Polygenic prediction of the risk of perinatal depressive symptoms

Depression and Anxiety. 2020;1–14. wileyonlinelibrary.com/journal/da | 1 Received: 14 March 2020 | Revised: 15 May 2020 | Accepted: 7 June 2020 DOI: 10.1002/da.23066 RESEARCH ARTICLE Polygenic prediction of the risk of perinatal depressive symptoms Ville Rantalainen 1 |Elisabeth B. Binder 2 |Marius Lahti‐Pulkkinen 1,3,4 | Darina Czamara 2 |Hannele Laivuori 5,6,7 |Pia M. Villa 8 |Polina Girchenko 1 | Tuomas Kvist 1 |Esa Hämäläinen 9 |Eero Kajantie 3 |Jari Lahti 10 |Katri Räikkönen 1 1 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland 2 Department of Translational Research in Psychiatry, Max‐Planck‐Institute of Psychiatry, Munich, Germany 3 Pulic Health Promotion Unit, National Institute for Health and Welfare, Helsinki, Finland 4 University/British Heart Foundation Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom 5 Department of Obstetrics and Gynecology, EBCOG Faculty of Medicine and Health Technology, Tampere University Hospital and Tampere University, Tampere, Finland 6 Department of Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland 7 Institute for Molecular Medicine, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland 8 Department of Obstetrics and Gynaecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland 9 Department of Clinical Chemistry, University of Eastern Finland, Kuopio, Finland 10 Department of Psychology and Logopedics, Faculty of Medicine, Helsinki, Finland Correspondence Rantalainen, Ville Juhani, Faculty of Medicine, Institute of Psychology and Logopedics, University of Helsinki, P.O BOX 21 (Haartmaninkatu 3), 00014 Helsinki, Finland. Email: [email protected] Funding information Foundation of Pediatric Research; European Commission Dynamics of Inequality Across the Life‐course: structures and processes (DIAL), Grant/Award Number: 724363 for PremLife; Academy of Finland; Juho Vainio Foundation; Signe ja Ane Gyllenbergin Säätiö; Novo Nordisk Foundation; Sigrid Juselius Foundation; European Commission, Grant/Award Number: Horizon 2020 Award SC1‐2016‐RTD‐733280 RECAP Abstract Background: Perinatal depression carries adverse effects on maternal health and child development, but genetic underpinnings remain unclear. We investigated the polygenic risk of perinatal depressive symptoms. Methods: About 742 women from the prospective Prediction and Prevention of Pre‐ eclampsia and Intrauterine Growth Restriction cohort were genotyped and completed the Center for Epidemiologic Studies Depression scale 14 times during the prenatal period and twice up to 12 months postpartum. Polygenic risk scores for major depressive disorder, bipolar disorder, schizophrenia, and cross‐disorder were calculated using multiple p‐value thresholds. Results: Polygenic risk scores for major depressive disorder, schizophrenia, and cross‐disorder, but not bipolar disorder, were associated with higher prenatal and postpartum depressive symptoms (0.8%–1% increase per one standard deviation increase in polygenic risk scores). Prenatal depressive symptoms accounted for and mediated the associations between the polygenic risk scores and postpartum depressive symptoms (effect size proportions‐mediated: 52.2%–88.0%). Further, the polygenic risk scores were associated with 1.24–1.45‐fold odds to belong to the -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Depression and Anxiety published by Wiley Periodicals LLC group displaying consistently high compared with consistently low depressive symptoms through out the prenatal and postpartum periods. Conclusions: Polygenic risk scores for major depressive disorder, schizophrenia, and cross‐disorder in non‐perinatal populations generalize to perinatal depressive symptoms and may afford to identify women for timely preventive interventions. KEYWORDS depression, epidemiology, genetics, mood disorders, pregnancy and postpartum 1|INTRODUCTION Maternal perinatal depression, defined as depression during pregnancy or within 12 months of childbirth, affects over 1 of 10 women in childbearing age (World Health Organizaton, 2015). Reported clinically relevant symptoms are even more common, affecting 1 of 5 women (Kumpulainen et al., 2018; Lahti et al., 2017). It causes a major burden on the health and well‐being of the women and is associated with problems such as gestational diabetes and hypertension in pregnancy (Bansil et al., 2009) and obesity (Kumpulainen et al., 2018). It is also a major risk factor of suicide, one of the most common causes of death among women during the perinatal period (Chang, Berg, Saltzman, & Herndon, 2005; Nock et al., 2008). In addition to the women themselves, perinatal depression affects their children: prenatal depression is associated with developmental adversities, including increased risk of low birth weight and preterm birth (Pesonen et al., 2016). Both prenatal and postpartum depression (PPD) are associated with lower rates and shorter duration of breastfeeding (Figueiredo, Canário, & Field, 2014), less secure mother‐child attachment (Martins & Gaffan, 2000), and higher risk of child neurodevelopmental, emotional, and behavioral problems (Lahti et al., 2017; Toffol et al., 2019; Tuovinen et al., 2018; Wolford et al., 2017). While the risk of perinatal depression is modified by psychosocial (Yim, Tanner Stapleton, Guardino, Hahn‐Holbrook, & Dunkel Schetter, 2015) and hormonal (Schiller, Meltzer‐Brody, & Rubinow, 2015) factors, twin (Treloar, Martin, Bucholz, Madden, & Heath, 1999; Viktorin et al., 2016), sibling (Murphy‐Eberenz et al., 2006;Viktorin et al., 2016) and family (Forty et al., 2006; Pearson et al., 2018)studies indicate that heritable factors also play a part. Heritability estimates of perinatal depression have varied between 25% (Treloar et al., 1999)and 54% (Viktorin et al., 2016)intwinstudies,and44%(Viktorin et al., 2016) in a sibling study. Furthermore, the daughters of prenatally depressed women have over the threefold risk of depression during their own pregnancy (Pearson et al., 2018). While perinatal depression and nonperinatal major depressive disorder (MDD) may represent at least partly distinct disorders, perinatal depression at least partially shares its genetic component with MDD (Viktorin et al., 2016)and bipolar disorder (BD; Payne et al., 2008). Moreover, the genetic component may also be shared with schizophrenia (SCZ), anorexia nervosa, attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), obsessive–compulsive disorder (OCD) and Tourette syndrome, due to the partially shared heritability between MDD and these disorders (Lee et al., 2013;Leeetal.,2019). Furthermore, candidate gene studies have linked single nucleotide polymorphisms (SNPs) with the risk of these disorders. However, it is unlikely that the heritabilities of these complex disorders are assigned to individual SNPs. Indeed, a recent meta‐analysis concluded that no candidate genes or gene sets reliably predict depression phenotypes (Border et al., 2019). Apromising alternative is the polygenic risk score (PRS) approach, which exploits findings from genome‐wide association studies (GWAS) using an aggregate measure of weighted genetic variants associated with the phenotype. One study showed that PRSs for BD, but not MDD, were associated with postpartum depression (Byrne et al., 2014). However, the study used polygenic profile scoring methodology instead of association analysis and exploited summary statistics from an older GWAS. In addition, to our best knowledge, no PRS study has sufficiently accounted for the other risk factors of perinatal depression and have focused only on PPD. However, women suffering from PPD often have elevated depressive symptoms already during early pregnancy (Evans et al., 2012;Kumpulainenetal.,2018; Tuovinen et al., 2018;vander Waerden et al., 2017), indicating that PPD is often a continuation of prenatal depression. Accordingly, we investigated PRSs based on the most recent and largest GWAS on MDD (Howard et al., 2019; Wray et al., 2018), BD (Ruderfer et al., 2018; Stahl et al., 2019), SCZ (Ripke, Neale, & Spiker, 2014; Ruderfer et al., 2018) and cross‐disorder (CD; including MDD, BD, SCZ, anorexia nervosa, ADHD, ASD, OCD, and Tourette syndrome; Lee et al., 2019) (a) predict prenatal and postpartum depressive symptoms, (b) if prenatal depressive symptoms account for, and hence mediate, or (c) moderate the associations of the PRSs and postpartum depressive symptoms, and (d) if these PRSs predict fluctuating or consistently high levels of depressive symptoms throughout the perinatal period. 2|MATERIALS AND METHODS 2.1 |Participants The participants come from the Prediction and Prevention of Pre‐eclampsia and Intrauterine Growth Restriction (PREDO) study (Girchenko et al., 2017). We enrolled 1,079 pregnant women to the clinical subsample: 969 had one or more, and 110 had none of the known risk factors for pre‐eclampsia and intrauterine growth restriction. 2 | RANTALAINEN ET AL. The women were recruited on their first ultrasound screening at 12–14 gestational weeks from 10 hospitals in Southern and Eastern the 1,079 women, 997 donated blood for DNA in early pregnancy (median = 13.0; interquartile range = 12.6–13.4 weeks). Of them, 742 (74.4%) had data available on prenatal or postpartum depressive symptoms; these women formed our analytic sample. Of them, data were available on prenatal depressive symptoms for 721 (97.2%), postpartum depressive symptoms for 726 (97.8%), and both for 705 (95.0%). Compared with the rest of the clinical subsample, the women in the analytic sample more often had attained tertiary education (42.7% vs. 55.0%; χ 2 = 15.29; p< .001), but did not significantly differ otherwise. 2.2 |Ethics statement All participants signed informed consent forms. The PREDO study protocol has been approved by the ethical committee of the Helsinki and Uusimaa Hospital District, and aligns with the Declaration of Helsinki. 2.3 |Genotyping Genotyping was performed on the IlluminaGlobal Screening array (Illumina Inc, San Diego, CA) at the Erasmus MC, The Netherlands and imputed with IMPUTE 2.3.2 and Eagle v2.3 against Finnish‐ specific SISu v2 reference panel (GRCh37) comprising 2690 high‐ coverage whole‐genome and 5093 high‐coverage whole‐exome sequences, which introduces less false polymorphisms than using global reference panels (Surakka et al., 2016). Before imputation, variants with call rate <0.95, MAF > 0.35, minor allele count <19 or HWE p<1×10 −6 were excluded. Samples were excluded based on call rate <0.95, heterozygosity F< 0.1, sex mismatch, and relatedness. Population outliers were excluded based on visual inspection. There were 15,544,584 variants after imputation. 2.4 |PRS of MDD, BD, SCZ, and CD PRS for MDD (Howard et al., 2019; Wray et al., 2018; hereafter referred to as MDD2018 and MDD2019), BD (Ruderfer et al., 2018; Stahl et al., 2019; BD2018 and BD2019), SCZ (Ripke et al., 2014; Ruderfer et al., 2018; SCZ2014 and SCZ2018), and CD (Lee et al., 2019) were calculated by taking genetic variants from the imputed best guess genotypes up to varying significance thresholds from the GWAS discovery sample and applying a score from these variants, weighted by the associations in the discovery sample, to predict a trait in an independent target sample. Clumping and calculation of PRSs were performed using PRSice‐2 (Choi & O'Reilly, 2019), using LD threshold (R 2 ) 0.1 and clumping window width of 500 kilobases. Before clumping, we excluded all SNPs with INFO score < 0.90. Other filtering criteria in the PREDO sample included HWE p<1×10 −6 and missingness > 0.05. PRSs were created using p‐value thresholds 5 × 10 −8 , .001, .01, and .05. The number of SNPs in each PRS is in Table 1. The correlations between the PRSs are in Tables S1–S4. 2.5 |Depressive symptoms The women completed the Center for Epidemiologic Studies Depression Scale (CES‐D; Radloff, 1977) biweekly up to 14 times throughout the perinatal period starting from 12 0/7 –13 6/7 until 38 0/7 –39 6/7 or delivery, and twice during the postpartum period (median 2.1 weeks and 6.4 months, interquartile range 2.0–2.4 weeks and 6.1–7.3 months, respectively). The 20 CES‐D questions were rated on a scale from none (0) to all of the time (3). Higher scores indicate more depressive symptoms during the past week. 2.6 |Covariates These included age at delivery (years), family structure (cohabiting/ married vs single parent), body mass index (BMI) in early pregnancy (kg/m2) and cigarette smoking (smoked through pregnancy/quit in early pregnancy vs no) with data from the Medical Birth Register. Education (secondary, tertiary vs basic) and alcohol use (yes vs no) were reported in early pregnancy. We conducted multidimensional scaling analyses on the whole genome genotypes with Plink v0.64 to control for population stratification. Four main components depicted the population substructure. TABLE 1 Numbers of SNPs in the polygenic risk scores p‐Value threshold MDD2018 MDD2019 BD2018 BD2019 SCZ2014 SCZ2018 CD p<5×10 –8 48 66 8 13 118 80 134 p< .001 2100 2529 1263 1514 2892 2352 3251 p< .01 7614 9104 5618 6070 8804 7859 10190 p< .05 19404 23301 15730 16380 20129 18951 23562 Abbreviations: BD2018, bipolar disorder (Ruderfer et al., 2018); BD2019, bipolar disorder (Stahl et al., 2019); CD, cross‐disorder; MDD2018, major depressive disorder (Wray et al., 2018); MDD2019, major depressive disorder (Howard et al., 2019); SCZ2014, schizophrenia (Ripke et al., 2014); SCZ2018, schizophrenia (Ruderfer et al., 2018). RANTALAINEN ET AL. | 3 2.7 |Statistical methods Linear regression analyses tested the associations between the PRSs and prenatal (mean of all 14 prenatal values) and postpartum (mean of the two postpartum values) depressive symptoms. We then tested if the associations between the PRSs and postpartum depressive symptoms were accounted for by prenatal depressive symptoms, by adding prenatal depressive symptoms to the regression equations. We pursued mediation if all variables were significantly interrelated and the regression coefficient diminished after introducing the mediator into the model (Baron & Kenny, 1986). To study if prenatal depressive symptoms moderated the associations between the PRSs and postpartum depressive symptoms, we included a prenatal depressive symptoms × PRS interaction termin the regression equation together with the main effects. Finally, to study if the PRSs were associated with fluctuating or persistently high levels of prenatal and postpartum depressive symptoms, we first applied latent class analysis (LCA) to identify subgroups of women based on their depressive symptoms levels across all measurements. We compared two to six subgroups solutions using the following criteria for the optimal solution: (a) Akaike and Bayesian Information Criteria of goodness‐of‐fit, (b) at least 10% of the sample in each subgroup, (c) high classification certainty identified by posterior probabilities, (d) clinical relevance (Kongsted & Nielsen, 2017), and (e) changes in log likelihoods for increasing the number of subgroups. Using multinomial and ordinal regression analyses, we then examined if the PRSs were associated with the depressive symptoms subgroups identified as optimal. We present the associations as adjusted for the MDS and maternal age, and further for all covariates. We normalized CES‐D values with square root transformation and standardized the CES‐D scores and the PRSs (Mean = 0; SD = 1) to facilitate interpretation. Unstandardized regression coefficients and odds ratios (OR) and 95% confidence intervals (95% CI) with two‐tailed p‐values present effect sizes. We controlled for multiple testing with a false detection rate (FDR) procedure (Benjamini & Hochberg, 1995). We corrected for 5% FDR over 28 tests (seven PRSs with four p‐value thresholds), separately for prenatal and postpartum CES‐D scores and for each statistical model. We conducted the analyses using SPSS v24 and PROCESS macro v3.3 (Hayes, 2017) with a custom model builder (Frank, 2018) using 10,000 bootsrapped samples. We corrected for FDR using the p.adjust function in R. 2.8 |Data availability statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Data requests may be subject to further review by the Finnish national register authorities, and by the ethical committees. 3|RESULTS Sample characteristics are in Table 2. The CES‐D scores were correlated across all 16 measurements (Pearson's r= .40–.81; p< .001). The correlation between the means of prenatal and postpartum measurements was Pearson's r= .68 (p< .001). Of the covariates, only alcohol use and early pregnancy BMI were correlated with prenatal depressive symptoms (Pearson's r≥.09; p≥.022), and none were correlated with postpartum depressive symptoms (Table S5). 3.1 |PRS and prenatal and postpartum depressive symptoms Table 3shows that the MDD2018, MDD2019, SCZ2014, SCZ2018, and CD PRSs with p‐value threshold .01 were significantly associated with higher levels of prenatal and postpartum depressive symptoms in both statistical models adjusting for covariates, except for TABLE 2 Sample characteristics Characteristic Mean (SD) or n (%) Maternal age at delivery (years) 33.4 (5.6) Missing data 0 (0.0%) Education Basic 24 (3.2%) Secondary 299 (40.3%) Tertiary 408 (55.0%) Missing data 11 (1.5%) Family structure Married or cohabit 720 (97.0%) Single 22 (3.0%) Missing data 0 (0.0%) Early pregnancy BMI (kg/m 2 ) 27.2 (6.7) Missing data 0 (0.0%) Smoking during pregnancy No 703 (94.7%) Yes 38 (5.1%) Missing data 1 (0.1%) Alcohol use in early pregnancy No 609 (82.1%) Yes 115 (15.5%) Missing data 18 (2.4%) CES‐D Prenatal mean score 11.4 (6.6) Above clinical cutoff (≥16) 152 (20.5%) Missing data 21 (2.8%) Postpartum mean score 10.2 (7.3) Above clinical cutoff (≥16) 138 (18.6%) Missing data 16 (2.2%) Abbreviations: BMI, body mass index; CES‐D, Center of Epidemiologic Studies Depression Scale; SD, standard deviation. 4 | RANTALAINEN ET AL. TABLE 3 Associations between standardized PRSs for MDD2018, MDD2019, SCZ2014, SCZ2018, and CD and prenatal and postpartum depressive symptoms Prenatal depressive symptoms Postpartum depressive symptoms Polygenic risk score p‐Threshold Statistical model B95% CI ppFDR R 2 Polygenic risk score p‐Threshold Statistical model B95% CI ppFDR R 2 MDD2018 p<5×10 −8 Model 1 0.09 0.01, 0.17 .022 .051 .007 MDD2018 p<5×10 −8 Model 1 0.07 −0.00, 0.15 .069 .114 .005 Model 2 0.08 0.01, 0.16 .034 .073 .006 Model 2 0.07 −0.01, 0.15 .077 .127 .004 Model 3 0.02 −0.04, 0.08 .496 .634 .000 p< .001 Model 1 0.05 −0.02, 0.13 .165 .231 .003 p< .001 Model 1 0.09 0.01, 0.16 .027 .058 .007 Model 2 0.06 −0.02, 0.13 .155 .220 .003 Model 2 0.08 0.02, 0.17 .019 .047 .008 Model 3 0.05 −0.01, 0.11 .084 .319 .002 p < 0.01 Model 1 0.10 0.03, 0.18 .008 .036 .010 p< .01 Model 1 0.12 0.04, 0.20 .002 .014 .013 Model 2 0.10 0.03, 0.18 .009 .044 .009 Model 2 0.12 0.05, 0.20 .002 .014 .014 Model 3 0.05 −0.01, 0.11 .091 .319 .002 p < 0.05 Model 1 0.10 0.03, 0.18 .008 .036 .010 p< .05 Model 1 0.13 0.05, 0.20 .001 .014 .015 Model 2 0.10 0.02, 0.17 .013 .044 .008 Model 2 0.13 0.05, 0.20 .001 .014 .014 Model 3 0.05 −0.01, 0.11 .083 .319 .002 MDD2019 p<5×10 −8 Model 1 0.08 −0.00, 0.16 .058 .102 .005 MDD2019 p<5×10 −8 Model 1 0.08 −0.01, 0.16 .065 .114 .005 Model 2 0.08 −0.00, 0.16 .058 .102 .005 Model 2 0.08 −0.00, 0.17 .052 .097 .005 Model 3 0.04 −0.03, 0.10 .231 .462 .001 p< .001 Model 1 0.06 −0.01, 0.14 .099 .163 .004 p< .001 Model 1 0.09 0.02, 0.17 .018 .046 .008 Model 2 0.05 −0.02, 0.13 .157 .220 .003 Model 2 0.09 0.02, 0.17 .019 .047 .008 Model 3 0.05 −0.01, 0.11 .109 .339 .002 p< .01 Model 1 0.09 0.02, 0.17 .013 .040 .008 p< .01 Model 1 0.09 0.02, 0.17 .016 .045 .008 Model 2 0.09 0.01, 0.16 .023 .059 .007 Model 2 0.09 0.01, 0.16 .022 .047 .007 Model 3 0.01 −0.01, 0.07 .798 .828 .000 p< .05 Model 1 0.10 0.02, 0.17 .009 .036 .009 p< .05 Model 1 0.11 0.03, 0.18 .006 .028 .010 Model 2 0.09 0.02, 0.17 .016 .045 .008 Model 2 0.10 0.03, 0.80 .009 .035 .009 Model 3 0.02 −0.04, 0.08 .498 .634 .000 SCZ2014 p<5×10 −8 Model 1 0.09 −0.00, 0.18 .058 .102 .005 SCZ2014 p<5×10 −8 Model 1 0.10 0.00, 0.19 .043 .086 .006 Model 2 0.09 0.00, 0.18 .041 .077 .006 Model 2 0.10 0.01, 0.20 .030 .060 .006 Model 3 0.03 −0.04, 0.10 .385 .599 .001 p< .001 Model 1 0.12 0.04, 0.20 .004 .036 .011 p< .001 Model 1 0.11 0.03, 0.19 .008 .032 .010 Model 2 0.13 0.05, 0.21 .002 .019 .013 Model 2 0.11 0.03, 0.19 .007 .033 .010 Model 3 0.03 −0.03, 0.09 .328 .574 .001 p< .01 Model 1 0.12 0.04, 0.20 .003 .036 .012 p< .01 Model 1 0.13 0.05, 0.21 .001 .014 .014 Model 2 0.12 0.04, 0.20 .002 .019 .013 Model 2 0.13 0.05, 0.21 .002 .014 .014 Model 3 0.05 −0.01, 0.11 .130 .364 .002 (Continues) RANTALAINEN ET AL. | 5 TABLE 3 (Continued) Prenatal depressive symptoms Postpartum depressive symptoms Polygenic risk score p‐Threshold Statistical model B95% CI ppFDR R 2 Polygenic risk score p‐Threshold Statistical model B95% CI ppFDR R 2 p< .05 Model 1 0.09 0.02, 0.17 .018 .046 .008 p< .05 Model 1 0.10 0.02, 0.18 .014 .044 .008 Model 2 0.10 0.02, 0.18 .012 .044 .009 Model 2 0.10 0.02, 0.18 .014 .044 .008 Model 3 0.03 −0.03, 0.09 .288 .538 .001 SCZ2018 p<5×10 −8 Model 1 0.10 0.00, 0.19 .047 .094 .005 SCZ2018 p<5×10 −8 Model 1 0.06 −0.01, 0.16 .188 .267 .002 Model 2 0.10 0.01, 0.20 .027 .063 .007 Model 2 0.07 −0.02, 0.17 .133 .206 .003 Model 3 0.00 −0.07, 0.07 .995 .995 .000 p< .001 Model 1 0.10 0.02, 0.18 .015 .042 .008 p< .001 Model 1 0.09 0.01, 0.18 .024 .056 .007 Model 2 0.10 0.02, 0.18 .013 .044 .008 Model 2 0.10 0.02, 0.18 .020 .047 .007 Model 3 0.02 ‐‐0.04, 0.08 .528 .643 .000 p< .01 Model 1 0.12 0.04, 0.20 .003 .036 .012 p< .01 Model 1 0.11 0.02, 0.19 .012 .042 .009 Model 2 0.13 0.05, 0.21 .002 .019 .013 Model 2 0.11 0.03, 0.19 .010 .035 .009 Model 3 0.02 −0.04, 0.09 .491 .634 .000 p< .05 Model 1 0.10 0.02, 0.16 .011 .039 .009 p< .05 Model 1 0.13 0.05, 0.21 .002 .014 .013 Model 2 0.11 0.03, 0.18 .008 .044 .009 Model 2 0.13 0.05, 0.21 .002 .014 .013 Model 3 0.06 −0.00, 0.12 .064 .319 .003 CD p<5×10 −8 Model 1 0.07 −0.02, 0.16 .134 .208 .003 CD p<5×10 −8 Model 1 0.07 −0.03, 0.16 .167 .260 .003 Model 2 0.08 −0.02, 0.17 .106 .175 .004 Model 2 0.07 −0.02, 0.17 .140 .206 .003 Model 3 0.03 −0.05, 0.10 .472 .634 .000 p< .001 Model 1 0.06 −0.02, 0.13 .164 .231 .003 p< .001 Model 1 0.07 −0.00, 0.15 .062 .114 .005 Model 2 0.06 ‐0.02, 0.13 .155 .220 .003 Model 2 0.08 −0.00, 0.15 .062 .109 .005 Model 3 0.01 −0.05, 0.08 .643 .692 .000 p< .01 Model 1 0.10 0.03, 0.18 .009 .036 .009 p< .01 Model 1 0.12 0.04, 0.19 .004 .022 .011 Model 2 0.10 0.02, 0.17 .014 .044 .008 Model 2 0.11 0.04, 0.19 .004 .022 .011 Model 3 0.03 –0.03, 0.09 .358 .590 .001 p< .05 Model 1 0.09 0.01, 0.16 .026 .056 .007 p< .05 Model 1 0.05 −0.03, 0.13 .191 .267 .002 Model 2 0.08 0.00, 0.15 .041 .077 .006 Model 2 0.05 −0.03, 0.13 .186 .260 .002 Model 3 −0.02 −0.08, 0.04 .577 .673 .000 Abbreviations: BD, bipolar disorder; CD, cross‐disorder; MDD2018, major depressive disorder (Wray et al., 2018); FDR, MDD2019, major depressive disorder (Howard et al., 2019); PRS, polygenic risk score; SCZ2014, schizophrenia (Ripke et al., 2014); SCZ2018, schizophrenia (Ruderfer et al., 2018). Note:Model 1 adjusting for maternal age at delivery and population stratification. Model 2 adjusting for maternal age at delivery, education, family structure, body mass index, smoking, alcohol use, and population stratification. Model 3 adjusting for prenatal depressive symptoms, maternal age at delivery, education, family structure, body mass index, smoking, alcohol use, and population stratification. B, unstandardized regression coefficient; R 2 , the proportion of variance in depressive symptoms explained by the PRS alone. 6 | RANTALAINEN ET AL. FIGURE 1 Mediation of the association between polygenic risk scores and postpartum depressive symptoms by prenatal depressive symptoms. The association between polygenic risk scores of MDD2018 (a), MDD2019 (b), SCZ2014 (c), SCZ2018 (d), and CD (e), and postpartum depressive symptoms is mediated by prenatal depressive symptoms. Thep‐values and regression coefficients shown are for the PRSs calculated using the p‐value threshold p< .01, and adjusted for maternal age at delivery and population stratification. p‐Values for indirect effects are from Sobel's test. PRS, polygenic risk score RANTALAINEN ET AL. | 7 MDD2019, which was not associated with prenatal depressive symptoms in the fully adjusted model. The associations were similar but less consistent with the other p‐value thresholds (Table 3). The PRSs for BD were not associated with prenatal or postpartum depressive symptoms (Table S6). The proportion of variance explained by the entire statistical models varied between 1.3% and 6.2% for prenatal and 1.5–5.9% for postpartum depressive symptoms; R 2 's of the PRSs alone are in Table 3. To further characterize the polygenic basis of perinatal depressive symptoms, we conducted stepwise linear regression analyses where we first entered Model 1 covariates and then tested using forward selection which of the PRSs with p‐value threshold 0.01 improved model fit the most. The PRSs for SCZ2018 and SCZ2014 were the best predictors of prenatal and postpartum depressive symptoms, respectively, and MDD2018 significantly predicted additional variance in prenatal and postpartum depressive symptoms: for adding the second PRS, R 2 changes were .7% and 1.0%, F's for change 5.20 and 7.14, and p= .02 and p= .008, respectively. 3.2 |PRS and prenatal and postpartum depressive symptoms: Mediation and moderation No PRSs were significantly associated with postpartum depressive symptoms in models including prenatal depressive symptoms (Table 3; Table S6). As the criteria were met (Table 3and significant correlations between prenatal and postpartum depressive symptoms), we pursued mediation tests. Prenatal depressive symptoms mediated the associations between the PRSs for MDD2018, MDD2019, SCZ2014, SCZ2018, and CD and postpartum depressive symptoms (effect size proportions mediated varied between 52.2% and 88.0%; Figure 1). After accounting for mediation, the direct effects of the PRSs for MDD2018, SCZ2014, and SCZ2018 remained significant, but MDD2019 and CD did not (Figure 1). Analyses testing if prenatal depressive symptoms moderated the associations between the PRSs and postpartum depressive symptoms revealed no significant interactions (prenatal depressive symptoms × PRS interactions p≥.152; data not shown). FIGURE 1 (Continued) 8 | RANTALAINEN ET AL. 3.3 |PRS and prenatal and postpartum depressive symptoms: Fluctuating or persistently high level of symptomatology In the LCA we compared two to six subgroups solutions, and based on the five criteria identified a solution with three latent classes as optimal (Table S7). Each of the three subgroups showed high depressive symptom stability throughout the prenatal and postpartum periods (Figure 2). The groups differed in symptom severity, showing consistently low (n= 158; 21.3%), moderate (n= 397; 53.5%) and high, clinically significant (CES‐D scores ≥16; Radloff, 1977;n=187; 25.2%) levels of depressive symptoms. Figure 3shows that for each SD increase in the PRSs for MDD2018, MDD2019, SCZ2014, SCZ2018, and CD with p‐value threshold .01 and for each SD increase in PRSs for MDD2018, SCZ2018, and CD with p‐value threshold .05, the odds to belong to the group with consistently high compared with low depressive symptoms throughout the prenatal and postpartum periods were significantly higher (OR = 1.24–1.45). The PRSs for CD and SCZ2014 at 0.01 and 0.05 p‐value thresholds were also associated with significantly higher odds (OR = 1.23–1.29) to belong to the group with consistently moderate compared with low depressive symptoms (Figure 3). The odds to belong to the LCA subgroups with consistently low, moderate, and high levels of depressive symptoms increased linearly with higher values of the PRSs (Figure 3). Figure 3also shows the means of the PRSs according to the LCA subgroups. The PRSs for BD2018 and BD2019 were not significantly associated with the LCA subgroups. 4|DISCUSSION We found that the PRSs for MDD2018, MDD2019, SCZ2014, and SCZ2018 and CD, but not BD2018 or BD2019, predicted higher levels of prenatal and postpartum depressive symptoms. While all the PRSs were correlated, only MDD2018, SCZ2014, and SCZ2018 significantly predicted unique variance in depressive symptoms. The associations were significant across PRSs calculated using multiple p‐value thresholds, but they were most consistent with the PRSs containing larger numbers of SNPs. This is likely related to the genetic variance reflected in the different PRSs, as the numbers of SNPs differ widely between different p‐value thresholds (Table 1). These findings thus suggest that the genetic risk of MDD, SCZ, and CD in nonperinatal populations may generalize to depressive symptoms in perinatal women. The effect sizes that predicted prenatal and postpartum depressive symptoms were small, but of similar magnitude as those of the covariates (Table S5). Moreover, as previously reported (Evans et al., 2012; Kumpulainen et al., 2018; Tuovinen et al., 2018; van der Waerden et al., 2017), the best predictor of postpartum depressive symptomatology was prenatal symptomatology. However, the effect size estimates were much larger when we took into account the high stability of depressive symptoms throughout the perinatal period. Our LCA indicated that the PRSs for MDD2018, MDD2019, SCZ2014, SCZ2018, and CD were associated with a trajectory of consistently high depressive symptoms across the perinatal period; one SD increase in these PRSs translated to 1.24–1.45 increase in the odds to belong to the group with consistently high compared with low depressive symptoms. Thus, genetic FIGURE 2 Latent profiles of maternal prenatal and postpartum depressive symptoms. The figure shows the mean levels of depressive symptoms in the three classes derived from latent class analysis of women who showed consistently low, consistently moderate and consistently high levels of depressive symptoms throughout pregnancy and up to 12 months after delivery. Error bars represent 95% CI's RANTALAINEN ET AL. | 9