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Get Free AccessBirth weight (BW) variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. These associations have been proposed to reflect the lifelong consequences of an adverse intrauterine environment. In earlier work, we demonstrated that much of the negative correlation between BW and adult cardio-metabolic traits could instead be attributable to shared genetic effects. However, that work and other previous studies did not systematically distinguish the direct effects of an individual’s own genotype on BW and subsequent disease risk from indirect effects of their mother’s correlated genotype, mediated by the intrauterine environment. Here, we describe expanded genome-wide association analyses of own BW (n=321,223) and offspring BW (n=230,069 mothers), which identified 278 independent association signals influencing BW (214 novel). We used structural equation modelling to decompose the contributions of direct fetal and indirect maternal genetic influences on BW, implicating fetal- and maternal-specific mechanisms. We used Mendelian randomization to explore the causal relationships between factors influencing BW through fetal or maternal routes, for example, glycemic traits and blood pressure. Direct fetal genotype effects dominate the shared genetic contribution to the association between lower BW and higher type 2 diabetes risk, whereas the relationship between lower BW and higher later blood pressure (BP) is driven by a combination of indirect maternal and direct fetal genetic effects: indirect effects of maternal BP-raising genotypes act to reduce offspring BW, but only direct fetal genotype effects (once inherited) increase the offspring’s later BP. Instrumental variable analysis using maternal BW-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring BP. In successfully separating fetal from maternal genetic effects, this work represents an important advance in genetic studies of perinatal outcomes, and shows that the association between lower BW and higher adult BP is attributable to genetic effects, and not to intrauterine programming.
Nicole M. Warrington, Robin N. Beaumont, Momoko Horikoshi, Felix R. Day, Øyvind Helgeland, Charles Laurin, Jonas Bacelis, Shouneng Peng, Ke Hao, Bjarke Feenstra, Andrew R. Wood, Anubha Mahajan, Jessica Tyrrell, Neil R. Robertson, N. William Rayner, Zhen Qiao, Gunn-Helen Moen, Marc Vaudel, Carmen J. Marsit, Jia Chen, Michael Nodzenski, Theresia M. Schnurr, Mohammad Hadi Zafarmand, Jonathan P. Bradfield, Niels Grarup, Marjolein N. Kooijman, Ruifang Li‐Gao, Frank Geller, Tarunveer S. Ahluwalia, Lavinia Paternoster, Rico Rueedi, Ville Huikari, Jouke‐Jan Hottenga, Leo‐Pekka Lyytikäinen, Alana Cavadino, Sarah Metrustry, Diana L. Cousminer, Ying Wu, Elisabeth Thiering, Carol A. Wang, Henri Theil, Natàlia Vilor‐Tejedor, Peter K. Joshi, Jodie N. Painter, Ιωάννα Ντάλλα, Ronny Myhre, Niina Pitkänen, Jin‐Moo Lee, Raimo Joro, Vasiliki Lagou, Rebecca C. Richmond, Ana Espinosa, Sheila J. Barton, Hazel Inskip, John W. Holloway, Loreto Santa‐Marina, Xavier Estivill, Wei Ang, Julie Marsh, Christoph Reichetzeder, Letizia Marullo, Berthold Hocher, Kathryn L. Lunetta, Joanne M. Murabito, Caroline L. Relton, Manolis Kogevinas, Leda Chatzi, Catherine Allard, Luigi Bouchard, Marie‐France Hivert, Ge Zhang, Louis J. Muglia, Jani Heikkinen, Camilla S. Morgen, Antoine H. C. van Kampen, Barbera D. C. van Schaik, Frank Mentch, Claudia Langenberg, Jian’an Luan, Robert A. Scott, Wei Zhao, Gibran Hemani, Susan M. Ring, Amanda J. Bennett, Kyle J. Gaulton, Juan Fernández‐Tajes, Natalie R. van Zuydam, Carolina Medina‐Gómez, Hugoline G. de Haan, Frits R. Rosendaal, Zoltán Kutalik, Pedro Marques‐Vidal, Shikta Das, Gonneke Willemsen, Hamdi Mbarek, Martina Müller‐Nurasyid, Marie Standl, Emil V. R. Appel, Cilius Esmann Fonvig, Cæcilie Trier (2018). Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. bioRxiv (Cold Spring Harbor Laboratory), DOI: 10.1101/442756.
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Type
Preprint
Year
2018
Authors
100
Datasets
0
Total Files
0
Language
English
Journal
bioRxiv (Cold Spring Harbor Laboratory)
DOI
10.1101/442756
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