Association between maternal genome-wide polygenic scores for psychiatric and neurodevelopmental disorders and perinatal risk factors: A Danish population-based study

Association between maternal genome-wide polygenic scores for psychiatric and neurodevelopmental disorders and perinatal risk factors: A Danish population-based study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Association between maternal genome-wide polygenic scores for psychiatric and neurodevelopmental disorders and perinatal risk factors: A Danish population-based study Fenfen Ge , Yue Wang , Xiaoqin Liu , Trine Munk-Olsen , Kathrine Bang Madsen , Emil Michael Pedersen , Clara Albiñana , Esben Agerbo , Cynthia M. Bulik , Liselotte Vogdrup Petersen , Unnur A. Valdimarsdottir , Bjarni Jóhann Vilhjálmsson doi: https://doi.org/10.1101/2025.01.15.25320615 Fenfen Ge 1 Centre of Public Health Sciences, Faculty of Medicine, University of Iceland , Reykjavík, Iceland 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ffge.ncrr{at}au.dk Yue Wang 1 Centre of Public Health Sciences, Faculty of Medicine, University of Iceland , Reykjavík, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoqin Liu 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Trine Munk-Olsen 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark 3 Child and Adolescent Psychiatry Research Unit, Department of Clinical Research, University of Southern Denmark , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathrine Bang Madsen 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark 3 Child and Adolescent Psychiatry Research Unit, Department of Clinical Research, University of Southern Denmark , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emil Michael Pedersen 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clara Albiñana 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esben Agerbo 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cynthia M. Bulik 4 Department of Medical Epidemiology & Biostatistics, Karolinska Institutet , Stockholm, Sweden 5 Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, USA 6 Department of Nutrition, University of North Carolina at Chapel Hill , Chapel Hill, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liselotte Vogdrup Petersen 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Unnur A. Valdimarsdottir 1 Centre of Public Health Sciences, Faculty of Medicine, University of Iceland , Reykjavík, Iceland 4 Department of Medical Epidemiology & Biostatistics, Karolinska Institutet , Stockholm, Sweden 7 Harvard T.H. Chan School of Public Health , Boston, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bjarni Jóhann Vilhjálmsson 2 NCRR-The National Centre for Register-based Research, Department of Public Health, Aarhus University , Aarhus, Denmark 8 Bioinformatics Research Centre, Aarhus University , Aarhus, Denmark 9 Novo Nordisk Foundation Centre for Genomic Mechanisms of Disease, the Broad Institute of MIT and Harvard , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Importance Adverse perinatal outcomes are common challenges for mothers and their newborns. Epidemiological studies indicate that mothers with psychiatric and neurodevelopmental disorders are at an increased risk of adverse pregnancy and neonatal outcomes; however, the underlying mechanisms behind these associations remain inadequately understood. Objective To investigate whether perinatal risk factors are driven by maternal genetic susceptibility to multiple psychiatric and neurodevelopmental disorders. Design, setting and participants This nationwide population-based case-control study identified 14,917 primiparous mothers with available genetic information, born between 1981 and 2008, from the iPSYCH cohort, which is nested in the Danish National Registers. Exposures Eight genome-wide polygenic scores (PGS) for psychiatric and neurodevelopmental disorders in mothers—ADHD, autism spectrum disorder (ASD), schizophrenia, depression, anxiety, bipolar disorder, obsessive-compulsive disorder, and anorexia nervosa—were calculated using LDPred2. Main outcomes and measures Six pregnancy-related and four neonatal-related risk factors were obtained from the Danish Medical Birth Registry. Odds ratios (ORs) and 95% CIs were estimated, adjusted for the year of delivery and the first 10 genetic principal components. Results Of 14,917 mothers, the mean age at childbirth was 24.9 years (standard deviation [SD]=3.9). Per SD increase in the PGS for ADHD (OR=1.07 [95%CI 1.00-1.14]), anxiety (1.10 [1.03-1.18]) and depression (1.12 [1.05-1.20]) were associated with maternal smoking exceeding 10 cigarettes per day during the early pregnancy. Stronger associations were observed for the depression PGS in relation to younger age at first birth (i.e. < 20 years; 1.15 [1.07-1.23]), mandatory education (1.15 [1.11-1.19]), and non-cohabitation status during pregnancy (1.07 [1.02-1.12]), compared to other PGS. Schizophrenia PGS was associated with reduced odds of maternal obesity (0.88 [0.84-0.93]) during early pregnancy. In contrast, little evidence was found for associations between maternal PGS for psychiatric and neurodevelopmental disorders and neonatal-related risk factors. We observed comparable associations when the analyses excluded mothers with any psychiatric or neurodevelopmental disorders prior to the conception date. Conclusions and relevance High genetic loading for psychiatric and neurodevelopmental disorders may partly explain the observed phenotypic associations between maternal mental illness and perinatal risk factors, particularly pregnancy related factors, but it is less likely to account for associations with neonatal related factors. Alternative mechanisms, e.g., psychological stress and medical treatment for psychiatric and neurodevelopmental disorders, should be further explored. Introduction Perinatal risk factors, such as preterm birth, small for gestational age, and younger or advanced maternal age at childbirth are among the leading causes of short- and long-term health issues for both mother and newborn. 1 – 3 Accumulating epidemiological evidence based on large-scale prospective national health registers or nationwide cohorts has shown that women with pre-existing or active psychiatric and neurodevelopmental disorders (e.g., schizophrenia, 4 anorexia nervosa, 5 obsessive-compulsive disorder, 6 bipolar disorder, 7 depression, 7 , 8 or autism spectrum disorder 9 ) are at increased risk of a variety of adverse pregnancy behaviours (e.g., more frequent smoking during pregnancy, 4 and caesarean sections 10 ) and neonatal outcomes (e.g., preterm birth, 5 – 7 low Apgar at 5 minutes, 6 , 10 and low birthweight 7 ). A recent meta-analysis on 43,611 deliveries of women with schizophrenia and 40,948,272 controls across 11 high-income countries found that schizophrenia was associated with a substantially increased risk of very preterm delivery and stillbirth. 11 However, the underlying mechanisms driving these associations remain inadequately understood, with unmeasured confounding by shared genetic or environmental factors potentially contributing to the observed associations. Genetic factors play an important role in the development of mental illness, as shown in genome-wide associations studies (GWAS) and twin studies. 12 , 13 Specifically, estimated SNP-based heritability on the liability scale is 8.9% for depression, 14 10% for anxiety, 15 11-17% for anorexia nervosa, 16 and 24% for schizophrenia 17 with hundreds or thousands of genetic loci identified via GWAS. Polygenic scores (PGS), which combine the effect sizes of multiple genetic variants using well-established summary data, can be used to assess genetic liability of an individual to specific mental illnesses, such as schizophrenia or depression. 18 Understanding the role of genetic factors for maternal mental illness in perinatal risk factors is important for improved understanding of factors influencing adverse pregnancy and neonatal outcomes. Yet, evidence on the associations between maternal genetic liability to mental illnesses and perinatal risk factors is lacking, likely due to the scarcity of maternal genetic information or well-documented pregnancy and birth characteristics. 19 – 21 Moreover, previous studies have not had sufficient long follow-up periods to assess the pre-pregnancy mental health history of the mothers, limiting the ability to exclude potential phenotypic effects of mental illness that has already manifested and to disentangle those factors from the underlying impact of maternal genetic liability to psychiatric and neurodevelopmental disorders. To this end, leveraging individual genotyping data from the Integrative Psychiatric Research (iPSYCH) cohort and detailed pregnancy, delivery, and birth information from the Danish National Registers, we aimed to assess the associations between eight genome-wide polygenic scores for psychiatric and neurodevelopmental disorders for mother and perinatal risk factors (six pregnancy-related factors and four neonatal-related factors). Methods Date Source and study population We used data from the Integrative Psychiatric Research (iPSYCH2015) study, 22 a population-based case-cohort sample that builds upon the design of iPSYCH2012. 23 The iPSYCH2015 sample was selected from the Civil Registration System, which includes all singletons born between 1981 and 2008 who were alive and living in Denmark at one year of age, with known maternal information. A total of 93,608 individuals diagnosed with one or more major psychiatric disorders were identified through the Danish Psychiatric Central Research Register and included in the case group. The anorexia nervosa (AN; ANGI-DK) samples were include from the Anorexia Nervosa Genetics Initiative (ANGI), 24 as they were samples within the same framework as iPSYCH 2015. Moreover, a random sample of 50,615 individuals from the same birth cohort was selected to serve as a population-representative control group. DNA was extracted from blood samples collected at birth and stored as dried blood spots in the Danish Newborn Screening Biobank. 25 Detailed information about iPSYCH2015 has been described in the cohort profile. 22 Information on demographic characteristics (e.g., birth year or sex), medical diagnoses (e.g., ICD-10 code), prescription and birth information were derived by cross-linkage with Danish national registers including, Medical Birth register, 26 Psychiatric Central Research Register, 27 National Prescription Registry, 28 and Civil Registration System. 29 , 30 Detailed descriptions of registers used are shown in Supplementary table 1. In the present study, among all individuals in the iPSYCH2015, we first excluded individuals who did not pass genotypic quality control, 31 showed significant heterogeneity (≥4.5 log distance units), or had a degree of relatedness greater than second-degree (0.0884), leaving 108,628 for further analysis. We then linked data from iPSYCH2015 to the Medical Birth register and included 14,917 primiparous mothers giving birth to singletons in the analysis. To examine the direct pathway of maternal psychiatric genetic susceptibility, we further excluded mothers with any psychiatric disorder diagnosis (ICD-10 F codes) or records of antidepressant use (ATC code N06A) from mother’s birth through six months prior to conception and included 7,816 mothers for analysis ( Figure 1 ). Download figure Open in new tab Figure 1. Study profile The present study obtained approval from the Danish Scientific Ethics Committee, the Danish Health Data Authority, the Danish Data Protection Agency, and the Danish Neonatal Screening Biobank Steering Committee. Genotyping Standard GWAS quality control procedures were applied in iPSYCH2015, which involved removing SNPs with a minor allele frequency (MAF) < 0.01 and a Hardy-Weinberg equilibrium p-value < 10⁻⁶, as well as restricting the analysis to HapMap3 variants in the LD reference panel. 31 This resulted in a final dataset of 1,053,299 SNPs. Principal component analysis (PCA) was conducted, yielding 20 principal components (PCs) and genetically homogeneous individuals were defined as those with a log distance of <4.5 units from the multidimensional centre of the 20 PCs. 32 The KING-relatedness robust coefficient was estimated and used to exclude participants with a degree of relatedness greater than second-degree (kinship > 0.0884). 33 Polygenic score for psychiatric and neurodevelopmental disorders We generated polygenic scores (PGS) for ADHD, ASD, schizophrenia, depression, anxiety, bipolar disorder, obsessive-compulsive disorder (OCD), and anorexia nervosa using the GWAS summary statistics from the Psychiatric Genomics Consortium, excluding iPSYCH2015 sample, with the LDpred2 method. 34 Detailed information about summary statistics and validation information for each PGS was shown in our previous study and Supplementary Table 2. 35 The PGS were standardised to the mean and standard deviation of the study population in the analysis. The distribution of the standardized PGSs of interest are shown in Supplementary Figure 1. The PGS for mothers without pre-pregnancy diagnosed psychiatric or neurodevelopmental disorders or antidepressant use was slightly lower than that of mothers with such diagnoses or medication use. Measurements of perinatal risk factors We categorised factors during pregnancy and delivery, pertaining to the mother, as pregnancy-related risk factors, and those concerning the newborn and the time surrounding delivery as neonatal-related risk factors. Pregnancy-related factors Information on maternal smoking (categorised as non-smoker, stopping smoking, 1-10 cigarettes/day, and greater than 10 cigarettes/day) and maternal BMI during early pregnancy (categorised as <25 [underweight and normal], 25 to 30 [overweight], and ⩾30 kg/m² [obesity]) was derived from the Medical Birth Register. Age at first birth was calculated by subtracting birth date of first baby derived from Medical Birth Register from birth date of the mother derived from civil registration system, and categorised as <20, 20-24, 25-29, and 30-36 years. Information on both maternal cohabitation status (i.e., living with a partner or not) and maternal education (i.e., mandatory or above mandatory) during pregnancy was extracted from the Medical Birth Register and the civil registration system, respectively. The caesarean section was recorded in Medical Birth Register and grouped as ‘with’ or ‘without’. Neonatal-related factors Gestational age was estimated from the ultrasound scan; if unknown, 280 days were used as a replacement. 36 It was then categorised as preterm (≤36 weeks), term (36-41 weeks), or post-term (>41 weeks). 37 We first classified birth weight as 3999g. We then calculated birth weight for gestational age using sex-specific reference curves for foetal growth 38 and categorized into 90 th percentile (large for gestational age). The Apgar score at five minutes was used and categorized into two groups: 7-10 for normal and <7 for low. 39 Covariates Information on the calendar year of delivery was obtained from the Medical Birth Register and treated as a continuous variable, ranging from 1981 to 2008. Additionally, the first 10 standardized genetic principal components were adjusted to account for population substructure in the genetic data. 23 Statistical analysis Main analyses Descriptive results are reported as frequencies and percentages. We assessed the associations between eight types of PGS and perinatal risk factors, using logistic regression models for binary variables (e.g., maternal cohabitation status) or multinomial logistic regression models for categorical variables (e.g., maternal age at first birth). The results were represented as odds ratios (ORs) and 95% confidence intervals (CIs), and the effect estimates are presented per 1-standard deviation (1-SD) increase in PGS to aid interpretation. The analyses included the calendar year of delivery to account for genotyping waves and the first 10 standardized genetic principal components to adjust for population substructure. All data analyses were conducted using R-4.0 software and the statistical significance was adjusted for multiple testing using the Bonferroni correction. 40 Secondary analyses Since the population controls were randomly selected from the entire Danish population, 23 we re-ran the analyses restricted to primiparous mothers within the representative population group to obtain estimates more reflective of the national population ( Figure 1 ). We performed a negative control analysis using left-handedness PGS, as left-handedness has not been associated with any perinatal risk factors. 41 Results Of 14,917 mothers included in the present study, the mean age at first birth was 24.9 years (standard deviation, 3.9 years). Compared to the population representative group, the entire included population was more likely to have a younger maternal age at first birth (i.e., <20 years), standard education level (i.e., mandatory education), and higher rates of non-cohabitation and smoking during the early pregnancy ( Table 1 ). View this table: View inline View popup Table 1. Characteristics of study population Pregnancy-related risk factors Associations between each maternal psychiatric or neurodevelopmental PGS and pregnancy-related risk factors are shown in Figure 2 . PGS for ADHD (OR=1.07 [95%CI 1.00-1.14]), anxiety (1.10 [1.03-1.18]), and depression (1.12 [1.05-1.20]) were associated with maternal smoking during pregnancy exceeding 10 cigarettes/day. PGS for ADHD (1.05 [1.02-1.09]) and anxiety (1.07 [1.03-1.11]) were associated with standard education level during pregnancy, and PGS for schizophrenia (1.08 [1.03-1.12]) was associated with non-cohabitation status during pregnancy. Download figure Open in new tab Figure 2. Associations between maternal polygenic scores for psychiatric and neurodevelopmental disorders and pregnancy-related factors. * p < 0.05; ** p.adjust < 0.0083 In contrast, stronger associations with younger age at first birth (i.e., < 20 years; 1.15 [1.07-1.23]), standard education level (1.15 [1.11-1.19]), and non-cohabitation status during pregnancy (1.07 [1.02-1.12]) were found for the depression PGS, and the observed association for depression PGS remained significant when restricted to mothers without diagnosed pre-pregnancy mental illness. The schizophrenia PGS was associated with reduced odds of being having maternal overweight (0.89 [0.86-0.93]) and obesity (0.88 [0.84-0.93]) during early pregnancy. Similar results were observed among mothers without diagnosed pre-pregnancy mental illness, although these were not significant after correcting for multiple testing. No or weak associations were found between the PGS for ASD, bipolar disorder, OCD, and anorexia nervosa and pregnancy-related risk factors, both in the full sample of included primiparous mothers and in mothers without diagnosed pre-pregnancy mental illness. Neonatal-related risk factors Associations between each maternal psychiatric or neurodevelopmental PGS and neonatal-related outcomes are shown in Figure 3 . Except for an observed association between PGS for anxiety and preterm birth (0.91 [0.84-0.97]), low birth weight (<2500g; 0.91 [0.84-0.97]), and being small for gestational age (<10th percentile, 0.93 [0.89-0.98]), no associations were found between other maternal psychiatric or neurodevelopmental PGS and neonatal-related factors. No results survive multiple testing when restricted to mothers without pre-pregnancy mental illness. Download figure Open in new tab Figure 3. Associations between maternal polygenic scores for psychiatric and neurodevelopmental disorders and neonatal-related factors. * p < 0.05; ** p.adjust < 0.0125 Secondary analyses Largely comparable results were observed among the representative population group (Supplementary Figure 2). As expected, no statistically significant associations were observed between negative control exposure (PGS for left-handedness) and any pregnancy- or neonatal-related risk factors (Supplementary Table 3). Discussion This study examined the association between maternal genetic liability—measured by eight psychiatric and neurodevelopmental disorder-specific PGS—and a range of perinatal risk factors. The results of our study indicate a link between maternal psychiatric or neurodevelopmental PGS and pregnancy related risk factors, including maternal smoking during pregnancy, younger age at first birth, non-cohabitation during pregnancy, standard education level, and a reduced likelihood of being overweight in early pregnancy. Moreover, we found that part of this association, particularly for depression PGS, persisted even when the analysis was restricted to mothers without diagnosed pre-pregnancy mental illness. In contrast, our findings do not support a link between maternal psychiatric or neurodevelopmental PGS and neonatal-related risk factors. Our maternal smoking results aligned with previous findings from the Norwegian Mother, Father, and Child Cohort Study (MoBa), 20 the Avon Longitudinal Study of Parents and Children (ALSPAC), 42 and the Nurses’ Health Study 2 (NHS2). 19 We extended prior epidemiological studies 43 , 44 and found that mothers with a high genetic risk for the ADHD, anxiety, and depression were more likely to smoke heavily during pregnancy. However, no association was noted between schizophrenia PGS and maternal smoking, which was inconsistent with findings from a previous study, suggesting that this discrepancy may be attributed to differences in the definition of the outcome. 19 Our results further suggest that maternal PGS, particularly for depression, is associated with a younger age at first birth, standard education level, and non-cohabitation during pregnancy. Expanding upon previous studies using both linkage disequilibrium score regression (LDSC) 45 and Mendelian randomization (MR) analyses, 46 we found that the associations persisted even among mothers with no diagnosed pre-pregnancy mental illness, suggesting a direct effect of maternal genetic liability to psychiatric disorders. For example, one study suggested associations of age at first birth and depression using univariable MR, 47 although another study did not totally support these findings based on multivariable MR when considering age at first sexual intercourse, first birth, last birth, and age at menopause. 48 Compared to methods that utilise summary data from GWAS (e.g., LDSC or MR) to calculate genetic correlations, associations derived from PGS offer specific indices relevant to aetiology, potentially improving the utility of PGS testing in clinical settings. 49 Both the negative associations between schizophrenia PGS and maternal overweight/obesity in early pregnancy and the absence of associations between other types of genetic risk for psychiatric and neurodevelopmental disorders (i.e. ADHD, ASD, anxiety, depression, bipolar disorder, anorexia nervosa, and OCD) and overweight/obesity in our study are novel. Indeed, mounting evidence suggest higher rates of overweight/obesity in individuals with mental illnesses, such as schizophrenia, bipolar disorder, and depression. 50 However, the findings of our study, and the Zhang et al. study, 49 based on UK Biobank data of 406, 929 individuals without a schizophrenia diagnosis, do not support the positive genetic associations between schizophrenia PGS and overweight or obesity. Psychiatric disorders are commonly recognized as chronic or recurrent conditions, often requiring maintenance therapy involving mood stabilisers, anxiolytics, antidepressants, or antipsychotics, all of which can cause weight gain by influencing appetite control or energy metabolism. 51 Thus, the effects of psychiatric medication might partly explain the aforementioned phenotypic association. Consistent with previous findings from ALSPAC 42 and NHS2 19 , we found little evidence of an association between maternal PGS for psychiatric or neurodevelopmental disorders and neonatal related risk factors, including birth weight and gestational age. Similarly, the study using data from the ALSPAC 42 suggested that neither maternal nor child neurodevelopmental genetic liability (i.e., ADHD, and ASD) were associated with low birth weight, preterm delivery, or low Apgar score at 5 minutes. Instead of maternal genetic liability to these disorders, factors such as psychotropic medication and psychological stress may explain the discrepancy between observed phenotypic associations and absent genetic associations. For example, several previous meta-analyses have found that the use of antidepressants during pregnancy is associated with an increased risk of preterm birth and low birth weight, regardless of whether the comparison group consists of all unexposed mothers or only depressed mothers without antidepressants. 52 – 54 Moreover, a meta-analysis that included 23 studies found that women with depression who were not receiving treatment for their depression had significantly increased infant risks of preterm birth and low birth weight compared to pregnant women without depression. 55 Thus, these findings suggest that medication may not be the only contributing factor, psychological stress, such as guilt or stigma associated with mental illness, 56 may mediate these associations. Strengths and Limitations To the best of our knowledge, this is the first study to comprehensively explore the associations between eight types of psychiatric and neurodevelopmental PGS and well-documented perinatal risk factors among primiparous mothers. Another major strength of our study is the restriction of analyses to mothers without any pre-existing mental illnesses, which allows for direct exploration of whether associations between maternal mental illnesses and pregnancy and neonatal-related risk factors are driven by genetic liability. There are several limitations that need to be considered. First, the iPSYCH2015 cohort includes individuals born between 1981 and 2008, with the Medical Birth Register updated until 2017. As a result, our study primarily captures relatively young mothers (average: aged 24.9 years), which may lead to systematic differences in the associations between genetic risk for mental illness and the outcomes tested. Some mother may not yet have traversed the age of risk for developing some of the disorders studied. Second, despite the large sample size, our study lacks sufficient power to investigate pregnancy-related conditions such as gestational hypertension and diabetes, given the predominance of younger mothers in the cohort. Thirdly, maternal smoking during the pregnancy was self-reported in our study, and we used a detailed classification based on the number of cigarettes smoked per day. While this provides more detailed information, it may introduce information bias compared to a simpler ‘Yes’ or ‘No’ classification. Additionally, it should be noted that, in our study, the maternal BMI in early pregnancy was assessed at early stage of pregnancy and nearly 11% of the data is missing. Finally, while our study identifies associations between PGS and pregnancy-related risk factors, it does not infer causal relationships between maternal genetic liability and these factors. Future research is needed to determine the causal direction of the relationship between them. Conclusions Within a large cohort covering a total of 14,917 primiparous mothers, we found that genetic risk may partly account for previously identified associations between maternal psychiatric and neurodevelopmental disorders and pregnancy-related risk factors, even in the absence of diagnosed disorders. In contrast, we found the associations between maternal mental illness and neonatal-related risk factors are less likely driven by the maternal genetic liability to psychiatric or neurodevelopmental disorders. Future studies could replicate our findings in larger, well-powered datasets and incorporate explorations of additional mechanisms, such as psychological stress or psychopharmacological treatment on maternal and neonatal outcomes. Data Availability Data availability is limited due to the sensitive nature. For more information please contact the authors. Acknowledgements The authors gratefully acknowledge the Psychiatric Genomics Consortium (PGC) and the research participants and employees of 23andMe, Inc., for providing the summary statistics used to generate the polygenic score. Footnotes Funding: This work was supported by NordForsk to UAV (PreciMent 164218). BJV is supported by the Lundbeck Foundation (R335-2019-2339, R335-2024-1234) and the Independent Research Fund Denmark (2034-00241B). CMB is supported by the National Institute of Mental Health (R01MH136149; R01MH134039; R56MH129437; R01MH120170; R01MH124871; R01MH119084; R01MH118278; R01MH124871). The Anorexia Nervosa Genetics Initiative (ANGI) was an initiative of the Klarman Family Foundation, and additional anorexia nervosa genotyping was supported by the Lundbeck Foundation (grant no. R276-2018-4581). The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724, and R248-2017-2003). 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