Eco de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatististics, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatististics, Karolinska Institutet, Stockholm, Sweden
| | - Hamdi Mbarek
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota, United States of America
| | - Dorret I Boomsma
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota, United States of America.,Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
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34
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Kim Y, Balbona JV, Keller MC. Bias and Precision of Parameter Estimates from Models Using Polygenic Scores to Estimate Environmental and Genetic Parental Influences. Behav Genet 2021; 51:279-288. [PMID: 33301082 PMCID: PMC8093160 DOI: 10.1007/s10519-020-10033-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/20/2020] [Indexed: 01/27/2023]
Abstract
In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing [Formula: see text] values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ([Formula: see text]), standard errors of these standardized estimates are reasonable ([Formula: see text]) for [Formula: see text] trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., [Formula: see text] as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS's on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.
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Affiliation(s)
- Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, USA.
| | - Jared V Balbona
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, USA
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, USA.
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, USA.
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Yang Q, Millard LAC, Davey Smith G. Proxy gene-by-environment Mendelian randomization study confirms a causal effect of maternal smoking on offspring birthweight, but little evidence of long-term influences on offspring health. Int J Epidemiol 2021; 49:1207-1218. [PMID: 31834381 PMCID: PMC7660158 DOI: 10.1093/ije/dyz250] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 12/27/2022] Open
Abstract
Background A lack of genetic data across generations makes transgenerational Mendelian randomization (MR) difficult. We used UK Biobank and a novel proxy gene-by-environment MR to investigate effects of maternal smoking heaviness in pregnancy on offspring health, using participants’ (generation one: G1) genotype (rs16969968 in CHRNA5) as a proxy for their mothers’ (G0) genotype. Methods We validated this approach by replicating an established effect of maternal smoking heaviness on offspring birthweight. Then we applied this approach to explore effects of maternal (G0) smoking heaviness on offspring (G1) later life outcomes and on birthweight of G1 women’s children (G2). Results Each additional smoking-increasing allele in offspring (G1) was associated with a 0.018 [95% confidence interval (CI): -0.026, -0.009] kg lower G1 birthweight in maternal (G0) smoking stratum, but no meaningful effect (-0.002 kg; 95% CI: -0.008, 0.003) in maternal non-smoking stratum (interaction P-value = 0.004). The differences in associations of rs16969968 with grandchild’s (G2) birthweight between grandmothers (G0) who did, versus did not, smoke were heterogeneous (interaction P-value = 0.042) among mothers (G1) who did (-0.020 kg/allele; 95% CI: -0.044, 0.003), versus did not (0.007 kg/allele; 95% CI: -0.005, 0.020), smoke in pregnancy. Conclusions Our study demonstrated how offspring genotype can be used to proxy for the mother’s genotype in gene-by-environment MR. We confirmed the causal effect of maternal (G0) smoking on offspring (G1) birthweight, but found little evidence of an effect on G1 longer-term health outcomes. For grandchild’s (G2) birthweight, the effect of grandmother’s (G0) smoking heaviness in pregnancy may be modulated by maternal (G1) smoking status in pregnancy.
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Affiliation(s)
- Qian Yang
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Louise A C Millard
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK.,Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
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36
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Evans DM, Medland SE, Prom-Wormley E. Introduction to the Special Issue on Statistical Genetic Methods for Human Complex Traits. Behav Genet 2021; 51:165-169. [PMID: 33864530 DOI: 10.1007/s10519-021-10057-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia. .,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Elizabeth Prom-Wormley
- The Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, USA
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Hwang LD, Evans DM. Commentary: Proxy gene-by-environment Mendelian randomization for assessing causal effects of maternal exposures on offspring outcomes. Int J Epidemiol 2021; 49:1218-1220. [PMID: 32356890 DOI: 10.1093/ije/dyaa069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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Hwang LD, Davies NM, Warrington NM, Evans DM. Integrating Family-Based and Mendelian Randomization Designs. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a039503. [PMID: 32122917 PMCID: PMC7919398 DOI: 10.1101/cshperspect.a039503] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Most Mendelian randomization (MR) studies published in the literature to date have involved analyses of unrelated, putatively independent sets of individuals. However, estimates obtained from these sorts of studies are subject to a range of biases including dynastic effects, assortative mating, residual population stratification, and horizontal pleiotropy. The inclusion of related individuals in MR studies can help control for and, in some cases, estimate the effect of these biases on causal parameters. In this review, we discuss these biases, how they can affect MR studies, and describe three sorts of family-based study designs that can be used to control for them. We conclude that including family information from related individuals is not only possible given the world's existing twin, birth, and large-scale population-based cohorts, but likely to reap rich rewards in understanding the etiology of complex traits and diseases in the near future.
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Affiliation(s)
- Liang-Dar Hwang
- The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland 4102, Australia
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit;,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS82BN, United Kingdom
| | - Nicole M. Warrington
- The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland 4102, Australia,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - David M. Evans
- The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland 4102, Australia,Medical Research Council Integrative Epidemiology Unit
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Are Mendelian randomization investigations immune from bias due to reverse causation? Eur J Epidemiol 2021; 36:253-257. [PMID: 33611685 PMCID: PMC8032609 DOI: 10.1007/s10654-021-00726-8] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/01/2021] [Indexed: 01/27/2023]
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40
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Mamluk L, Jones T, Ijaz S, Edwards HB, Savović J, Leach V, Moore THM, von Hinke S, Lewis SJ, Donovan JL, Lawlor DA, Davey Smith G, Fraser A, Zuccolo L. Evidence of detrimental effects of prenatal alcohol exposure on offspring birthweight and neurodevelopment from a systematic review of quasi-experimental studies. Int J Epidemiol 2021; 49:1972-1995. [PMID: 31993631 PMCID: PMC7825937 DOI: 10.1093/ije/dyz272] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 11/25/2019] [Accepted: 01/08/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Systematic reviews of prenatal alcohol exposure effects generally only include conventional observational studies. However, estimates from such studies are prone to confounding and other biases. OBJECTIVES To systematically review the evidence on the effects of prenatal alcohol exposure from randomized controlled trials (RCTs) and observational designs using alternative analytical approaches to improve causal inference. SEARCH STRATEGY Medline, Embase, Web of Science, PsychINFO from inception to 21 June 2018. Manual searches of reference lists of retrieved papers. SELECTION CRITERIA RCTs of interventions to stop/reduce drinking in pregnancy and observational studies using alternative analytical methods (quasi-experimental studies e.g. Mendelian randomization and natural experiments, negative control comparisons) to determine the causal effects of prenatal alcohol exposure on pregnancy and longer-term offspring outcomes in human studies. DATA COLLECTION AND ANALYSIS One reviewer extracted data and another checked extracted data. Risk of bias was assessed using customized risk of bias tools. A narrative synthesis of findings was carried out and a meta-analysis for one outcome. MAIN RESULTS Twenty-three studies were included, representing five types of study design, including 1 RCT, 9 Mendelian randomization and 7 natural experiment studies, and reporting on over 30 outcomes. One study design-outcome combination included enough independent results to meta-analyse. Based on evidence from several studies, we found a likely causal detrimental role of prenatal alcohol exposure on cognitive outcomes, and weaker evidence for a role in low birthweight. CONCLUSION None of the included studies was judged to be at low risk of bias in all domains, results should therefore be interpreted with caution. SYSTEMATIC REVIEW REGISTRATION This study is registered with PROSPERO, registration number CRD42015015941.
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Affiliation(s)
- Loubaba Mamluk
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Timothy Jones
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Sharea Ijaz
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Hannah B Edwards
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Jelena Savović
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Verity Leach
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Theresa H M Moore
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Stephanie von Hinke
- Department of Economics, School of Economics, Finance and Management, University of Bristol, Bristol, UK
| | - Sarah J Lewis
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jenny L Donovan
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR ARC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Luisa Zuccolo
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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41
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Hart SA, Little C, van Bergen E. Nurture might be nature: cautionary tales and proposed solutions. NPJ SCIENCE OF LEARNING 2021; 6:2. [PMID: 33420086 PMCID: PMC7794571 DOI: 10.1038/s41539-020-00079-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 11/12/2020] [Indexed: 05/27/2023]
Abstract
Across a wide range of studies, researchers often conclude that the home environment and children's outcomes are causally linked. In contrast, behavioral genetic studies show that parents influence their children by providing them with both environment and genes, meaning the environment that parents provide should not be considered in the absence of genetic influences, because that can lead to erroneous conclusions on causation. This article seeks to provide behavioral scientists with a synopsis of numerous methods to estimate the direct effect of the environment, controlling for the potential of genetic confounding. Ideally, using genetically sensitive designs can fully disentangle this genetic confound, but these require specialized samples. In the near future, researchers will likely have access to measured DNA variants (summarized in a polygenic scores), which could serve as a partial genetic control, but that is currently not an option that is ideal or widely available. We also propose a work around for when genetically sensitive data are not readily available: the Familial Control Method. In this method, one measures the same trait in the parents as the child, and the parents' trait is then used as a covariate (e.g., a genetic proxy). When these options are all not possible, we plead with our colleagues to clearly mention genetic confound as a limitation, and to be cautious with any environmental causal statements which could lead to unnecessary parent blaming.
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Affiliation(s)
- Sara A Hart
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA.
| | - Callie Little
- Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA
- Department of Psychology, University of New England, Armidale, NSW, Australia
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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42
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Direct and Indirect Effects of Maternal, Paternal, and Offspring Genotypes: Trio-GCTA. Behav Genet 2021; 51:154-161. [PMID: 33387132 DOI: 10.1007/s10519-020-10036-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/07/2020] [Indexed: 10/22/2022]
Abstract
Indirect genetic effects from relatives may result in misleading quantifications of heritability, but can also be of interest in their own right. In this paper we propose Trio-GCTA, a model for separating direct and indirect genetic effects when genome-wide single nucleotide polymorphism data have been collected from parent-offspring trios. The model is applicable to phenotypes obtained from any of the family members. We discuss appropriate parameter interpretations and apply the method to three exemplar phenotypes: offspring birth weight, maternal relationship satisfaction, and paternal body-mass index, using real data from the Norwegian Mother, Father and Child Cohort Study (MoBa).
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43
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Estimation of Parental Effects Using Polygenic Scores. Behav Genet 2021; 51:264-278. [PMID: 33387133 PMCID: PMC8093180 DOI: 10.1007/s10519-020-10032-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/20/2020] [Indexed: 12/11/2022]
Abstract
Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents' nontransmitted alleles, one can estimate the effect of "genetic nurture"-a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to (a) account for the influence of different types of assortative mating, (b) estimate the total variation due to additive genetic effects and their covariance with the familial environment (i.e., the full genetic nurture effect), and (c) model situations where a parental trait influences a different offspring trait. By utilizing structural equation modeling techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.
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44
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Diemer EW, Labrecque JA, Neumann A, Tiemeier H, Swanson SA. Mendelian randomisation approaches to the study of prenatal exposures: A systematic review. Paediatr Perinat Epidemiol 2021; 35:130-142. [PMID: 32779786 PMCID: PMC7891574 DOI: 10.1111/ppe.12691] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. OBJECTIVES We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. DATA SOURCES We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. STUDY SELECTION AND DATA EXTRACTION Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. SYNTHESIS We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. RESULTS Forty-three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty-four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). CONCLUSIONS Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.
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Affiliation(s)
- Elizabeth W. Diemer
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands
| | | | - Alexander Neumann
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands,Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada
| | - Henning Tiemeier
- Department of Child and Adolescent PsychiatryErasmus MCRotterdamThe Netherlands,Department of Social and Behavioral ScienceHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Sonja A. Swanson
- Department of EpidemiologyErasmus MCRotterdamThe Netherlands,Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
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A cautionary note on using Mendelian randomization to examine the Barker hypothesis and Developmental Origins of Health and Disease (DOHaD). J Dev Orig Health Dis 2020; 12:688-693. [DOI: 10.1017/s2040174420001105] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractRecent 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.
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46
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Hwang LD, Tubbs JD, Luong J, Lundberg M, Moen GH, Wang G, Warrington NM, Sham PC, Cuellar-Partida G, Evans DM. Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs. PLoS Genet 2020; 16:e1009154. [PMID: 33104719 PMCID: PMC7646364 DOI: 10.1371/journal.pgen.1009154] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 11/05/2020] [Accepted: 09/28/2020] [Indexed: 02/03/2023] Open
Abstract
Indirect parental genetic effects may be defined as the influence of parental
genotypes on offspring phenotypes over and above that which results from the
transmission of genes from parents to their children. However, given the
relative paucity of large-scale family-based cohorts around the world, it is
difficult to demonstrate parental genetic effects on human traits, particularly
at individual loci. In this manuscript, we illustrate how parental genetic
effects on offspring phenotypes, including late onset conditions, can be
estimated at individual loci in principle using large-scale genome-wide
association study (GWAS) data, even in the absence of parental genotypes. Our
strategy involves creating “virtual” mothers and fathers by estimating the
genotypic dosages of parental genotypes using physically genotyped data from
relative pairs. We then utilize the expected dosages of the parents, and the
actual genotypes of the offspring relative pairs, to perform conditional genetic
association analyses to obtain asymptotically unbiased estimates of maternal,
paternal and offspring genetic effects. We apply our approach to 19066 sibling
pairs from the UK Biobank and show that a polygenic score consisting of imputed
parental educational attainment SNP dosages is strongly related to offspring
educational attainment even after correcting for offspring genotype at the same
loci. We develop a freely available web application that quantifies the power of
our approach using closed form asymptotic solutions. We implement our methods in
a user-friendly software package IMPISH (IMputing
Parental genotypes In Siblings and
Half Siblings) which allows users to quickly and efficiently
impute parental genotypes across the genome in large genome-wide datasets, and
then use these estimated dosages in downstream linear mixed model association
analyses. We conclude that imputing parental genotypes from relative pairs may
provide a useful adjunct to existing large-scale genetic studies of parents and
their offspring. Indirect parental genetic effects may be defined as the influence of parental
genotypes on offspring phenotypes over and above that which results from the
transmission of genes from parents to children. Estimating indirect parental
genetic effects on offspring outcomes at the genotype level has been challenging
because it requires large-scale, individual level genotypes from both parents
and their offspring, and there is a paucity of cohorts around the world with
this information. Here we present a new approach to estimate indirect parental
genetic effects without the requirement of physically genotyped parents. Our
method creates virtual parental genotypes based on the genotypes of offspring
pairs, and then uses these virtual genotypes in downstream genetic association
analyses. We developed a software package “IMPISH” that allows users to impute
virtual parental genotypes in their own genome-wide datasets and then use these
in downstream genome-wide association analyses, as well a series of power
calculators to estimate the power to detect indirect parental genetic effects on
offspring phenotypes. We apply our method to educational attainment data from
the UK Biobank and show that indirect parental genetic effects are related to
offspring educational attainment even after correcting for offspring genotype at
the same loci.
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Affiliation(s)
- Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
| | - Justin D. Tubbs
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR,
China
| | - Justin Luong
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
| | - Mischa Lundberg
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
- Transformational Bioinformatics, Commonwealth Scientific and Industrial
Research Organisation, Sydney, New South Wales, Australia
| | - Gunn-Helen Moen
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo,
Oslo, Norway
- Population Health Science, Bristol Medical School, University of Bristol,
Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health
and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim,
Norway
| | - Geng Wang
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
| | - Nicole M. Warrington
- The University of Queensland Diamantina Institute, 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
| | - Pak C. Sham
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR,
China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong
SAR, China
- Centre of Brain and Cognitive Sciences, The University of Hong Kong, Hong
Kong SAR, China
| | - Gabriel Cuellar-Partida
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
- 23andMe Inc, Sunnyvale, California, United States of
America
| | - David M. Evans
- The University of Queensland Diamantina Institute, The University of
Queensland, Brisbane, Australia
- Medical Research Council Integrative Epidemiology Unit at the University
of Bristol, Bristol, United Kingdom
- * E-mail:
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47
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Moen GH, Brumpton B, Willer C, Åsvold BO, Birkeland KI, Wang G, Neale MC, Freathy RM, Smith GD, Lawlor DA, Kirkpatrick RM, Warrington NM, Evans DM. Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort. Nat Commun 2020; 11:5404. [PMID: 33106479 PMCID: PMC7588432 DOI: 10.1038/s41467-020-19257-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 10/02/2020] [Indexed: 12/11/2022] Open
Abstract
There is a robust observational relationship between lower birthweight and higher risk of cardiometabolic disease in later life. The Developmental Origins of Health and Disease (DOHaD) hypothesis posits that adverse environmental factors in utero increase future risk of cardiometabolic disease. Here, we explore if a genetic risk score (GRS) of maternal SNPs associated with offspring birthweight is also associated with offspring cardiometabolic risk factors, after controlling for offspring GRS, in up to 26,057 mother-offspring pairs (and 19,792 father-offspring pairs) from the Nord-Trøndelag Health (HUNT) Study. We find little evidence for a maternal (or paternal) genetic effect of birthweight associated variants on offspring cardiometabolic risk factors after adjusting for offspring GRS. In contrast, offspring GRS is strongly related to many cardiometabolic risk factors, even after conditioning on maternal GRS. Our results suggest that the maternal intrauterine environment, as proxied by maternal SNPs that influence offspring birthweight, is unlikely to be a major determinant of adverse cardiometabolic outcomes in population based samples of individuals.
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Affiliation(s)
- Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia.
- 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.
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Cristen Willer
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kåre I Birkeland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Geng Wang
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - George Davey Smith
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Robert M Kirkpatrick
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Nicole M Warrington
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia.
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
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48
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Hanswijk SI, Spoelder M, Shan L, Verheij MMM, Muilwijk OG, Li W, Liu C, Kolk SM, Homberg JR. Gestational Factors throughout Fetal Neurodevelopment: The Serotonin Link. Int J Mol Sci 2020; 21:E5850. [PMID: 32824000 PMCID: PMC7461571 DOI: 10.3390/ijms21165850] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/24/2020] [Accepted: 08/11/2020] [Indexed: 12/21/2022] Open
Abstract
Serotonin (5-HT) is a critical player in brain development and neuropsychiatric disorders. Fetal 5-HT levels can be influenced by several gestational factors, such as maternal genotype, diet, stress, medication, and immune activation. In this review, addressing both human and animal studies, we discuss how these gestational factors affect placental and fetal brain 5-HT levels, leading to changes in brain structure and function and behavior. We conclude that gestational factors are able to interact and thereby amplify or counteract each other's impact on the fetal 5-HT-ergic system. We, therefore, argue that beyond the understanding of how single gestational factors affect 5-HT-ergic brain development and behavior in offspring, it is critical to elucidate the consequences of interacting factors. Moreover, we describe how each gestational factor is able to alter the 5-HT-ergic influence on the thalamocortical- and prefrontal-limbic circuitry and the hypothalamo-pituitary-adrenocortical-axis. These alterations have been associated with risks to develop attention deficit hyperactivity disorder, autism spectrum disorders, depression, and/or anxiety. Consequently, the manipulation of gestational factors may be used to combat pregnancy-related risks for neuropsychiatric disorders.
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Affiliation(s)
- Sabrina I. Hanswijk
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, 6525 EN Nijmegen, The Netherlands; (S.I.H.); (M.S.); (M.M.M.V.); (O.G.M.)
| | - Marcia Spoelder
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, 6525 EN Nijmegen, The Netherlands; (S.I.H.); (M.S.); (M.M.M.V.); (O.G.M.)
| | - Ling Shan
- Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, The Netherlands;
| | - Michel M. M. Verheij
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, 6525 EN Nijmegen, The Netherlands; (S.I.H.); (M.S.); (M.M.M.V.); (O.G.M.)
| | - Otto G. Muilwijk
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, 6525 EN Nijmegen, The Netherlands; (S.I.H.); (M.S.); (M.M.M.V.); (O.G.M.)
| | - Weizhuo Li
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China; (W.L.); (C.L.)
| | - Chunqing Liu
- College of Medical Laboratory, Dalian Medical University, Dalian 116044, China; (W.L.); (C.L.)
| | - Sharon M. Kolk
- Department of Molecular Neurobiology, Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands;
| | - Judith R. Homberg
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, 6525 EN Nijmegen, The Netherlands; (S.I.H.); (M.S.); (M.M.M.V.); (O.G.M.)
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49
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Tubbs JD, Porsch RM, Cherny SS, Sham PC. The Genes We Inherit and Those We Don’t: Maternal Genetic Nurture and Child BMI Trajectories. Behav Genet 2020; 50:310-319. [DOI: 10.1007/s10519-020-10008-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/09/2020] [Indexed: 01/06/2023]
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50
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Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li S, Auton A, Windmeijer F, Chen WM, Bjørngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Davey Smith G, Åsvold BO, Hemani G, Davies NM. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 2020; 11:3519. [PMID: 32665587 PMCID: PMC7360778 DOI: 10.1038/s41467-020-17117-4] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 06/12/2020] [Indexed: 01/24/2023] Open
Abstract
Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.
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Affiliation(s)
- Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Karl Heilbron
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Fernando Pires Hartwig
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Sean Harrison
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gunnhild Åberge Vie
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Spångbergveien 25, 0853, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Sandakerveien 24 C, 0473, Oslo, Norway
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Michael Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michel G Nivard
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Andrew Grotzinger
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Laurence Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Adam Auton
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Frank Windmeijer
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Department of Economics, University of Bristol, 2 Priory Road, Bristol, BS8 1TU, UK
| | - Wei-Min Chen
- Center for public health genomics, Department of public health sciences, University of Virginia, Charlottesville, VA, USA
| | - Johan Håkon Bjørngaard
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
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