Behavior Genetics

Neuropsychiatric polygenic indices (NPGIs) are used as genetic predictors of poor mental health. However, NPGIs are also associated with environmental factors which could affect mental health in adulthood, including the rearing environment. Hence, their "genetic" effects are both direct and environmentally mediated. There is a need to identify alternative genetic predictors without environmental signal. Endophenotype-based polygenic indices (EPGIs) trained on brain structure and function are under-studied alternatives which, due to their relative biological proximity, may exhibit associations with mental health outcomes which are less environmentally mediated than those of NPGIs. Using four representative UK samples (Understanding Society; UKHLS, NCDS, BCS70 and MCS) we employ sex-stratified path models to estimate the direct and environmentally mediated effects of eleven NPGIs and 30 EPGIs on adult mental health, focussing on the rearing environment. The depression NPGI is consistently associated with mental health symptoms across most sex-stratified sub-samples (best meta-analysis beta = 0.091, p 0.001) but demonstrates 1.6 - 24.5% environmental mediation. Seven other NPGIs and three EPGIs show sample- and sex-specific associations with mental health symptoms. NPGIs for attention deficit hyperactivity disorder, depression and substance use disorder are robustly associated with measures of the rearing environment, which in turn are frequently associated with mental health symptoms. Sensitivity analyses find that NPGI associations vary substantially depending on who is included in the sample. In conclusion, the rearing environment likely mediates a substantial portion of NPGIs' so-called "genetic" effects on mental health symptoms, but EPGIs are not currently powerful enough to replace them.

Animals fight for resources to obtain fitness benefits; most contests are intrasexual, and males tend to fight more than females. Although the genetic basis of male aggression is well studied, we know little about the genetic variation of female aggression. Female aggression varies with reproductive status and is potentially influenced not only by her genotype, but also by the genotype of her mate. Here we measured both male and female aggression in a set of Drosophila melanogaster inbred lines by competing each line against a standard competitor. Aggression varied among lines for both sexes, but male and female aggression were not correlated. Female aggression for many lines increased with mating, as expected, but not all lines changed aggression. However, when females were mated to males of different lines, male genotype did not affect the post-mating change in aggression, suggesting that ejaculate-mediated effects do not vary across these lines. The aggression level of the standard opponent was positively correlated with that of focal individuals indicating that individuals modulate their behavior according to the genotype of their opponent.

Importance: Avoidant/restrictive food intake disorder (ARFID) is a feeding and eating disorder characterized by extremely restricted dietary variety and/or quantity resulting in significant physical health impairment and psychosocial dysfunction. ARFID frequently co-occurs with neurodevelopmental conditions, yet the extent to which this co-occurrence reflects shared genetic or environmental influences remains largely unknown, as few twin or genetic studies of ARFID have been conducted. Objective: To examine the extent to which genetic and environmental influences contribute to the association between a broad ARFID phenotype and neurodevelopmental traits. Design, Setting, and Participants: Population-based twin study using data from the Child and Adolescent Twin Study in Sweden, including 30,374 twins born 1992-2008. Main Outcomes and Measures: A broad ARFID phenotype was identified using a composite measure derived from parent reports and national health registers between ages 6 and 12 years. Parents completed measures of neurodevelopmental traits at age 9 or 12 years, including autism (subdomains: social communication problems and restricted/repetitive behaviors), attention-deficit/hyperactivity disorder (ADHD, subdomains: inattention and impulsivity/hyperactivity), tic disorders, learning disorders, oppositional defiant disorder, conduct disorder, obsessive-compulsive disorder (OCD), sensory perception problems, and sleep problems. Phenotypic associations were estimated using polyserial correlations. Bivariate twin models decomposed variance and covariance into genetic and environmental components. Results: Phenotypic correlations with the broad ARFID phenotype ranged from 0.18 (95% CI: 0.15-0.21) for OCD to 0.36 (95% CI: 0.33-0.38) for autism. Broad genetic correlations (rH; additive plus dominant genetic influences) ranged from 0.27 (95% CI: 0.21-0.33) for conduct disorder to 0.52 (95% CI: 0.44-0.60) for autism-restricted/repetitive behaviors. Genetic factors explained 77% to 95% of all phenotypic correlations. Non-shared environmental correlations were minimal to small, with the largest observed for autism (0.17; 95% CI: 0.08-0.26). Conclusions and Relevance: The broad ARFID phenotype shares substantial genetic influences with a number of neurodevelopmental traits. These findings suggest that the frequent co-occurrence of ARFID with neurodevelopmental traits largely reflects shared genetic influences rather than overlapping environmental influences, supporting the conceptualization of ARFID within a broader neurodevelopmental framework.

Differences in physical features and disease prevalence between men and women are examples of sexual dimorphisms. However, sex differences can manifest not only in trait means but also in how strongly risk factors are linked to diseases (e. g. BMI to cardiovascular disease), a question heavily under-researched. To fill this gap, we set out to identify sex differences in phenotype correlations (rP) and decompose them into genetic (rG) and environmental (rE) contributions. Our analysis revealed 250 trait pairs with significant sex-different phenotypic correlations in the UK Biobank. Overall, we observed a predominance of environmental contributions to sex-different effects: 182 trait pairs (73%) exhibited exclusively sex-different rE, while 68 (27%) showed sex differences in both rE and rG, and no trait pair was affected solely by sex-specific rG. For example, we detected sex-different environmental correlation between C-reactive protein and BMI (rE(men) = 0.07 vs rE(women) = 0.25), but no sex-difference in genetic correlation. On the contrary, glycated haemoglobin and LDL cholesterol showed genetic correlation only in women (rG(women) = 0.17; 95% CI = [0.1, 0.23]), but environmental correlation only in men (rE(men) = -0.18; 95% CI = [-0.19, -0.16]). Some of the observed sex differences - including those involving testosterone, SHBG, urate, waist-hip ratio, and triglycerides - may reflect underlying sex-specific genetic architectures, as evidenced by low between-sex genetic correlations. In conclusion, environmental factors are the predominant contributors to sex differences in phenotypic correlations between complex traits, with modest detectable contributions from sex-specific genetic architectures. Recognising these patterns can inform the development of more effective, sex-informed interventions.

Although female rats are typically less aggressive than male rats, lactating females will vigorously defend their nests and pups against an intruder. Much attention has been directed at the consequences of this aggression on the intruder and less on the consequences for the mother and her subsequent interactions with her pups. Here, we exposed resident Sprague-Dawley dams to the resident-intruder paradigm twice daily for five consecutive days, beginning when the dam's (RES) pups were 7 days old, to assess social stress effects on maternal behavior and neurobiology. Controls were dams that had time-matched (TMC) separation from their pups but were not exposed to intruders, and naive moms which were never separated nor exposed to an intruder (CTL). We assessed the dam's subsequent behavior and interactions with her pups on Day 1 and Day 5, and Fos expression after Day 5 in select regions of the prefrontal cortex, amygdala, hypothalamus and periaqueductal gray of the midbrain. In separate cohorts, after pups were weaned, the dams underwent restraint stress and plasma corticosterone assayed. PCA analysis of the dam's behaviors identified three components: normal self-focused behaviors; nurturing behaviors and rough non-nurturing behaviors. Relative to CTL, RES dams exhibited more disrupted behaviors towards their pups, including, rough transport, stepping on pups, and flinging/tossing pups around the cage. In contrast, TMC Dams showed some, but fewer changes relative to CTL, suggesting that separation from pups alone does not account for all disrupted behavior in RES dams. The bulk of these behavioral effects occurred in the first 5-10 min after reunion with the pups and were seen on both the first and fifth day of testing. Of the brain regions examined, the prefrontal cortex was activated by both the defeat/intruder stress (RES) and separation stress (TMC), whereas the dorsal PAG was activated specifically by the defeat/intruder stress. The medial and basolateral amygdala exhibited differential neuronal activity between the RES defeat/intruder-exposed dams and the other two groups. The RES moms exhibited an insufficient adrenocortical response to acute restraint stress. The results suggest that amygdala-dPAG activity is important for dissociating disrupted maternal care in RES (due to defense of the nest against an intruder) from simple pup separation, both of which activate the mPFC. The experience of repeatedly defending the nest may induce subsequent disruptions in HPA responses. The amygdala-dPAG pathway may regulate aspects of stress and emotional regulation exhibited by mothers who defend their offspring against intruders.

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contributing to model decisions. Method: We analyzed participants from the Oregon ADHD-1000 cohort split into training, validation, and test subsets. We trained modular neural network models classifying ADHD case-control status using SNP-level genotype data with biological annotations, polygenic scores, demographics, parenting and family conflict, stress and trauma, geocoded measures, cognitive task measures, temperament factor scores, and missingness indicators. Hyperparameter optimization selected model architecture and feature block inclusion. We evaluated model performance using AUC, precision-recall curves, calibration analyses, prediction certainty analyses, and decision curve analysis. We used integrated gradients to quantify block-level, feature-level, and individualized feature importance. Results: The best model using temperament features had an AUC of 0.97 in the held-out test subset, with high accuracy, sensitivity, and specificity and a Brier score of 0.06. The best model excluding temperament had an AUC of 0.75. Feature importance analyses highlighted temperament, demographic, and cognitive domains in the temperament-inclusive model. Individualized explanations showed that prediction drivers varied across participants and could help reveal conflicting or supporting evidence across domains. Conclusion: Explainable, multi-modal classification models can integrate heterogeneous ADHD-relevant information and identify features that contribute to individual predictions. These types of models may advance ADHD risk modeling research and clinician-led decision support, especially in complex or diagnostically uncertain cases.

Objective: To develop and validate clinically relevant polygenic scores (PGS) for alcohol (AUD), cannabis (CanUD), opioid (OUD), tobacco (TUD), and polysubstance use disorders (polySUD) across African (AA), European (EA), and Latinx (LA) ancestry populations. Methods: Using multiple genome-wide association study summary statistics and PGS methods, substance use disorder PGS were developed and evaluated in Indiana Biobank samples (IB, N: 1,356-24,989), then top-performing PGS were validated in All of Us Research Program samples (AOU, N: 62,389-209,952). Case and controls were defined using ICD-9/10 codes. All participants were aged 18 years or older (>=21 years for AUD controls). Clinical relevance was defined as an odds ratio (OR) >=2 for individuals with the highest PGS determined based on disorder prevalence compared to everyone else. Results: In EA and LA, all PGS achieved clinically relevant performance in both IB and AOU (ORs: 2.00-9.10; P <= 3.87E-4). In AA, PGS met this threshold in IB (ORs: 2.02-2.71; P <= 2.20E-4) but not in AOU (ORs: 1.28-1.56; P <=0.03). Overall, OUD PGS showed the strongest associations in most analyses, followed by CanUD and polySUD. Generally, compared to female PGS, male PGS had higher or comparable ORs, but the differences were not significant except AUD PGS in AOU LA. Conclusions: PGS demonstrated clinically meaningful risk prediction for substance use disorders in EA and LA, supporting the feasibility of future clinical implementation for population-level screening. However, reduced performance in AA underscores the urgent need for more genetic studies in that population.

Recently, Hu et al. (2024) conducted a benchmarking study showing that most existing Mendelian randomization (MR) methods exhibit substantial bias and inflated type-I error rates in real data. They attributed these failures to two largely neglected sources of bias: winner's curse and polygenicity-induced bias. Although a few methods have been developed to address one or both of these issues, existing approaches either do not fully account for both biases or are restricted to the univariable setting. In this paper, we propose a multivariable Rao-Blackwellization that corrects winner's curse while accounting for polygenicity and sample structure in a unified framework. Unlike univariable Rao-Blackwellization, where instrument selection yields a truncated normal statistic amenable to a Mills-ratio correction, multivariable Rao-Blackwellization conditions on a noncentral $\chi^2$ statistic, for which no analogous correction is available. We derive closed-form conditional moments under this instrument selection model and use them to construct bias-corrected summary statistics that can be integrated into a wide range of existing MR methods. Simulations and real data analyses show that, when combined with methods such as MR-cML and MR-BEE, the proposed correction substantially improves type-I error control and yields more robust inference.

Background Individuals with severe mental illness (SMI), including schizophrenia spectrum disorders (SSD) and bipolar disorder (BD), have been shown to have an elevated risk of violent perpetration. However, no population-wide study has systematically examined how this risk varies across psychiatric comorbidity patterns and specific violent crime types. Methods Using the first nationwide Norwegian registry linkage comprising mental health and crime data, we included 3,612,215 individuals aged 15-79 years living in Norway on Jan 1, 2008, and followed them until Dec 31, 2022. We estimated absolute and relative risks (RRs) of violent offending overall and by specific violent crimes among individuals with SSD and BD. To capture clinically relevant comorbidity patterns, we included substance use disorders (SUD), common personality disorders (PD), and hyperkinetic disorders (ADHD). RR models were adjusted first for sex and age, and subsequently for co-occurring mental disorders. Findings At the population level, individuals with SMI accounted for a minority of violent offenders (SSD: 8.7%; BD: 4.6%), whereas SUD was present among a substantially larger proportion (36.8%). Absolute risk of violent offending increased markedly with psychiatric comorbidity, from e.g., 5.0% among individuals with SSD alone to 43.9% for SSD combined with SUD and PD. Compared with the remaining general population, the RR of violent offending for SSD decreased from 6.58 (95% CI 6.4-6.8, adjusted for sex and age), to 2.0 (2.0-2.1) after further adjustment for other mental disorders. Similar attenuation patterns were observed across specific violent crime types, although varying in magnitude. In contrast to SMI, elevated risks associated with SUD remained substantial after full adjustment across most crime categories. Interpretation The association between SMI and violent offending is strongly influenced by psychiatric comorbidity, particularly SUD, and varies across crime types. Our findings underscore the importance of identifying and treating co-occurring mental disorders and substance use, both in the clinical management of SMI and in population-level violence prevention strategies.

Background: Individuals with attention-deficit hyperactivity disorder (ADHD) are at increased risk for cannabis misuse, with increasing prevalence among young adults. Existing evidence suggests that cannabis can have therapeutic effects on ADHD symptoms, and continued use may be partly driven by perceived improvements in symptom-related deficits. To investigate the neural evidence for these associations, we integrated functional neuroimaging and Allen Human Brain Atlas transcriptomic data to assess neural correlates of ADHD in regions targeted by cannabinoids as predictors of cannabis use. We hypothesized that greater ADHD symptoms would lead to higher cannabis use frequency through associations of ADHD symptoms with functional deficits in cannabinoid receptor type 1 (CB1R; encoded by the CNR1 gene) expressing brain regions. Methods: We tested 466 college students (ages 18-19) with varying ADHD symptom severity and cannabis use, self-reported at baseline and three yearly-follow up questionnaires. ADHD-related neural deficits were tested in a subset of 144 participants using an fMRI stop-signal task at baseline. Growth mixture modelling categorized participants with similar cannabis use into three latent classes. The covariance between the CNR1 gene expression map and differences in stop-signal task activation were tested as a mediator linking ADHD symptoms and cannabis use. Results: Greater ADHD symptoms significantly predicted reduced activation within CNR1-expressing regions, which predicted higher-use cannabis class membership. Conclusions: Our results add support for the self-medication hypothesis for higher rates of cannabis use among individuals with greater ADHD symptoms, which may be mechanistically linked through CB1R-enriched attention and inhibitory networks, highlighting neural targets for prevention and treatment.

Purpose: This study examined longitudinal trajectories of adaptive functioning in 331 individuals with Angelman syndrome (AS) using the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3) and examined differences by molecular subtype. Methods: A total of 331 individuals (156 females, 47%) with genetically confirmed AS (ages 6 months to 52 years) were assessed between 2018 and 2025, including 207 with a deletion subtype, 63 with uniparental disomy or imprinting defect, and 61 with a UBE3A point mutation. Growth scale values were analyzed using linear mixed-effects models with log2-transformed age. Results: Individuals with deletion subtypes demonstrated significantly lower adaptive functioning across domains compared to those with non-deletion subtypes. Adaptive skills across all Vineland-3 subdomains increased nonlinearly with age, showing faster growth early in life that slowed over time, with largely parallel trajectories across subtypes. Conclusion: Individuals with AS demonstrate slow but steady growth in adaptive functioning that continues into adulthood, with progress varying by molecular subtype. These findings provide updated natural history benchmarks and demonstrate the utility of the Vineland-3 for clinical trials.

Social hierarchies are a nearly universal feature of animal groups, but whether dominance reflects a single generalized trait or a collection of context-specific competitive abilities remains unclear. Here, we assess social hierarchy in three strains of laboratory mice (BALB/c, C57BL/6, and Shank3B knockouts) of both sexes using three established paradigms: the tube test, the warm spot assay, and the void spot assay. Hierarchies emerged in all strains and both sexes across all three assays, but how animals established rank differed markedly by strain and sex. In the tube test, Shank3b-/- knockout females, but not males, lacked the winner effects seen in wild-type mice, indicating that the ability to build a winning streak depends on social recognition in a sex-specific manner. In the warm spot assay, females formed stronger hierarchies than males, particularly among mice on a C57BL/6 background, with high-ranking females actively displacing others from the warm platform. In the void spot assay, BALB/c mice of both sexes frequently displayed territory-marking behavior, a pattern that was less common in the other strains. Overall, individual rank rarely generalized across domains, despite high trial-to-trial repeatability for individuals within each assay. Together, these findings indicate that mice behave as dominance specialists rather than generalists, with strain- and sex-specific strategies for establishing rank in different competitive contexts, suggesting that distinct neural circuits likely underlie these separable components of competitive ability.

Background: Cannabis is one of the most widely used psychoactive substances worldwide. {Delta}-tetrahydrocannabinol ({Delta}-THC) is the main contributor to cannabis-induced effects such as euphoria, anxiety, and psychotomimetic effects, and is metabolized by several hepatic enzymes, including CYP3A4. There are interindividual differences in how cannabis affects users, which have substantial genetic contributors. Methods: We examined how real-time effects of {Delta}-THC on psychotomimetic measures and on subjective effects of "high", sadness and anxiety in 188 healthy volunteers in a laboratory infusion paradigm, relate to polygenic risk scores (PRS) for cannabis lifetime use (CanLU), cannabis use disorder (CanUD), and CYP3A4 expression. Results: CYP3A4 expression PRS was significantly associated with {Delta}-THC-induced psychotomimetic effects. Genetic liability to use and misuse cannabis is potentially associated with lower {Delta}-THC-induced psychotomimetic symptoms. CanLU PRS nominally predicted enhanced {Delta}-THC-induced "high", while CanUD PRS predicted it to be lower. Conclusions: Our findings suggest that genetic liability to produce more CYP3A4 enzyme may be associated with faster {Delta}-THC degradation and the consequential diminution of the latter's effects. Nominal effects suggest that aversive outcomes may reduce cannabis use and use disorder genetic liability, and that CanUD subjects may need higher {Delta}-THC doses to experience euphoria ("high"). In total, this study provides novel insights regarding some of the specific genetic factors that influence interindividual variability in {Delta}-THC effects, mainly via {Delta}-THC metabolism.

Introduction Limitations of current classifications of schizophrenia, schizoaffective disorder, and bipolar disorder are evident from their overlapping symptoms, aetiologies, treatments, and outcomes, and present a barrier to novel treatment discovery. Alternative conceptualisations are needed to address nosological validity, align diagnosis to aetiology, and improve prognostication and treatment choice. We aimed to identify latent classes across the psychosis spectrum based on premorbid functioning and outcomes, and assess these in relation to genetic liability and symptom dimensions. Method Participants with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder type 1, were ascertained from four UK clinical cohorts (total n=5,043). Latent class analysis was conducted using phenotypes not included within the diagnostic criteria, including premorbid functioning, age at illness onset, and measures of severity and course. Polygenic scores (PGS) for psychiatric disorders and behavioural traits were tested for associations with latent classes. We tested if diagnosis explained associations between PGS and classes. Results A three-class model provided the best fit. Class one had poorer premorbid functioning, lower rates of recovery, and higher PGS for schizophrenia and ADHD. Class three had the highest functioning, higher rates of psychosocial stressors before onset, higher intelligence PGS and lower PGS for psychiatric disorders. Class two was intermediate between classes one and three on measures of functioning, but was characterised by high levels of involuntary hospital admissions and high bipolar disorder PGS. Diagnosis only partially explained associations between PGS and class membership. Conclusions We identified classes across the psychosis spectrum characterised by different premorbid functioning and outcomes, that cut across diagnostic categories and captured genetic liability not explained by diagnosis. Our findings suggest alternative conceptualisations of psychotic disorders may complement diagnoses in mapping to the aetiology of these conditions, and could be useful to advance precision psychiatry.

Polygenic scores are imperfect measures of the additive genetic effects of common genetic variants. The resulting measurement error biases estimates of quantities of interest in epidemiological analyses integrating polygenic scores. For example, how much of an exposure-outcome association is genetically confounded can be substantially underestimated when using polygenic scores alone. Here we present extensions to Gsens, a genetic sensitivity analysis, which aims to correct for such measurement error using both polygenic scores and heritability estimates. Gsens now allows for multiple exposures and estimates several quantities of interest, i.e. genetic confounding, adjusted residual association (net of genetic confounding), genetic overlap and environmentally mediated genetic effects. We present derivations and simulations showing how Gsens accounts for measurement error in the polygenic score; we also show how estimation may be affected by misspecifications of the causal structure between exposures. Applying Gsens in the Norwegian Mother, Father and Child Cohort Study (MoBa), we uncover, among other results, substantial genetic confounding in the associations between multiple known risk factors for attention deficit hyperactivity disorder (ADHD), such as low birth weight and temperament, and measures of ADHD in childhood. The updated Gsens R package offers multiple options, including for missing data handling and customisable syntax. Our extended version of Gsens is applicable to a broad range of substantive questions in multiple disciplines.

The leading cause of mortality and morbidity in children under the age of 5 is preterm birth. The timing of birth is influenced by both genetic and environmental factors, but the underlying mechanisms remain poorly understood, making its prediction difficult. In this study, we investigated the potential of using machine learning models to predict preterm birth based on genetic data from the Norwegian Mother, Father and Child Cohort Study (MoBa). We trained and evaluated several classification algorithms on individual-level genetic data from over 15,000 mothers and children. Our results indicate that the predictive capacity of maternal gestational duration-associated loci for preterm birth is limited, with the highest AUC values around 0.57. Additionally, incorporating more SNPs within the associated loci did not improve prediction performance. As expected, the contribution of the maternal genome to preterm birth prediction was found to be larger than that of the fetal genome. Overall, our findings suggest that while genetic testing provides some information about an individual's risk for preterm birth, further research incorporating additional factors is necessary to enhance predictability.

Although romantic partners tend to resemble each other on many characteristics, the geographical processes underlying partner similarities remain poorly understood. Using Finnish nationwide registry data from cohabiting or married partners (N = 1,500,204 couples; partnerships were formed between 1990-2023), we examined regional differences in partner similarity in mental disorders, educational attainment, and adolescent school performance. We also analysed geographical variation in partner similarity within three major cities. Accounting for local demographic composition of potential partners attenuated the partner similarity from r=0.43 to r=0.34 for highest obtained educational attainment, but increased partner similarity in any mental disorders from r=.40 to r=.42. In urban municipalities partners were more similar in educational attainment, but less in mental disorders, compared to more rural regions. We found no clear within-city variation in partner similarity. These findings highlight the role regional demographic composition plays in partnership formation and suggest different partnering dynamics depending on societal organization.

In multiply-mating species, male-female postcopulatory, prezygotic interactions can influence reproductive outcomes. In Drosophila melanogaster, females can bias sperm storage and usage and thereby influence paternity outcomes. One mechanism by which females may regulate paternity contributions from specific males is through modulation of mating plug ejection timing. The D. melanogaster mating plug is composed of seminal fluid proteins, and some female-derived proteins, that coagulate in the female reproductive tract during mating. The mating plug facilitates sperm storage; thus, timing of female mating plug ejection is associated with sperm storage and relative paternity contributions in cases of multiple mating. However, whether there is natural genetic variation among females that shapes mating plug ejection timing, and genes or phenomena that might mediate it are unknown. We examined mating plug ejection in females from 69 lines of the Drosophila Genetic Reference Panel and observed dramatic differences in median plug ejection timing ranging from less than 1 to over 6 hours. We used this variation to perform a genome-wide association study to identify gene candidates associated with this phenotype. Many gene candidates are expressed in the brain and/or function in neurodevelopment. The candidate pool was also enriched for genes expressed in the ovary and functioning in oogenesis, indicating a link between female reproductive physiology and mating plug ejection. Consistent with this interpretation, females without a germline delay mating plug ejection. Our results demonstrate that female mating plug ejection is a physiologically integrated reproductive trait with a genetic basis that can be shaped by selection. Article SummaryThe D. melanogaster mating plug is composed of seminal fluid proteins and some female-derived proteins that coagulate in the female reproductive tract during mating. The mating plug facilitates sperm storage; thus, timing of female mating plug ejection is associated with sperm storage and relative paternity contributions in cases of multiple mating. Using the DGRP, we observed heritable genetic variation in female timing of mating plug ejection and through a GWAS find associated gene candidates. Gene candidates are enriched for neurodevelopment function and oogenesis function. We experimentally validate the connection between female mating plug ejection and the ovary.

Importance Prenatal alcohol exposure (PAE) is associated with lasting cognitive and neurodevelopmental deficits and can quadruple risk for depression later in life. However, it remains unknown whether there are specific trimesters when PAE is more strongly associated with longitudinal trajectories of internalizing symptoms - an indicator of depression risk - across childhood and adolescence. Objective To investigate how PAE timing and dosage are associated with internalizing symptom trajectories from ages 4 to 16.5 years. Design, Setting and Participants We analyzed prospective data from the Avon Longitudinal Study of Parents and Children (ALSPAC), an ongoing longitudinal birth cohort from the United Kingdom. Internalizing symptom trajectories were estimated for 6,409 participants. Primary analyses were conducted on 2,254 participants with complete data on PAE in all three trimesters, covariates, and trajectories. Main Outcomes and Measures We used growth mixture modelling to identify latent trajectories of depressive symptoms measured using the internalizing symptom scale from the Strengths and Difficulties Questionnaire (SDQ) at seven occasions between ages 4 to 16.5 years. Prospective alcohol consumption during each trimester were categorized into three PAE dosages: unexposed (0 drinks/week), low (1-7 drinks/week) and high (7+ drinks/week). Results We identified five distinct depressive symptom trajectories: stable low (75.9% of participants), moderate childhood peak (11.2%), progressive increase (5.57%), high early childhood (4.73%), and early adolescent peak (2.61%). PAE in the second (relative risk [RR]=2.08, 95% CI=1.15-3.76) and third trimesters (RR=1.83, 95% CI=1.05-3.21), as well as total PAE burden across pregnancy (RR=1.33, 95% CI=1.06-1.68) increased risk for the progressive increase trajectory, versus the stable low trajectory. High PAE in the second (RR=2.71, 95% CI=1.41-5.21) and third (RR=2.27, 95% CI=1.27-4.05) trimesters drove elevated risk for this trajectory. PAE in the first trimester or at low dosages showed no associations with depressive symptom trajectories. Negative control analyses of paternal drinking also found no associations. Conclusions and Relevance Our results highlight the second and third trimesters as potential sensitive periods for the impact of PAE on rising depressive symptoms from childhood to adolescence. Ultimately, these findings could inform the design of prevention programs, and facilitate targeted interventions to youth at elevated risk for depression.

Adolescent externalizing behavior is a major risk factor for later substance use and other psychiatric outcomes. Understanding its genetic architecture and its relationship with brain imaging phenotypes requires scalable genome-wide methods applied to youth cohorts. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we implemented a pipeline for genome-wide association studies (GWAS) of longitudinally measured externalizing traits and multimodal neuroimaging-derived phenotypes (IDPs). We performed quality-controlled genotype processing and constructed harmonized phenotype and covariate datasets. GWAS analyses were conducted using REGENIE in a two-step framework, with Step 1 ridge regression models trained on LD-pruned variants and Step 2 association testing performed genome-wide. Externalizing traits measured at baseline and summarized as longitudinal means and slopes, together with approximately 200 IDPs measured at baseline and summarized as longitudinal means and slopes, were analyzed. We further constructed a custom linkage disequilibrium (LD) reference panel using unrelated individuals and computed LD scores using LDSC. Genetic correlations between externalizing traits and imaging phenotypes were estimated using LD Score Regression. This exploratory study systematically evaluated genome-wide genetic correlations between regional cortical morphology and externalizing phenotypes in adolescence. Although several associations reached nominal significance, none remained significant after correction for multiple comparisons. These findings should not be interpreted as demonstrating an absence of shared genetic architecture. Rather, the precision of the estimates was constrained by the available imaging GWAS sample size, uncertainty in SNP-heritability estimates, and the large number of regional comparisons. Larger imaging-genetics samples and independent replication will be required to determine whether modest or regionally specific genetic correlations exist.