Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder.
Andrea StoccoroRoberta GalloSara CalderoniRomina CagianoFilippo MuratoriLucia MiglioreEnzo GrossiFabio CoppedèPublished in: Epigenomics (2022)
Aim and methods: Artificial neural networks were used to unravel connections among blood gene methylation levels, sex, maternal risk factors and symptom severity evaluated using the Autism Diagnostic Observation Schedule 2 (ADOS-2) score in 58 children with autism spectrum disorder (ASD). Results: Methylation levels of MECP2 , HTR1A and OXTR genes were connected to females, and those of EN2 , BCL2 and RELN genes to males. High gestational weight gain, lack of folic acid supplements, advanced maternal age, preterm birth, low birthweight and living in rural context were the best predictors of a high ADOS-2 score. Conclusion: Artificial neural networks revealed links among ASD maternal risk factors, symptom severity, gene methylation levels and sex differences in methylation that warrant further investigation in ASD.
Keyphrases
- genome wide
- neural network
- birth weight
- autism spectrum disorder
- gestational age
- weight gain
- risk factors
- preterm birth
- dna methylation
- copy number
- intellectual disability
- attention deficit hyperactivity disorder
- pregnancy outcomes
- body mass index
- genome wide identification
- low birth weight
- physical activity
- gene expression
- preterm infants
- south africa
- pregnant women
- genome wide analysis
- transcription factor