Multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycaemic traits during pregnancy reveals limited evidence of pregnancy-specific genetic effects

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Cisse , Frédérique White , Nancy McBride , Alan Kuang , Catherine Allard , Justiina Ronkainen , Alice Hughes , Amanda Elliott , Gudmar Thorleifsson , Marc Vaudel , Triin Laisk , Yuqin Gu , Amel Lamri , Li Chen , Johanna Tuhkanen , Jari Lahti , Lucinda Calas , Manon Muntaner , Ville Karhunen , Jaewon Choi , Anni Heiskala , Gad Hatem , Nicolas Fragoso-Bargas , Sheryl L. Rifas-Shiman , Guillermo Paz-Lopez , Sara E. Stinson , Bishwajit Bhowmik , Emma Ahlqvist , “Estonian Biobank Research Team” , “Genes & Health Research Team” , Jean-Francois Deleuze , Johan G. Eriksson , Jongseok Park , Koon Teo , Katri Räikkönen , Kari Stefansson , Maria Molina-Vega , Padmaja Subbarao , Robin N. Beaumont , Stefan Johansson , Tiinamaija Tuomi , Torben Hansen , Line Engelbrechtsen , Sonsoles Morcillo , Emily Oken , Elisabeth Quigstad , Kåre I. Birkeland , Marja Vääräsmäki , Andrew T. Hattersley , Sylvain Sebert , Graham A Hitman , Soo Heon Kwak , Marjo-Riitta Järvelin , Rashmi B Prasad , Barbara Heude , Aline Meirhaeghe , Luigi Bouchard , Pierre-Étienne Jacques , Hannele Laivuori , Kok Hian Tan , Sonia S. Anand , David A. van Heel , Siyang Liu , Pål R. Njølstad , Valgerdur Steinthorsdottir , Elisabeth Widén , Elina Keikkala , Denise M Scholtens , William L. Lowe , Sarah Finer , Andrew P Morris , Reedik Mägi , Julia Zöllner , Maria Carolina Borges , Deborah A. Lawlor , View ORCID Profile Marie-France Hivert , View ORCID Profile Rachel M. Freathy , David M Evans , View ORCID Profile Gunn-Helen Moen doi: https://doi.org/10.1101/2025.08.19.25333735 Caroline Brito Nunes 1 Institute for Molecular Bioscience, The University of Queensland , St. Lucia, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caroline Brito Nunes For correspondence: c.britonunes{at}uq.edu.au Valentina Rukins 2 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valentina Rukins Aminata H. Cisse 3 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frédérique White 4 Département de Biologie, Faculté des Sciences, Université de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nancy McBride 5 MRC Integrative Epidemiology Unit at the University of Bristol , UK 6 Population Health Science, Bristol Medical School, University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alan Kuang 7 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine , Chicago, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Catherine Allard 8 Centre de recherche du Centre Hospitalier Universitaire de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Justiina Ronkainen 9 Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alice Hughes 3 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amanda Elliott 10 Analytic and Translational Genetics Unit, Massachusetts General Hospital , Boston, MA, USA 11 Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT , Cambridge, MA, USA 12 Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gudmar Thorleifsson 13 deCODE genetics/Amgen, Inc. , 102 Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc Vaudel 14 Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen , Bergen, Norway 15 Department of Genetics and Bioinformatics, Norwegian Institute of Public Health , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Triin Laisk 2 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuqin Gu 16 School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amel Lamri 17 Department of Medicine, McMaster University , Hamilton, Ontario, Canada 18 Population Health Research Institute, Hamilton Health Sciences , Hamilton, Ontario, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Li Chen 19 Institute for Human Development and Potential, Agency for Science, Technology and Research (A*STAR) , Singapore, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johanna Tuhkanen 20 Department of Psychology, Faculty of Medicine, University of Helsinki , Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jari Lahti 20 Department of Psychology, Faculty of Medicine, University of Helsinki , Finland 21 Folkhälsan Research Centre , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lucinda Calas 22 Université Paris Cité and Université Sorbonne Paris Nord , Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manon Muntaner 23 Université de Lille , Inserm, CHU Lille, Institut Pasteur de Lille, U1167 – RID-AGE – Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ville Karhunen 9 Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jaewon Choi 24 Department of Biomedical Sciences, Seoul National University College of Medicine , Seoul, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anni Heiskala 25 Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gad Hatem 26 Department of Clinical Sciences , Genetics and Diabetes, Lund University , Malmö, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicolas Fragoso-Bargas 14 Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen , Bergen, Norway 27 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo,Oslo , Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sheryl L. Rifas-Shiman 28 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guillermo Paz-Lopez 29 Unidad de Bioinformática, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina - IBIMA Plataforma BIONAND , Málaga, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara E. Stinson 30 Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen , Copenhagen, Denmark 31 Section for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bishwajit Bhowmik 32 Centre for Global Health Research , Diabetic Association of Bangladesh, Dhaka, Bangladesh Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Ahlqvist 26 Department of Clinical Sciences , Genetics and Diabetes, Lund University , Malmö, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jean-Francois Deleuze 33 Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay , CEA, Evry, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johan G. Eriksson 19 Institute for Human Development and Potential, Agency for Science, Technology and Research (A*STAR) , Singapore, Singapore 21 Folkhälsan Research Centre , Helsinki, Finland 34 Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 35 Department of General Practice and Primary Health Care, University of Helsinki , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jongseok Park 24 Department of Biomedical Sciences, Seoul National University College of Medicine , Seoul, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Koon Teo 18 Population Health Research Institute, Hamilton Health Sciences , Hamilton, Ontario, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katri Räikkönen 21 Folkhälsan Research Centre , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kari Stefansson 13 deCODE genetics/Amgen, Inc. , 102 Reykjavik, Iceland 36 Faculty of Medicine, University of Iceland , 101 Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Molina-Vega 37 Unidad de Gestión Clínica Endocrinología y Nutrición, Hospital Universitario Virgen de la Victoria , Málaga, Spain 38 Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND , Málaga, Spain 39 Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Padmaja Subbarao 40 Hospital for Sick Children (SickKids), Department of Pediatrics, University of Toronto , Toronto, Canada 41 Dalla Lana School of Public Health, University of Toronto , Toronto, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robin N. Beaumont 3 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefan Johansson 14 Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen , Bergen, Norway 42 Department of Medical Genetics, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tiinamaija Tuomi 21 Folkhälsan Research Centre , Helsinki, Finland 43 Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki , Helsinki, Finland 44 Department of Endocrinology, Abdominal Centre, Helsinki University Hospital , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Torben Hansen 30 Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Line Engelbrechtsen 30 Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen , Copenhagen, Denmark 45 Department of Gynaecology and Obstetrics, Herlev Hospital, University of Copenhagen , Herlev, Denmark 46 Department of Clinical Medicine, Faculty of Health, University of Copenhagen Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonsoles Morcillo 37 Unidad de Gestión Clínica Endocrinología y Nutrición, Hospital Universitario Virgen de la Victoria , Málaga, Spain 38 Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND , Málaga, Spain 39 Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Oken 28 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elisabeth Quigstad 27 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo,Oslo , Norway 47 Department of Endocrinology , Morbid Obesity and Preventive Medicine, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kåre I. Birkeland 27 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo,Oslo , Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marja Vääräsmäki 48 Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu , Oulu, Finland 49 Finnish Institute for Health and Welfare, Department of Public Health , Welfare Epidemiology and Monitoring Unit, Helsinki and Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew T. Hattersley 3 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sylvain Sebert 9 Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Graham A Hitman 50 Centre for Genomics and Child Health, Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Soo Heon Kwak 51 Department of Internal Medicine, Seoul National University Hospital , Seoul, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marjo-Riitta Järvelin 25 Research Unit of Population Health, Faculty of Medicine, University of Oulu , Oulu, Finland 52 School of Public Health , Dept of Epidemiology and Biostatistics, Imperial College London, St Mary’s Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rashmi B Prasad 26 Department of Clinical Sciences , Genetics and Diabetes, Lund University , Malmö, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Barbara Heude 22 Université Paris Cité and Université Sorbonne Paris Nord , Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aline Meirhaeghe 23 Université de Lille , Inserm, CHU Lille, Institut Pasteur de Lille, U1167 – RID-AGE – Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luigi Bouchard 53 Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke , Sherbrooke, Québec, Canada 54 Clinical Department of Laboratory Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay–Lac-St-Jean , Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierre-Étienne Jacques 4 Département de Biologie, Faculté des Sciences, Université de Sherbrooke , Sherbrooke, Québec, Canada 8 Centre de recherche du Centre Hospitalier Universitaire de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hannele Laivuori 43 Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki , Helsinki, Finland 55 Center for Child, Adolescent and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University , Tampere, Finland 56 Department of Obstetrics and Gynecology, Tampere University Hospital , The Wellbeing Services County of Pirkanmaa, Tampere, Finland 57 Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kok Hian Tan 58 KK Women’s and Children’s Hospital , Singapore, Singapore 59 Duke-NUS Medical School , Singapore, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonia S. Anand 17 Department of Medicine, McMaster University , Hamilton, Ontario, Canada 18 Population Health Research Institute, Hamilton Health Sciences , Hamilton, Ontario, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. van Heel 60 Blizard Institute, Queen Mary University of London Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siyang Liu 16 School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pål R. Njølstad 14 Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen , Bergen, Norway 61 Department of Pediatrics and Adolescents, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valgerdur Steinthorsdottir 13 deCODE genetics/Amgen, Inc. , 102 Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elisabeth Widén 43 Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elina Keikkala 48 Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu , Oulu, Finland 49 Finnish Institute for Health and Welfare, Department of Public Health , Welfare Epidemiology and Monitoring Unit, Helsinki and Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Denise M Scholtens 7 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine , Chicago, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site William L. Lowe 62 Department of Medicine, Northwestern University Feinberg School of Medicine , Chicago, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Finer 63 Wolfson Institute of Population Health, Queen Mary University of London Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew P Morris 64 Centre for Musculoskeletal Research, The University of Manchester , Manchester, UK 65 NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre , Manchester, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reedik Mägi 2 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julia Zöllner 63 Wolfson Institute of Population Health, Queen Mary University of London 66 Women’s Health, University College London , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Carolina Borges 5 MRC Integrative Epidemiology Unit at the University of Bristol , UK 6 Population Health Science, Bristol Medical School, University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Deborah A. Lawlor 5 MRC Integrative Epidemiology Unit at the University of Bristol , UK 6 Population Health Science, Bristol Medical School, University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marie-France Hivert 28 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, MA, USA 67 Diabetes Unit, Massachusetts General Hospital , Boston, USA 68 Departement de Medicine, Faculty of Medicine and Health Sciences , Universite de Sherbrooke, Quebec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marie-France Hivert Rachel M. Freathy 3 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel M. Freathy David M Evans 1 Institute for Molecular Bioscience, The University of Queensland , St. Lucia, Queensland, Australia 5 MRC Integrative Epidemiology Unit at the University of Bristol , UK 69 University of Queensland Frazer Institute, University of Queensland , Woolloongabba, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gunn-Helen Moen 1 Institute for Molecular Bioscience, The University of Queensland , St. Lucia, Queensland, Australia 27 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo,Oslo , Norway 69 University of Queensland Frazer Institute, University of Queensland , Woolloongabba, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gunn-Helen Moen For correspondence: c.britonunes{at}uq.edu.au Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Gestational diabetes mellitus (GDM) affects ∼14% of pregnancies and is linked to adverse pregnancy outcomes and increased maternal type 2 diabetes mellitus (T2DM) risk. The GenDiP Consortium conducted trans-generational, multi-ancestry genome-wide association study meta-analyses of GDM and pregnancy glycemic traits in up to 38,305 GDM cases and 776,145 controls. We identified 37 GDM-associated loci (19 novel) and five novel loci for glycemic traits, all operating through the maternal genome. Most GDM loci overlapped with T2DM and non-pregnant glycemic traits, with limited evidence for pregnancy-specific effects. MTNR1B showed pregnancy-enhanced effects on 2-hour glucose, potentially mediated by interaction with GPR61 , a novel GDM locus, suggesting a gestation-specific melatonin-glucose signalling axis. We also observed ancestry-specific effects at the fasting glucose locus ABCB11 , with opposite directions in European and East Asian populations. Our findings provide new insights into the genetic architecture of GDM and highlight the need for larger, ancestrally diverse studies. Gestational diabetes mellitus (GDM) affects ∼14.0% of pregnancies worldwide, and poses risks to both maternal and fetal health 1 , 2 . Prevalence varies across regions, with the highest rates in Middle Eastern and North African countries (27.6%) and the lowest in North America and the Caribbean (7.1%). This variation is influenced by population characteristics (e.g. ancestry and obesity rates), and differences in screening strategies and diagnostic criteria 3 . Pregnancy induces metabolic changes essential for fetal development, including increased insulin resistance, heightened insulin secretion, and fluctuations in glucose levels 4 – 10 . While fasting glucose (FG) levels typically decline in the first trimester before stabilising, postprandial glucose levels tend to rise due to insulin resistance 4 , 8 , 10 . GDM arises when these adaptations are disrupted - either by excessive insulin resistance and/or insufficient insulin secretion - leading to maternal hyperglycemia 5 , 11 , 12 . While maternal glucose crosses the placenta, maternal insulin does not. In response, elevated maternal glucose levels trigger fetal insulin secretion, contributing to fetal overgrowth and potentially adverse outcomes such as obstructed labour, preterm delivery, operative delivery, stillbirth and neonatal hypoglycaemia 13 . A previous diagnosis of GDM carries an 8-10-fold higher risk of type 2 diabetes mellitus (T2DM) 13 – 17 . GDM and T2DM share genetic and lifestyle risk factors 18 – 22 , with recent genome-wide association studies (GWASs) reporting a strong genetic correlation between the two conditions (rG ∼ 0.70), and only a few genome-wide significant loci unique to GDM 21 , 23 . However, while large-scale multi-ancestry GWAS of T2DM have identified hundreds of loci and included millions of individuals 24 – 26 , GDM GWAS have been small in comparison 21 , 23 , 27 – 31 . The disparity in sample size between GDM and T2DM meta-analyses limits attempts to understand whether GDM and T2DM represent the same condition (i.e. the physiological stress of pregnancy unmasking a predisposition to T2DM) or the extent to which there are distinct genetic determinants of both. The largest and most recent GDM GWASs to date include the FinnGen study (12,332 cases; 131,109 controls; European ancestry) 23 and the MONN study (12,024 cases; 67,845 controls; East Asian ancestry) 31 , which together identified 27 loci associated with GDM. Both these large GWASs, however, were limited to single ancestral populations, unlike the previous Genetics of Diabetes in Pregnancy (GenDiP) GWAS (5,485 cases, 347,856 controls) 21 and this current GWAS. Further insights into the genetic aetiology of GDM may come from GWAS of glycemic traits during pregnancy, such as FG, 1-hour (1hG) and 2-hour (2hG) post-oral glucose tolerance test (OGTT) glucose levels and glycated haemoglobin (HbA1c) 30 – 35 . These traits capture variation in maternal glucose regulation and may reveal loci influencing glycemic physiology not detectable through binary GDM analyses. A recent GWAS in East Asian mothers (up to 85,856 individuals) identified distinct genetic architectures for fasting versus post-OGTT glucose regulation, suggesting pregnancy-specific effects at several loci 30 , 31 . However, this study was limited to East Asians, and critical gaps remain in determining whether glycemic trait regulation during pregnancy differs across populations. In addition to maternal genetic contributions, recent studies have highlighted the role of fetal genetics in shaping the prenatal environment independently of the maternal genome. While it is expected that fetal genetic variants influence fetal traits such as birth weight and placental weight, emerging evidence suggests that the fetal genome may also influence maternal conditions, such as pre-eclampsia, gestational hypertension, and gestational duration - even after accounting for maternal genetics 36 – 43 . Studies also support a potential role of fetal imprinted genes in modulating maternal insulin resistance and fasting responses via placental hormone signalling 44 , 45 . However, studies often fail to account for the correlation between maternal and fetal genotypes, limiting conclusions about independent fetal effects on GDM. To address these gaps, we performed the largest multi-ancestry and trans-generational GWAS meta-analyses of GDM and glycemic traits during pregnancy to date ( Fig. 1 ). We further performed downstream analyses with our two largest ancestry groups - Europeans and East Asians - to elucidate the degree to which GDM and T2DM represent the same or distinct conditions and whether genetic effects on glycemic traits differ across population ns. Download figure Open in new tab Figure 1. Study overview. Summary of the data resources, study sample and analyses conducted in this study. GDM; Gestational Diabetes Mellitus. FG; Fasting glucose. 1hG; 1-hour glucose after oral-glucose tolerance test (OGTT). 2hG; 2-hour glucose after OGTT. AFR: African. EAS; East Asian. EUR; European. SAS; South Asian. HIS; Hispanic. Results Discovery of maternal and fetal genetic associations in multi-ancestry meta-analyses Our GWAS meta-analysis included data from up to thirty cohorts across European (EUR), East Asian (EAS), South Asian (SAS), admixed Africans (AFR), and admixed Hispanics (HIS) populations ( Supplementary Tables 1-3 ). We conducted both ancestry-specific and multi-ancestry meta-analyses of maternal and fetal GWAS for five traits: i) GDM diagnosis (N maternal =38,305 cases and 776,145 controls; N fetal =3,126 cases and 90,877 controls), ii) FG (N maternal =55,371; N fetal =15,855), iii) 1hG post-OGTT (N maternal =38,439; N fetal =9,365), iv) 2hG post-OGTT (N maternal =46,401; N fetal =15,046) and v) HbA1c (N maternal =9,724; N fetal =7,035). In the multi-ancestry maternal GWAS meta-analyses, we identified 37 loci associated with GDM at genome-wide significance (P<5×10 - 8 ), including 19 novel associations ( Fig. 2 ; Table 1 ; Supplementary Table 4 , Manhattan plot: Supplementary Figure 1 ; Plots of the axes of genetic variation separating cohorts: Supplementary Figure 2 ; Locus zoom plots: Supplementary Figures 3-11 ). We also detected several maternal loci associated with glycemic traits during pregnancy: 13 for FG, nine for 1hG, eight for 2hG, and four for HbA1c ( Supplementary Table 5; Manhattan plots: Supplementary Figures 12-15 ; Plots of the axes of genetic variation separating cohorts: Supplementary Figures 16-19 ; Locus zoom plots: Supplementary Figures 20-27 ). Of these, five glycemic trait-locus associations were novel ( RBM47 and IRAG2 in the FG analyses; TTC27 in 1hG; TCF7L2 and ABCC8 in 2hG). Notably, several loci identified overlapped between GDM and glycemic traits ( Fig. 2 ). No additional loci were identified in the ancestry-specific meta-analysis. Download figure Open in new tab Figure 2. Summary of the associations for the maternal multi-ancestry GDM GWAS meta-analyses. (A) Manhattan plot for the maternal multi-ancestral GDM meta-analysis (38,305 cases and 776,145 controls), with putative gene names labelled at genome-wide significant loci. (B) Venn diagram illustrating the genetic overlap between GDM and glycemic traits reaching genome-wide significance in the maternal multi-ancestry GWAS meta-analyses. Several loci showed evidence of shared genetic architecture between GDM and glycemic traits. MTNR1B variants were associated with all four traits. CDKAL1 showed overlap with GDM, 1hG, and 2hG, while shared signals at BCL11A , CAST-PCSK1 , and FOXA2 (GDM and FG), as well as IGF2BP2 and SLC30A8 (GDM and 1hG) were also observed. Additional loci with trait overlap included GCK (GDM, FG, 2hG), SNX17-GCKR (GDM, FG, 1hG), as well as TCF7L2 , HKDC1 and TENT5C (GDM and 2hG). Sample sizes for the GWAS of glycemic traits were as follows: FG (N=55,371), 1hG (N=38,439), and 2hG (N=46,401). GDM; Gestational Diabetes Mellitus. FG; Fasting glucose. 1hG; 1-hour glucose after oral-glucose tolerance test (OGTT). 2hG; 2-hour glucose after oral-glucose tolerance test (OGTT). View this table: View inline View popup Table 1. Summary of the independent lead variants identified in the trans-ancestry maternal meta-analysis of GDM. The association P-value indicates the association between the variant and GDM after accounting for heterogeneity correlated with ancestry. The ancestry P-values were obtained from a test of heterogeneity in effect sizes across ancestries, whereas the residual P-values were obtained from a test of residual heterogeneity assessing the degree of variation in the data that is not captured by the principal coordinates. Asterisks indicate novel GDM loci. For the columns with the results from follow-up analyses, dashes (-) indicate variants that could not be investigated as they were not present in the GWASs used in the analysis. GDM; Gestational Diabetes Mellitus. T2DM; Type 2 Diabetes Mellitus. AFR; African. EAS; East Asian. EUR; European. SAS; South Asian. HIS; Hispanic. Fetal GWAS meta-analyses identified four loci associated with GDM and/or glycemic traits (Manhattan plots: Supplementary Figures 28-32 ; Plots of the axes of genetic variation separating cohorts: Supplementary Figures 33-37) . However, DINGO analyses conditioning on maternal genotype suggested all signals were maternally driven, likely reflecting correlations with maternal genotypes rather than independent fetal effects ( Supplementary Figures 38-45 ). In EUR, observed SNP heritability estimates ranged from 0.014 ± 0.002 (liability SNP h 2 = 0.108 ± 0.014) for GDM to 0.1767 ± 0.085 for HbA1c, with intermediate values for FG (0.092 ± 0.025), 1hG (SNP h² = 0.056 ± 0.058), and 2hG (SNP h² = 0.076 ± 0.039) ( Supplementary Table 6 ). EAS populations showed higher SNP heritability for GDM (observed SNP h² = 0.053 ± 0.021; liability SNP h 2 = 0.119 ± 0.047) and post-load glucose traits (1hG: SNP h² = 0.144 ± 0.025; 2hG: SNP h² = 0.112 ± 0.023) compared to EUR, while FG heritability was similar (SNP h² = 0.075 ± 0.017) ( Supplementary Table 6 ). Ancestry-specific variation in allelic effects Our maternal multi-ancestry meta-analyses revealed significant heterogeneity in allelic effects across populations (P ancestry <0.05) ( Supplementary Tables 4 and 5 ), with several lead variants exhibiting discordant direction and/or magnitude of effect across ancestries ( Supplementary Figures 46-55 ). This was captured by the meta-regression used in MR- MEGA, which partitions heterogeneity associated with population differences (represented by principal components (PCs)) and residual sources of variation (heterogeneity not captured by PCs). These differences were most evident between the two largest ancestry groups in our study - EAS and EUR - though effect estimates in the remaining populations in our analysis were accompanied by wide confidence intervals. For GDM, notable differences in effect estimates between EUR and EAS were observed for IGF2BP2 (lead variant rs113851927), CDKAL1 (rs9348441), SLC30A8 (rs11558471), SLCO4A1 (rs6122141) and RMST (rs77864822) ( Supplementary Figure 46 ). Similar differences were observed for glycemic traits, including variants at GCK (rs730497), LOC157273 (rs4841132), IRAG2 (rs1683163), and MTNR1B (rs3847554) associated with maternal FG as well as MTNR1B (rs10765572) and HKDC1 (rs5030937) detected in the maternal 1hG and 2hG analyses, respectively ( Supplementary Figures 47-49 ). A striking example of ancestry-specific divergence was observed at ABCB11 (rs2685806), where the effect on maternal FG was in opposite directions across EUR (β= -0.047, 95% CI: -0.058 to - 0.035) and EAS (β= 0.066, 95% CI: 0.051 to 0.080) populations ( Supplementary Figures 47 and 51). Residual heterogeneity and sensitivity analyses We observed significant residual heterogeneity (P residual <0.05) for several variants associated with glycemic traits and GDM in the multi-ancestry maternal meta-analysis ( Table 1 ; Supplementary Tables 4 and 5 ). This suggests that the PCs included as covariates in the meta-regression do not fully explain between-study variability, potentially due to differences in GDM screening/diagnostic criteria, OGTT load, the gestational age at which glycemic traits were measured, or other differences in the populations included. To investigate potential sources of this variation, we conducted a sensitivity analysis, comparing cohorts based on the GDM ascertainment method to assess the contribution to residual variation in allelic effects detected in the GDM analyses. Out of 40 contributing datasets, 26 employed universal OGTT testing for GDM diagnosis (N cases =6,770, N controls =37,992), while 14 relied on risk-based testing or self-report (N cases =31,605, N controls =738,083). Among the 37 variants identified in the maternal multi-ancestry meta- analysis of GDM, only one lead variant (rs4665972 at the GCKR-SNX17 locus) exhibited significant variability in the effect estimates between the testing approaches (P sensitivity =3.96×10 -4 ) ( Supplementary Table 7, Supplementary Figure 56 ). After accounting for the testing strategy, no significant residual heterogeneity was observed for this locus (P residual >0.05). We then explored whether body mass index (BMI) could explain the opposite-direction effects observed across ancestries for maternal FG lead variant rs2685806. However, sensitivity analysis revealed no evidence for BMI-correlated heterogeneity in the allelic effects at this locus (P=0.78). Identification of additional genes and masking effects through gene-based testing Given the modest SNP-based heritability estimates for GDM and glycemic traits during pregnancy, we employed gene-based association tests in EUR and EAS to capture additional signals not detected by single-variant analyses ( Supplementary Tables 8 and 9 ). Although there was strong overlap between the genes identified, mBAT-combo identified additional genes not detected in the multi-ancestry GWAS meta-analyses ( Supplementary Figures 57-66; Supplementary Tables 8 and 9 ). In the EUR GDM analysis, novel genes detected included TYR , SIX3 , HLA-DQA2 , and PLEKHA7 , all showing evidence of masking effects (i.e. significant in mBAT but not in fastBAT analyses after multiple test correction where α=0.05/n genes). Other novel GDM- associated genes detected in the EUR analysis were KCNJ11 , HMGA2 , DNAJC27 , SLC38A11 , SLC9B1 , FOXP4 , and LMO1 . In EAS, novel associations included EXOSC8 (associated with both GDM and 2hG), and SHOX2 , S100P , VPS13C , and MNX1 identified in the 1hG analysis, with VPS13C showing evidence of masking effects. For 2hG, mBAT- combo further detected MNX1 and HHEX in EAS. Genetic correlations with T2DM, glycemic traits and pregnancy complications To understand the shared genetic architecture between glycemic traits during and outside of pregnancy and assess the genetic overlap between these traits in pregnancy and pregnancy complications, we performed genetic correlation analyses. Our analyses revealed strong genetic correlations between diabetes-related traits in EUR ( Figure 3 ; Supplementary Table 10; Supplementary Figure 67 ), with GDM and T2DM showing high genetic correlation (rG = 0.77, 95% CI = 0.70–0.84). GDM showed significant positive genetic correlations with most glycemic traits measured both during and outside of pregnancy, with rG ranging from 0.31 to 0.87. Notably, pregnancy measures of glycemic traits demonstrated moderate to strong genetic correlations with their counterparts outside of pregnancy. These patterns were similar, though less precise in EAS, with wider confidence intervals ( Supplementary Figure 68 ). In EUR, GDM also showed positive genetic correlations with adverse pregnancy outcomes, including gestational hypertension (rG = 0.32, 95% CI = 0.23-0.42) and pre- eclampsia (rG = 0.32, 95% CI = 0.19-0.44) ( Figure 3 ). Download figure Open in new tab Figure 3: Genetic correlation analyses in Europeans. Panels display trait-specific genetic correlations for fasting glucose during pregnancy, GDM and T2DM. Each panel shows correlations with: (i) T2DM (purple; DIAMANTE consortium 71 ), (ii) hypertension, adverse pregnancy outcomes and women’s reproductive health (blue; FinnGen R12 72 ), (iii) pregnancy-specific glycemic traits (green; from our European GWAS meta-analyses), and (iv) glycemic trait measures outside of pregnancy (red; MAGIC consortium 46 ). Genetic correlation estimates (rG) are presented as forest plots with 95% confidence intervals. Asterisks indicate statistically significant associations after false discovery rate correction (FDR < 0.05). The vertical dashed line at rG = 0 represents no genetic correlation. GDM; Gestational Diabetes Mellitus. T2DM; Type 2 Diabetes Mellitus. Heterogeneity in genetic effects between GDM and T2DM We used SCOUTJOY to assess the degree of heterogeneity in genetic effects between GDM and T2DM, and between glycaemic traits measured during and outside of pregnancy using ancestry-specific effect estimates. A significant positive relationship in genetic effects was observed between GDM and T2DM before and after outlier removal in both EUR and EAS, using either sex-combined or female- only T2DM GWAS data ( Supplementary Figures 69-72 ; Supplementary Table 11 ). Despite this overall concordance, several variants exhibited significant heterogeneity in effect sizes: 16 and three outlier SNPs were identified in EUR and EAS, respectively, when using sex-combined T2DM, and 12 and three outliers when using the female-only T2DM data. We also observed significant positive relationships between inside and outside pregnancy for FG and 2hG traits, although the strength of associations with 2hG was smaller in magnitude ( Supplementary Figure 73; Supplementary Table 11 ). Similarly to the GDM analysis, we detected outlier SNPs: four (EUR) and two (EAS) in FG analyses, and two (EUR) and one (EAS) in 2hG analyses. Classification of GDM-associated loci into GDM- or T2DM-predominant effects To characterise the heterogeneity in genetic effects observed between GDM and T2DM in the SCOUTJOY analyses, we classified each GDM-associated lead variant based on whether its genetic effect was stronger for GDM or T2DM in both EAS and EUR populations. The classifications were largely consistent across ancestries; however, several variants could not be classified in the EAS analyses due to imprecise effect estimates arising from smaller GWAS sample sizes ( Supplementary Table 12; Supplementary Figure 74-75 ). Across both ancestries, 12 GDM lead variants showed strong evidence of GDM-predominant effects ( TENT5C, SNX17-GCKR, G6PC2, CAST-PCSK1, ESR1, GCK, HKDC1, MTNR1B, RMST, FOXA2 , NFATC2 and SLCO4A1 ), while 14 variants were predominantly associated with T2DM ( Figure 4 ). Of the remaining variants, 10 could not be confidently classified in either population, and one (at the RNF144B locus) was not available in the EAS or EUR T2DM GWAS ( Figure 4 ). Download figure Open in new tab Figure 4. SNP and GRS associations with fasting and 2-hour glucose for GDM-associated loci. This figure presents the 37 lead SNPs identified in the maternal multi-ancestry GDM GWAS meta-analyses. Panels are vertically arranged by classification of SNPs into GDM-predominant, T2DM-predominant, and unclassified variants, based on the combined results from shared variant analyses in EUR and EAS. For each SNP, glucose effect estimates are plotted in the following order: (i) during pregnancy (N=11,117 for FG and 9,850 for 2hG), (ii) after pregnancy in previously pregnant individuals (N=14,028 for FG and 4,307 for 2hG), (iii) outside of pregnancy in individuals of European ancestry (EUR) from the MAGIC consortium (N=200,622 for FG and 63,396 for 2hG), and (iv) outside of pregnancy in individuals of East Asian ancestry (EAS) from the MAGIC consortium (N=35,619 for FG and for 8,509 2hG). The effects on fasting glucose are shown in the left panel and 2-hour glucose in the right panel, aligned to the GDM risk-increasing allele. Effect estimates during pregnancy are shown in dark red, while those after or outside of pregnancy are indicated in blue shades. Shape annotations reflect colocalization results for GDM and T2DM: squares indicate SNPs only associated with GDM, upward-pointing triangles indicate a shared causal variant between GDM and T2DM, downward-pointing triangles reflect distinct causal variants for GDM and T2DM, and diamonds represent inconclusive or null colocalization results. In general, classifications were based on the overall pattern across populations. If colocalization suggested a shared causal variant in one ancestry and was inconclusive in the other, the locus was still classified as having a shared causal variant. In the GDM- and T2DM-predominant panels, we also show glucose effects for genetic risk scores (GRS) constructed from GDM- or T2DM-predominant SNPs, respectively, with the effects of the GDM-predominant GRS (11 SNPs) and the T2DM-predominant GRS (13 SNPs) classified in EUR evaluated during and after pregnancy adjusted for principal components and maternal age. SNP and GRS effects during and after pregnancy were derived from our analyses investigating the effects of GDM-lead SNPs on glucose levels in pregnancy and postpartum, including up to ten cohorts of multiple ancestries, while estimates outside of pregnancy were obtained from the MAGIC consortium for EUR and EAS populations 46 . Arrows at the ends of confidence intervals indicate that the intervals extend beyond the plotted axis limits. GDM; Gestational Diabetes Mellitus. T2DM; Type 2 Diabetes Mellitus. SNP; Single Nucleotide Polymorphism. EUR; Europeans. EAS; East Asians. GRS; Genetic Risk Score. FG; Fasting glucose. 2hG; 2-hour glucose after oral-glucose tolerance test (OGTT). Colocalization analysis We then conducted colocalization analyses to assess whether loci associated with GDM and glycemic traits during pregnancy share causal variants with T2DM and glycemic traits outside of pregnancy. Among the 37 genetic variants associated with GDM, colocalization analysis using data from EUR mothers revealed that six SNPs were associated only with GDM ( GPR61 , G6PC2 , CAST-PCSK1 , ESR1 , NFATC2 , and SLCO4A1 ), seven SNPs were associated with both GDM and T2DM but harboured distinct causal variants, 22 SNPs showed evidence of a shared causal variants with T2DM ( Figure 4 , Supplementary Table 13; Supplementary Figures 76-112 ). GDM and T2DM colocalization results were generally consistent between populations, although in EAS analyses, several loci showed inconclusive results or indicated that variants were not strongly associated with either trait. Colocalization analyses of lead SNPs from multi-ancestry meta-analyses of glycemic traits in pregnancy - 13 for FG, eight for 2hG, and four for HbA1c - identified nine loci with shared causal variants for glycemic traits during and outside pregnancy in at least one ancestry ( Supplementary Figures 113-121 ). Of these, three FG loci ( GCKR, GCK, MTNR1B ) colocalized with FG outside of pregnancy in both EUR and EAS datasets, while six loci colocalized with glycemic traits outside of pregnancy in only one ancestry: RFX6 (FG) and TCF7L2 (2hG) in EUR; KANK1, FOXA2 (FG), CDKAL1 (2hG), and HBS1L (HbA1c) in EAS. ABCB11 (FG) and GCK (2hG) during and outside of pregnancy showed different causal variants in EUR and EAS, respectively, with inconclusive results in the other populations ( Supplementary Figures 122-123) . Five loci showed associations only during pregnancy: ABCC8 (2hG) in both ancestries; RBM47 (FG) in EUR analyses; and IRAG2 , TENT5C , and CRHR2 (2hG) in EAS analyses ( Supplementary Figures 124-128 ). Four loci showed ancestry-specific differences: CAST-PCSK1 , GRB10 (FG), and HKDC1 (2hG) shared causal variants in EUR but were only associated with the trait during pregnancy in EAS; LOC157273 (FG) showed distinct causal variants in EUR and associations only in pregnancy in EAS ( Supplementary Figures 129-132 ). Results for the remaining variants were inconclusive in both ancestries ( Supplementary Figures 133-137 ). Associations of GDM-associated lead SNPs with glucose levels during and after pregnancy To further explore whether GDM-associated variants reflect pregnancy-specific disruption of glucose regulation, we compared genetic risk score (GRS) associations with FG and 2hG levels measured during and after pregnancy. We analysed data from across six multi-ancestry cohorts, including women with glucose measurements during pregnancy (N=11,117 for FG and 9,850 for 2hG) and post-pregnancy (N=14,028 for FG and 4,307 for 2hG) ( Supplementary Table 14 ). A GRS including the 37 variants associated with GDM (All-GRS) and a partitioned GRS including 11 SNPs showing GDM-predominant effects in EUR (based on shared variant analyses - Supplementary Figure 74 ; G-GRS) showed similar associations with FG levels during pregnancy and post-pregnancy. In contrast, the partitioned T2DM-predominant GRS (T-GRS; 13 SNPs based on shared variant analyses in EUR - Supplementary Figure 74 ) showed weaker associations with FG during pregnancy than post-pregnancy ( Supplementary Figure 138; Supplementary Tables 15-17 ). The All-GRS and T-GRS were strongly and consistently associated with 2hG levels during pregnancy and post- pregnancy. We observed positive associations for the G-GRS with 2hG during pregnancy, and while the post-pregnancy was seemingly weaker with 95% CI crossing the null, CIs overlapped ( Supplementary Figure 139; Supplementary Tables 15-17 ). The results remained consistent when adjusting for maternal age, restricting to EUR only, or only including women with glucose measurements both during and post-pregnancy and when weighting the GRSs by the T2DM effect estimates ( Supplementary Table 16 ). Leave-one-out analyses suggested that no individual study influenced the between-study heterogeneity, except for the removal of i) HAPO (AFR), which strengthened the G-GRS association with 2hG during pregnancy and removed evidence of heterogeneity, and ii) HAPO (HIS) reduced the G-GRS association with 2hG post-pregnancy and removed evidence of heterogeneity ( Supplementary Table 17 ). Analyses of individual SNPs suggested greater effects on 2hG during pregnancy than post-pregnancy for the MTNR1B and HKDC1 loci in class G, but power was limited ( Figure 4 ). Expression quantitative trait locus (eQTL) Associations in the Placenta As placental gene regulation may mediate maternal-fetal glucose dynamics, we examined whether GDM- and glycemic trait-associated variants identified in the multi-ancestry analyses were associated with placental gene expression from the Gen3G cohort (placenta biopsies from the maternal-facing side). In total, 570 SNP-gene pairs were tested, involving 408 genes within 500 Kb of at least one lead SNP. Linear regression analysis, adjusting for offspring sex, maternal genetic PCs, expression PCs and offspring genotype, revealed 16 maternal eQTLs, involving 14 SNPs and 15 genes ( Supplementary Table 18 ). The strongest maternal signals included rs9379084 (RNA transcript: SSR1 ; P=2.00×10 -3 ), rs4869273 ( ELL2 , P=5.80×10 -3 ), rs4389513 ( SENP2 , P=6.30×10 -3 ), and rs2299620 ( CDKN1C , P=8.50×10 -3 ). Of note, rs2299620 was also an eQTL for placental KCNQ1 expression with opposite direction depending on whether the SNP was maternal versus offspring origin. None of the associations surpassed the multiple testing correction threshold (FDR < 0.05). Discussion Our multi-ancestry GWAS meta-analyses identified 37 independent genome-wide significant variants associated with GDM, including 19 novel loci, as well as multiple genetic variants associated with glycemic traits during pregnancy. We found no evidence of fetal-specific genetic effects for any of the traits analysed. While most loci overlapped with T2DM and glycemic traits outside of pregnancy, a few loci showed stronger associations with GDM and context-dependent modulation of genetic effects in pregnancy. A key strength of our study was the inclusion of diverse populations, which enabled the detection of ancestry-specific differences in allelic effects at several GDM- and glycemic trait-associated loci. A striking example is ABCB11 (rs2685806), where the lead variant showed opposite directions of association in EUR (β=-0.047, SE= 0.006) and EAS (β=0.066, SE= 0.007) for FG during pregnancy - a pattern also observed for the same variant in FG outside of pregnancy after adjusting for body mass index (BMI), although not explicitly discussed by the authors 46 . ABCB11 encodes the bile salt export pump (BSEP), a key transporter in hepatic bile acid secretion 47 , 48 . Variants in this gene have been previously implicated in cholestatic liver diseases, including intrahepatic cholestasis of pregnancy (ICP) 49 , 50 - a condition which disrupts bile acid homeostasis and can impact glucose metabolism. Sensitivity analyses for FG incorporating mean cohort-level BMI as a meta- regression covariate found no evidence that BMI could account for this difference between ancestries, and imputation artifacts were unlikely given the use of distinct reference panels across cohorts. The consistency of ancestry-specific effects across cohorts also supported a genuine ancestry-related difference, perhaps reflecting differences in linkage disequilibrium (LD) structure, where the same SNP tags different causal variants across populations. Consistent with this, colocalization in EUR suggested distinct causal variants for FG during versus outside pregnancy, whereas, although not definitive, EAS analysis suggested a shared causal variant (PP=0.68). Nevertheless, population-specific gene-environment or gene-gene interactions - arising due to different environmental exposures, dietary patterns, or genetic backgrounds - could also explain these population differences. We also observed that SNP heritability estimates for GDM and pregnancy-related glycemic traits remained modest across ancestries (e.g., EUR liability-scale SNP h² for GDM = 0.108 ± 0.014). This may be explained by differences in screening strategies and GDM diagnosis criteria across the cohorts. However, another explanation is the presence of masking effects, where LD between causal variants with opposing or reinforcing effects attenuates marginal SNP associations 51 . Supporting this, our gene-based tests identified multiple genes in EAS and EUR showing evidence of masking effects and not detected in our standard GWAS, suggesting heritability may reside partly in loci with complex LD patterns or multiple weakly associated variants, which are poorly captured by traditional GWAS and LDSC regression. To elucidate the genetic architecture of GDM and its relationship with T2DM, we examined loci with GDM-predominant effects, integrating findings from several of our analyses ( Table 2 ). Unlike previous studies that variably labelled loci as “pregnancy-specific” often without robust comparison to non-pregnant contexts, our study classifies GDM-predominant loci into five categories reflecting more precise biological interpretations: (i) loci influencing diabetes and glucose metabolism exclusively during pregnancy, (ii) glycemic trait loci with no T2DM effect, (iii) glycemic trait loci with distinct causal variants affecting GDM and T2DM, (iv) loci sharing causal variants with T2DM but with effects modified by pregnancy status, and (v) glycemic trait loci sharing causal variants with T2DM, but with evidence of effect heterogeneity across testing approaches. View this table: View inline View popup Table 2. Classification of GDM-associated variants showing GDM-predominant effects. This table summarises the classification of lead GDM-associated variants that exhibited GDM-predominant effects based on shared variant analyses in at least one ancestry. For each locus, we report the lead gene and rsID, individual SNP effects on fasting and 2-hour glucose (from Figure 4 ), whether the variant was flagged as an outlier in SCOUTJOY (in either Europeans or East Asians), and whether it showed heterogeneity in allelic effects attributable to GDM diagnostic testing (from sensitivity analyses). We further indicate whether each variant reached genome-wide significance for T2DM or glycemic traits outside pregnancy (as reported in the GWAS Catalog) and present colocalization results for (i) GDM vs T2DM and (ii) glycemic traits during vs outside of pregnancy. Colocalization analyses were conducted separately in European and East Asian ancestries using lead SNPs for fasting glucose (FG) or 2-hour glucose (2hG), which may differ from the GDM lead variant; loci not detected in glycemic trait analyses were excluded from colocalization results. GDM-predominant variants were classified into categories reflecting the underlying genetic architecture: (1) Pregnancy-specific locus – Loci with no evidence of genome-wide association with T2DM or glycemic traits outside pregnancy (in GWAS Catalog), supported by distinct colocalization, SCOUTJOY outlier status and SNP estimates show some suggestive trend where effects on glycemic traits are greater during pregnancy. (2) Glycemic trait loci with no T2DM effect – Loci with no genome-wide association with T2DM, but associated with other glycemic traits outside of pregnancy (in GWAS-Catalog), supported by distinct colocalization patterns (distinct causal variants or associations with GDM only), SCOUTJOY outlier status, and SNP estimates show some suggestive trend where effects on FG and 2hG are i) greater during pregnancy or ii) confidence intervals are wide so it is possible they have greater effects during pregnancy; (3) Glycemic trait loci with distinct causal variants affecting GDM and T2DM – Loci at glycemic trait regions with distinct causal variants for GDM vs T2DM, SCOUTJOY outlier status and associated with glycemic traits outside of pregnancy; (4) Loci sharing causal variants with T2DM but with effects modified by pregnancy status – Loci at glycemic trait regions with shared causal variants between GDM and T2DM, SCOUTJOY outlier status, suggestive evidence of stronger effects in pregnancy and associated with glycemic traits outside of pregnancy and; (5) Glycemic trait loci sharing causal variants with T2DM, but with evidence of effect heterogeneity across testing approaches – Loci with shared causal variants between GDM and T2DM, SCOUTJOY outlier status, associated with glycemic traits outside of pregnancy and showing evidence of heterogeneity in allelic effects due to GDM diagnostic testing. GDM, Gestational Diabetes Mellitus; T2DM, Type 2 Diabetes Mellitus; FG, fasting glucose; 2hG, 2-hour glucose. No loci met the criteria for pregnancy-specific effects (Category i), suggesting we have limited evidence of variants acting exclusively during pregnancy without influencing glycemia outside gestation. Consistent with this, our GDM-predominant GRS showed similar associations with FG and 2hG during and after pregnancy, reinforcing limited, if existing, pregnancy[specific genetic effects. However, we identified four GDM loci - G6PC2 , CAST- PCSK1 , HKDC1 , and FOXA2 - that colocalized only with GDM or harboured distinct causal variants from those affecting T2DM (Category ii). These loci lacked genome-wide associations with T2DM despite associations with other glycemic traits outside of pregnancy. Although individual SNP confidence intervals were wide, two loci showed suggestive trends toward stronger effects on glycemic traits during pregnancy: CAST-PCSK1 in FG and HKDC1 in 2hG. This pattern suggests that the effects of these variants may be amplified by the metabolic demands of pregnancy or may reflect differences in diagnostic thresholds between GDM and T2DM, contributing to their apparent pregnancy[specificity. Additionally, for one locus, GCK , we observed evidence of distinct causal variants for GDM and T2DM (Category iii). Although this gene is strongly associated with glycemic traits outside of pregnancy, our colocalization results suggest locus-level pleiotropy and potentially separate regulatory mechanisms for GDM and T2DM. We also observed evidence of pregnancy-dependent effect modification at MTNR1B , a well- established locus associated with glycemic traits outside of pregnancy (Category[iv). While the causal variant is shared between GDM and T2DM, MTNR1B shows markedly stronger associations with 2hG during pregnancy. This pregnancy-amplified effect may be explained by its regulatory interaction with GPR61 - a novel locus detected in our maternal multi- ancestry GDM meta-analysis - via receptor heteromerization 47 . Although GPR61 remained unclassified in the shared variant analyses, it did not colocalize with T2DM or show genome- wide associations with glycemic traits outside of pregnancy. However, GPR61 showed genome-wide significant associations in GWAS of anthropometric traits 53 – 55 , lipid biomarkers 56 – 58 , and metabolic syndrome 59 - pointing to a broader metabolic role. Altogether, these findings support the existence of a gestation-specific melatonin-glucose signalling axis, where MTNR1B-GPR61 interaction potentially modulates the effect of MTNR1B during pregnancy. Similar to MTNR1B , GCKR is known to influence glycemic traits outside of gestation and was shown to share a causal variant between GDM and T2DM. Although individual SNP effects appeared to indicate a potentially stronger effect in FG during pregnancy, consistent with pregnancy-dependent effect modification, our sensitivity analyses revealed substantial heterogeneity in GDM effect estimates depending on glucose testing approach (i.e. universal vs risk-based) (Category v). This suggests that clinical practice variations could account for differences in effects for this locus and reinforces the importance of standardised testing and diagnostic criteria for GDM. Finally, several GDM-predominant loci ( TENT5C, ESR1, RMST, NFATC2, SLCO4A1 ) remained unclassified due to incomplete evidence. Overall, evidence suggests that GDM- predominant loci represent a spectrum of genetic effects shaped by context, locus-level pleiotropy, and testing variability rather than uniformly “pregnancy-specific” mechanisms. In addition to GDM-predominant loci, we identified several GDM loci with stronger T2DM effects. Colocalization analyses indicated distinct causal variants for GDM and T2DM at all but one locus - KCNQ1 . Located in an imprinted region, KCNQ1 harbours a likely shared causal variant for GDM and T2DM and has been previously implicated in parent-of-origin effects, where maternally inherited risk alleles show stronger effects on diabetes-related traits 60 . In our eQTL analyses, rs2299620 exhibited allele-origin-dependent effects, with maternal alleles reducing KCNQ1 expression while fetal alleles increasing it, consistent with imprinting mechanisms and prior evidence linking KCNQ1 methylation with maternal insulin sensitivity 61 . This interplay of opposing maternal and fetal regulatory effects at this locus may modulate its influence during pregnancy, potentially attenuating GDM effects and contributing to its stronger apparent effect in T2DM. Our study has several limitations. First, while MR-MEGA enabled the integration of multi- ancestry genetic data, some signals (e.g. rs77090130) may reflect cohort-specific differences rather than true biological signals, as indicated by substantial heterogeneity and lack of significance in ancestry-specific analyses. Second, sample size imbalance - GDM cases predominantly EUR and glycemic traits largely powered by EAS cohorts - limited ancestry- specific discovery, stratified analyses and interpretation. Third, colocalization results should be interpreted cautiously as differences between EUR and EAS may partly reflect disparities in sample size and power, and the assumption of a single causal variant per locus may not hold in complex regions with multiple independent signals. Phenotypic heterogeneity across cohorts due to varying GDM diagnostic criteria may also have contributed to reduced power and heterogeneity in the analyses. Interpretation of HbA1c results was further limited by small sample size, pregnancy-related changes in red cells turnover, and heritability in glycation. Finally, fetal GWAS sample sizes were small, and the lack of observed fetal- specific effects may reflect limited power. In conclusion, this multi-ancestry study provides the most comprehensive genomic analysis of GDM and pregnancy glycemic traits to date, identifying novel loci and uncovering both shared and distinct components with T2DM. Our findings demonstrate that GDM largely shares its genetic architecture with T2DM, with limited evidence for pregnancy-specific loci, while revealing ancestry-related differences in allelic effects and glycemic regulation during pregnancy. These findings underscore the importance of integrating diverse statistical approaches to refine locus interpretation and highlight the need for larger, ancestrally representative studies to guide future precision medicine approaches. Methods Study cohorts This was a multi-ancestry meta-analysis that included thirty cohorts of EUR, EAS, SAS, AFR, and HIS ancestries. A detailed overview of cohorts can be found in Supplementary Table 1 . All studies were approved by the relevant institutional ethics review boards. Traits analysed GWAS analyses were conducted for maternal genotype data (i.e. maternal genotypes and maternal GDM diagnosis/glucose measurement) and fetal genotype data (i.e. offspring genotypes and maternal GDM diagnosis/glucose measurement). When available, individual cohorts provided GWAS on i) Fasting glucose (FG; mmol/l), ii) 1-hour post-OGTT glucose (1hG; mmol/l), iii) 2-hour post-OGTT glucose (2hG; mmol/l), iv) HbA1c (%), and v) GDM diagnosis (self-reported, ICD code or calculated from OGTT). Cohort contributions, phenotype definitions and individual cohort covariate adjustments can be found in Supplementary Tables 1-3 . Individual cohort analyses Individuals were genotyped in each participating study/cohort, and stringent quality control measures were applied to both samples and SNPs. Sample data were pre-phased and subsequently imputed using reference panels appropriate to each cohort’s ancestry and design. Associations between genetic variants and both glycaemic traits and GDM were assessed using additive models based on the minor allele dosage and employing a regression framework involving principal components and study-specific covariates (such as maternal age, assessment centre, etc). Studies addressed population structure concerns by either excluding related samples and incorporating PCs as additional covariates in the regression model or by including a random effect for a genetic relatedness matrix within a mixed model. Study-level quality control, pre-phasing, imputation, and GWAS analysis procedure (including covariate and analysis tools) can be found in Supplementary Table 3 . Quality control of GWAS Prior to the meta-analysis and subsequent downstream analyses, quality control was performed by the GenDiP analysts, excluding SNPs exhibiting poor imputation quality (R 2 <0.3 or info score<0.4) and/or with a minor allele count <10. If required, genetic data were transformed to align with the NCBI’s Genome Reference Consortium human build 38 (GRCh38) using SNPTracker 62 , providing a standardised reference for subsequent analyses. Further, for the multi-ancestry analyses, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated from the beta coefficients and standard errors provided by the original GWAS summary statistics. Meta-analyses Ancestry-specific meta-analyses were performed using METAL (v 2017) 63 , aggregating either maternal or fetal GWAS summary statistics using a fixed-effect approach with inverse- variance weights to obtain effect size estimates within each of the five broad ancestry groups. The multi-ancestry fetal and maternal GWAS meta-analyses were performed using MR- MEGA (v 0.2) 64 . Three PCs that defined axes of genetic variation were derived and employed as covariates, distinguishing GWAS within the five ancestry groups (i.e. AFR, SAS, EAS, HIS, and EUR). After performing the multi-ancestry and ancestry-specific meta-analyses, we removed variants that were not present in at least half of the total meta-analysis sample size and variants with a MAF of less than 1%. Manhattan plots illustrating multi-ancestry GWAS results were generated using topr R package (v 2.0.2) 65 . Novel variants were defined as those not previously reported in the literature and/or located >1Mb away from any previously reported variant. Multi-ancestry lead variants and functional mapping From our multi-ancestry meta-analysis, independent lead variants were identified by first filtering for variants reaching genome-wide significance (P < 5×10 -8 ) and then sorting these variants by chromosome and genomic position. Variants were clustered into genomic loci using a 1-megabase (Mb) proximity threshold, and within each locus, we selected the variant with the smallest P-value as the lead variant. Locus zoom plots were generated for genomic regions surrounding lead variants using locuszoomr R package (v0.3.8) to visualise association signals and define locus boundaries 66 . Gene track information for Homo sapiens (GRCh38, Ensembl v113) was obtained through AnnotationHub R package (v3.14.0). In case no protein-coding genes were present within +250 kb of the lead variant, available long non- coding RNAs and microRNAs were visualised instead. Putative genes were assigned to each genomic locus based on positional proximity to lead variants. DINGO analyses Maternal and offspring genetic effects on GDM, FG, 1hG, and 2hG were further analysed using the Direct and INdirect effects analysis of Genetic lOci (DINGO) software 67 . DINGO employs i) conditional maternal analyses (adjusted for fetal genotype), ii) conditional fetal analyses (adjusted for maternal genotype). Effective sample overlap between offspring and maternal GWAS was calculated using bivariate linkage disequilibrium score (LDSC) regression 68 . As LDSC regression does not perform well with small samples, DINGO was only performed with the EUR GWAS meta-analyses, as both the maternal and fetal sample sizes were the largest when compared to the other ancestry groups; However, the EUR HbA1c GWAS were too small to be included. For GDM, a binary outcome, genetic effects were transformed to the liability scale using the linear approximation before implementing DINGO 69 . GDM sensitivity analyses To characterise the impact of GDM diagnostic criteria on the heterogeneity of allelic effects, we conducted a sensitivity analysis using cohort-level summary statistics of maternal GDM from 40 datasets, corresponding to 28 cohorts. We performed a meta-regression analysis using MR-MEGA (v0.2), where we employed universal diagnostic testing status (binary variable, i.e. use of universal blood-based diagnostic test) as an additional covariate alongside three principal components to account for diversity between ancestry groups. The universal diagnostic testing status was determined according to the phenotype ascertainment provided by contributing studies ( Supplementary Table 1) . We then examined the extent of heterogeneity arising from phenotype definition among lead variants identified in the multi- ancestry meta-analysis of maternal GDM. A likelihood-ratio test with one degree of freedom was used to assess the goodness of the extended model over the baseline model. The level of significance was set at 0.05/37=1.35×10 -3 , taking into account the number of tested lead variants. Additionally, we characterised the impact of BMI on rs2685806 ( ABCB11 locus), the lead variant from the maternal FG multi-ancestry meta-analysis exhibiting opposite-direction effects in EAS and EUR populations. We extended the baseline model containing three ancestry-specific PCs in MR-MEGA by adding the mean BMI reported for 29 cohorts. Similarly to the GDM diagnostic criteria sensitivity analysis, the effect of BMI was assessed via a one-degree-of-freedom likelihood-ratio test. Heritability and genetic correlations The LD-score regression software 68 ( https://github.com/bulik/ldsc ) implemented in the Complex-Traits Genetics Virtual Lab 70 (CTG-VL, https://vl.genoma.io/ ) was used to estimate SNP heritability (h 2 ) of GDM and glycemic traits during pregnancy in both the EUR and EAS populations. We then assessed pairwise genetic correlations (rG) of our traits with publicly available summary statistics for T2DM (DIAMANTE consortium) 71 , hypertension (FinnGen R12 for EUR 72 and KoGES for EAS 73 ), and glycemic traits outside of pregnancy 46 (MAGIC). For the EUR analysis, we additionally looked at 8 complications of pregnancy and delivery, as well as diabetic nephropathy and polycystic ovarian syndrome, using the FinnGen publicly available dataset. The overview of traits is provided in Supplementary Table 10 . Summary statistics were filtered to the common, well-imputed SNPs from the HapMap 3 reference panel, and variants from the HLA region were removed. Pre-computed 1000 Genomes Project EUR and EAS LD scores available on the CTG-VL platform were used. Statistical significance was corrected for a false discovery rate of 5% using p.adjust function in R (v4.4.2). Observed-scale SNP h 2 of GDM and corresponding SE were transformed to the liability scale 74 , where EUR and EAS population prevalences were presumed to be 12.3% and 14.7% 1 . Gene-based test Gene-based analyses were performed using mBAT-combo 51 implemented in GCTA (v 1.94.3) 75 to identify putative genes associated with GDM, FG, 1hG, and 2hG. mBAT-combo assesses gene-trait associations by combining mBAT and fastBAT approaches using the Cauchy combination method, enhancing the detection of gene-level associations even in the presence of masking effects (e.g. SNPs with the same direction of effect but in negative LD). These analyses were conducted separately for both EUR and EAS using population-specific LD reference panels from 1000G and against the recommended gene list (GENCODE v40). The default settings were used, with mBAT-combo assigning SNPs to genes if they fell within 50kb upstream or downstream of the gene boundaries and allowing an allele frequency difference of 0.2 between the GWAS summary and LD reference dataset. A genome-wide significant threshold was used to identify genes significantly associated with the traits analysed (α =0.05/n genes; α =2.66×10 -6 for EUR; α =2.75×10 -6 for EAS). Heterogeneity in genetic effect estimates across traits Significant Cross-Trait Outliers and Trends in Joint York regression (SCOUTJOY) was employed to assess the relationship and compare the heterogeneity of genetic effects among glycaemic traits and diabetes-associated loci 23 . This method extends the techniques introduced in MR-PRESSO to address heterogeneity detection by considering sample overlap and estimation error in both GWAS datasets 76 . In this approach, estimators for the York regression with a fixed intercept were employed to estimate the slope of the average relationship of the variant’s effect sizes for two phenotypes. This is followed by a global heterogeneity test based on the overall goodness of fit of the York regression model to the observed distribution of effect size estimates (i.e. testing if the observed effect sizes across top hits follow a single uniform relationship). Outlier variants were detected if they presented larger than expected York regression residuals. SCOUTJOY was implemented in R (v0.0.5). The lead SNPs from the multi-ancestry GWAS meta-analyses were used for the analyses comparing the heterogeneity in genetic effects between i) GDM and T2DM (sex-combined sample 25 and female-only sample 77 , 78 unadjusted for BMI), ii) FG during and outside of pregnancy (sex-combined) 46 , and iii) 2hG values during and outside of pregnancy (sex- combined) 46 . These analyses were conducted in both EUR and EAS; utilising SNPs identified in the multi-ancestry meta-analysis but with ancestry-specific effect sizes and standard errors (obtained from our ancestry-specific meta-analyses). Lead SNPs not present in the GWAS summary statistics downloaded or in the meta-analyses of glycemic traits conducted were excluded from the analyses. If the SNPs were not available in the datasets, we attempted to identify proxies; however, some SNPs were not present in the 1000G reference panel used in LDproxy 79 , had no variants in high LD, or did not have a proxy that reached genome-wide significance in the multi-ancestry GWAS (and was therefore not considered a suitable variant). Intercepts of the univariate and bivariate LDSC regression analyses required for SCOUTJOY were calculated before the analyses. Shared variant analysis To explore whether lead SNPs identified in the multi-ancestry GDM GWAS meta-analysis exhibit genetic effects more strongly associated with T2DM or GDM, we applied the linemodels R-package (v.03) 80 to summary statistics from both the GDM GWAS (Multi-ancestry: 38,241 cases and 775,169 controls; EUR: 29,625 cases and 736,101 controls; EAS: 4,064 cases and 22,276) and the T2DM GWAS in females (EUR: 30,053 cases and 434,336 controls 77 ; EAS: 27,370 cases and 135,055 controls 78 ). These analyses were run separately using EAS or EUR-specific GWAS. Although lead SNPs from the multi-ancestry GDM analysis were initially selected, comparisons used ancestry-specific effect estimates and standard errors; GDM SNPs absent from the T2DM dataset were excluded. The analyses ultimately included 34 GDM lead SNPs and 73 T2DM lead SNPs in the EUR analyses and 32 GDM lead SNPs and 40 T2DM lead SNPs in the EAS analyses. Using bivariate effect sizes, SNPs were classified into two groups based on line models fitted with an Expectation-Maximization (EM) algorithm as implemented in Elliot et al., 2024 23 , producing slope estimates of 1.747 for one group (designated class T) and 0.2497 for the other (designated class G) in EUR and 1.082 for class T and 0.1443 for class G in EAS. Scale parameters for both groups were set to 0.2, while correlation parameters that defined the permissible deviation from the line were set to 0.99. Membership probabilities for each SNP in the two classes were calculated by a Bayesian classifier, assuming equal prior probability for each class. Due to the non-overlapping samples in the two GWAS datasets, the correlation of effect size estimates was set to 0. Variants were assigned to class T or G based on the highest posterior probability; those with a posterior probability ≥0.95 were labelled as "GDM" or "T2DM," while those below this threshold were categorised as "Unspecified." Colocalization analysis We conducted genetic colocalization analyses using the coloc R package 81 to determine whether there were shared causal variants between GDM and T2DM for each locus identified in the maternal multi-ancestry GDM GWAS meta-analysis. These analyses were restricted to the maternal meta-analysis from participants of EUR and EAS ancestry due to sample size and availability of matching ancestry summary statistics. Additionally, we performed these analyses assessing the presence of shared variants for continuous traits - specifically FG, 2hG, and HbA1c - during and outside of pregnancy. Summary data for pregnancy-related traits were derived from our meta-analyses, while publicly available data from the DIAMANTE (T2DM 71 ) and MAGIC (FG, 2hG, and HbA1c 46 ) consortia were used for non- pregnancy glycemic traits. For each analysis, we extracted summary data for the relevant traits for SNPs within a +/- 500kb window from the multi-ancestry GWAS meta-analyses lead SNPs. We assumed a prior probability that any SNP in the region is associated with trait 1 only (in this case, GDM) (p1) of 1 × 10 -4 , trait 2 only (in this case, T2DM) (p2) of 1 × 10 -4 , or both traits (p12) 1 × 10 -5 . Coloc computes all possible configurations of causal variants for each of two traits and uses a Bayesian approach to calculate support for each causal model (H 0 : no association; H 1 : association with trait 1 only; H 2 : association with trait 2 only; H 3 : association with both traits owing to distinct causal variants; H 4 : association with both traits owing to a shared causal variant). We consider a posterior probability of association (PPA) for H 4 > 0.7 to provide strong evidence for colocalization and PPA for H 3 > 0.5 as evidence against colocalization, PPA for H 1 > 0.5 as evidence for association with the pregnancy trait only, and PPA for H 2 > 0.5 as evidence for association with the non-pregnancy trait only. Associations of GDM-associated lead SNPs with glucose levels during and after pregnancy To test whether GDM-associated variants exert pregnancy-specific or consistent effects on glucose regulation, we compared the associations of genetic risk scores (GRS) with FG and 2hG levels measured both during and after pregnancy. We analysed data from 10 datasets, corresponding to six cohorts (some including multiple ancestries): EFSOCH (EUR), Gen3G (EUR), FinnGeDi (EUR), ALSPAC (EUR), HAPO (EUR, AFR, EAS, SAS, HIS), and ELGH (SAS). We analysed a total of 11,117 women with FG and 9,850 with 2hG levels during pregnancy (measured at a mean of between 24-28.4 weeks of gestation, mean maternal age ranged from 25.7 to 31.2 years at pregnancy) and 14,028 women with FG and 4,307 women with 2hG levels outside of pregnancy, with mean maternal ages across cohorts ranging from 27.0 to 47.4 years ( Supplementary Table 14 ). Women were included if they did not have a diagnosis of diabetes (type 1 or 2) prior to pregnancy. We analysed individual SNP associations but also grouped SNPs into GRSs to increase power. GRSs were based on the GDM-predominant (class G; n= 11 SNPs) and T2DM- predominant (class T; n=13 SNPs) SNP classifications derived from the shared variant analyses, and an overall GRS composed of all GDM variants identified in this study (n=37 SNPs), which additionally incorporated the 13 unclassified SNPs (G-GRS, T-GRS, and All- GRS, respectively). An individual’s GRS was calculated as the sum of the genotype dosages for each GDM increasing risk allele, weighted by its corresponding beta value (effect size, log-odds of GDM) from the European-ancestry GWAS, given in Supplementary Table 4 . Associations were first estimated separately within each cohort using appropriate linear regression models, adjusting for the first 4 or 5 PCs and any study-specific covariables such as genotyping batches. Results from individual cohorts were combined using an inverse- variance weighted random-effects meta-analysis. We additionally analysed associations of individual SNPs and performed sensitivity analyses (i) including only women with glucose measurements both during and after pregnancy, (ii) conducting a leave-one-out cohort meta- analysis to assess heterogeneity, (iii) performing the meta-analysis in women of EUR ancestry only, and (iv) weighting genetic scores by log-odds of T2DM (given in Supplementary Table 12 ). eQTL Associations in the Placenta Placental Sample Collection and RNA Sequencing We used data from the Gen3G cohort to explore associations with placental expression data. Placentas were processed within 30 minutes of delivery following standard protocols previously described in Hivert et al. (2024) 82 . A 1 cm³ sample was excised from the maternal- facing side of the placenta, approximately 5 cm from the umbilical cord insertion (if transverse). The samples were preserved in RNAlater™ solution at 4°C for 24 hours before being stored at -80°C until RNA extraction. Total RNA was extracted using the mirVana™ Paris™ Kit, and library preparation was performed with the Illumina TruSeq Stranded mRNA Sample Preparation Kit using 250 ng of RNA per sample. RNA sequencing was conducted on an Illumina HiSeq 4000 platform, generating 101-bp paired-end reads. Sequencing reads were processed following the Genotype-Tissue Expression (GTEx) V8 pipeline 83 , with alignment performed using the hg38 reference genome and gene quantification based on GENCODE V30 annotations, using RNASeQC v2.3.6. The Gen3G placental RNA-seq dataset is available on dbGaP 84 . Gene Expression Normalisation and Covariate Adjustment To process gene expression data for quantitative trait locus (eQTL) discovery, we followed the approach used by the GTEx project by applying rimmed Mean of M-values (TMM) normalisation to expression data from 58,011 genes in a subset of 375 European-ancestry samples with both placental RNA-seq data and maternal genotype information available 85 . Genes with low abundance were filtered out, retaining 20,578 genes with a minimum read count of six and transcript per million (TPM) values > 0.1 in at least 20% of samples 85 . An inverse normal transformation was then applied to each gene across samples 85 . To account for unmeasured sources of variation, we performed principal component analysis (PCA) on the normalised expression data. The first seven PCs, which primarily captured variance attributable to placenta cell-type composition as estimated through cell deconvolution, were retained as covariates. eQTL Analysis eQTL associations were evaluated within a 500 Kb window upstream and downstream of each lead SNP (1 Mb total). Linear regression models were used to test associations between each SNP and nearby genes, with gene expression as the dependent variable and the following covariates: offspring sex, five maternal genetic PCs, and the seven expression PCs. To assess the potential contribution of offspring genotype to eQTL effects, we repeated the analysis in a subset of 267 samples with available offspring genotypes (from whole genome sequencing). An eQTL was considered under maternal genotype control if the maternal SNP- eQTL association had a p-value 0.05). Data availability GWAS summary statistics are available via the University of Queensland eSpace repository: maternal multi-ancestry GWAS ( https://espace.library.uq.edu.au/view/UQ:0aa6ba4 ), maternal ancestry-specific GWAS (EUR/EAS; https://espace.library.uq.edu.au/view/UQ:2e6c470 ) and DINGO analyses ( https://espace.library.uq.edu.au/view/UQ:ed470a2 ). Code availability Genetic data were transformed to align GRCh38 using SNPTracker ( http://pmglab.top/snptracker/ ). Multi-ancestry meta-analyses were conducted with MR-MEGA v0.2 ( https://genomics.ut.ee/en/tools ). Ancestry-specific meta-analyses were performed with METAL v2017 ( https://github.com/statgen/METAL ). mBAT-combo ( https://github.com/Share-AL-work/mBAT.combo ) was implemented in GCTA v1.94.3 ( https://yanglab.westlake.edu.cn/software/gcta/#Overview ). Maternal and fetal conditional analyses were conducted using the DINGO software ( https://github.com/danielldhwang/DINGO ) implemented in the CTG-VL platform ( https://vl.genoma.io/ ). Functional mapping and annotation of ancestry-specific signals was performed with FUMA v1.7.0 ( https://fuma.ctglab.nl/ ). Genetic correlation and heritability estimates were performed using LDSC v1.0.1 ( https://github.com/bulik/ldsc ) in the CTG-VL platform ( https://vl.genoma.io/ ). Comparison of heterogeneity of cross-trait genetic effects was performed using SCOUTJOY v0.0.5 ( https://github.com/aelliott08/SCOUTJOY ). Classification of variants based on their bivariate effect size was carried out using Linemodels 0.2.0 ( https://github.com/mjpirinen/linemodels ). Colocalization analyses were performed with the coloc package implemented in R ( https://github.com/chr1swallace/coloc ). eQTL analyses were carried out following GTEx steps for normalisation ( https://github.com/broadinstitute/gtex-pipeline/tree/master/qtl ) and using custom R scripts to test associations ( https://github.com/whitef19/conditional-eQTL ) Meta-analyses of lead SNPs were conducted in R using the implemented package “metafor” ( https://wviechtb.github.io/metafor/ ) Data Availability GWAS summary statistics are available via the University of Queensland eSpace repository: maternal multi-ancestry GWAS (https://espace.library.uq.edu.au/view/UQ:0aa6ba4), maternal ancestry-specific GWAS (EUR/EAS; https://espace.library.uq.edu.au/view/UQ:2e6c470) and DINGO analyses (https://espace.library.uq.edu.au/view/UQ:ed470a2). Author contributions CBN, VR, AHC and FW contributed equally to this work. CBN and GHM conceived the study. CBN, VR, AHC, FW, NMB, AH, SF, APM, RM, JZ, MCB, DAL, MFH, RMF, DME, GHM designed the study and were a part of the analysis and writing group of this paper. CBN, VR, AHC, FW, NMB and MCB performed the meta-analysis and/or follow-up analysis. CBN, VR, AHC, FW, NMB, AK, CA, JR, AE, GT, MV, TL, YG, AL, LC, JT, JL, LC, MM, VK, JC, AH, GH, NFB, SLRS, GPL, SES, BB, EA, JFD, JGE, JP, KT, KR, KS, MMV, PS, RNB, SJ, TT, TH, LE, SM, EO, EQ, KIB, MV, ATH, SS, GAH, SHK, MRJ, RBP, BH, AM, LB, PEJ, HL, KHT, SSA, DAvH, SL, PRN, VS, EW, EK, DMS, WLL, SF, RM, JZ, MCB, DAL, MFH, RMF, DME and GHM provided cohort level analysis and/or contributed to gathering, processing and curating data. MFH, RMF, DME, and GHM jointly supervised the project. All authors had access to all the data in the study. All authors contributed to the review and the editing of the paper. All authors approved the paper before submission. Footnotes ↵ * Joint first ↵ # Joint last References 1. ↵ Wang , H. et al. 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Cisse , Frédérique White , Nancy McBride , Alan Kuang , Catherine Allard , Justiina Ronkainen , Alice Hughes , Amanda Elliott , Gudmar Thorleifsson , Marc Vaudel , Triin Laisk , Yuqin Gu , Amel Lamri , Li Chen , Johanna Tuhkanen , Jari Lahti , Lucinda Calas , Manon Muntaner , Ville Karhunen , Jaewon Choi , Anni Heiskala , Gad Hatem , Nicolas Fragoso-Bargas , Sheryl L. Rifas-Shiman , Guillermo Paz-Lopez , Sara E. Stinson , Bishwajit Bhowmik , Emma Ahlqvist , “Estonian Biobank Research Team” , “Genes & Health Research Team” , Jean-Francois Deleuze , Johan G. Eriksson , Jongseok Park , Koon Teo , Katri Räikkönen , Kari Stefansson , Maria Molina-Vega , Padmaja Subbarao , Robin N. Beaumont , Stefan Johansson , Tiinamaija Tuomi , Torben Hansen , Line Engelbrechtsen , Sonsoles Morcillo , Emily Oken , Elisabeth Quigstad , Kåre I. Birkeland , Marja Vääräsmäki , Andrew T. Hattersley , Sylvain Sebert , Graham A Hitman , Soo Heon Kwak , Marjo-Riitta Järvelin , Rashmi B Prasad , Barbara Heude , Aline Meirhaeghe , Luigi Bouchard , Pierre-Étienne Jacques , Hannele Laivuori , Kok Hian Tan , Sonia S. Anand , David A. van Heel , Siyang Liu , Pål R. Njølstad , Valgerdur Steinthorsdottir , Elisabeth Widén , Elina Keikkala , Denise M Scholtens , William L. Lowe , Sarah Finer , Andrew P Morris , Reedik Mägi , Julia Zöllner , Maria Carolina Borges , Deborah A. Lawlor , Marie-France Hivert , Rachel M. 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