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. 2024 Mar 30;14(1):171.
doi: 10.1038/s41398-024-02866-3.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

Vincent-Raphael Bourque  1 , Cécile Poulain  1 , Catherine Proulx  1 , Clara A Moreau  2 , Ridha Joober  3 , Baudouin Forgeot d'Arc  1 , Guillaume Huguet  1 , Sébastien Jacquemont  4
Affiliations

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

Vincent-Raphael Bourque et al. Transl Psychiatry. .

Abstract

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study selection.
Fig. 2
Fig. 2. Degree of similarity between disorders, measured at different levels of observation.
Each point represents the estimate of similarity (coefficient of correlation, r) between two disorders, which was measured in different studies and at different levels of observation. Descriptive statistics across studies for each level of observation are represented as boxplots; the box indicates the median, 25th, and 75th percentiles. The numbers linked to the points refer to the different studies: 1. Lee et al. [54], 2. Anttila et al. [2], 3. Lee et al. [55], 4. Schork et al. [33], 5. Grotzinger et al. [56], 6. Gerring et al. [58], 7. Li et al. [57], 8. Selzam et al. [34], 9. Wang et al. [20], 10. Gandal et al. [6], 11. Sadeghi et al. [59], 12. Kaufmann et al. [60], 13. Opel et al. [7], 14. Patel et al. [8], 15. Radonjic et al. [22], 16. Patel et al. [61], 17. Moreau et al. [5]. One study (Sey et al. [19]) that presented metrics of similarity on a scale not comparable to that of correlation coefficients was excluded from this figure.
Fig. 3
Fig. 3. Comparisons and consensus ranking of the pattern of disorder similarities across studies.
A Pairwise comparison matrix between 18 studies (Table 1) reporting on six levels of biological observation. For each pair of studies, we use the Pearson correlation coefficient to compare the pattern of similarities across disorder pairs. Significant correlations are marked as *unadjusted p < 0.05, **FDR < 0.05. Correlations with less than five pairwise disorder comparisons are marked with a gray dashed line. B The correlation matrix in panel A is summarized at the level of disorder pairs using principal component analysis (PCA), with a first dimension that accounts for 51.4% (95% CI: 43.2, 65.4) of variance among the 18 studies. Error bars represent 95% confidence intervals. A higher score on Dimension 1 implies that a pair of disorders has consistently higher similarity across all studies and levels of observation. C Contributions of individual studies to the first dimension of PCA, color-coded by the level of observation.
Fig. 4
Fig. 4. Risks in first-degree relatives across disorders.
Probability in first-degree relatives across disorders displayed as relative risk (RR) compared to the lifetime risk present in the general population. Genetically predicted risks were computed using published methods and in two independent analyses, using either family-based or twin-based heritability, both of which are compared here with observed risk estimates from published population-based studies. The risks observed clinically were systematically higher than those predicted on the basis of the genetic overlap. Note that the risk ratios are valid in both directions from one disorder to another (RRdiagnosis1|diagnosis2 = RRdiagnosis2|diagnosis1).
Fig. 5
Fig. 5. Cross-disorder research trends.
Proportion of studies referenced on PubMed that address two disorders at a time, for each pair of disorders, as compared to the total number of studies for the same pair of disorders, as a function of publication year. Locally estimated scatterplot smoothing (LOESS) regression lines indicate trends over time. Shaded background colors correspond to the Diagnostic and Statistical Manual of Mental Disorders (DSM) editions.
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