The complexity of autism’s phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with other psychiatric and behavior traits in the same manner as autism case-control status. In N = 6064 autistic children in the SPARK cohort, we investigated the common genetic properties of twelve subscales from three clinical autism instruments measuring autistic traits: the Social Communication Questionnaire (SCQ), the Repetitive Behavior Scale-Revised (RBS-R), and the Developmental Coordination Disorder Questionnaire (DCDQ). Educational attainment polygenic scores (PGS) were significantly negatively correlated with eleven subscales, while ADHD and major depression PGS were positively correlated with ten and eight of the autism subscales, respectively. Loneliness and neuroticism PGS were also positively correlated with many subscales. Significant PGS by sex interactions were found—surprisingly, the autism case-control PGS was negatively correlated in females and had no strong correlation in males. SNP-heritability of the DCDQ subscales ranged from 0.04 to 0.08, RBS-R subscales ranged from 0.09 to 0.24, and SCQ subscales ranged from 0 to 0.12. GWAS in SPARK followed by estimation of polygenic scores (PGS) in the typically-developing ABCD cohort (N = 5285), revealed significant associations of RBS-R subscale PGS with autism-related behavioral traits, with several subscale PGS more strongly correlated than the autism case-control PGS. Overall, our analyses suggest that the clinical autism subscale traits show variability in SNP-heritability, PGS associations, and significant PGS by sex interactions, underscoring the heterogeneity in autistic traits at a genetic level. Furthermore, of the three instruments investigated, the RBS-R shows the greatest evidence of genetic signal in both (1) autistic samples (greater heritability) and (2) general population samples (strongest PGS associations).

The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is unclear whether clinical autism instruments such as the Social Communication Questionnaire (SCQ), Repetitive Behaviors Scale-Revised (RBS-R), and Developmental Coordination Disorder Questionnaire (DCDQ) are more genetically informative than case-control. We employed the SPARK autism cohort (N = 6,449) to illuminate the genetic etiology of these twelve subscales. In comparison to the heritability of autism case-control at 0.12, the RBS-R subscales were increased, ranging from 0.18 to 0.30 (all p < 0.05). Heritability of the DCDQ subscales ranged from 0.07 to 0.09 and the SCQ subscales from 0 to 0.09 (all p > 0.05). We also found evidence for genetic correlations among the RBS-R, SCQ, and DCDQ. GWAS followed by projection of polygenic scores (PGS) into ABCD revealed significant associations with CBCL social and thought problems, while the autism case-control PGS did not significantly associate. In phenotypic correlation analyses, the autism case-control PGS did not predict the subscales in SPARK, and sex-stratified correlations showed no effect in males and a surprising negative effect in females. Notably, other PGS did predict the subscales, with the strongest being educational attainment negatively correlated, while ADHD and major depression were positively correlated. Overall, our analyses suggest that clinical subscales are more genetically powerful than case-control, and that of the three instruments investigated, the RBS-R shows the greatest evidence of common genetic signal in both autistic and general population samples.

Both sex and gender are characteristics that play a key role in risk and resilience in health and well-being. Current research lacks the ability to quantitatively describe gender and gender diversity, and is limited to endorsement of categorical gender identities, which are contextually and culturally dependent. A more objective, dimensional approach to characterizing gender diversity will enable researchers to advance the health of gender-diverse people by better understanding how genetic factors interact to determine health outcomes. To address this research gap, we leveraged the Gender Self-Report (GSR), a questionnaire that captures multiple dimensions of gender diversity. We then performed polygenic score associations with brain-related traits like cognitive performance, personality, and neuropsychiatric conditions. The GSR was completed by N = 818 independent adults with or without autism in the SPARK cohort, and GSR factor analysis identified two factors: Binary (divergence from gender presumed by designated sex to the opposite) and Nonbinary (divergence from male and female gender norms) Gender Diversity (BGD and NGD, respectively). We performed polygenic associations (controlling for age, sex, and autism diagnostic status) in a subset of N = 452 individuals and found higher polygenic propensity for cognitive performance was associated with greater BGD (β = 0.017, p = 0.049) and NGD (β = 0.036, p = 0.002), and higher polygenic propensity for educational attainment was also associated with greater NGD (β = 0.030, p = 0.015). We did not observe any significant associations with personality or neuropsychiatric polygenic scores in this sample. Overall, our results suggest cognitive processes and gender diversity share overlapping genetic factors, indicating the biological utility of the GSR while also underscoring the importance of quantitatively measuring gender diversity in health research contexts.