Correlated factors model

When modelling psychotic experiences, negative dimension items, depression and anxiety as separate, correlated, latent constructs (correlated factors model), we found that genetic risk for schizophrenia was associated with an increase in all four adolescent psychopathology constructs. Schizophrenia PRS association effect sizes were similar and confidence intervals overlapped across all psychopathology factors. The smallest effect size was for the association between the schizophrenia PRS and depression which is consistent with our previous publication using binary outcome measures within this sample16. Our results from the correlated factors model showing stronger evidence of association with psychotic experiences compared to our previous publication16, suggest that accounting for measurement error through use of latent models might be particularly important for these phenomena.

Bifactor model

Bifactor models have a number of advantages over standard univariate approaches and are a popular approach in modelling construct-relevant multidimensionality52,53, improving psychiatric phenotype definition and, in comparison to a summed-score approach, can provide higher statistical power to detect larger effect sizes54. Bifactor models have been used in twin studies to decompose additive genetic and environmental effects across phenotypes55,56 and in a cohort study to investigate associations with candidate genes implicated in affective disorders54, but have not been utilized previously to understand phenotypic manifestation of polygenic liability for psychiatric disorders as far as we are aware.

The high correlation between the psychopathology factors and large share of the score variance as a result of the general factor (indicated by the omegas) suggest that covariance between responses to items measuring psychotic experiences, negative dimension, depression and anxiety can be explained by an underlying general psychopathology latent construct within the general population, distinct from latent constructs specific for each trait. In comparison to the correlated model results, there was only weak evidence of association between schizophrenia genetic risk and remaining variance for psychotic experiences after accounting for the general psychopathology factor. This suggests that psychotic experiences resulting from higher genetic risk for schizophrenia usually occur, at this age, in the presence of other psychopathology too. This is perhaps not surprising; for example, it is hard to imagine holding paranoid beliefs or hearing hostile voices without some comorbid anxiety or low mood.

The evidence of association between schizophrenia genetic risk and remaining variance for anxiety, and especially for depression, was even weaker when taking into account the general psychopathology factor. However, there was stronger evidence of association with the remaining variance relating to the negative dimension items, although our IPW results suggest that this association might not be robust. This indicates that schizophrenia genetic risk may manifest particularly strongly as negative dimension traits in adolescence, above and beyond the occurrence of general psychopathology, confirming our previous observation16. It is also possible that risk variants for schizophrenia identified in the GWAS may only weakly index risk for hallucinations and delusions and more strongly reflect genetic risk for other characteristics such as negative symptoms that index severity or chronicity of illness and that might be selected for in clinically ascertained samples57.

Interpretation in context of previous studies

Whilst family studies have shown that negative symptoms may have higher familial aggregation compared to positive or depressive symptoms in people with schizophrenia58, as yet there are no clear patterns of heritability in clinical samples for phenotype dimensions as they are currently conceived59,60. A population-based twin study of trait psychopathology showed that self-reported anhedonia and parent-rated negative symptoms were more heritable than hallucinations, though no more heritable than paranoia61. Our findings indicate that negative dimension traits as well as other psychopathology during adolescence, whilst not necessarily at levels of clinical significance, are indeed influenced by common genetic variants that increase the risk for schizophrenia.

Other studies have examined the relationship between schizophrenia genetic risk and psychopathology, both in clinical and population-based samples. One study of people with schizophrenia reported that polygenic risk was associated with negative/disorganized factor scores but not with positive symptom or mood dimensions62, and more recently associations were reported between genetic risk scores and both anxiety symptoms and general psychopathology, but not with positive or negative symptom dimensions, in patients with first episode psychosis63. Our correlated model findings are consistent with associations reported with depression and anxiety in ALSPAC and the Netherlands Twin Register64. Other studies have not found evidence of associations between schizophrenia genetic risk and dimensions of psychopathology65,66, although statistical power may have been limited due to the size of the discovery or target samples used.

The lack of consistency of findings across studies to date may be partly due to the difficulty of teasing out psychopathology-specific effects from those that are shared across symptom domains. By using a bifactor modelling approach, our study is the first to test whether genetic risk is manifest as a common psychopathology, or as specific symptoms related to one or more underlying psychopathology constructs. Whilst we show that genetic risk for schizophrenia is manifested primarily as general psychopathology and possibly negative dimension traits, it is possible that with greater power, for example from risk scores derived using yet larger discovery samples, we might also find evidence of specific effects on psychotic experiences, anxiety and depression above and beyond the effect on general psychopathology. This might be difficult, however, as specific traits appear to offer very little variability above that explained by general psychopathology at this age. More detailed analyses, for example using risk scores for specific sets of functionally related genes or more detailed psychopathology items, might also allow us to better understand the biological pathways that lead to specific, as well as shared, psychopathology through use of approaches such as latent trait modelling as we use here.

Genetic risk for MDD, neuroticism and bipolar disorder

We found no robust evidence of association between the bipolar disorder genetic risk score and adolescent psychopathology, though this might be due to the smaller discovery sample used to derive PRSs for this phenotype compared to those for schizophrenia, MDD and neuroticism. As compared to the schizophrenia and neuroticism associations, the MDD PRS was only weakly associated with the general factor which may be due to the lower SNP-based heritability for MDD reported by the GWAS used within the current study (0.06–0.07)34 as compared to the other phenotypes.

We found that genetic risk for neuroticism was strongly associated with anxiety, depression and negative dimension constructs within the correlated factors model but, unlike our results for schizophrenia genetic risk, not with psychotic experiences. Within the bifactor model, genetic risk for neuroticism was strongly associated with the general psychopathology construct, and less strongly with the remaining variance for anxiety. Evidence for association with remaining variance for negative dimension items as well as that for psychotic experiences was weaker than those for schizophrenia genetic risk, indicating that genetic risk for schizophrenia may have a more specific effect on these phenotypes than genetic risk for neuroticism.

Strengths and limitations

The use of a large population-based sample with a broad range of measures of psychopathology during adolescence allows us to infer how genetic risk for psychiatric disorders is likely manifested in the general population at this age. However, whilst the ALSPAC cohort is broadly representative of the UK population, attrition and missing data means that selection bias might have affected our results. Genetic risk for schizophrenia is associated with increased likelihood of attrition67, and if presence of psychopathology is also related to missingness this could introduce collider bias in our results.

Whilst self-report measures may perform less well for psychotic experiences than other psychopathological domains, we used self-report measures as we wanted all psychopathology domains assessed at similar ages using questionnaire data, and we previously reported that associations with schizophrenia genetic risk were consistent when comparing self-report and interview-assessed psychotic experiences16. Unfortunately, additional data were not collected to ascertain the test–retest reliability of the questionnaires used. We can therefore not assess whether intra-individual variability in responses has biased our results.

A strength of our study is that use of a latent modelling framework allowed us to tease out the effects that explain the shared variance across measures from those that are specific to constructs separate from general psychopathology. However, we do not know the source or relevance of the specific-construct variance, particularly where this has only modest specific variance over and above the general factor, as for example, the negative dimension construct. Furthermore, whilst the symptoms assessed using the CAPE measure of negative symptoms were derived from the Scale for Assessment of Negative Symptoms and load onto a separate factor from depressive symptoms in other studies29, as in ours, they might not accurately index negative symptoms as conceptualized in schizophrenia.

Furthermore, item contamination may have occurred, whereby, for example, similarity in items between the 12-item Eysenck Personality Questionnaire-Revised used to generate the neuroticism PRS and the depression and anxiety measures used in our study may have led to an overestimate of association between the neuroticism PRS and the general factor. However, a previous study identified a genetic overlap between neuroticism (negative emotionality) and general psychopathology using an item pool designed to exclude synonyms or antonyms of psychopathology symptoms56, suggesting that such a bias is unlikely to adequately explain our findings. Similarity in question wording was also evident between the CAPE and MFQ items used to construct the negative dimension of psychosis and depression factors, respectively. For example, both scales contain items relating to loss of motivation. This may explain their high correlation within the correlated factors model.

The particular measures used in the current study may also have introduced confounding by question time-frames. Questions from two domains referred to past month experiences, one to experiences in the past 2 weeks and one to experiences since age 15 years. This may reduce/increase the covariances between each pair of latent factors and hence the degree of support for the bifactor model.

Finally, our models do not include measures of externalizing psychopathology or cognition, thus limiting comparison to the general factor in bifactor models that have been derived in studies that have incorporated such measures.