Jean-Baptiste Pingault

Educational attainment and mental health are associated from childhood, but less is known about how this association is reflected within families, where children’s genetic variation is correlated with both parental genotypes and family environments. We examine whether associations involving children’s own and parental polygenic scores differ for cognitive and non-cognitive genetic components of educational attainment. These scores aggregate common DNA variants using weights from genome-wide association studies. We analyse 3,223 mother-father-child trios from the UK Millennium Cohort Study using structural equation models that jointly estimate associations involving children’s own and parental polygenic scores. Here we show that cognitive and non-cognitive education-related polygenic scores are associated with externalising behaviour, including hyperactivity and conduct problems, and internalising behaviour, including anxiety and low mood, across childhood and adolescence. Associations are generally larger in magnitude for externalising than internalising behaviour, with clearer component-specific differences for externalising behaviour. These findings highlight the heterogeneity in education-related polygenic score associations with youth mental health within families.

Background: Anhedonia is a core symptom of major depressive disorder, and a key feature of schizophrenia and bipolar disorder and shows limited improvement with current treatments. Genetically informed drug repurposing can help prioritise existing compounds to identify new treatment avenues for anhedonia. Method: We applied cis-Mendelian randomisation (MR) to 1,412 druggable genes using genetic instruments from genome‑wide association studies (GWASs) of protein or gene expression levels measured in blood, brain, and cerebrospinal fluid. For our primary outcome, we used GWAS summary statistics for anhedonia in the UK Biobank (N=375,275). To prioritise targets for anhedonia, we integrated genetic analyses, including cis-MR, robust MR sensitivity analyses, and colocalisation analyses, with pharmacological data. We finally conducted systematic literature reviews to identify preclinical and clinical evidence linking each of our prioritised targets to anhedonia-like or reward-related behaviours. Findings: We identified 17 targets with robust genetic evidence, of which six were selected as high‑priority. These span inflammatory signalling (IL-6, CX3CL1, CD6), mitochondrial complex I subunits (NDUFA6, NDUFS1) and synaptic signalling (PTK2B). Among 14 repurposing compounds, sirukumab, an IL-6 neutralising monoclonal antibody, has shown preliminary improvement in anhedonia. Preclinical studies indicate that metformin, acting on mitochondrial complex I, increases reward-related behaviour in treated mice compared to controls. Interpretation: Our findings support roles for inflammatory signalling, mitochondrial complex I, and synaptic signalling in anhedonia, and identify novel repurposing candidates. Further validation is needed to test whether modulating these targets improves anhedonia and to clarify whether their relevance is confined to inflammatory or metabolic subtypes or generalises more broadly.

While BMI is phenotypically and genetically associated with depression, the extent to which BMI causes depression and the mechanisms underlying this effect remain unclear. We estimated the causal effect of BMI on depression symptoms, accounting for complexity in both the exposure and the outcome. We applied PheWAS-based Clustering of Mendelian Randomization instruments (PWC-MR) to partition 324 BMI-associated SNPs into six genetic clusters and estimated the effect of each cluster on nine depression symptoms from the largest available genome-wide meta-analysis of PHQ-9 depression symptoms (N = 224,535–308,421). Across all six clusters, BMI had the largest effect on appetite changes (β ranges from 0.19 to 0.34), which was robust across estimators, homogeneous across clusters, and survived correction for multiple testing. Smaller effects were observed for several other symptoms, including concentration changes, fatigue, anhedonia, and sleep problems, but these were heterogeneous across clusters and often attenuated under pleiotropy-robust estimators, indicating likely bias from horizontal pleiotropy. Little evidence of an effect was detected for depressed mood and suicidal ideation. Cluster-level heterogeneity was observed for the majority of symptoms, demonstrating that the causal effect of BMI varies by instrument group and indicating likely violation of the exclusion restriction assumption. These findings indicate that the causal effect of BMI on depression detected in typical non-clustered Mendelian randomization is driven to an extent by appetite, with no or inconsistent evidence for effects on core psychological symptoms such as anhedonia and depressed mood.

Background: Cord blood DNA methylation profile scores (MPSs) based on genetic and pre-/perinatal risk factors for neurodevelopmental conditions (NDCs) may capture downstream biological effects and help understand how combined exposure signals contribute to NDC risk. Methods: Using data from two longitudinal birth cohorts, Generation R (N _train = 1856, N _test = 476) and ALSPAC (N _validation = 832), we developed cord blood MPSs based on genetic and pre-/perinatal NDC risk factors. We assessed individual and combined predictive performance of risk factors and MPSs for eight childhood psychiatric outcomes (four broad, four specific), measured between ages 5 and 14 years. We also evaluated if the MPSs could be combined into a composite “transmission load” MPS. Results: We validated four novel MPSs: maternal age, birthweight, and genetic liability for ADHD and schizophrenia (r range = 0.08 to 0.29) and included two previously validated MPSs: maternal smoking and gestational age (r range = 0.42 to 0.63). Jointly modeling the six MPSs with their corresponding risk factors explained on average 3.3% of variance in outcomes, higher than that explained by risk factors (1.8%) or MPSs alone (1.6%), indicating complementary sources of risk. The “transmission load” MPS did not replicate due to heterogeneous contributions of the predictors across cohorts. Conclusions: The four novel MPSs based on genetic and pre-/perinatal risk factors can serve as valuable tools for future research. Integrating genetic and prenatal risk factors with DNA methylation at birth can provide insights into their individual and joint contributions to early psychiatric risk and may improve prediction.

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.

Major depression (MD) treatments have limited efficacy and target few mechanisms, highlighting the need for innovative drug discovery. Drugs targeting genetically supported proteins are 2.6 times more likely to succeed in drug development. Here, we use genetic methods to identify and prioritise MD drug targets, leveraging genome-wide association study (GWAS) summary statistics from >525,000 MD cases. We derived exposure data from 10 datasets measuring protein quantitative trait loci (pQTLs) and gene expression levels (eQTLs) in blood, cerebrospinal fluid, and brain tissues. We performed cis -Mendelian randomisation (MR) on 3469 druggable targets (genes encoding proteins targeted by existing compounds or experimentally predicted to be druggable). To strengthen causal inference, we implemented robust MR estimators, colocalisation, external replication, and assessed directional consistency across tissues. We integrated cis -MR effect directions with drug mechanisms and clinical annotations to infer potential therapeutic effects. Validation analyses showed that 82% of drugs approved for depression/anxiety had ≥1 significant MR target, compared to 51% for compounds in clinical trials. For repurposing, we prioritised 54 targets of compounds developed for other conditions with estimated beneficial effects on MD (e.g., an inhibitor for a risk-increasing target). Ten high-priority targets of brain-penetrating compounds included ACE and NISCH (cardiovascular drugs), NDUFA2, NDUFB6, and NDUFS1 (metformin), CDK4, NTRK3, and MET (oncology inhibitors), and GLS and NOS2 (enzyme inhibitors). We found genetic evidence for established and novel MD targets across the drug development pipeline. Novel targets point to mechanisms beyond monoaminergic systems, most with approved drugs for other conditions, offering immediate repurposing opportunities.

Educational attainment is associated with mental health across development. What drives this association remains unclear, as it may reflect both genetic transmission and nongenetic intergenerational processes, which may operate differently for the cognitive and non-cognitive components of educational attainment. Using polygenic scores as proxies for the cognitive and non-cognitive components of educational attainment, we examined their developmental associations with early markers of mental health, i.e. externalising and internalising behaviour. We analysed data from 3,223 mother-father-child trios from the UK Millennium Cohort Study, applying a genetically informed design to distinguish genetic transmission (direct inheritance) from indirect genetic effects (non-transmitted parental genetic associations). For externalising behaviour, cognitive and non-cognitive components showed a nominally significant developmental trend: genetic transmission increased from early childhood (age 3) to adolescence (age 14), while indirect genetic effects were evident in early childhood but less consistent at later ages. In contrast, genetic influences on internalising behaviour were relatively stable across development, with small and overlapping indirect genetic effects for cognitive and non-cognitive components in early childhood and modest, overlapping genetic transmission in adolescence. Our results underscore the importance of considering both cognitive and non-cognitive dimensions in a developmentally informed framework to fully understand the role of education-related traits in the intergenerational transmission of mental health.

Background: Early adolescence is a common period of onset for depressive symptoms. In part, this may reflect a developmental manifestation of individual's genetic propensities as they undergo physiological and hormonal changes and interact with new environments. Many commonly proposed mechanisms assume direct effects of an individual's own genes on emerging variation in their depressive symptomatology. However, estimates of genetic influence based on analyses in unrelated individuals capture not only direct genetic effects but also genetic effects from parents and other biologically related family members. Aim: In data from the Norwegian Mother, Father and Child Cohort (MoBa), we used linear mixed models to distinguish developmentally-stable and adolescence-specific direct and parental indirect genetic effects. We examined effects of polygenic scores for major depressive disorder (MDD), ADHD, anxiety disorders, and educational attainment (EA) on depressive symptoms, which were assessed by maternal reports at ages 8 and 14. Results: Children's own MDD polygenic scores showed adolescence-specific effects on depressive symptoms ( b_PGS*wave=0.041, [95% CI: 0.017, 0.065]). Developmentally-stable direct effects from children's polygenic scores for MDD (b=0.016, [0.006, 0.039]), ADHD (b=0.024, [0.008, 0.041]) and EA (b=-0.02, [ -0.038, -0.002]) were also evident. The only evidence of indirect genetic effects was a stable effect of maternal EA polygenic scores (b=0.04, [0.024, 0.054]). Conclusion: Direct genetic effects linked to genetic liability to MDD accounted for emerging variation in depressive symptoms in adolescence. These results imply that specific etiological mechanisms related to MDD may become particularly relevant for depressive symptoms during early adolescence compared to at earlier ages.

Substance use disorders run in families, yet the mechanisms underlying intergenerational transmission remain unclear. We investigated indirect genetic effects, pathways through which parental genotypes influence offspring phenotypes via the family environment, for alcohol use disorder (AUD), nicotine dependence (ND), and related quantitative outcomes, and aimed to identify family environmental factors through which such effects may operate. Using transmitted and non-transmitted polygenic scores (PGS) constructed for problematic alcohol use, tobacco use disorder, and general addiction liability, we analyzed 5972 European-ancestry adult offspring with at least one genotyped parent from the population-based Lifelines cohort (Netherlands). Offspring outcomes included lifetime DSM-5 AUD diagnosis, AUD symptom count, maximum drinks in 24 hours, Fagerström Test for Nicotine Dependence score, and cigarettes per day. AUD findings were meta-analyzed with data from the Brisbane Longitudinal Twin Study ( N = 1368; Australia). We also examined parent-of-origin effects and mediation by parental substance use and socioeconomic status using structural equation modeling. Transmitted PGS robustly predicted all AUD and ND outcomes ( β = 0.07-0.16; OR = 1.20 for AUD diagnosis). Non-transmitted PGS, indexing indirect genetic effects, were negligible for all clinical syndrome outcomes. The only significant indirect genetic effect was on cigarettes per day ( β = 0.03, p = 0.01), mediated by parental smoking behavior but not socioeconomic status. These findings indicate that intergenerational transmission of risk for AUD and ND is driven primarily by direct genetic effects, with modest indirect genetic effects on smoking quantity. Larger samples and cross-trait analyses are needed to further elucidate these mechanisms.