Prematurity and genetic liability for autism spectrum disorder.

Abstract

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by diverse presentations and a strong genetic component. Environmental factors, such as prematurity, have also been linked to increased liability for ASD, though the interaction between genetic predisposition and prematurity remains unclear. This study aims to investigate the impact of genetic liability and preterm birth on ASD conditions.

Methods: We analyzed phenotype and genetic data from two large ASD cohorts, the Simons Foundation Powering Autism Research for Knowledge (SPARK) and Simons Simplex Collection (SSC), encompassing 78,559 individuals for phenotype analysis, 12,519 individuals with genome sequencing data, and 8104 individuals with exome sequencing data. Statistical significance of differences in clinical measures was evaluated between individuals with different ASD and preterm status. We assessed the rare variants burden using generalized estimating equations (GEE) models and polygenic load using the ASD-associated polygenic risk score (PRS). Furthermore, we developed a machine learning model to predict ASD in preterm children using phenotype and genetic features available at birth.

Results: Individuals with both preterm birth and ASD exhibit more severe phenotypic outcomes despite similar levels of genetic liability for ASD across the term and preterm groups. Notably, preterm-ASD individuals showed an elevated rate of de novo variants identified in exome sequencing (GEE model, p = 0.005) in comparison to non-ASD-preterm group. Additionally, a GEE model showed that a higher ASD PRS, preterm birth, and male sex were positively associated with a higher predicted probability for ASD in SPARK, reaching a probability close to 90%. Lastly, we developed a machine learning model using phenotype and genetic features available at birth with limited predictive power (AUROC = 0.65).

Conclusions: Preterm birth may exacerbate multimorbidity present in ASD, which was not due to ASD-associated genetic variants. However, increased ASD-associated rare variants may elevate the likelihood of a preterm child being diagnosed with ASD. Additionally, a polygenic load of ASD-associated variants had an additive role with preterm birth in the predicted probability for ASD, especially for boys. Future integration of genetic and phenotypic data in larger preterm or population-based cohorts will be crucial for advancing early ASD identification in preterm subgroup.