Author Response
Reviewer #1 (Public Review):
This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:
- The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.
We clarified the specific longitudinal paths explored in our study in the end of the Introduction section (line 149~160). We also added a figure of the proposed path model (Figure 1) and refer to it in the Method section (line 232~239).
- There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.
We corrected and tone-downed all causal languages used in our manuscript. Per your suggestion, we deleted statements like ‘unbiased estimates’ and used expressions such as ‘adjustment for observed/unobserved confounding’ instead.
- I feel that the limitation section is a bit brief, and can be developed further.
We specified additional potential constraints of our study, including limited representativeness, limited periods of follow-up data, possible sample selection bias, and the use of non-randomized, observational data. These corrections can be found in line 518~538.
- I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?
We believe that the Reviewer #1’s comment for the correlations between PLEs derived from PQ-BC (total score and distress score PLEs) and from CBCL (parent-rated PLEs) might have been due to the fact that she/he was referring to the prior version of our manuscript submitted to a different journal. We obtained Pearson’s correlation coefficients between the PLEs (baseline year: r = 0.095~0.0989, p<0.0001; 1-year follow-up: r = 0.1322~0.1327, p<0.0001; 2-year follow-up: r = 0.1569~0.1632, p<0.0001) and added this information in the Method section for PLEs (line 198~201).
- What was the correlation between CP and EA PGSs?
We also added the Pearson’s correlation between the two PGSs (r =0.4331, p<0.0001) in the Methods section for PGS (line 214~215).
- Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.
We thank the reviewer for the feedback to clarify that educational attainment (EA) is not only a genetic marker of cognitive ability but also that of socioeconomic outcomes. Per your suggestion, we included the associations of EA PGS with multiple biological and socioeconomic outcomes found in prior studies (e.g., Abdellaoui et al., 2022) in the Introduction (line 131~142).
Abdellaoui, A., Dolan, C. V., Verweij, K. J. H., & Nivard, M. G. (2022). Gene–environment correlations across geographic regions affect genome-wide association studies. Nature Genetics. doi:10.1038/s41588-022-01158-0
- Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.
We conducted this adjustment considering that PLEs often precede the onset of schizophrenia. In addition, prior studies found that schizophrenia PGS is significantly associated with cognitive intelligence within psychosis patients (Shafee et al., 2018) and individuals at-risk of psychosis (He et al., 2021), and that significant distress psychotic-like experiences had greater positive correlation with schizophrenia PGS than PGS for psychotic-like experiences (Karcher et al., 2018).
For these reasons, we thought that it is necessary to assess whether the effects of cognitive phenotypes PGS (i.e., CP PGS and EA PGS) in the linear mixed model are significant after adjusting for schizophrenia PGS. We believe our results from the mixed linear model showed the sensitivity and specificity of the association between cognitive phenotype PGS and PLEs.
He, Q., Jantac Mam-Lam-Fook, C., Chaignaud, J., Danset-Alexandre, C., Iftimovici, A., Gradels Hauguel, J., . . . Chaumette, B. (2021). Influence of polygenic risk scores for schizophrenia and resilience on the cognition of individuals at-risk for psychosis. Translational Psychiatry, 11(1). doi:10.1038/s41398-021-01624-z
Karcher, N. R., Paul, S. E., Johnson, E. C., Hatoum, A. S., Baranger, D. A. A., Agrawal, A., . . . Bogdan, R. (2021). Psychotic-like Experiences and Polygenic Liability in the Adolescent Brain Cognitive Development Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. doi:https://doi.org/10.1016/j.bpsc.2021.06.012
Shafee, R., Nanda, P., Padmanabhan, J. L., Tandon, N., Alliey-Rodriguez, N., Kalapurakkel, S., . . . Robinson, E. B. (2018). Polygenic risk for schizophrenia and measured domains of cognition in individuals with psychosis and controls. Translational Psychiatry, 8(1). doi:10.1038/s41398-018-0124-8
- How did the FDR correction for multiple testing affect the results?
For all analysis results presented in our study, False Discovery Rate (FDR) correction for multiple testing compared p-values of nine key study variables: PGS (cognitive performance or educational attainment), family income, parental education, family’s financial adversity, Area Deprivation Index, years of residence, proportion of population below -125% of the poverty line, positive parenting behavior, and positive school environment. An exception was the sensitivity analysis that included schizophrenia PGS in the linear mixed model for adjustment: with another PGS variable added, FDR correction compared p-values of ten key variables. Overall, the effects of FDR correction on the results were limited; i.e., the majority of associations between the key variables and the outcomes, which were deemed highly significant, remained unchanged after the FDR correction.
Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.
We express our appreciation to the reviewer for the constructive feedback and guidance, which has significantly contributed to the improvement of our manuscript. As addressed in the preceding sections, we have implemented the necessary corrections and clarifications in response to the reviewer's suggestions. We remain open to making further amendments as needed, and thus invite any additional comments should any aspect of our revisions be deemed inadequate or inappropriate.
Reviewer #2 (Public Review):
This paper tried to assess the link between genetic and environmental factors on psychotic-like experiences, and the potential mediation through cognitive ability. This study was based on data from the ABCD cohort, including 6,602 children aged 9-10y. The authors report a mediating effect, suggesting that cognitive ability is a key mediating pathway in the link between several genetic and environmental (risk and protective) factors on psychotic-like experiences.
While these findings could be potentially significant, a range of methodological unclarities and ambiguities make it difficult to assess the strength of evidence provided.
Strengths of the methods:
The authors use a wide range of validated (genetic, self- and parent-reported, as well as cognitive) measures in a large dataset with a 2-year follow-up period. The statistical methods have the potential to address key limitations of previous research.
We sincerely thank the reviewer for recognizing these methodological strengths of our study. The reviewer’s positive comments are highly supportive and encouraging for us.
Weaknesses of the methods:
The rationale for the study is not completely clear. Cognitive ability is probably a more likely mediator of traits related to negative symptoms in schizophrenia, rather than positive symptoms (e.g., psychosis, psychotic-like symptom). The suggestion that cognitive ability might lead to psychotic-like symptoms in the general population needs further justification.
We sincerely thank and highly appreciate the concerns that the reviewer has raised regarding our proposal that cognitive ability may serve as a mediator of psychotic-like experiences. To the best of our knowledge, it has been proposed that cognitive ability can be a mediator of positive symptoms in schizophrenia (including psychotic-like experiences), as well as negative symptoms. This mediating role of cognitive ability was proposed in several prior studies on cognitive model of schizophrenia/psychosis. Per your suggestion, we included further justification in the Introduction section of our study (line 104~107). Specifically, we highlighted that cognitive ability has been theoretically proposed as a potential mediator of genetic & environmental influence on positive symptoms of schizophrenia such as psychotic-like experiences. We refer to studies conducted by Howes & Murray (2014) and Garety et al. (2001).
Howes, O. D., & Murray, R. M. (2014). Schizophrenia: an integrated sociodevelopmental-cognitive model. The Lancet, 383(9929), 1677-1687. doi:https://doi.org/10.1016/S0140-6736(13)62036-X
Garety, P. A., Kuipers, E., Fowler, D., Freeman, D., & Bebbington, P. E. (2001). A cognitive model of the positive symptoms of psychosis. Psychological Medicine, 31(2), 189-195. doi:10.1017/S0033291701003312
Terms are used inconsistently throughout (e.g., cognitive development, cognitive capacity, cognitive intelligence, intelligence, educational attainment...). It is overall not clear what construct exactly the authors investigated.
Thank you for your comment. We corrected the term ‘cognitive capacity’ to ‘cognitive phenotypes’ throughout our manuscript. We also added in the Introduction (line 141~143) that we will collectively refer to these two PGSs of focus as ‘cognitive phenotypes PGSs’, which is similar to the terms used in prior research (Joo et al., 2022; Okbay et al., 2022; Selzam et al., 2019).
Joo, Y. Y., Cha, J., Freese, J., & Hayes, M. G. (2022). Cognitive Capacity Genome-Wide Polygenic Scores Identify Individuals with Slower Cognitive Decline in Aging. Genes, 13(8), 1320. doi:10.3390/genes13081320
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., . . . Young, A. I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449. doi:10.1038/s41588-022-01016-z
Selzam, S., Ritchie, S. J., Pingault, J.-B., Reynolds, C. A., O’Reilly, P. F., & Plomin, R. (2019). Comparing Within- and Between-Family Polygenic Score Prediction. The American Journal of Human Genetics, 105(2), 351-363. doi:https://doi.org/10.1016/j.ajhg.2019.06.006
Not the largest or most recent GWASes were used to generate PGSes.
Thank you for mentioning this point. The reason why we were not able to use the largest GWAS for cognitive intelligence, educational attainment and schizophrenia is because (unfortunately) our study started earlier than the point when the GWAS studies by Okbay et al. (2022) and Trubetskoy et al. (2022) were published. We corrected that our study used ‘a GWAS of European-descent individuals for educational attainment and cognitive performance’ instead of the largest GWAS (line 206~208).
It is not fully clear how neighbourhood SES was coded (higher or lower values = risk?). The rationale, strengths, and assumptions of the applied methods are not fully clear. It is also not clear how/if variables were combined into latent factors or summed (weighted by what). It is not always clear when genetic and when self-reported ethnicity was used. Some statements might be overly optimistic (e.g., providing unbiased estimates, free even of unmeasured confounding; use of representative data).
Consistent with the illustration of neighborhood SES in the Methods section, higher values of neighborhood SES indicate risk. In the original Figure 2, higher values of neighborhood SES links to lower intelligence (direct effects: β=-0.1121) and higher PLEs (indirect effects: β=-0.0126~ -0.0162). We think such confusion might have been caused by the difference between family SES (higher values = lower risk) neighborhood SES (higher values = higher risk). Thus, we changed the terms to ‘High Family SES’ and ‘Low Neighborhood SES’ in the corrected figure (Figure 3) for clarification.
Considering that shorter duration of residence may be associated with instability of residency, it may indicate neighborhood adversity (i.e., higher risk). This definition of the ‘years of residence’ variable is in line with the previous study by Karcher et al. (2021).
We represented PGSs, family SES, neighborhood SES, positive family and school environment, and PLEs as composite indicators (derived from a weighted sum of relevant observed variables). To the best of our knowledge, it has been suggested from prior studies that these variables are less likely to share a common factor and were assessed as a composite index during analyses. For instance, Judd et al. (2020) and Martin et al. (2015) analyze genetic influence of educational attainment and ADHD as composite indicators. Also, as mentioned in Judd et al. (2020), socioenvironmental influences are often analyzed as composite indicators. Studies on psychosis continuum (e.g., van Os et al., 2009) suggest that psychotic disorders are likely to have multiple background factors instead of having a common factor, and notes that numerous prior research uses composite indices to measure psychotic symptoms. These are the reasons why we used components for these constructs instead of generating latent factors (which is done in the standard SEM method). On the contrary, we represented general intelligence as a common factor that determines the underlying covariance pattern of fluid and crystallized intelligence, based on the classical g theory of intelligence. We added this explanation in line 269~285.
Moreover, during estimation, the IGSCA determines weights of each observed variable in such a way as to maximize the variances of all endogenous indicators and components. We added this explanation in the description about the IGSCA method (line 266~268).
We deleted overly optimistic statements like ‘unbiased estimates’ and used expressions such as ‘adjustment for observed/unobserved confounding’ instead, throughout our manuscript.
Judd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., ... & Klingberg, T. (2020). Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment. Proceedings of the National Academy of Sciences, 117(22), 12411-12418.
Karcher, N. R., Schiffman, J., & Barch, D. M. (2021). Environmental Risk Factors and Psychotic-like Experiences in Children Aged 9–10. Journal of the American Academy of Child & Adolescent Psychiatry, 60(4), 490-500. doi:10.1016/j.jaac.2020.07.003
Martin, J., Hamshere, M. L., Stergiakouli, E., O'Donovan, M. C., & Thapar, A. (2015). Neurocognitive abilities in the general population and composite genetic risk scores for attention‐deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, 56(6), 648-656.
van Os, J., Linscott, R., Myin-Germeys, I., Delespaul, P., & Krabbendam, L. (2009). A systematic review and meta-analysis of the psychosis continuum: Evidence for a psychosis proneness–persistence–impairment model of psychotic disorder. Psychological Medicine, 39(2), 179-195. doi:10.1017/S0033291708003814
It appears that citations and references are not always used correctly.
We thoroughly checked all citations and specified the references for each statement.
We deleted Plomin & von Stumm (2018) and Harden & Koellinger (2020) and cited relevant primary studies (e.g., Lee et al., 2018; Okbay et al., 2022; Abdellaoui et al., 2022) instead. We also specified the references supporting the statement that educational attainment PGS links to brain morphometry (Judd et al., 2020; Karcher et al., 2021). As Okbay et al. (2022) use PGS of cognitive intelligence (which mentions the analyses results in their supplementary materials) as well as educational attainment, we decided to continue citing this reference. These corrections can be found in line 131~141.
Strengths of the results:
The authors included a comprehensive array of analyses.
We thank the reviewer for the positive comment.
Weaknesses of the results:
Many results, which are presented in the supplemental materials, are not referenced in the main text and are so comprehensive that it can be difficult to match tables to results. Some of the methodological questions make it challenging to assess the strength of the evidence provided in the results.
As you rightly identified, we inadvertently failed to reference Table S2 in the main text. We have since corrected this omission in the Results section for the IGSCA (SEM) analysis (line 375). The remainder of the supplementary tables (Table S1, S3~S7) have been appropriately cited in the main manuscript. We recognize that the quantity of tables provided in the supplementary materials is substantial. However, given the comprehensiveness and complexity of our analyses, which encompass a wide array of study variables, these tables offer intricate results from each analysis. We deem these results, which include valuable findings from sensitivity analyses and confound testing, too significant to exclude from the supplementary materials. That said, we are open to, and would greatly welcome, any further suggestions on how to present our supplementary results in a more accessible and digestible format. We are ready and willing to implement any necessary modifications to ensure clarity and ease of comprehension. Your guidance in this matter is highly valued.
Appraisal:
The authors suggest that their findings provide evidence for policy reforms (e.g., targeting residential environment, family SES, parenting, and schooling). While this is probably correct, a range of methodological unclarities and ambiguities make it difficult to assess whether the current study provides evidence for that claim.
Impact:
The immediate impact is limited given the short follow-up period (2y), possibly concerns for selection bias and attrition in the data, and some methodological concerns.
We added as study limitations (line 518~538) that the impact of our findings for understanding cognitive and psychiatric development during later childhood may be limited due to the relatively short follow-up period, the possibility of sample selection bias, and the problems of interpreting analyses results from an observational study as causality (despite the novel causal inference methods, designed for non-randomized, observational data, that we used).
As responded above, we made necessary corrections and clarifications for the points suggested by the reviewer. As we are willing to make additional revisions, please feel free to give comments if you feel that our corrections are insufficient or inappropriate.
