Prematurity and genetic liability for autism spectrum disorder.

This study looked at how being born early (premature) affects autism risk and severity. Researchers studied nearly 79,000 people and found that premature babies who develop autism tend to have more severe symptoms, even though their genetic risk for autism is similar to full-term babies with autism. Boys born prematurely with high genetic risk scores had the highest chance (up to 90%) of developing autism. However, predicting autism in premature babies using information available at birth was still quite difficult.

This large-scale genetic study examined the interaction between prematurity and genetic liability for autism spectrum disorder (ASD) using data from 78,559 individuals across two major cohorts (SPARK and SSC). Researchers found that children born prematurely who develop ASD show more severe outcomes despite similar genetic liability compared to full-term children with ASD. Preterm children with ASD had elevated rates of de novo genetic variants. The study developed predictive models showing that higher ASD polygenic risk scores, preterm birth, and male sex were associated with increased ASD probability, reaching nearly 90% in some cases.

However, a machine learning model using genetic and phenotypic features available at birth showed limited predictive power (AUROC = 0.65).

Clinicians should monitor preterm children more closely for ASD signs, particularly boys with high polygenic risk scores. The findings suggest prematurity may independently worsen ASD outcomes, emphasizing the need for tailored interventions. Current genetic screening at birth shows limited utility for ASD prediction in preterm populations, requiring further research development.

The study does not specify exact sample sizes for different analyses. The machine learning model's limited predictive power (AUROC = 0.65) indicates insufficient accuracy for clinical use. The abstract does not detail potential confounding factors or specify the timeline for ASD diagnosis assessment.

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. 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. 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). 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.