2024-07-22

090_ Shared genetics and symptoms between schizophrenia (SCZ) and Autism Spectrum Disorder (ASD)

Research Question and Aims

Autism spectrum disorder (ASD) is a heterogeneous, heritable neurodevelopmental condition, that affects ~1% of the population across cultures, with a ~4:1 male/female ratio. ASD is characterized by impairments in reciprocal social interaction and communication, as well as restricted, repetitive and stereotyped patterns of behavior, interests and activities (1). Autistic phenotypes, however, transcend diagnostic categories. Different expressions of autistic phenotypes can also be observed in patients with psychiatric diagnoses other than ASD, being schizophrenia (SCZ) one of the psychiatric disorders manifesting ASD features (2). Particularly, the very heterogeneous diagnostic category of SCZ harbors a distinct subgroup of individuals with severe autistic features (2). Moreover, subthreshold deficits in social communication and restricted interests which do not meet formal criteria for an ASD diagnosis can also be found in the general population (3). This evidence supports the continuum dimensional nature of autistic traits.
Both ASD and SCZ present a complex etiology in which genetic factors play a role. The most recent GWAS meta-analysis of ASD has identified 5 genome-wide significant loci for ASD (4) and the latest GWAS meta-analysis on SCZ has identified 287 hits (5). In addition, recent cross-disorder GWAS meta-analyses in psychiatry have shown significant genetic correlations for most pairs of disorders, including ADS and SCZ (rG=0.24-0.26), suggesting a complex, higher-order genetic structure underlying psychopathology, and indicating that a substantial fraction of genetic influences on psychopathology transcend clinical diagnostic boundaries (6). However, little is known about the specific genetic variants with pleiotropic effects in ASD and SCZ, and whether these genetic variants contribute to behavioral phenotypes shared between the two disorders, which would allow clinicians to better diagnose individuals and give more personalized behavioral and pharmacological treatments.
Taken all together, this study aims to investigate the common underlying aetiology between ASD and SCZ using different approaches. On one side, we will study the polygenic effect of ASD on SCZ diagnosis. On the other side, we will investigate whether the polygenic effect of ASD is associated with autistic phenotypes in SCZ patients.

Analytic Plan

General Hypotheses: We hypothesize that ASD and SCZ share genetic risk factors and that the polygenic effect of shared genomic loci will be associated with autistic phenotypes in SCZ.

Participants:
In this study, we will use the summary statistics from the largest GWAS meta-analyses from the Psychiatric Genomics Consortium (PGC) for ASD (38,717 cases and 232,735 controls, unpublished) and SCZ (5) as well as cross-sectional data derived from the PsyCourse dataset. PsyCourse dataset: This dataset includes sociodemographic, clinical and genomic data of patients with SCZ (N=647) and controls (N=466).

- Phenotype data – Autistic phenotypes:
Based on specific items of the Positive and Negative Syndrome Scale (PANSS) (7), a PANSS autism severity score (PAUSS) will be generated to assess autistic phenotypes (2).

- Genotype data:
Genotyping was conducted based on The Illumina Global Screening Array. Imputation was carried out using the Haplotype Reference Consortium panel in the Michigan Imputation Server. High-quality post-imputation dosage files will be used for Polygenic Score analyses.

Analytic plan:

Shared genetic etiology between ASD and SCZ
We will run a cross-trait PRS framework to study shared etiology between ASD and SCZ. For this, the PRS-CS tool (8) will be used to infer posterior SNP effect sizes of the largest ASD-GWAS meta-analysis of ASD (unpublished). Subsequently, ASD-PRSs will be calculated in the PsyCourse sample using Plink v1.9 (9). The associations between the ASD-PRS and SCZ status will be tested including sex, age and the first 10 ancestry principal components (PCs) as covariates.

Polygenic effects on autistic phenotypes in SCZ
The ASD-PRS calculated from the largest ASD-GWAS meta-analysis from the PGC (unpublished) will be tested for association with autistic phenotypes, measured by means of the PAUSS scale (2), in schizophrenia patients from the PsyCourse cohort including sex, age and the first 10 ancestry principal components (PCs) as covariates.
As a second step, and with the aim to improve- the prediction of autistic phenotypes in SCZ, we will identify overlapping variants with concordant and discordant effect direction in the largest ASD-GWAS meta-analysis from the PGC (unpublished) and SCZ (5), as previously described (10). PRSs will be calculated based on the summary statistics of the largest ASD-GWAS meta-analysis separately for the concordant and discordant variants using the PRS-CS tool (8) and plink (9). The obtained PRSs will be tested for association with autistic phenotypes in SCZ patients from the PsyCourse cohort including sex, age and the first 10 ancestry principal components (PCs) as covariates.
Since the prevalence and the manifestation of autistic traits differ in males and females, sex will be included as a covariate in all analyses. Sex-stratified analyses will also be performed.

Resources needed

v1_id
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