Before describing the approaches and results of each objective, we provide a general summary of the data collection of the eight CAPICE cohorts (see Tables 1, 2). All longitudinal cohorts have started either at birth or in childhood and use quantitative measures of psychopathology. These measures have been associated with clinical diagnoses [31,32,33,34] and are therefore widely used in clinical practice. The advantage of these dimensional measures is that they capture more of the variation present in the common population than dichotomous measures that only specify the presence or absence of a diagnosis. For genome-wide SNP data, methods to impute the genotypes are widely available allowing for the same genetic variants to be analysed over cohorts. All cohorts have been described in more detail elsewhere: ALSPAC [35,36,37], TEDS , GenR [39, 40], NTR [41,42,43], TCHAD , NFBC’86 , CATSS [46, 47], MOBA . For a link to cohort-specific websites and for a detailed description of the cohorts (see Table 1). All data used for the analyses were collected under protocols that have been approved by the appropriate ethics committees, and studies were performed in accordance with the ethical standards.
Objective 1: Elucidate the role of genetic and environmental factors in mental health symptoms across childhood and adolescence, and to establish the overlap in genetic risk factors with other traits related to childhood mental health symptoms.
We refer to several excellent overviews for a description of the methods that can be used in genetic epidemiological studies analysing twin/family data and/or molecular genetic data [15, 49, 50]. In short, twin and family studies estimate the proportion of variance in a trait attributable to genetic and environmental factors by comparing the resemblance between pairs of relatives that differ in their relatedness. For example, if monozygotic twins, who are essentially 100% genetically identical, are more alike than dizygotic twins, who share on average 50% of their co-segregating alleles, this is an indication that genetic factors play a role in explaining differences between individuals for a certain trait. This model can be extended to include other family members, to longitudinal or multivariate designs, and to study gene–environment interaction (G × E), even without a direct measure of the environment [51,52,53].
It is also possible to address these questions using genotypic data obtained for GWAS. In GWAS, common single nucleotide polymorphisms (SNPs) (positions in the DNA sequence that vary between individuals) measured across the whole genome are tested for their association with a trait. Many complex traits, like mental disorders, are influenced by multiple genetic loci. In this case, polygenic analyses, taking into account many SNPs, can be applied to investigate the cumulative impact of these SNPs on a trait, as well as on the co-occurrence of phenotypes or the persistence of symptoms over time [15, 49]. To this point, CAPICE researchers have performed twin and polygenic risk score analyses of the correlation between common mental health symptoms, such as internalizing problems and ADHD problems, and the stability of symptoms over time, including into adulthood.
Regarding the co-occurrence of symptoms, the covariance could be explained by one common factor, the so-called p-factor, which was found to be for 50–60% heritable. Moreover, genetic factors explained stability in this factor across ages. A polygenic p-factor risk score based on adult psychiatric disorders was also associated with the childhood p-factor .
The genetic association between adult psychiatric disorders and childhood and adolescents traits was further investigated with polygenic analyses. The PGS for adult depression, neuroticism, BMI, and insomnia were significantly positively associated with childhood ADHD, internalizing and social problems, while the PGS for subjective well-being and educational attainment showed negative associations . Only bipolar disorder PGS did not yield any significant associations. Effect sizes were in general similar across age and phenotype, although the PGS for educational attainment was more strongly associated with ADHD and the BMI PGS with ADHD and social problems . A follow-up study is currently on the way, performing multivariate analyses to shed more light on the pattern of associations (https://osf.io/7nkw8).
Genetic data can also be leveraged to estimate the average causal effect of specific environmental factors, such as perinatal factors, on child or adolescent psychopathology, using a method known as Mendelian randomization (MR). Several ongoing CAPICE projects have applied this approach to estimate the effect of prenatal risk factors, such as maternal smoking, on childhood mental health problems. It is important to recognize that the assumptions that are required for MR may not hold for all prenatal exposures and offspring outcomes . Therefore, methods to identify violations of the MR assumptions are also evaluated and approaches that require fewer assumptions are tested.
Another method to analyse the mechanisms underlying parent-offspring associations is maternal genome-wide complex trait analysis (M-GCTA), which can be applied when parental genotypic data are also available. M-GCTA can be used to calculate whether the association between parent and offspring psychopathology is explained by an environmental effect on top of the effect of the genetic transmission . Applying this method to the MoBa data indicated no such effects for anxiety and depression at age 8 . Analyses on a larger so more powerful sample and including externalizing problems are currently performed.
Objective 2: Identify genetic, epigenetic, and transcriptomic variants associated with mental health symptoms during childhood and adolescence.
GWAS have provided insight into the genetic basis of quantitative variation in complex traits in the past decade . By increasing the sample size and performing meta-analyses across the EAGLE cohorts, including the CAPICE cohorts, it may be possible to detect genome-wide significant associations and to detect age effects. A large-scale GWAMA using a multivariate method to analyse summary statistics that are not independent  focused on identifying genetic variants that influence the development and course of internalising symptoms from ages 3 to 18 (https://osf.io/w5adg/). Three gene-wide significant effects were detected as well as significant genetic associations with adult depression and related traits as well as with childhood traits. Another study explores the effect of (ultra) rare and common variation in genes specific to brain cell types on neuropsychiatric disorders (https://osf.io/uyv2s).
Previous work has also suggested that the impact of environmental factors on childhood psychopathology may be mediated by epigenetic variation, which consists of functional alterations in the genome that do not involve a change to DNA sequence . While there are several forms of epigenetic variation, most epigenetic studies have focused on alterations in DNA methylation. Epigenome-wide association studies (EWAS) are performed to test the effect of maternal mid-pregnancy vitamin D on offspring cord blood methylation and of the association between variation in child peripheral and cord blood methylation and the subsequent development of ADHD.
Under very strong assumptions, mediation of the possible average causal effect of prenatal exposures on offspring psychiatric outcomes might be tested using an extension of the MR approach, incorporating genetic variants as proposed instruments for a particular prenatal exposure and for methylation at a specific locus. In certain contexts, this might be considered as a follow up to the present studies.
The association of prenatal maternal smoking with offspring blood DNA methylation has been investigated in individuals aged 16–48 years, and MR and mediation analyses have been performed to evaluate whether methylation markers have causal effects on disease outcomes in the offspring . 69 differentially methylated CpGs in 36 genomic regions (P-value < 1 × 10−7) were found to be associated with exposure to maternal smoking in adolescents and adults and MR analyses delivered evidence for a causal role of four maternal smoking-related CpG sites on an increased risk of SCZ or inflammatory bowel disease . Further studies analyse whether alcohol, tobacco, and caffeine use in pregnancy might be causally related to ADHD in the offspring using negative control and MR approaches (https://osf.io/wxu58) (https://osf.io/aqrxp).
Objective 3: Identify biological pathways associated with mental health symptoms and to validate potential drug targets based on these pathways.
Applying drug pathway analyses to the CAPICE GWAS results may permit us to derive hypotheses about potential drug targets and consequently possibilities for drug repurposing. The GWAMA on internalizing problems did not detect biological pathways (https://osf.io/w5adg/) so could not identify drug targets. This is not surprising as there were not many significantly associated genes.
Objective 4: Build a prediction model that identifies children at the highest risk of developing chronic mental health symptoms.
Using cohort data, as well as Swedish registry data, studies have been performed to predict outcomes of psychiatric symptoms in childhood and adolescence, focusing not only on mental disorders but also on somatic medical outcomes. As part of these analyses, a machine learning model including 474 predictors has been developed that can predict mental health problems in adolescence using data from the Child and Adolescent Twin Study in Sweden (CATSS) . The suggested model would not be appropriate for medical purposes, but it helps to build better models to predict mental health outcomes .
Moreover, longitudinal analyses of data from the Swedish and Dutch twin registers indicated that adolescent anxiety is associated with psychiatric disorders later in life, even when adjusting for other mental health issues .
Objective 5: Develop a sustainable international network of researchers in which collaboration is facilitated by data harmonization and information technology (IT) solutions enabling a joint analysis of data over cohorts.
Using Item-Response Theory (IRT) based test linking it has been evaluated whether internalizing and ADHD symptoms assessed by different instruments can be mapped onto dimensions that are shared across instruments. These analyses were possible as some of the EAGLE cohorts (ABCD , Raine , and TEDS ), had measured mental health symptoms of the same individuals at the same age with two or more instruments. This could allow combining individual raw item data from different instruments to maximize statistical power.
In addition, to facilitate data analyses over cohorts, a searchable data catalogue is created. The variables important for the current project include demographic and family characteristics, individual’s school achievements, mental health measures (both psychopathology as well as wellbeing) by various raters (mother, father, self-report, teacher), pregnancy/perinatal measures, several general health and anthropometric measures, parenting, parental mental health, and several genomic measures and biomarkers in children and parents. To build a search engine that returns items including the searched term as well as related terms, text mining of available data documentation has been used to identify relationships between words. These results can then be used to develop an advanced search engine for the data catalogue. If “mental health” is, for example, the search term, the results will also include “emotional problems”, “behavioural problems”, and “psychiatric history of the mother”.
Objective 6: Build a structure to disseminate the results to a broad audience of scientists, clinicians, patients and their parents, and the general public.
To engage the general public with the results from these studies, CAPICE researchers have also created content designed for a lay audience on the website (http://www.capice-project.eu/index), Twitter (https://twitter.com/capice_project), YouTube (https://www.youtube.com/channel/UCgq8uIHiHE69IlcHoYCjwKg/featured?view_as=subscriber), LinkedIn and Facebook. CAPICE was also represented at the Greenman Festival in Wales, UK, and ESRs presented multiple times on several international conferences.