Genetic Architecture of Placental Efficiency for Term Infants: Evidence from Monoaminergic Pathways and Placental Tissue Expression in the Norwegian Mother, Father and Child Cohort Study (MoBa)

Genetic Architecture of Placental Efficiency for Term Infants: Evidence from Monoaminergic Pathways and Placental Tissue Expression in the Norwegian Mother, Father and Child Cohort Study (MoBa) | 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 Genetic Architecture of Placental Efficiency for Term Infants: Evidence from Monoaminergic Pathways and Placental Tissue Expression in the Norwegian Mother, Father and Child Cohort Study (MoBa) View ORCID Profile Jonas Østerhaug Andersen , View ORCID Profile Stener Nerland , Piotr Pawel Jaholkowski , Gianluca Ursini , Srdjan Djurovic , Anne Cathrine Staff , Anders Dale , View ORCID Profile Ole A. Andreassen , Ingrid Agartz , Alexey Shadrin , Laura A. Wortinger doi: https://doi.org/10.1101/2025.10.08.25337553 Jonas Østerhaug Andersen 1 Department of Adult Psychiatry, Division for Mental Health and Substance Abuse, Diakonhjemmet Hospital , Oslo, Norway 2 Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonas Østerhaug Andersen For correspondence: jonasoan{at}uio.no l.a.w.bakke{at}medisin.uio.no Stener Nerland 1 Department of Adult Psychiatry, Division for Mental Health and Substance Abuse, Diakonhjemmet Hospital , Oslo, Norway 2 Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stener Nerland Piotr Pawel Jaholkowski 3 Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gianluca Ursini 4 Lieber Institute for Brain Development, Johns Hopkins University School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Srdjan Djurovic 3 Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 5 Department of Medical Genetics, Oslo University Hospital and University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne Cathrine Staff 6 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo , Oslo, Norway 7 Division of Obstetrics and Gynaecology, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anders Dale 8 Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine , La Jolla, CA, USA 9 Department of Radiology, University of California, San Diego School of Medicine , La Jolla, CA, USA 10 Department of Cognitive Science, University of California , San Diego, La Jolla, CA, USA 11 Department of Neurosciences, University of California, San Diego School of Medicine , La Jolla, CA, USA 12 Department of Psychiatry, University of California, San Diego School of Medicine , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ole A. Andreassen 3 Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 13 KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ole A. Andreassen Ingrid Agartz 1 Department of Adult Psychiatry, Division for Mental Health and Substance Abuse, Diakonhjemmet Hospital , Oslo, Norway 3 Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 14 Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences , Stockholm Region, Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexey Shadrin 3 Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 13 KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura A. Wortinger 1 Department of Adult Psychiatry, Division for Mental Health and Substance Abuse, Diakonhjemmet Hospital , Oslo, Norway 2 Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo , Oslo, Norway 15 Department of Psychology, Oslo New University College , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: jonasoan{at}uio.no l.a.w.bakke{at}medisin.uio.no Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract The placenta plays a central role in supporting fetal growth. Placental efficiency (PlE) defined as the birthweight-to-placental weight ratio proves to be a key measure of its capacity to adapt to the fetal developmental demands. Although the genetic architecture of birthweight (BW) and placental weight (PW) have been explored, the biology underlying PlE remains largely unknown. Here, we report the first genome-wide association study (GWAS) of PlE in 63,894 at term singleton births from the Norwegian Mother, Father and Child cohort (MoBa), complemented by maternal (N = 60,472) and paternal (N = 40,116) analyses. Across offspring and maternal genomes, we identified multiple genome-wide significant loci, with TSNAX-DISC1 consistently implicated across analyses. Comparative genetic analyses revealed strong overlap between PlE and PW, but minimal overlap with BW, suggesting that PlE captures distinct aspects of placental adaptation beyond overall growth. Gene-set enrichment highlighted significant involvement of monoaminergic pathways, particularly norepinephrine uptake and transport, while tissue-specific analyses demonstrated strong enrichment in placental tissue. Notably, mapped genes including SLC6A2, SLC22A2, and SLC22A3 link PlE to regulation of monoamine signaling, aligning with the placenta’s potential role in neurodevelopmental vulnerability. Together, these findings establish PlE as a genetically distinct phenotype, provide insight into the biology of placental adaptation, and suggest shared genetic pathways connecting placental function and offspring neurodevelopment. Introduction The human placenta is a transient organ that forms the critical interface between mother and fetus. Beyond mediating the exchange of oxygen, nutrients, and waste, it supports maternal physiology and fetal growth ( 1 - 4 ). As a highly dynamic structure, the placenta adapts its morphology and nutrient transport capacity in response to both normal and adverse gestational environments, thereby influencing fetal development and shaping long-term health outcomes ( 5 ). Placental dysfunction has been associated with obstetric complications such as fetal growth restriction ( 6 ), preeclampsia ( 7 , 8 ), spontaneous preterm birth ( 9 , 10 ), and adverse outcomes for the developing child such as altered neurodevelopment ( 11 , 12 ) and later somatic health risks ( 2 ). Placental efficiency (PlE) is defined as the ratio of birth weight (BW) to placenta weight (PW) ( 13 ). The BW:PW ratio acts as a proxy for placental structural adaptation in response to fetal development demands ( 4 , 13 ). Abnormal PLE has been associated with increased risks of adverse outcomes, including fetal growth restriction ( 14 ), fetal death ( 15 , 16 ), and neurodevelopmental delay in childhood ( 17 ). These findings suggest that the BW:PW ratio is not only a descriptive metric but also a meaningful indicator of placental adaptation to intrauterine conditions ( 13 ). As such, PLE provides valuable insights into fetal health at birth and developmental consequences ( 18 ). While the genetic architecture of both PW ( 19 ) and BW ( 20 ) has been studied in the past, no such GWAS has been performed on PLE as measured through the BW:PW ratio. Consequently, little is known about the genetic architecture of PLE itself, and whether it shares or diverges from the biology underlying placental and fetal growth. To investigate the biological basis of placental efficiency, we conducted GWAS of the PLE ratio in term, singleton pregnancies from the Norwegian Mother, Father and Child cohort (MoBa). Analyses were performed in offspring, maternal, and paternal genotype data, with secondary sex-stratified analyses in the offspring. For comparison, we also performed GWAS of PW and BW using the same covariates. These analyses enabled us to assess the genetic architecture of placental efficiency, quantify its overlap with related growth traits, and identify loci and pathways contributing to fetal development. Results We analyzed data from the Norwegian Mother, Father and Child cohort (MoBa ( 21 )). The offspring GWAS included 63,894 individuals (N male = 32,711, N female = 31,183), all born at term (38–43 weeks gestational age) from singleton pregnancies. Mean gestational age was 40 weeks (SD = 1.1). Placental weights ranged from 200 to 1500 g (median = 3680g), and birth weights from 1340g to 6300g (median = 3680g). Mothers (N = 60,472) and fathers (N = 40,116) had a mean age of 29.9 (SD = 4.6) and 32.4 (SD = 5.3) years at birth respectively. PlE was defined as the ratio of birth weight to placental weight measured in grams. For comparison, GWAS were also performed on PW and BW in the offspring. Cohort characteristics are summarized in Supplementary Table 1.1 . Genome-wide associations All GWAS analyses were performed using REGENIE (v4.1) ( 22 ) with inverse-rank normalized phenotypes, adjusting for sex, offspring gestational age, and the first 10 genetic principal components. For sex stratified offspring GWAS, sex was removed as a covariate. Across the offspring, maternal, and paternal samples, we identified multiple genome-wide significant loci associated with PlE. Gene mapping was performed through positional mapping and gene significance was assessed through Benjamini-Hochberg false discovery rate (FDR) correction of p-values acquired through MAGMA gene analysis. Offspring GWAS In the offspring GWAS of PlE (Manhattan plot of total offspring PlE illustrated in Figure 1 and QQ plot shown in Supplementary figure 1A ) we identified 16 genomic risk loci, comprising 20 lead SNPs and 46 independent significant. Based on positional and functional mapping, 26 genes were mapped to the identified risk loci, of which 19 were significant after FDR correction for multiple testing (p < .05). Download figure Open in new tab Figure 1. Annotated Manhattan plot. Manhattan plot with annotated genes, and most significant SNP mapped for the gene. Sex Stratified GWAS To evaluate differences between female and male offspring we performed a sex stratified GWAS. In females, 5 loci were detected, represented by 6 lead SNPs and 12 independent significant variants mapping to 8 genes of which 6 survived FDR correction (p < .05). In males, 3 loci were identified, with 4 lead SNPs, 6 independent significant variants, corresponding to 6 mapped genes of which 2 survived FDR correction (p < .05). Maternal and Paternal GWAS The maternal GWAS of placental efficiency identified 4 loci, including 4 lead SNPs, 5 independent significant variants mapping to 7 genes of which 3 survived FDR correction (p < .05). No significant GWAS was observed for the paternal sample. Shared and Unique Gene Associations Across PlE GWAS To integrate findings across GWAS, we next examined overlapping genes between analyses. Overlapping genetic variants across all GWAS are reported in Table 1 . TSNAX-DISC1 emerged as a mapped and FDR-significant gene in all PlE GWAS. TSNAX was present in both the total offspring and sex-stratified offspring GWAS, while EPAS1, EXOC8, SPRTN , and TBX20 were shared between the total offspring and female-specific GWAS. By contrast, the maternal GWAS yielded two mapped significant genes, MYNN and ACTRT3 , only present in the maternal GWAS. View this table: View inline View popup Download powerpoint Table 1. Placenta Weight and Birth Weight GWAS To facilitate comparison with PlE, we performed GWAS of placental weight (PW) and birth weight (BW) in the fetal sample using the same covariates. In the GWAS of placenta weight, we identified 37 genomic risk loci, represented by 44 lead SNPs and 100 independent significant variants. Gene mapping implicated 68 genes of which 19 remained significant after FDR correction (p < .05). For birth weight, the analysis revealed 43 loci with 50 lead SNPs and 110 independent significant variants leading to the mapping of 137 genes of which 96 remained significant after FDR correction (p < .05). Gene-set enrichment analysis of PlE MAGMA gene-set enrichment analyses identified 9 significant pathways for the fetal sample and 1 significant functional pathway for the male offspring sample, after FDR correction ( Table 2 ). The top strongest enrichments were observed for the fetal sample was GOBP_NOREPINEPHRINE_UPTAKE (p < .001), GOCC_CHROMATIN (p < .05), and GOCC_CHROMOSOME (p < .05) functional gene sets. The GOBP_NOREPINEPHRINE_UPTAKE set was also the single significant gene set for the male offspring sample (β = 1.7, p < .05). Of the nine fetal pathways, five were related to norepinephrine or other monoaminergic functions. Within these, the genes SLC6A2, SLC22A2 , and SLC22A3 appeared in three pathways and were also mapped and significant genes. No significant pathways were detected for the female offspring or maternal samples. For all functional gene set analyses see Supplementary Table 2.1-2.5 . View this table: View inline View popup Download powerpoint Table 2. MAGMA Gene set analysis Gene property analysis for tissue expression To assess tissue specific gene expression, we performed gene property analysis through MAGMA using all PlE GWAS as well as BW and PW GWAS. Gene property analysis for tissue expression in PlE revealed significant tissue expression for the total offspring ( Figure 2A ), male offspring, and maternal sample (see figure X). The total offspring sample exhibited significant tissue expressions in the placenta ( p = 9.8304 × 10 -5 ), bladder ( p = 1.5785 × 10 -4 ), esophagus gastroesophageal junction ( p = 3.9353 × 10 -4 ), esophagus muscularis ( p = 4.9185 × 10 -4 ), and the uterus ( p = 5.6648 × 10 -4 ). The male offspring only revealed a significant expression in esophagus muscularis ( p = 0.0009), while the maternal sample exhibited significant expression in both the placenta ( p = .0003) and the fallopian tubes ( p = .0007). No significant gene expression was discovered in either PW- ( Figure 2B ) or BW-GWAS ( Figure 2C ). Download figure Open in new tab Figure 2: MAGMA tissue enrichment analysis. MAGMA gene property analysis of tissue expression was performed on (A) Placental Efficiency GWAS revealing significant expression (red) in the placenta (highlighted in red), bladder, esophagus gastrointestinal junction, esophagus muscularis, and the uterus. No significant tissue enrichment was discovered in (B) Placental Weight GWAS or (C) birth weight GWAS. Heritability and Genetic Correlations Using LD score regression (LDSC), we estimated SNP-heritability for PlE, PW, and BW. Heritability was 0.109 (SE = 0.012) for PlE, 0.145 (SE = 0.014) for PW, and 0.187 (SE = 0.016) for BW. Genetic correlation analyses showed a strong, statistically significant negative correlation between PlE and PW (rg = –0.78, SE = .03, p < 0.001), and a modest but statistically significant negative correlation between PlE and BW (rg = –0.14, SE = .06, p < 0.05). Comparison of loci and genes identified in PlE, PW and BW GWAS To further characterize shared architecture, we compared lead SNPs and genomic risk loci across PlE, PW and BW offspring GWAS using Jaccard Index and Szymkiewicz–Simpson overlap coefficients. We observed no shared genome-wide significant SNPs between PlE and BW (JI = 0, overlap = 0), whereas PlE and PW shared six lead SNPs (JI = 0.10, overlap = 0.30). At the locus level, we observed a JI = 0.387, indicating that 38.7% of all base pairs were shared between PlE and PW, and an overlap coefficient of 0.709, indicating that 70.9% of PlE locus base pairs were contained within PW loci. In contrast, the JI for PlE and BW was 0.038, meaning that only 3.8% of base pairs overlapped, with an overlap coefficient of 0.102, showing that 10.2% of PlE base pairs were contained within BW loci. Locus-level comparisons further showed that 87.5% of PlE loci contained PW SNPs, while 58.3% of PW loci contained PlE SNPs, corresponding to an average locus-level overlap of 72.9%. To assess overlap at the gene level we performed FDR correction of raw MAGMA p-values and assessed the overlap. At the gene level, we compared FDR-significant genes identified through MAGMA analysis, which revealed 44 overlapping genes between PlE (n = 51 unique genes) and PW (n = 215 unique genes; See Supplementary Figure 12 ). Taken together, these results suggest that while PlE and PW share substantial global genetic architecture, the primary signals driving each trait, particularly the lead SNPs, remain largely distinct. Discussion We present the first GWAS of PlE, a trait defined by the ratio of BW to PW. Across offspring, maternal, and paternal genomes, we identified multiple loci associated with PlE, with distinct contributions from offspring and maternal genotypes but no significant paternal associations. Gene-based analyses revealed several significant genes, including TSNAX-DISC1 , which was consistently implicated across all PlE GWAS. Comparative analyses showed that PlE shares substantial genetic architecture with PW, but largely diverges from BW, suggesting that the genetic architecture of PlE is only partially aligned with fetal growth. Functional pathway analyses further highlighted a strong enrichment for norepinephrine and monoaminergic signaling, supported by convergent evidence from mapped and significant genes such as SLC6A2, SLC22A2 , and SLC22A3 . We also demonstrated significant placental expression of PlE genes, which was not the case for PW or BW GWAS. One consistent finding across our PlE GWAS was the involvement of TSNAX-DISC1 , a read-through transcript formed when TSNAX transcription continues into DISC1 . Importantly, in our analyses, TSNAX was mapped and significant both in the total offspring GWAS and in sex-stratified GWAS, while DISC1 showed significance only in the total offspring sample. The TSNAX gene encodes for the protein Translin-associated factor X which binds to the translin protein and has been implicated in DNA repair functions and neural plasticity ( 23 ). DISC1 has also been associated with several functions such as coordination of intracellular trafficking which in turn affects neuronal development and connectivity ( 24 ), dopamine regulation ( 25 ), and synaptic plasticity ( 26 ). Prior studies have implicated this region in psychiatric risk: the TSNAX-DISC1 locus shows association with disorders such as schizophrenia, bipolar disorder ( 27 ), and major depressive disorder ( 28 - 31 ). Notably both TSNAX and DISC1 have been shown to be expressed in the placenta ( 32 , 33 ) and recent work has further identified TSNAX–DISC1 as a chimeric transcript that promotes endometrial carcinoma progression ( 34 ). The convergence of placental expression, neuropsychiatric association, and cancer-related dysregulation supports the hypothesis that regulation at the TSNAX-DISC1 locus could act as a developmental link between placental inefficiency and offspring vulnerability to psychiatric disorders. We observed a strong negative genetic correlation between PW and PlE, alongside substantial overlap of significant genes and loci, suggesting that these traits share a broad underlying genetic architecture. In contrast, the weaker correlation between PlE and BW points to a more limited genetic relationship with fetal growth. Large-scale BW GWAS have shown that BW is shaped by both direct fetal genetic effects and indirect maternal genetic effects, primarily through pathways related to fetal insulin secretion, maternal glucose, maternal height, and blood pressure ( 20 ). These findings indicate that BW reflects a combination of maternal and fetal growth regulation, driven by traits such as maternal glucose and blood pressure and fetal insulin secretion. The limited genetic overlap between BW and PlE suggests that PlE is influenced by mechanisms not central to BW genetic signals, but instead by genetic contributions to placental adaptation. This interpretation is consistent with the shared global genetic architecture between PlE and PW, which may reflect a connection to placental hypertrophy. Importantly, despite the global overlap between PW and PlE, the lead SNPs driving PlE associations were largely unique, implying that PlE captures distinct, more specialized aspects of placental function not fully represented in PW. This distinction aligns with recent GWAS of PW ( 19 ), which revealed a heterogeneous architecture with predominant fetal contributions but additional maternal and paternal influences, spanning pathways in placental development, nutrient and antibody transport, immune function, and metabolism, and showing partial overlap with BW signals. While PW reflects diverse and partly nonspecific influences on placental mass, PlE appears to isolate genetic signals directly tied to placental adaptation making it a more targeted phenotype for studying placental biology and its downstream consequences for offspring development. The offspring GWAS revealed 9 significant functional gene sets, where one gene set was shared with the male offspring. No other GWAS revealed functional gene sets. Of these 9 gene sets 5 were associated with norepinephrine and monoamine functions. Within these functional groups three of them contained the SLC6A2, SLC22A2 , and SLC22A3 genes, which were mapped and significant in the total offspring GWAS. SLC6A2 codes for the norepinephrine (NE) transporter NET ( 35 ), which mainly transports NE through cellular membranes, but has also been shown to be permeable to dopamine and serotonin ( 36 ). This is also the case for SLC22A2 and SLC22A3 which codes for the OCT2 and OCT3 proteins respectively. OCT3 has been shown to regulate norepinephrine, serotonin, and dopamine ( 36 - 39 ), while OCT2 has been shown to mainly be permeable towards epinephrine, but exhibits an affinity towards dopamine and norepinephrine though to a lesser extent ( 40 , 41 ). Our findings are consistent with the discovery of the maternal facing NET and fetal facing OCT3 transporters in the syncytiotrophoblast, regulating monoaminergic concentrations in both fetal and maternal circulations ( 36 ). Gene property analysis for tissue expression revealed significant expression in the placenta, bladder, esophagus, and uterus for the total offspring sample, while maintaining expression in the male offspring sample. We also discovered significant gene expression in the fallopian tubes for the maternal GWAS. Placental enrichment aligns with the central role of placental biology in PlE, while uterine and fallopian tube signals in the maternal genome underscore the contribution of maternal reproductive tissues. This is consistent with previous observations of TSNAX-DISK1 being implicated in development of endometrial carcinomas ( 34 ) and observation of SLC6A2 and SLC22A3 expression in the syncytiotrophoblast of the placenta ( 36 ). The absence of significant signals in PW and BW GWAS further suggests that these expression patterns are either unique to PlE or offers a more specific genetic structure, supporting a distinct biological profile despite shared global architecture with related PW traits. Our PlE GWAS findings suggest important developmental and implications by associating placental regulation of monoaminergic pathways in offspring neurodevelopment. The enrichment of norepinephrine-related gene sets, alongside signals in genes such as TSNAX-DISC1 , points to a potential mechanism through which placental adaptation may influence vulnerability to neuropsychiatric disorders. This aligns with emerging literature linking placental genetic risk to schizophrenia ( 42 , 43 ) and with studies showing altered DNA methylation in placental tissue ( 44 ), as well as prior reports of neurodevelopmental alterations associated with abnormal PlE ratios ( 17 ). Expression studies further support this interpretation. NET and OCT3 expression are significantly reduced in pre-eclamptic placentae, suggesting that impaired monoamine clearance may contribute to elevated circulating norepinephrine, placental vasoconstriction, and reduced fetal perfusion ( 45 ). Furthermore, monoamine transporters from multiple families have been identified in endometrium, decidua, and placenta, where they regulate extracellular monoamine concentrations essential for implantation, placentation, and fetal development ( 46 ). Given the placenta’s role in synthesizing and regulating monoamines ( 36 , 47 ), our results suggest a dual function whereby the placenta supports its own health ( 45 , 46 ) while also influencing neuronal migration, proliferation, and differentiation ( 48 , 49 ), highlighting a direct genetic connection between placental monoamine function and offspring neural development. Together, these findings suggest that common genetic pathways may underlie both proper placental function and neuronal development in the offspring ( 12 , 45 - 47 ). By highlighting this shared biology, our results provide new insight into how genetic disruptions of placental processes can contribute to long-term neurodevelopmental risk, reinforcing the placenta’s role as a critical mediator of maternal and offspring health outcomes. Materials and Methods Participants and phenotype definition Our sample consists of a subset of the Norwegian Mother, Father and Child cohort study ( https://www.fhi.no/en/ch/studies/moba/ ). MoBa is a population-based cohort study conducted by the Norwegian Institute of Public Health, recruiting mothers, fathers, and their children. The Norwegian Mother, Father and Child Cohort Study (MoBa) is a population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health. Participants were recruited from all over Norway from 1999-2008. The women consented to participation in 41% of the pregnancies. The cohort includes approximately 114.500 children, 95.200 mothers and 75.200 fathers. MoBa is regulated by the Norwegian Health Registry Act. The current study was approved by The Regional Committees for Medical and Health Research Ethics (2016/1226). Pregnancy related factors were acquired through the Medical Birth Registry of Norway (MBRN). The MBRN is a national health registry containing information about all births in Norway. We analyzed data from single birth, at term births (38 weeks – 43 weeks gestational age), and placental weights ranging from 200g-1500g. GWAS were performed on offspring (N = 63,894, N male = 32,711, N female = 31,183), maternal (N = 60,472), and paternal (N = 40,116) samples, all white-European heritage. Placental efficiency was computed as the birth weight-placenta weight (BW:PW) ratio. GWAS was also performed on birth weight and placental weight for the fetal sample, for the purpose of genetic correlation analysis. Genome-wide association analysis Genotyping and QC Blood samples were collected from mothers and fathers around the 17th week of gestation, and additional samples were obtained from the mother postpartum and from the child’s umbilical cord at birth. DNA was extracted at the Norwegian Institute of Public Health using standard procedures ( https://www.fhi.no/en/publ/2012/protocols-for-moba/ ). Genotyping was conducted across several research projects over multiple years, resulting in 238,001 samples processed across 26 technical batches using different arrays and genotyping centers. Some individuals were genotyped more than once for quality control or due to overlapping projects (for details see ( 50 )). Genotype quality control and imputation were conducted using the standardized MoBaPsychGen pipeline v.1( 50 ). The MoBaPsychGen pipeline is a 9-module pipeline, accounting for complex relatedness structures. Variants with low imputation quality were excluded resulting in 6,981,748 high-quality common autosomal variants from 207,569 unique individuals ( 50 ). GWAS Genome-wide association analyses were performed using REGENIE (v4.1; ( 22 )), a two-step whole-genome regression method. In Step 1, we used ∼500,000 variants that were either genotyped or imputed with very high quality. Variants were filtered for minor allele frequency (MAF) ≥ 1%, genotype missingness ≤ 10%, and Hardy–Weinberg equilibrium p ≥ 1 × 10 −15 . This step was used to fit the prediction model for phenotypes while accounting for relatedness and population structure. In Step 2, we tested all imputed variants passing the following filters: minor allele count (MAC) ≥ 20 and imputation INFO score ≥ 0.8. Association analyses were conducted using linear regression adjusted for sex, gestational age, and the first 10 genetic principal components. All phenotypes were inverse-rank normalized prior to analysis. Genomic Risk Loci and Lead SNPs Genomic risk loci were identified using the SNP2GENE function in Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) v1.5.2 ( 51 ). Independent significant SNPs were defined as those with a P-value < 5 × 10 −8 and in low linkage disequilibrium (r 2 < 0.6) with one another. Among these, lead SNPs were identified as a subset with r 2 < 0.1. Genomic risk loci located within 250 kilobases of each other were merged and considered a single locus. Functional mapping and annotation Functional mapping and annotation were conducted using FUMA. Independent significant SNPs (p < 5 × 10 −8 ) and their LD-expanded candidate SNPs (r 2 ≥ 0.6, based on the 1000 Genomes Phase 3 European reference panel, EUR population) were annotated using ANNOVAR (2017-07-17) with Ensembl build v102 gene definitions. Functional scores were assigned to candidate SNPs using Combined Annotation Dependent Depletion (CADD), RegulomeDB (RDB), and 15-core chromatin state annotations from the Roadmap Epigenomics Project. SNPs were mapped to protein-coding genes using positional mapping, defined as ±10 kb from the transcription start site of each gene. Genetic correlation analysis To assess genetic similarity between placental efficiency, birth weight, and placenta weight we performed genetic correlation analysis using linkage disequilibrium score regression (LDSC). Top hit SNP and loci comparison For further analysis of genetic similarity between PlE, BW, and PW, we compared the genetic structure through assessing similarity in lead SNPS. To evaluate overlapping lead SNPs and genomic risk loci we computed Jaccard index, and the Szymkiewicz–Simpson overlap coefficient, For this purpose, each genomic risk locus was defined by its base-pair span (start to end coordinates). The indices were calculated based on the total number of overlapping base pairs across loci. To further assess overlap between loci across GWASs, we represented each locus by its start and end coordinates as defined by FUMA. For each PlE locus, we examined whether at least one genome-wide significant SNP (p < 5 × 10 −8 ) from the PW GWAS fell within the locus boundaries. Loci containing ≥1 significant SNP from the second GWAS were counted as shared; otherwise, they were considered unique to PlE. Since all GWASs were performed in MoBa using nearly identical SNP sets, locus boundaries were approximated using the set of candidate SNPs reported by FUMA. Each locus contributed equally ( w = 1) to the final proportion of shared versus unique loci. Genetic overlap was also assessed descriptively and visualized through venn-diagrams, where we visually inspected the intersection of FDR significant genes between PlE and PW. FDR was performed on genes based on raw p-values obtained through MAGMA gene analysis (described below). MAGMA analysis Using Multi-marker Analysis of GenoMic Annotation (MAGMA) v1.08 ( 52 ) we performed gene analysis, gene-set analysis and gene-property analysis for tissue specificity, where both gene analysis and gene-set analysis were performed using FUMA v1.5.2. Gene-property analysis for tissue specificity was performed locally. MAGMA gene analysis GWAS summary statistics were input into MAGMA with SNP-level p-values. SNPs were mapped to protein-coding genes based on their physical location with no additional upstream or downstream window. Linkage disequilibrium (LD) between SNPs was accounted for using the 1000 Genomes Phase 3 European reference panel. Gene-level p-values were calculated by aggregating SNP-level summary statistics within each gene while correcting for LD structure. Gene enrichment analysis Gene enrichment analysis was conducted using MAGMA, including both functional gene set and tissue-specific expression analyses. Functional gene set analysis was conducted through the FUMA implementation of MAGMA which tests 17023 predefined biological pathways (MsigDB v2023.1). Post hoc Benjamini-Hochberg FDR correction was subsequently performed in R. Gene property analysis for tissue specificity Gene property analysis for tissue specificity was not performed through FUMA, as FUMA’s implementation of MAGMA is based on GTEx data, where placental tissue was not available by default. To include placenta, we obtained gene expression data from the POPS study through the POPS Placenta Transcriptome project ( https://www.obgyn.cam.ac.uk/placentome/ ), described in Gong et al. (2021). The transcriptomic data was harmonized through converting Fragments per kilobase million (FPKM) to transcripts per million (TPM; ( 53 )), subsequently winsorized, And log transformed, as reported in FUMA preprocessing prior to MAGMA tissue enrichment analysis. Tissue enrichment analysis was then performed using MAGMA (v1.10), using GTEx v8 with 54 and 30 tissues, alongside the placental tissue. Data Availability The data that support the findings of this study are derived from the Norwegian Mother, Father and Child Cohort Study and the Medical Birth Registry of Norway, both managed by the Norwegian Institute of Public Health (NIPH). Individual-level data are protected under the Norwegian Health Registry Act and are therefore not publicly available. Access to data requires approval from the Regional Committees for Medical and Health Research Ethics and from the data owners. Qualified researchers may apply for data access through the Health Research Portal of Norway. 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