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# Multitrait Analysis to Decipher the Intertwined Genetic Architecture of Neuroanatomical Phenotypes and Psychiatric Disorders

Antoine Auvergne, Nicolas Traut, Léo Henches, Lucie Troubat, Arthur Frouin, Christophe Boetto, Sayeh Kazem, Hanna Julienne, Roberto Toro, Hugues Aschard

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## ► To cite this version:

Antoine Auvergne, Nicolas Traut, Léo Henches, Lucie Troubat, Arthur Frouin, et al. Multitrait Analysis to Decipher the Intertwined Genetic Architecture of Neuroanatomical Phenotypes and Psychiatric Disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2025, 10 (7), pp.740-749. 10.1016/j.bpsc.2024.08.018. hal-05163886

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# Multitrait Analysis to Decipher the Intertwined Genetic Architecture of Neuroanatomical Phenotypes and Psychiatric Disorders

Antoine Auvergne, Nicolas Traut, Léo Henches, Lucie Troubat, Arthur Frouin, Christophe Boetto, Sayeh Kazem, Hanna Julienne, Roberto Toro, and Hugues Aschard

## ABSTRACT

**BACKGROUND:** There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven to be challenging, and new approaches are needed to infer the genetic structures that may underlie those phenotypes. Multivariate analyses are a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

**METHODS:** First, we conducted univariate and multivariate genome-wide association studies for 9 MRI-derived brain volume phenotypes in 20,000 UK Biobank participants. Next, we performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches could distinguish disorder-associated and non-disorder-associated variants from 6 psychiatric disorders: bipolar disorder, attention-deficit/hyperactivity disorder, autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized *k*-medoids approach.

**RESULTS:** A univariate MRI genome-wide association study revealed only negligible genetic correlations with psychiatric disorders, while a multitrait genome-wide association study identified multiple new associations and showed significant enrichment for variants related to both attention-deficit/hyperactivity disorder and schizophrenia. Clustering analyses also detected 2 clusters that showed not only enrichment for association with attention-deficit/hyperactivity disorder and schizophrenia but also a consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways in both MRI phenotypes and schizophrenia.

**CONCLUSIONS:** Our results show that multitrait association signature can be used to infer genetically driven latent MRI variables associated with psychiatric disorders, thereby opening paths for future biomarker development.

https://doi.org/10.1016/j.bpsc.2024.08.018

Psychiatric disorders are characterized by strong heterogeneity in their clinical presentation, and identifying biomarkers remains a critical research area to facilitate diagnosis and implement more focused treatment options (1,2). The study of neuroimaging phenotypes and their association with mental disorders has become a major direction to develop robust and reliable biomarkers and pathological features (3). Brain magnetic resonance imaging (MRI) phenotypes have the potential to capture endophenotypes related to the molecular and cellular mechanisms involved in the etiology of the disorders and thus improve our understanding of the underlying pathophysiology (4). Thousands of studies have been published in the field, with approximately one-third of studies being based on the analysis of structural MRI data (5). This research has contributed to the identification of links between brain regions and psychiatric disorders, such as increased ventricular size in schizophrenia (SCZ) (6), but other associations remain

controversial, with mixed directions of effect in the genetic overlap (7), e.g., both a higher (8) and lower (9) putamen volume being associated with higher risk for SCZ.

Neuroimaging genomics offers opportunities to address these limitations through the integration of genomic and imaging data (10). Both regional volumetric phenotypes and psychiatric disorders have a strong genetic component, with estimated heritability ($h^2$) commonly above 50% (6,11–20). Over the past years, there has been increasing evidence of genetic correlation between those disorders and MRI phenotypes, which has shed new light on potential mechanisms that underlie the relationships between MRI phenotypes and mental disorders (14,21–27). There exist multiple possible causal models underlying shared genetics between MRI and disorders (Figure S1) that can be difficult to disentangle from one another. However, due to the invariant nature of germline genetic variation, solving this causal relationship is not a
prerequisite if the goal is to examine the links between MRI phenotypes and mental disorders and identify biological pathways involved in the disorders.

Here, we investigated the potential of multivariate MRI ge-
netic approaches, that is, jointly modeling the genetics of
multiple MRI phenotypes to inform the genetics of mental
disorders. As a proof of concept, we considered 8 MRI phe-
notypes that had already been investigated in previous studies
(28) and the following 6 disorders: attention-deficit/
hyperactivity disorder (ADHD), autism spectrum disorder
(ASD), bipolar disorder, major depressive disorder, obsessive-
compulsive disorder, and SCZ. We first conducted univariate
and multitrait genome-wide association screenings of the MRI
phenotypes and estimated the genetic correlation with each of
the 6 disorders. Next, we investigated the potential utility of
both approaches to improve the detection of genetic variants
associated with those disorders. Finally, we hypothesized that
shared genetics across the MRI phenotypes might point to-
ward genetically driven latent MRI variables that display
increased association with the disorders. Testing this hy-
pothesis, we identified 2 clusters of variants associated with
SCZ and ADHD, which we characterized using in silico func-
tional annotations.

METHODS

Neuroanatomical Brain Phenotypes and Mental
Disorder Genome-Wide Association Studies

We conducted genome-wide association studies (GWASs) of 8 regional brain volumes: intracranial, accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus, which were selected based on a recent study from our group (28) of a subset of 20,744 unrelated UK Biobank participants (10,861 females, 9883 males) using PLINK2 (29) and standard quality control parametrization (Supplementary Notes). Brain phenotypes were derived from raw MRI data using FreeSurfer software (30). For comparison purposes, we used publicly available GWAS results for the same regional phenotypes from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium (31), which included 13,171 individuals and approximately 7.5 million variants, with 6 million variants overlapping with the UK Biobank GWAS. Genetic associations with brain phenotype were compared with GWASs of 6 mental disorders: ADHD, ASD, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and SCZ. GWASs of these disorders were conducted by the Psychiatric Genomics Consortium (PGC) (32–37) and did not include UK Biobank participants (Table S5).

Multitrait GWAS Analysis

Multiple methods have been proposed to conduct multitrait GWASs (Supplementary Notes). Here, we used JASS (https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeine) (38), a polyvalent and efficient Python package that allows for the computation of various joint tests from GWAS summary statistics. We applied an omnibus test, where a joint statistic T<sub>omni</sub> is derived as T<sub>omni</sub> = z<sup>T</sup>Σ<sup>-1</sup>z, where z = (z<sub>1</sub>, z<sub>2</sub>, ..., z<sub>k</sub>) is a vector of k z scores (for k GWAS results), derived from the summary statistics as z<sub>i</sub> = ̂β<sub>i</sub> / ̂σ<sub>i</sub> where ̂β<sub>i</sub> and ̂σ<sub>i</sub> are the

estimated regression coefficient and its standard error for
study *i*; and *Σ* is the covariance matrix between the z scores
under the null. The latter covariance, *Σ*, is approximated by the
intercept covariance matrix from multitrait linkage disequilib-
rium (LD) score regression (39,40) applied to the GWAS sum-
mary statistics. Under the null hypothesis of no association
between the variant tested and any of the *k* phenotypes tested
jointly, *T*<sub>omni</sub> follows a *χ*<sup>2</sup> distribution with *k* degrees of
freedom. For univariate GWAS analyses, comparisons of re-
sults were conducted using the top associated variants from
predefined independent loci.

LD Region-Based Comparison

Unless specified otherwise, comparisons across GWAS re-
sults were done using the top associated variants from inde-
pendent loci defined based on LD blocks. A total of 1668 loci
had GWAS summary statistics for at least one phenotype.
Focusing on the 7,099,802 variants with data on both MRI and
disorders, loci included 4256 variants on average, with a
maximum of 9859 and a minimum of 16. Using those inde-
pendent loci for comparison purposes avoids overcounting
association signals for correlated variants and addresses the
issue of missing summary statistics (e.g., when the top asso-
ciated variant for one phenotype is not available for another
phenotype).

Enrichment for Association With Psychiatric
Disorders

We estimated whether genetic variants selected based on univariate and multivariate association results with the MRI phenotypes were enriched for association signal with the psychiatric disorders. Enrichment was conducted for each brain phenotype–psychiatric disorder pair using stepwise filtering of associated loci based on the strength of significance. We tested the enrichment using a joint association statistic between selected variants and each disorder derived as $S_u = \sum_{i=1...n} \chi_i^2$, where $\chi_i^2 = (\chi_1^2, ..., \chi_n^2)$ is a vector of $n$ $\chi^2$ (for $n$ variants). Moreover, we generated an empirical $p$ value distribution to account for potential biases in the data (Supplementary Notes).

Supervised Learning of Disorder Genetic Risk
Based on MRI Association

We performed a supervised learning of the disorder GWAS
summary statistics using MRI GWASs as predictors. The goal
was to determine whether association of variants with the
disorder could be predicted based on a linear combination of
the MRI association statistics. In practice, we used a multiple
regression model as $Z_{disorder} = \sum_{i=1...8} \gamma_i Z_{MRIi}$ where the outcome
$Z_{disorder}$ is the vector of disorder variants z score and $Z_{MRIi}$ is
the vector z score for MRI phenotype *i* for those same variants.
The regression was applied for ADHD and SCZ using the top
associated variant per locus from each disorder, including
variants significant over a *p*-value threshold (*p*<sub>T</sub>) that we varied
from 1 to 2.5 × 10<sup>-10</sup>. For each variant set, we derived the R<sup>2</sup>
and the model *p* value.
## Assessing Concordant Associations Between Psychiatric Disorders and Brain Phenotypes

We assessed the concordance of effects' directions between brain phenotypes and each disorder, that is, whether the alleles associated with an increase in phenotypic value were consistently associated with either a decreased or an increased risk of the disorders. For the enrichment analysis, we used independent genetic variants selected based on their *p* value for association with the brain phenotypes, using the most associated variant per LD locus. Concordance was tested sequentially using the same method of stepwise filtering of associated loci based on the strength of significance. We tested for concordance of effect using a weighted sum of *z* score statistics defined as $S_s = \sum_{q=1...Q} Z_q \times w_q$, where $Z_q$ is the *z* score of variant *q* for the disorder and $w_q$ is the *z* score for the phenotype tested. Under the null, the variance of $S_s$ is $var(S_s) = \sum_{q=1...Q} w_q^2$, and a test for concordance of effect can be derived as $T_s = S_s^2 / \sum_{q=1...Q} w_q^2$. Under the null, $T_s$ follow a 1 degree-of-freedom $\chi^2$ distribution.

## Enrichment for Concordant Associations After Clustering of Variants Based on Multitrait Association

We conducted a clustering analysis to examine whether subsets of variants selected based on multitrait association similarity display concordant association with disorders. The clustering was performed on variants with an association *p* value under $5 \times 10^{-5}$ for either the multitrait test or the univariate brain phenotype test using a spherical *k*-medoids method, as implemented in the R Cluster (41) package, and where the clustering is applied to the absolute cosine distance between data points. The cosine distance is defined as $\frac{G_1 \cdot G_2}{||G_1|| \cdot ||G_2||}$ with $G_i$ a vector containing the coordinates of single nucleotide polymorphism (SNP) *i*. These coordinates are the first 5 principal components from a principal component analysis that we conducted on the suggestive variants we selected. All variants that were not used in the clustering (i.e., with a *p* value above $5 \times 10^{-5}$) were assigned a posteriori to the closest cluster, again using the absolute cosine distance to the closest medoids. To further increase the homogeneity of the multitrait effect across variants within clusters, we filtered out variants that displayed an absolute cosine distance to the medoid >0.5.

For each cluster, we computed the enrichment for association with each disorder with prior on the direction of effects using the same approach as was used for the concordant enrichment of brain phenotypes with a psychiatric disorder, that is $S_c = \sum_{i \in N} Z_i w_i$, where $Z_i$ is the *z* score for a disorder for variant *i*. The weight $w_i$ was defined as follows: $w_i = \sum_{p \in P} (\tilde{Z}_{pi} \times w_{pc})$ with $\tilde{Z}_{pi}$ the *z* score of variant *i* for phenotype *p*, *P* the number of phenotypes, and $w_{pc}$ the *z* score of the medoid of cluster *c* for phenotype *p*. Under the null hypothesis of no enrichment, $\sum_{i \in N} (w_i^2) \sim \chi_{1df}^2$. For a threshold from 0 to 10 with increments of 0.5, we tested each cluster using SNPs with

$a - \log_{10}(\text{minimum } p \text{ value})$ above the threshold, and we kept the most associated cluster for each threshold. To avoid clusters with a signal led by only a few SNPs, we applied 2 additional conditions. First, we only considered clusters with at least 5 SNPs above the threshold. Second, when a cluster was significant, we removed the 3 variants with the highest *|z* score| within the cluster and computed a second $S_{\text{signed}}$ value. If it was not significant, it was removed from the analysis.

## RESULTS

### Univariate and Multitrait GWAS of MRI Phenotypes

We conducted univariate and multitrait GWASs on 11,993,198 variants of 9 MRI phenotypes, i.e., total brain volume and 8 regional volumes: the nucleus accumbens, amygdala, caudate nucleus, pallidum, putamen, thalamus, hippocampus, and intracranial volume (Figure S2). The total brain volume GWAS detected 5 genome-wide significant phenotype-locus associations (Table S1A). The regional brain volume GWASs detected a total of 44 genome-wide significant phenotype-locus associations, involving 33 loci, with 6 loci displaying significant associations for at least 2 phenotypes (Table S1A, B). We compared these results against previous GWASs of the same phenotypes conducted in independent samples as part of the ENIGMA consortium (31). All associations reported in the ENIGMA study were replicated, being either genome-wide significant or suggestive genome-wide significant in our analysis (Table S2). Of the 44 associations detected in the current analysis, we observed a strong enrichment for association in ENIGMA, with 47% (21 of 44) of the closest top variants being nominally significant ($p < .05$) (Table S1B).

Multitrait analysis detected 49 loci associated at the genome-wide significance level ($p < 5 \times 10^{-8}$), corresponding to a 48% increase in power as compared with univariate analysis. This included 23 of the 33 loci that were identified with the univariate analysis and 26 new associations that were not identified by the univariate screening (Figure S3; Table S3). Minimum univariate *p* value at these new signals ranged from .003 to $5.8 \times 10^{-9}$, highlighting the ability of the multitrait test to detect associations with limited single-trait signal. The univariate association pattern varied substantially across these new signals. Notably, we observed enrichment for association with caudate and putamen volumes, with 15 of 26 univariate *p* values being nominally significant. Of the 26 new associated variants, 7 were associated with brain or psychiatric disorders (Table S3). For comparative purposes, we also conducted multitrait GWASs on 2 sets of related MRI phenotypes (Supplementary Notes). Although those targeted multitrait analyses identified a few additional loci, they missed a large fraction of the loci identified using our heterogeneous set of 9 phenotypes (Figure S4; Table S4).

### Shared Genetics Between MRI Phenotypes and Disorders

We estimated the genetic correlation between the 9 MRI phenotypes and 6 psychiatric disorders, ADHD, ASD, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and SCZ, using both top variants and genome-wide results (Table S5; Supplementary Notes). Although we observed high genetic correlation across MRI phenotypes and across disorders, correlations between MRI phenotypes and
psychiatric disorders were low to negligible (Supplementary Notes and Figure S5; Table S5). However, the absence of a genetic correlation does not necessarily imply that there are no shared genetics. Some variants may be associated with both mental disorder and MRI without showing a concordant effect across the genome. To address this question, we tested whether genetic variants selected based on univariate and multivariate association results with the MRI phenotypes were enriched for association signal with the psychiatric disorders (Supplementary Notes). Except for the hippocampus and ASD, which displayed an enrichment just above the significance threshold after correction for multiple testing ($p_T = 5.4 \times 10^{-6}$), variants selected through univariate neuroanatomical GWAS results did not show significant associations with disorders (Figure 1). Conversely, the variants selected through the multivariate analysis showed highly significant associations with ADHD and SCZ ($p < 10^{-7}$, the smallest $p$ value achievable in our analysis) (see Supplementary Notes) across a range of multitrait $p$-value thresholds.

As a complementary analysis, we investigated whether this significant enrichment for signal could be translated into improved polygenic risk models using the multitrait results and using SCZ as a case study. We derived 2 polygenic risk scores (PRSs), one using a standard approach applied to the SCZ GWAS only and a weighted PRS using the JASS association signal as weight (see Supplementary Notes). The standard model produced an area under the receiver operating curve of 0.603 (SD = 0.013), and the JASS-weighted PRS produced an

area under the receiver operating curve of 0.621 (SD = 0.013). Although the difference between the approaches was small ($p = .095$), it is consistent with previous work (7), suggesting that multitrait neuroanatomical GWAS results could not only help to identify disorder-associated variants but also improve the predictive accuracy of mental disorder PRSs.

## Supervised Learning of Disorder Genetic Risk Based on MRI Association

Enrichment analyses based on multitrait MRI association confirmed some shared genetic factors between neuroanatomical traits and mental disorders. However, this enrichment is not informative regarding potential latent biomarkers involved in the genetics of these 2 outcomes. The omnibus multitrait test that we used is an 8 degree of freedom test, the significance of which is independent of the direction of the effects across the 8 traits. That is, the multitrait test can equally capture a range of heterogeneous associations between each variant tested and the MRI phenotypes. In parallel, the univariate MRI GWAS showed neither concordant association with the disorder nor enrichment for association (Figure 1; Figure S5). Given these 2 results, we wondered whether a naïve supervised learning approach applied to the entire genome could recover potential linear combinations of the MRI phenotypes' association statistics that could explain the enrichment for association with ADHD and SCZ. In practice,

**Figure 1.** Enrichment for genetic association with disorders conditional on brain phenotype association. We estimated the extent to which genetic signal observed with each of the 8 neuroanatomical phenotypes were enriched for genetic association with 6 disorders: ADHD, ASD, BIP, MDD, OCD, and SCZ. Each panel presents the $-\log_{10}(p)$ value for enrichment for a given disorder for 170 subsets of independent genetic variants selected based on their association with the neuroanatomical phenotype. We applied the same approach using the $p$ value from the multitrait association test (JASS, red curve). $p$ Values for enrichment were derived using a permutation approach, which limited the minimum $p$ that could be reached. Dashed red lines represent this maximum, indicating that the empirical $p$ value is $<10^{-7}$. The dashed blue line represents a stringent Bonferroni-corrected significance threshold. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; BIP, bipolar disorder; ICV, intracranial volume; MDD, major depressive disorder; OCD, obsessive-compulsive disorder; SCZ, schizophrenia.

<figure><img src="image_4.png" /></figure>
we performed a multiple regression where the z score of the disease was treated as the outcome, and the z scores of the 8 MRI phenotypes at the same variants were used as predictors. We applied this approach using the top associated variant per locus and considered a range of $p_T$ on the p value with the disorder (Supplementary Notes). As shown in Table S6, we did not identify any significant linear combination that was able to predict the disorder genetic association after accounting for multiple testing. The adjusted $R^2$ was very low for most of the thresholds considered ($R^2 < 0.004$ for $p_T = 5 \times 10^{-4}$), although 1 subset showed nominal significance and a slightly larger $R^2$ for SCZ ($R^2 < 0.069$, $p = .028$ for $p_T = 5 \times 10^{-4}$).

## Unsupervised Learning to Identify Latent Genetic Pathways

The null results from both the genetic correlation study and the supervised learning suggest that the genetic relationship between mental disorder and MRI phenotypes cannot be summarized at the genome-wide level and may instead be heterogeneous across the genome. One possibility is that the neuroanatomical phenotypes depend on multiple genetic pathways with various effects on the disorders. Given the strong genetic correlation across MRI phenotypes (Figure S5), we considered using an unsupervised learning approach in

which variants are first grouped based on the clustering of the multitrait matrix of variants-trait association and then tested jointly for association with the disorders. Briefly, we applied a spherical k-medoids clustering algorithm to the MRI association matrix, considering 2 to 12 clusters, and tested the association between genetic risk scores derived for each cluster and each of the 6 disorders (Figure 2). Intuitively, this approach assumes that subsets of variants exist with similar multitrait association patterns (e.g., variants positively associated with 2 traits and negatively associated with other traits) that encode distinct biological functions associated with psychiatric disorders.

The associations between the top clusters and the 6 disorders, while filtering out variants based on their multitrait association *p* values, are provided in Figure 3. We observed significant associations for the 2 disorders that were also identified in the omnibus multitrait enrichment analyses. One cluster was significantly associated with ADHD (minimum *p* = 3.0 × 10⁻⁹) and another one was significantly associated with SCZ (minimum *p* = 2.0 × 10⁻⁹). There was no significant association with the other disorders. The top ADHD-associated cluster was observed for a 2-cluster model and included 939 independent variants (Table S7). The strongest association was observed when using a JASS $p_T$ of 1.0 × 10⁻⁵, which resulted in a subset of 104 variants. The top SCZ-associated

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Clustering approach. We used univariate brain phenotype association signals to build clusters of variants showing similar multitrait association patterns. Panel (A) illustrates the clustering pipeline. Top independent associated variants are selected based on their univariate and multivariate association (*p* < 5 × 10⁻⁵). A dissimilarity matrix is derived based on the absolute cosine distance. k-Medoids clustering is applied on this distance matrix, which minimizes a cost function depending on the distance between each point x and medoid o. Finally, independent nonsignificant variants (*p* > 5 × 10⁻⁵) are assigned to cluster a posteriori. Panel (B) displays the real absolute cosine dissimilarity matrix derived from the top 479 variants. The k-medoids clustering was applied to this matrix assuming 2 and 12 clusters. Variants from each cluster were then selected based on their *p* value for association with the brain phenotypes, and the resulting sets were evaluated for enrichment in concordant effects with each of the 6 disorders using a weighted z score approach. GWAS, genome-wide association study.</figcaption></figure>
Deciphering the Genetics of Psychiatric Disorders

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Concordance of effects between brain phenotypes and psychiatric disorders. Enrichment for concordant genetic associations signal between MRI phenotypes and each of the 6 psychiatric disorders: ADHD, ASD, BIP, MDD, OCD, and SCZ. Concordant association for a set of variants corresponds to a case where, given that the coded allele is defined as the increasing phenotype allele, the associations with the disorder for those variants are consistently positive or consistently negative. For univariate MRI phenotypes, concordance was derived over subsets of independent variants that were selected based on their univariate association *p* value. For clusters, we selected variants based on their multivariate MRI *p* value. The x-axis displays the *p*-value threshold. The y-axis represents the −log₁₀(*p*) value of the concordance obtained from a test of the weighted sum of each disorder's z score. The dashed blue line shows the Bonferroni-corrected significance threshold. The green line shows the enrichment for the most associated cluster. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; BIP, bipolar disorder; ICV, intracranial volume; MDD, major depressive disorder; MRI, magnetic resonance imaging; OCD, obsessive-compulsive disorder; SCZ, schizophrenia.</figcaption></figure>

cluster was observed when using an 8-cluster model and included 349 independent variants (Table S8). The strongest association was observed for a JASS $p_T$ of $3.1 \times 10^{-5}$ that resulted in a subset of 60 variants. Note that there is substantial overlap of variants across the 77 clusters (e.g., cluster 1 from the 2-cluster model necessarily overlaps with some of the clusters from the 3 and more cluster models) (see Figure 2). As shown in Figure S6, clusters that overlapped with the best SCZ and ADHD clusters displayed enrichment that was approximately proportional to the overlap, confirming that these top clusters captured most of the association with the disorders. Finally, consistent with Figure S5, the application of the *S* test on subsets of variants selected based on univariate MRI association signal did not identify any association (Figure 3), except for pallidum volume and ADHD ($p = 8.19 \times 10^{-7}$), which just reached the Bonferroni-corrected significance threshold.

## Characteristics of the Latent Neuroanatomical Phenotypes

To identify whether a latent variable exists, we mapped variants within each of the 2 clusters to their nearest genes and conducted a functional annotation overrepresentation analysis (Supplementary Notes; Table S11). The ADHD-associated cluster was poorly specific, including many variants from all

over the genome (Figure S6). It showed highly significant overrepresentation of annotations but with low fold enrichment (Figure S7; Table S9). The largest fold enrichment ($5.28, Bonferroni-corrected p = 6.7 \times 10^{-5}$) was observed for protein phosphorylation. The most significant one was for polar residues (minimum $p = 1.1 \times 10^{-10}$), but enrichment was modest (average = 1.7). The top significant annotation for the SCZ-associated cluster was the neurotrophin signaling pathway (Bonferroni-corrected $p = 2.2 \times 10^{-3}$), which showed very strong enrichment (up to 27.06) (Figure S8; Table S10) and was the best candidate mechanism underlying the shared genetics between MRI phenotypes and SCZ. Peak significance for that pathway strongly coincided with the association with SCZ. Moreover, recent meta-analyses suggest that neurotrophins may play a role in the pathophysiology of SCZ (42–44), and it has been reported as a potential biomarker for brain function, structure, and cognition (45). The neurotrophin pathway overrepresentation was highly specific to this cluster, and we found no similar enrichment from top MRI variants after excluding the variants within that cluster. Overrepresented annotations from the 2 clusters, in addition to those cited previously, included kinase, plasma membrane, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, and serine/threonine protein kinase. Several of these annotations have also been discussed in SCZ studies (46–48), suggesting that other mechanisms could be at play. Figure 4A summarizes the
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Overview of ADHD- and SCZ-associated clusters. (A) Summary of the functional annotation analysis for the ADHD and SCZ clusters. Edge thickness and vertex size of the annotation are proportional to fold enrichment. Colors of the vertex indicate potential evidence for a role in SCZ and ADHD from a PubMed Central search. Panels (B, C) present the weights of the cluster with the highest concordant signal for ADHD and SCZ, respectively. Left panels present the absolute value of the weights with a radar plot. Right panels present the weights plotted on the corresponding brain area. These weights are the standardized z scores of the medoid for each magnetic resonance imaging phenotype: Acc, Amy, Ca, Hip, ICV, Pa, Pu, and Th. Acc, accumbens volume; ADHD, attention-deficit/hyperactivity disorder; Amy, amygdala volume; BV, brain volume; Ca, caudate volume; EGFR, epidermal growth factor receptor; Hip, hippocampus volume; ICV, intracranial volume; Pa, pallidum volume; Pu, putamen volume; Th, thalamus volume; SCZ, schizophrenia.</figcaption></figure>

annotation mapped to the 2 clusters together with potential evidence for association with SCZ and ADHD extracted from a quantitative literature search.

By construction, clusters that were inferred from the spherical k-medoids captured association effects in a specific direction in the multidimensional MRI space. Therefore, weights on each phenotype from each cluster can be used to form a composite latent variable associated with the genetic variants (Figure 4B, C). We computed this latent variable for the cluster associated with SCZ using the 20,744 UK Biobank participants. A GWAS analysis of this variable identified 12 associated loci (Figure S9; Table S12), 5 of which were not identified by the univariate MRI GWAS: rs9874537 ($p = 4.5 \times 10^{-8}$, minimum $p_{\text{MRI}} = 3.4 \times 10^{-5}$), rs768519054 ($p = 4.6 \times 10^{-8}$, minimum $p_{\text{MRI}} = 3.4 \times 10^{-5}$), rs298619 ($p = 4.7 \times 10^{-10}$, minimum $p_{\text{MRI}} = 3.3 \times 10^{-7}$), rs12762089 ($p = 7.0 \times 10^{-9}$, minimum $p_{\text{MRI}} = 2.8 \times 10^{-4}$), and rs28678082 ($p = 3.4 \times 10^{-8}$, minimum $p_{\text{MRI}} = 4.8 \times 10^{-7}$). Although several of these variants can be mapped to genes related to SCZ [e.g., AUTS2 (49,50), ABCC5 (51)], there was limited direct association with the disorder. Moreover, repeating the DAVID (52) analysis on the top associated variants at various thresholds did not point toward any significant annotation enrichment, including neurotrophins. This is consistent with the supervised clustering analysis that did not identify a linear combination of phenotypes relevant for the genetics of any of the disorders studied.

It suggests that the MRI phenotypes studied were more likely influenced by specific genetic mechanisms shared with SCZ (Figure 1C), 2 of which were identified in our clustering analysis, rather than on a mediation path of the disorder (Figure 1A, B).

## DISCUSSION

Neuroimaging technologies hold great promise for linking symptoms of mental health disorders to abnormal patterns of brain activity. Characterizing these links can help us understand disorder etiology and ultimately improve diagnostic systems and suggest new classifications of patients to implement more personalized treatments. Here, we investigated the potential of multivariate neuroimaging genomic approaches to inform the genetics of 6 psychiatric disorders. First, we showed that multitrait MRI GWASs could increase the detection of genetic variants associated with MRI phenotypes compared with univariate analysis. In these data, the multivariate analysis led to a 48% increase in statistical power while also capturing most of the variants identified in univariate analyses. It is important to note that if the focus is on a particular MRI phenotype, the multitrait test comes at the cost of decreased precision and would be more challenging to interpret. Second, we showed that multitrait analysis can be much more informative about the genetics of psychiatric disorders.
We observed a statistically significant enrichment for genetic variants associated with both ADHD and SCZ from the multi-trait MRI screening, where univariate MRI conveyed no information on the genetic risk of any of the 6 disorders considered. Third, we demonstrated that breaking down the specific and shared genetic components of MRI phenotypes could help map subsets of disease-associated variants with biological mechanisms. Our clustering analysis of MRI genetics identified 2 distinct clusters of variants associated with ADHD and SCZ, respectively. Those 2 clusters were significantly enriched for multiple pathways related to psychiatric disorders, including neurotrophins, plasma membrane, and EGFR in particular.

Neurotrophins play an important role in the regulation of neuronal activity during the development and in the maintenance of the mature nervous system (42). Association with both brain volume and SCZ have already been widely discussed (42,43,45), and BDNF (brain-derived neurotrophic factor), the most widely distributed neurotrophin in the brain, has been associated with several psychiatric disorders including SCZ (53). With age, the changes induced in the lipid composition of the neuronal plasma membrane lead to changes in receptors' coupling efficiency to ligands, signal attenuation, and pathway desensitization. This altered membrane composition has several effects, including reduced response to neurotrophins and alteration in cell surface growth factor receptors such as the EGFR (54). The EGFR is also involved in the differentiation and functions of neurons and neuroglia (55) and dopaminergic/GABAergic (gamma-aminobutyric acidergic) maturation in the brain and has been widely associated with SCZ (56). These altered functions of membrane receptors due to altered plasma membrane lead to an increased vulnerability to neuropathology in aging (54). The plasma membrane annotation has also been associated with ADHD through its dopaminergic maturation role. ADHD has been associated with dopaminergic insufficiencies that are attributed to a synaptic dysfunction of dopamine transporter that is regulated by several protein kinases (57), which was another annotation detected in our analysis.

The current study also has some limitations. First, we used a set of 8 MRI phenotypes without prior evaluation of their relevance to the disorders being considered. Future work using an optimal set of univariate phenotypes for a given disorder or simply a much larger set of brain phenotypes may maximize the detection of new associations and help investigators to infer more refined latent variables. The proposed framework may also be used together with other approaches, including machine learning for example, to extract MRI features of interest (58). Second, we used an omnibus test, as implemented in the JASS software, to conduct our multitrait test. We previously showed that this fairly simple and standard approach performed well in multitrait GWAS analyses (59). However, many alternative implementations of the omnibus test [e.g., (60,61)] and alternative multitrait association models (62–65) (see Supplementary Notes) have been proposed. Using those or other multitrait methods increasingly used for the analysis of brain imaging data (10,66) might further boost detection of new associations. Third, many methods exist to perform data clustering. Here we used spherical *k*-means because its properties fitted well with the objective of identifying variants showing similar multitrait association direction.

The identification of 2 clusters of interest confirms the relevance of this approach. Nevertheless, future work could consider alternative clustering methods. Fourth, we used mental disorder GWASs that typically use a broad disorder definition to maximize sample size. It is well accepted that mental disorders such as SCZ (67) can be difficult to diagnose, and even if the disorder is diagnosed, it can be divided into subtypes. Studying those subtypes may help refine potential links with MRI phenotypes. Currently, there are few large-scale GWASs of such disorder subtypes, but the proposed approach can easily be applied to those data as they become available.

Altogether, our analysis illustrates how multivariate approaches can help solve one important challenge in neuroimaging genomics: MRI phenotypes, including those used in this study, are derived variables, and their relevance to psychiatric disorders is partially unknown. All univariate MRI analyses that we conducted confirmed limited correspondence between single MRI genetics and the 6 disorders. Multitrait association tests circumvent this limitation, allowing for the detection of genetic variants associated with any linear combination of the original MRI, which proved much more significant for linking the genetics of brain neuroanatomy with those disorders in the current study. Furthermore, deriving latent variables from multitrait MRI genetics as was done in the current study provides a compelling foundation to uncover shared pathways between brain neuroanatomy and psychiatric disorders. Ultimately, the proposed approaches can provide new opportunities for future biomarker development and mediation effect analyses by targeting those shared genetically driven pathways.

## ACKNOWLEDGMENTS AND DISCLOSURES

This work was supported by the Fondation pour la Recherche Médicale (Grant No. ECO202106013759 [to AA]). It was conducted as part of the INCEPTION program (Investissement d'Avenir Grant No. ANR-16-CONV-0005 [to AA, LH, LT, AF, CB, SK, HJ, HA]). This research was conducted using the UK Biobank Resource (Application No. 18584 [to AA, NT, LH, RT, HA]).

A previous version of this article was published as a preprint on bioRxiv: https://doi.org/10.1101/2023.11.01.564329.

The authors report no biomedical financial interests or potential conflicts of interest.

## ARTICLE INFORMATION

From the Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France (AA, NT, LH, LT, AF, CB, SK, HJ, RT, HA); and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (HA).

Address correspondence to Antoine Auvergne, M.S., at ant.auvergne@gmail.com, or Hugues Aschard, Ph.D., at hugues.aschard@pasteur.fr.

Received May 21, 2024; revised Jun 28, 2024; accepted Aug 12, 2024.

Supplementary material cited in this article is available online at https://doi.org/10.1016/j.bpsc.2024.08.018.

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