Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-14T19:18:06.018Z Has data issue: false hasContentIssue false
  • English
  • Français

Maternal antenatal depression and child mental health: Moderation by genomic risk for attention-deficit/hyperactivity disorder

Published online by Cambridge University Press:  11 January 2021

Lawrence M. Chen Open the ORCID record for Lawrence M. Chen [Opens in a new window] ,
Marieke S. Tollenaar Open the ORCID record for Marieke S. Tollenaar [Opens in a new window] ,
Shantala A. Hari Dass ,
Andrée-Anne Bouvette-Turcot ,
Irina Pokhvisneva ,
Hélène Gaudreau ,
Carine Parent ,
Josie Diorio ,
Lisa M. McEwen  and
Julia L. MacIsaac
...Show all authors
Lawrence M. Chen
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Marieke S. Tollenaar
Affiliation:
Clinical Psychology Unit, Institute of Psychology, Leiden University, Leiden, the Netherlands Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands
Shantala A. Hari Dass
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Andrée-Anne Bouvette-Turcot
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Irina Pokhvisneva
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Hélène Gaudreau
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Carine Parent
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Josie Diorio
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada Sackler Program for Epigenetics & Psychobiology, McGill University, Montreal, QC, Canada
Lisa M. McEwen
Affiliation:
Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
Julia L. MacIsaac
Affiliation:
Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
Michael S. Kobor
Affiliation:
Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada Child and Brain Development Program, CIFAR, Toronto, ON, Canada
Roseriet Beijers
Affiliation:
Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition & Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
Carolina de Weerth
Affiliation:
Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition & Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
Patricia P. Silveira
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Sherif Karama
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
Michael J. Meaney
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada Sackler Program for Epigenetics & Psychobiology, McGill University, Montreal, QC, Canada Child and Brain Development Program, CIFAR, Toronto, ON, Canada Singapore Institute for Clinical Sciences, Agency for Science, Technology & Research (A*STAR), Singapore
Kieran J. O'Donnell*
Affiliation:
Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada Child and Brain Development Program, CIFAR, Toronto, ON, Canada Yale Child Study Center & Department of Obstetrics, Gynecology & Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA
*
Author for Correspondence: Kieran J. O'Donnell, Yale Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Maternal antenatal depression strongly influences child mental health but with considerable inter-individual variation that is, in part, linked to genotype. The challenge is to effectively capture the genotypic influence. We outline a novel approach to describe genomic susceptibility to maternal antenatal depression focusing on child emotional/behavioral difficulties. Two cohorts provided measures of maternal depression, child genetic variation, and child mental health symptoms. We constructed a conventional polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) (PRSADHD) that significantly moderated the association between maternal antenatal depression and internalizing problems at 60 months (p = 2.94 × 10−4, R2 = .18). We then constructed an interaction PRS (xPRS) based on a subset of those single nucleotide polymorphisms from the PRSADHD that most accounted for the moderation of the association between maternal antenatal depression and child outcome. The interaction between maternal antenatal depression and this xPRS accounted for a larger proportion of the variance in child emotional/behavioral problems than models based on any PRSADHD (p = 5.50 × 10−9, R2 = .27), with similar findings in the replication cohort. The xPRS was significantly enriched for genes involved in neuronal development and synaptic function. Our study illustrates a novel approach to the study of genotypic moderation on the impact of maternal antenatal depression on child mental health and highlights the utility of the xPRS approach. These findings advance our understanding of individual differences in the developmental origins of mental health.

Keywords

ADHDchild developmentgene by environment (GxE)perinatal mental healthpolygenic risk score
Type
Special Section 2: Early Adversity and Development: Contributions from the Field
Information
Development and Psychopathology , Volume 32 , Special Issue 5: Early Adversity, Stress, and Neurobehavioral Development: Honoring Megan R. Gunnar's Contributions to Developmental Science , December 2020 , pp. 1810 - 1821
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achenbach, T. M. (1991). Integrative Guide to the 1991 CBCL/4–18, YSR, and TRF Profiles. Burlington, VT: University of Vermont, Department of Psychology.Google Scholar
Arnau-Soler, A., Macdonald-Dunlop, E., Adams, M. J., Clarke, T.-K., MacIntyre, D. J., Milburn, K., … Thomson, P. A. (2019). Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland. Translational Psychiatry, 9, 14. doi:10.1038/s41398-018-0360-yCrossRefGoogle ScholarPubMed
Babineau, V., Green, C. G., Jolicoeur-Martineau, A., Bouvette-Turcot, A.-A., Minde, K., Sassi, R., … MAVAN project. (2015). Prenatal depression and 5-HTTLPR interact to predict dysregulation from 3 to 36 months – A differential susceptibility model. Journal of Child Psychology and Psychiatry, 56, 21–29. doi:10.1111/jcpp.12246CrossRefGoogle ScholarPubMed
Beijers, R., Jansen, J., Riksen-Walraven, M., & de Weerth, C. (2010). Maternal prenatal anxiety and stress predict infant illnesses and health complaints. Pediatrics, 126, e401–e409. doi:10.1542/peds.2009-3226CrossRefGoogle ScholarPubMed
Belsky, J. (1997). Variation in susceptibility to environmental influence: An evolutionary argument. Psychological Inquiry, 8, 182–186.CrossRefGoogle Scholar
Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16, 300–304. doi:10.1111/j.1467-8721.2007.00525.xCrossRefGoogle Scholar
Belsky, J., Jonassaint, C., Pluess, M., Stanton, M., Brummett, B., & Williams, R. (2009). Vulnerability genes or plasticity genes? Molecular Psychiatry, 14, 746–754. doi:10.1038/mp.2009.44CrossRefGoogle ScholarPubMed
Belsky, J., Pluess, M., & Widaman, K. F. (2013). Confirmatory and competitive evaluation of alternative gene-environment interaction hypotheses. Journal of Child Psychology and Psychiatry, 54, 1135–1143. doi:10.1111/jcpp.12075CrossRefGoogle ScholarPubMed
Bentley, A. R., Sung, Y. J., Brown, M. R., Winkler, T. W., Kraja, A. T., Ntalla, I., … Cupples, L. A. (2019). Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nature Genetics, 51, 636–648. doi:10.1038/s41588-019-0378-yCrossRefGoogle ScholarPubMed
Biederman, J., Newcorn, J. H., & Sprich, S. (1991). Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. American Journal of Psychiatry, 148, 564–577. doi:10.1176/ajp.148.5.564Google ScholarPubMed
Border, R., Johnson, E. C., Evans, L. M., Smolen, A., Berley, N., Sullivan, P. F., & Keller, M. C. (2019). No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. American Journal of Psychiatry, 176, 376–387. doi:10.1176/appi.ajp.2018.18070881CrossRefGoogle ScholarPubMed
Brikell, I., Larsson, H., Lu, Y., Pettersson, E., Chen, Q., Kuja-Halkola, R., … Martin, J. (2020). The contribution of common genetic risk variants for ADHD to a general factor of childhood psychopathology. Molecular Psychiatry, 25, 1809–1821. doi:10.1038/s41380-018-0109-2.CrossRefGoogle ScholarPubMed
Brody, G. H., Yu, T., & Beach, S. R. H. (2015). A differential susceptibility analysis reveals the “who and how” about adolescents’ responses to preventive interventions: Tests of first- and second-generation Gene x Intervention hypotheses. Development and Psychopathology, 27, 37–49. doi:10.1017/S095457941400128xCrossRefGoogle Scholar
Chang, C. C., Chow, C. C., Tellier, L. C. A. M., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience, 4, 1–16. doi:10.1186/s13742-015-0047-8CrossRefGoogle Scholar
Chen, L. M., Yao, N., Garg, E., Zhu, Y., Nguyen, T. T. T., Pokhvisneva, I., … O'Donnell, K. J. (2018). PRS-on-Spark (PRSoS): A novel, efficient and flexible approach for generating polygenic risk scores. BMC Bioinformatics, 19, 295. doi:10.1186/s12859-018-2289-9CrossRefGoogle ScholarPubMed
Cicchetti, D., Toth, S. L., & Handley, E. D. (2015). Genetic moderation of interpersonal psychotherapy efficacy for low-income mothers with major depressive disorder: Implications for differential susceptibility. Development and Psychopathology, 27, 19–35. doi:10.1017/S0954579414001278CrossRefGoogle ScholarPubMed
Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786.CrossRefGoogle ScholarPubMed
Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013). Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. The Lancet, 381, 1371–1379. doi:10.1016/S0140-6736(12)62129-1CrossRefGoogle Scholar
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., … Neale, B. M. (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics, 51, 63–75. doi:10.1038/s41588-018-0269-7CrossRefGoogle ScholarPubMed
Duncan, L. E., Pollastri, A. R., & Smoller, J. W. (2014). Mind the gap: Why many geneticists and psychological scientists have discrepant views about gene–environment interaction (G×E) research. American Psychologist, 69, 249–268. doi:10.1037/a0036320CrossRefGoogle ScholarPubMed
Dunn, E. C., Wiste, A., Radmanesh, F., Almli, L. M., Gogarten, S. M., Sofer, T., … Smoller, J. W. (2016). Genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) of depressive symptoms in African American and Hispanic/Latina women. Depression and Anxiety, 33, 265–280. doi:10.1002/da.22484CrossRefGoogle ScholarPubMed
Du Rietz, E., Coleman, J., Glanville, K., Choi, S. W., O'Reilly, P. F., & Kuntsi, J. (2018). Association of polygenic risk for attention-deficit/hyperactivity disorder with co-occurring traits and disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3, 635–643. doi:10.1016/j.bpsc.2017.11.013Google ScholarPubMed
Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2011). Differential susceptibility to the environment: A neurodevelopmental theory. Development and Psychopathology, 23, 7–28. doi:10.1017/S0954579410000611CrossRefGoogle Scholar
Ficks, C. A., & Waldman, I. D. (2009). Gene-environment interactions in attention-deficit/hyperactivity disorder. Current Psychiatry Reports, 11, 387–392. doi:10.1007/s11920-009-0058-1CrossRefGoogle ScholarPubMed
Finsaas, M. C., Bufferd, S. J., Dougherty, L. R., Carlson, G. A., & Klein, D. N. (2018). Preschool psychiatric disorders: Homotypic and heterotypic continuity through middle childhood and early adolescence. Psychological Medicine, 48, 2159–2168. doi:10.1017/S0033291717003646CrossRefGoogle ScholarPubMed
Franke, B., Neale, B. M., & Faraone, S. V. (2009). Genome-wide association studies in ADHD. Human Genetics, 126, 13–50. doi:10.1007/s00439-009-0663-4CrossRefGoogle ScholarPubMed
Gentile, S. (2017). Untreated depression during pregnancy: Short- and long-term effects in offspring. A systematic review. Neuroscience, 342, 154–166. doi:10.1016/j.neuroscience.2015.09.001CrossRefGoogle Scholar
Gillman, M. W., & Blaisdell, C. J. (2018). Environmental influences on Child Health Outcomes, a research program of the National Institutes of Health. Current Opinion in Pediatrics, 30, 260–262. doi:10.1097/MOP.0000000000000600CrossRefGoogle Scholar
Goodman, S. H., Cullum, K. A., Dimidjian, S., River, L. M., & Kim, C. Y. (2018). Opening windows of opportunities: Evidence for interventions to prevent or treat depression in pregnant women being associated with changes in offspring's developmental trajectories of psychopathology risk. Development and Psychopathology, 30, 1179–1196. doi:10.1017/S0954579418000536CrossRefGoogle ScholarPubMed
Goodman, S. H., Rouse, M. H., Connell, A. M., Broth, M. R., Hall, C. M., & Heyward, D. (2011). Maternal depression and child psychopathology: A meta-analytic review. Clinical Child and Family Psychology Review, 14, 1–27. doi:10.1007/s10567-010-0080-1CrossRefGoogle ScholarPubMed
Graffi, J., Moss, E., Jolicoeur-Martineau, A., Moss, G., Lecompte, V., & Pascuzzo, K., … MAVAN Project. (2017). The dopamine D4 receptor gene, birth weight, maternal depression, maternal attention, and the prediction of disorganized attachment at 36 months of age: A prospective gene x environment analysis. Infant Behavior and Development, 50, 64–77. doi:10.1016/j.infbeh.2017.11.004CrossRefGoogle Scholar
Grizenko, N., Fortier, M. E., Zadorozny, C., Thakur, G., Schmitz, N., Duval, R., & Joober, R. (2012). Maternal stress during pregnancy, ADHD symptomatology in children and genotype: Gene-environment interaction. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 21, 9–15.Google ScholarPubMed
Groen-Blokhuis, M. M., Middeldorp, C. M., Kan, K.-J., Abdellaoui, A., van Beijsterveldt, C. E. M., Ehli, E. A., … Boomsma, D. I. (2014). Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 1123–1129. doi:10.1016/j.jaac.2014.06.014CrossRefGoogle Scholar
Hayman, V., & Fernandez, T. V. (2018). Genetic insights into ADHD biology. Frontiers in Psychiatry, 9, 251. doi:10.3389/fpsyt.2018.00251CrossRefGoogle ScholarPubMed
Hendershot, T., Pan, H., Haines, J., Harlan, W. R., Marazita, M. L., McCarty, C. A., … Hamilton, C. M. (2015). Using the PhenX Toolkit to add standard measures to a study. Current Protocols in Human Genetics, 86, 1.21.21–21.21.17. doi:10.1002/0471142905.hg0121s86CrossRefGoogle ScholarPubMed
Hüls, A., Ickstadt, K., Schikowski, T., & Krämer, U. (2017a). Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression. BMC Genetics, 18, 55. doi:10.1186/s12863-017-0519-1CrossRefGoogle Scholar
Hüls, A., Krämer, U., Carlsten, C., Schikowski, T., Ickstadt, K., & Schwender, H. (2017b). Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies. BMC Genetics, 18, 115. doi:10.1186/s12863-017-0586-3CrossRefGoogle Scholar
Keller, M. C. (2014). Gene x environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biological Psychiatry, 75, 18–24. doi:10.1016/j.biopsych.2013.09.006CrossRefGoogle Scholar
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1–26. doi:10.18637/jss.v028.i05.CrossRefGoogle Scholar
Lesch, K.-P., Timmesfeld, N., Renner, T. J., Halperin, R., Roser, C., Nguyen, T. T., … Jacob, C. (2008). Molecular genetics of adult ADHD: Converging evidence from genome-wide association and extended pedigree linkage studies. Journal of Neural Transmission, 115, 1573–1585. doi:10.1007/s00702-008-0119-3CrossRefGoogle ScholarPubMed
Lin, W.-Y., Huang, C.-C., Liu, Y.-L., Tsai, S.-J., & Kuo, P.-H. (2018). Polygenic approaches to detect gene-environment interactions when external information is unavailable. Briefings in Bioinformatics, 20, 2236–2252. doi:10.1093/bib/bby086.CrossRefGoogle Scholar
Manuck, S. B., & McCaffery, J. M. (2014). Gene-environment interaction. Annual Review of Psychology, 65, 41–70. doi:10.1146/annurev-psych-010213-115100CrossRefGoogle ScholarPubMed
Martin, J., Hamshere, M. L., Stergiakouli, E., O'Donovan, M. C., & Thapar, A. (2014). Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population. Biological Psychiatry, 76, 664–671. doi:10.1016/j.biopsych.2014.02.013CrossRefGoogle ScholarPubMed
Martin, J., Walters, R. K., Demontis, D., Mattheisen, M., Lee, S. H., Robinson, E., … Neale, B. M. (2018). A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder. Biological Psychiatry, 83, 1044–1053. doi:10.1016/j.biopsych.2017.11.026CrossRefGoogle ScholarPubMed
McCarthy, S., Das, S., Kretzschmar, W., Delaneau, O., Wood, A. R., Teumer, A., … Haplotype Reference Consortium. (2016). A reference panel of 64,976 haplotypes for genotype imputation. Nature Genetics, 48, 1279–1283. doi:10.1038/ng.3643Google ScholarPubMed
Meaney, M. J. (2018). Perinatal maternal depressive symptoms as an issue for population health. American Journal of Psychiatry, 175, 1084–1093. doi:10.1176/appi.ajp.2018.17091031CrossRefGoogle ScholarPubMed
Mullins, N., Power, R. A., Fisher, H. L., Hanscombe, K. B., Euesden, J., Iniesta, R., … Lewis, C. M. (2016). Polygenic interactions with environmental adversity in the aetiology of major depressive disorder. Psychological Medicine, 46, 759–770. doi:10.1017/S0033291715002172CrossRefGoogle ScholarPubMed
Neale, B. M., Medland, S. E., Ripke, S., Asherson, P., Franke, B., Lesch, K.-P., … Nelson, S. (2010). Meta-analysis of genome-wide association studies of attention deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 884–897. doi:10.1016/j.jaac.2010.06.008CrossRefGoogle ScholarPubMed
O'Donnell, K. A., Gaudreau, H., Colalillo, S., Steiner, M., Atkinson, L., Moss, E., … MAVAN Research Team. (2014). The Maternal Adversity, Vulnerability and Neurodevelopment project: Theory and methodology. Canadian Journal of Psychiatry, 59, 497–508. doi:10.1177/070674371405900906CrossRefGoogle ScholarPubMed
O'Donnell, K. J., Glover, V., Barker, E. D., & O'Connor, T. G. (2014). The persisting effect of maternal mood in pregnancy on childhood psychopathology. Development and Psychopathology, 26, 393–403. doi:10.1017/S0954579414000029CrossRefGoogle ScholarPubMed
O'Donnell, K. J., Glover, V., Holbrook, J. D., & O'Connor, T. G. (2014). Maternal prenatal anxiety and child brain-derived neurotrophic factor (BDNF) genotype: Effects on internalizing symptoms from 4 to 15 years of age. Development and Psychopathology, 26, 1255–1266. doi:10.1017/S095457941400100XCrossRefGoogle ScholarPubMed
O'Donnell, K. J., Glover, V., Lahti, J., Lahti, M., Edgar, R. D., Räikkönen, K., & O'Connor, T. G. (2017). Maternal prenatal anxiety and child COMT genotype predict working memory and symptoms of ADHD. PLoS One, 12, 1–16. doi:10.1371/journal.pone.0177506Google ScholarPubMed
O'Donnell, K. J., & Meaney, M. J. (2017). Fetal origins of mental health: The Developmental Origins of Health and Disease hypothesis. American Journal of Psychiatry, 174, 319–328. doi:10.1176/appi.ajp.2016.16020138CrossRefGoogle ScholarPubMed
Patterson, N., Price, A. L., & Reich, D. (2006). Population structure and eigenanalysis. PLoS Genetics, 2, 2074–2093. doi:10.1371/journal.pgen.0020190CrossRefGoogle ScholarPubMed
Pearson, R. M., Bornstein, M. H., Cordero, M., Scerif, G., Mahedy, L., Evans, J., … Stein, A. (2016). Maternal perinatal mental health and offspring academic achievement at age 16: The mediating role of childhood executive function. Journal of Child Psychology and Psychiatry, 57, 491–501. doi:10.1111/jcpp.12483CrossRefGoogle ScholarPubMed
Pearson, R. M., Evans, J., Kounali, D., Lewis, G., Heron, J., Ramchandani, P. G., … Stein, A. (2013). Maternal depression during pregnancy and the postnatal period: Risks and possible mechanisms for offspring depression at age 18 years. JAMA Psychiatry, 70, 1312–1319. doi:10.1001/jamapsychiatry.2013.2163CrossRefGoogle ScholarPubMed
Peyrot, W. J., Milaneschi, Y., Abdellaoui, A., Sullivan, P. F., Hottenga, J. J., Boomsma, D. I., & Penninx, B. W. J. H. (2014). Effect of polygenic risk scores on depression in childhood trauma. British Journal of Psychiatry, 205, 113–119. doi:10.1192/bjp.bp.113.143081CrossRefGoogle ScholarPubMed
Peyrot, W. J., Van der Auwera, S., Milaneschi, Y., Dolan, C. V., Madden, P. A. F., Sullivan, P. F., … Penninx, B. W. J. H. (2018). Does childhood trauma moderate polygenic risk for depression? A meta-analysis of 5765 subjects from the Psychiatric Genomics Consortium. Biological Psychiatry, 84, 138–147. doi:10.1016/j.biopsych.2017.09.009CrossRefGoogle ScholarPubMed
Plomin, R., & Simpson, M. A. (2013). The future of genomics for developmentalists. Development and Psychopathology, 25, 1263–1278. doi:10.1017/S0954579413000606CrossRefGoogle ScholarPubMed
Pluess, M., & Belsky, J. (2009). Differential susceptibility to rearing experience: The case of childcare. Journal of Child Psychology and Psychiatry and Allied Disciplines, 50, 396–404. doi:10.1111/j.1469-7610.2008.01992.xCrossRefGoogle ScholarPubMed
Poelmans, G., Pauls, D. L., Buitelaar, J. K., & Franke, B. (2011). Integrated genome-wide association study findings: Identification of a neurodevelopmental network for attention deficit hyperactivity disorder. American Journal of Psychiatry, 168, 365–377. doi:10.1176/appi.ajp.2010.10070948CrossRefGoogle ScholarPubMed
Pop, V. J., Komproe, I. H., & van Son, M. J. (1992). Characteristics of the Edinburgh post natal depression scale in the Netherlands. Journal of Affective Disorders, 26, 105–110.CrossRefGoogle ScholarPubMed
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904–909. doi:10.1038/ng1847CrossRefGoogle ScholarPubMed
Qiu, A., Shen, M., Buss, C., Chong, Y. S., Kwek, K., Saw, S. M., … Meaney, M. J. (2017). Effects of antenatal maternal depressive symptoms and socio-economic status on neonatal brain development are modulated by genetic risk. Cerebral Cortex, 27, 3080–3092. doi:10.1093/cercor/bhx065CrossRefGoogle ScholarPubMed
Radloff, L. S. (1977). The CES-D scale: A self-reported depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. doi:10.1177/014662167700100306CrossRefGoogle Scholar
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., … Koellinger, P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467–1471. doi:10.1126/science.1235488CrossRefGoogle ScholarPubMed
Roisman, G. I., Newman, D. A., Fraley, R. C., Haltigan, J. D., Groh, A. M., & Haydon, K. C. (2012). Distinguishing differential susceptibility from diathesis-stress: Recommendations for evaluating interaction effects. Development and Psychopathology, 24, 389–409. doi:10.1017/S0954579412000065CrossRefGoogle ScholarPubMed
Rutter, M. (2007). Gene-environment interdependence. Developmental Science, 10, 12–18. doi:10.1111/j.1467-7687.2007.00557.xCrossRefGoogle ScholarPubMed
Sfelinioti, S., & Livaditis, M. (2017). Association of maternal depression with children's attention deficit hyperactivity disorder. Psychiatriki, 28, 251–258. doi:10.22365/jpsych.2017.283.251CrossRefGoogle ScholarPubMed
Shevlin, M., McElroy, E., & Murphy, J. (2017). Homotypic and heterotypic psychopathological continuity: A child cohort study. Social Psychiatry and Psychiatric Epidemiology, 52, 1135–1145. doi:10.1007/s00127-017-1396-7CrossRefGoogle ScholarPubMed
Silveira, P. P., Pokhvisneva, I., Gaudreau, H., Atkinson, L., Fleming, A. S., Sokolowski, M. B., … MAVAN research team. (2018). Fetal growth interacts with multilocus genetic score reflecting dopamine signaling capacity to predict spontaneous sugar intake in children. Appetite, 120, 596–601. doi:10.1016/j.appet.2017.10.021CrossRefGoogle ScholarPubMed
Silveira, P. P., Pokhvisneva, I., Parent, C., Cai, S., Rema, A. S. S., Broekman, B. F. P., … Meaney, M. J. (2017). Cumulative prenatal exposure to adversity reveals associations with a broad range of neurodevelopmental outcomes that are moderated by a novel, biologically informed polygenetic score based on the serotonin transporter solute carrier family C6, member 4 (SLC6A4) gene expression. Development and Psychopathology, 29, 1601–1617. doi:10.1017/S0954579417001262CrossRefGoogle ScholarPubMed
Stergiakouli, E., Martin, J., Hamshere, M. L., Heron, J., St Pourcain, B., Timpson, N. J., … Davey Smith, G. (2017). Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population. International Journal of Epidemiology, 46, 421–428. doi:10.1093/ije/dyw216Google ScholarPubMed
Stergiakouli, E., Martin, J., Hamshere, M. L., Langley, K., Evans, D. M., St Pourcain, B., … Davey Smith, G. (2015). Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) traits in children and clinical ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 322–327. doi:10.1016/j.jaac.2015.01.010CrossRefGoogle ScholarPubMed
Sullivan, P. F., Agrawal, A., Bulik, C. M., Andreassen, O. A., Børglum, A. D., Breen, G., … O'Donovan, M. C. (2018). Psychiatric genomics: An update and an agenda. American Journal of Psychiatry, 175, 15–27. doi:10.1176/appi.ajp.2017.17030283CrossRefGoogle Scholar
Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012). Genetic architectures of psychiatric disorders: The emerging picture and its implications. Nature Reviews Genetics, 13, 537–551. doi:10.1038/nrg3240CrossRefGoogle ScholarPubMed
Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., … Alzheimer's Disease Neuroimaging Initiative, E. C. I. C. S. Y. S. G. (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8, 153–182. doi:10.1007/s11682-013-9269-5CrossRefGoogle ScholarPubMed
Van der Auwera, S., Peyrot, W. J., Milaneschi, Y., Hertel, J., Baune, B., Breen, G., … Grabe, H. (2018). Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 177B, 40–49. doi:10.1002/ajmg.b.32593CrossRefGoogle Scholar
van der Toorn, S. L. M., Huizink, A. C., Utens, E. M. J. W., Verhulst, F. C., Ormel, J., & Ferdinand, R. F. (2010). Maternal depressive symptoms, and not anxiety symptoms, are associated with positive mother–child reporting discrepancies of internalizing problems in children: A report on the TRAILS Study. European Child and Adolescent Psychiatry, 19, 379–388. doi:10.1007/s00787-009-0062-3CrossRefGoogle Scholar
van Ijzendoorn, M. H., & Bakermans-Kranenburg, M. J. (2015). Genetic differential susceptibility on trial: Meta-analytic support from randomized controlled experiments. Development and Psychopathology, 27, 151–62. doi:10.1017/S0954579414001369CrossRefGoogle ScholarPubMed
Verhulst, F. C., Akkerhuis, G. W., & Althaus, M. (1985). Mental health in Dutch children: (I). A cross-cultural comparison. Acta Psychiatrica Scandinavica. Supplementum, 72, 1–108. doi:10.1111/j.1600-0447.1985.tb10512.xGoogle Scholar
Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era—concepts and misconceptions. Nature Reviews Genetics, 9, 255–266. doi:10.1038/nrg2322CrossRefGoogle ScholarPubMed
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007). Prediction of individual genetic risk to disease from genome-wide association studies. Genome Research, 17, 1520–1528. doi:10.1101/gr.6665407.1CrossRefGoogle ScholarPubMed
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A. E., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55, 1068–1087. doi:10.1111/jcpp.12295CrossRefGoogle ScholarPubMed
Supplementary material: File

Chen et al. supplementary material

Chen et al. supplementary material 1

Download Chen et al. supplementary material(File)
File 5.7 MB
Supplementary material: File

Chen et al. supplementary material

Chen et al. supplementary material 2

Download Chen et al. supplementary material(File)
File 198.6 KB