Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas.
Fetal death
Genetic association
Gestational diabetes
Miscarriage
Preeclampsia
Pregnancy loss
Preterm birth
Stillbirth
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
07 May 2024
07 May 2024
Historique:
received:
29
09
2023
accepted:
02
05
2024
medline:
8
5
2024
pubmed:
8
5
2024
entrez:
7
5
2024
Statut:
epublish
Résumé
Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.
Identifiants
pubmed: 38714721
doi: 10.1038/s41598-024-61218-9
pii: 10.1038/s41598-024-61218-9
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10514Subventions
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL119990
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL120034
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL119990
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : R01LM013327
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL120034
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10- HL119992
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL120006
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL120018
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10- HL120019
Organisme : Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U10-HL120034
Organisme : National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health to Clinical and Translational Science Institutes
ID : UL1TR000153
Organisme : National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health to Clinical and Translational Science Institutes
ID : UL1TR001108
Organisme : Precision Health Initiative of Indiana University, National Institutes of Health Award
ID : R01HD101246
Organisme : Precision Health Initiative of Indiana University, National Institutes of Health Award
ID : R01HD101246
Informations de copyright
© 2024. The Author(s).
Références
Lawn, J. E. & Kinney, M. Preterm birth: now the leading cause of child death worldwide. Sci. Transl. Med. 6, 263ed21 (2014).
pubmed: 25411468
doi: 10.1126/scitranslmed.aaa2563
Martin, J. A., Hamilton, B. E., Osterman, M. J. K. & Driscoll, A. K. Births: Final data for 2019. Natl. Vital Stat. Rep. 70, 1–51 (2021).
pubmed: 35157571
Sibai, B., Dekker, G. & Kupferminc, M. Pre-eclampsia. Lancet 365, 785–799 (2005).
pubmed: 15733721
doi: 10.1016/S0140-6736(05)17987-2
Deputy, N. P., Kim, S. Y., Conrey, E. J. & Bullard, K. M. Prevalence and changes in preexisting diabetes and gestational diabetes among women who had a live birth - United States, 2012–2016. MMWR Morb. Mortal. Wkly. Rep. 67, 1201–1207 (2018).
pubmed: 30383743
pmcid: 6319799
doi: 10.15585/mmwr.mm6743a2
Wang, X. et al. Conception, early pregnancy loss, and time to clinical pregnancy: a population-based prospective study. Fertil. Steril. 79, 577–584 (2003).
pubmed: 12620443
doi: 10.1016/S0015-0282(02)04694-0
Zinaman, M. J., Clegg, E. D., Brown, C. C., O’Connor, J. & Selevan, S. G. Estimates of human fertility and pregnancy loss. Fertil. Steril. 65, 503–509 (1996).
pubmed: 8774277
doi: 10.1016/S0015-0282(16)58144-8
Kim, C., Newton, K. M. & Knopp, R. H. Gestational diabetes and the incidence of Type 2 Diabetes: A systematic review. Diabetes Care 25, 1862–1868 (2002).
pubmed: 12351492
doi: 10.2337/diacare.25.10.1862
Mongraw-Chaffin, M. L., Cirillo, P. M. & Cohn, B. A. Preeclampsia and cardiovascular disease death: prospective evidence from the child health and development studies cohort. Hypertension 56, 166–171 (2010).
pubmed: 20516394
doi: 10.1161/HYPERTENSIONAHA.110.150078
Haas, D. M. et al. Pregnancy as a window to future cardiovascular health: Design and implementation of the nuMoM2b heart health study. Am. J. Epidemiol. 183, 519–530 (2016).
pubmed: 26825925
pmcid: 4782765
doi: 10.1093/aje/kwv309
Haas, D. M. et al. A description of the methods of the Nulliparous pregnancy outcomes study: Monitoring mothers-to-be (nuMoM2b). Am. J. Obstet. Gynecol. 212(539), e1-539.e24 (2015).
Catov, J. M. et al. Patterns of leisure-time physical activity across pregnancy and adverse pregnancy outcomes. Int. J. Behav. Nutr. Phys. Act. 15, 68 (2018).
pubmed: 29996930
pmcid: 6042402
doi: 10.1186/s12966-018-0701-5
Goretsky, A. et al. Data preparation of the nuMoM2b dataset. https://doi.org/10.1101/2021.08.24.21262142 .
Facco, F. L. et al. Association between sleep-disordered breathing and hypertensive disorders of pregnancy and gestational diabetes mellitus. Obstet. Gynecol. 129, 31–41 (2017).
pubmed: 27926645
pmcid: 5512455
doi: 10.1097/AOG.0000000000001805
Galanter, J. M. et al. Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: the Genes-environments & Admixture in Latino Americans study. J. Allergy Clin. Immunol. 134, 295–305 (2014).
pubmed: 24406073
pmcid: 4085159
doi: 10.1016/j.jaci.2013.08.055
Bien, S. A. et al. Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping array. PLoS One 11, e0167758 (2016).
pubmed: 27973554
pmcid: 5156387
doi: 10.1371/journal.pone.0167758
Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).
pubmed: 31217584
pmcid: 6785182
doi: 10.1038/s41586-019-1310-4
Rentería, M. E., Cortes, A. & Medland, S. E. Using PLINK for genome-wide association studies (GWAS) and data analysis. Methods Mol Biol https://doi.org/10.1007/978-1-62703-447-0_8 (2013).
doi: 10.1007/978-1-62703-447-0_8
pubmed: 23756892
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
pubmed: 20926424
pmcid: 3025716
doi: 10.1093/bioinformatics/btq559
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: Faster genotype imputation. Bioinformatics 31, 782–784 (2015).
pubmed: 25338720
doi: 10.1093/bioinformatics/btu704
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443–1448 (2016).
pubmed: 27694958
pmcid: 5096458
doi: 10.1038/ng.3679
Cnattingius, S., Forman, M. R., Berendes, H. W. & Isotalo, L. Delayed childbearing and risk of adverse perinatal outcome A population-based study. JAMA 268, 886–890 (1992).
pubmed: 1640617
doi: 10.1001/jama.1992.03490070068044
Fuchs, F., Monet, B., Ducruet, T., Chaillet, N. & Audibert, F. Effect of maternal age on the risk of preterm birth: A large cohort study. PLoS One 13, e0191002 (2018).
pubmed: 29385154
pmcid: 5791955
doi: 10.1371/journal.pone.0191002
Mägi, R. & Morris, A. P. GWAMA: Software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).
pubmed: 20509871
pmcid: 2893603
doi: 10.1186/1471-2105-11-288
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630
pmcid: 4495769
doi: 10.1038/ng.3211
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
pubmed: 26854917
pmcid: 4767558
doi: 10.1038/ng.3506
Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Dong, S. et al. Annotating and prioritizing human non-coding variants with RegulomeDB vol 2. Nat. Genet. 55, 724–726 (2023).
pubmed: 37173523
pmcid: 10989417
doi: 10.1038/s41588-023-01365-3
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
doi: 10.1038/nature11247
Jansen, R. et al. Conditional eQTL analysis reveals allelic heterogeneity of gene expression. Hum. Mol. Genet. 26, 1444–1451 (2017).
pubmed: 28165122
pmcid: 6075455
doi: 10.1093/hmg/ddx043
Zeharia, A. et al. Acute infantile liver failure due to mutations in the TRMU gene. Am. J. Hum. Genet. 85, 401–407 (2009).
pubmed: 19732863
pmcid: 2771591
doi: 10.1016/j.ajhg.2009.08.004
Uusimaa, J. et al. Reversible infantile respiratory chain deficiency is a unique, genetically heterogenous mitochondrial disease. J. Med. Genet. 48, 660–668 (2011).
pubmed: 21931168
doi: 10.1136/jmg.2011.089995
Zhao, J., Zou, W. & Hu, T. Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study. PLoS One 15, e0238940 (2020).
pubmed: 32915913
pmcid: 7485790
doi: 10.1371/journal.pone.0238940
Tissir, F., De-Backer, O., Goffinet, A. M. & Lambert de Rouvroit, C. Developmental expression profiles of Celsr (Flamingo) genes in the mouse. Mech. Dev. 112, 157–160 (2002).
pubmed: 11850187
doi: 10.1016/S0925-4773(01)00623-2
Shima, Y. et al. Differential expression of the seven-pass transmembrane cadherin genes Celsr1-3 and distribution of the Celsr2 protein during mouse development. Dev. Dyn. 223, 321–332 (2002).
pubmed: 11891983
doi: 10.1002/dvdy.10054
Feng, J., Han, Q. & Zhou, L. Planar cell polarity genes, Celsr1-3, in neural development. Neurosci. Bull. 28, 309–315 (2012).
pubmed: 22622831
pmcid: 5560321
doi: 10.1007/s12264-012-1232-8
Tissir, F. & Goffinet, A. M. Atypical cadherins Celsr1-3 and planar cell polarity in vertebrates. Prog. Mol. Biol. Transl. Sci. 116, 193–214 (2013).
pubmed: 23481196
doi: 10.1016/B978-0-12-394311-8.00009-1
Matsunaga, E., Nakamura, H. & Chédotal, A. Repulsive guidance molecule plays multiple roles in neuronal differentiation and axon guidance. J. Neurosci. 26, 6082–6088 (2006).
pubmed: 16738252
pmcid: 6675224
doi: 10.1523/JNEUROSCI.4556-05.2006
Ramírez, J. et al. Thirty loci identified for heart rate response to exercise and recovery implicate autonomic nervous system. Nat. Commun. 9, 1947 (2018).
pubmed: 29769521
pmcid: 5955978
doi: 10.1038/s41467-018-04148-1
Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiat. 24, 169–181 (2019).
doi: 10.1038/s41380-017-0001-5
Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).
pubmed: 29942086
pmcid: 6411041
doi: 10.1038/s41588-018-0152-6
Cordeddu, V. et al. Mutations in ZBTB20 cause Primrose syndrome. Nat. Genet. 46, 815–817 (2014).
pubmed: 25017102
doi: 10.1038/ng.3035
Zhang, Y. et al. The zinc finger protein ZBTB20 regulates transcription of fructose-1,6-bisphosphatase 1 and β cell function in mice. Gastroenterology 142, 1571-1580.e6 (2012).
pubmed: 22374165
doi: 10.1053/j.gastro.2012.02.043
Meng, R. et al. Changes in gene expression in rat placenta at gestational day 16.5 in response to hyperglycemia. Gen. Comp. Endocrinol. 320, 113999 (2022).
pubmed: 35217063
doi: 10.1016/j.ygcen.2022.113999
Davis, A. P. et al. Comparative toxicogenomics database (CTD): Update 2023. Nucleic Acids Res. 51, D1257–D1262 (2023).
pubmed: 36169237
doi: 10.1093/nar/gkac833
Ressler, S. J. et al. WFDC1 is a key modulator of inflammatory and wound repair responses. Am. J. Pathol. 184, 2951–2964 (2014).
pubmed: 25219356
pmcid: 4215025
doi: 10.1016/j.ajpath.2014.07.013
Cappelletti, M., Della Bella, S., Ferrazzi, E., Mavilio, D. & Divanovic, S. Inflammation and preterm birth. J. Leukoc. Biol. 99, 67–78 (2016).
pubmed: 26538528
doi: 10.1189/jlb.3MR0615-272RR
Denney, J. M. et al. Longitudinal modulation of immune system cytokine profile during pregnancy. Cytokine 53, 170–177 (2011).
pubmed: 21123081
doi: 10.1016/j.cyto.2010.11.005
Rotimi, C. N. et al. The genomic landscape of African populations in health and disease. Hum. Mol. Genet. 26, R225–R236 (2017).
pubmed: 28977439
pmcid: 6075021
doi: 10.1093/hmg/ddx253
Liu, Y.-J., Papasian, C. J., Liu, J.-F., Hamilton, J. & Deng, H.-W. Is replication the gold standard for validating genome-wide association findings?. PLoS One 3, e4037 (2008).
pubmed: 19112512
pmcid: 2605260
doi: 10.1371/journal.pone.0004037
Jiang, W., Xue, J.-H. & Yu, W. What is the probability of replicating a statistically significant association in genome-wide association studies?. Brief. Bioinform. 18, 928–939 (2017).
pubmed: 27687799
Virolainen, S. J., VonHandorf, A., Viel, K. C. M. F., Weirauch, M. T. & Kottyan, L. C. Gene-environment interactions and their impact on human health. Genes Immun. 24, 1–11 (2023).
pubmed: 36585519
doi: 10.1038/s41435-022-00192-6
Pagel, K. A. et al. The influence of genetic predisposition and physical activity on risk of gestational diabetes mellitus in the nuMoM2b cohort. https://doi.org/10.1101/2022.03.08.22271868 .
Momozawa, Y. & Mizukami, K. Unique roles of rare variants in the genetics of complex diseases in humans. J. Hum. Genet. 66, 11–23 (2021).
pubmed: 32948841
doi: 10.1038/s10038-020-00845-2