Evidence for the Placenta-Brain Axis: Multi-Omic Kernel Aggregation Predicts Intellectual and Social Impairment in Children Born Extremely Preterm

Background Children born extremely preterm are at heightened risk for intellectual and social impairment, including Autism Spectrum Disorder (ASD). There is increasing evidence for a key role of the placenta in prenatal developmental programming, suggesting that the placenta may explain origins of neurodevelopmental outcomes.

Methods We examined associations between placental genomic and epigenomic profiles and assessed their ability to predict intellectual and social impairment at age 10 years in 379 children from the Extremely Low Gestational Age Newborn (ELGAN) cohort. Assessment of intellectual ability (IQ) and social function was completed with the Differential Ability Scales-II (DAS-II) and Social Responsiveness Scale (SRS), respectively. Examining IQ and SRS allows for studying ASD risk beyond the diagnostic criteria, as IQ and SRS are continuous measures strongly correlated with ASD. Genome-wide mRNA, CpG methylation and miRNA were assayed with the Illumina Hiseq 2500, HTG EdgeSeq miRNA Whole Transcriptome Assay, and Illumina EPIC/850K array, respectively. We conducted genome-wide differential mRNA/miRNA and epigenome-wide placenta analyses. These molecular features were integrated for a predictive analysis of IQ and SRS outcomes using kernel aggregation regression. We lastly examined associations between ASD and the genomically-predicted component of IQ and SRS.

Results Genes with important roles in placenta angiogenesis and neural function were associated with intellectual and social impairment. Kernel aggregations of placental multi-omics strongly predicted intellectual and social function, explaining approximately 8% and 12% of the variance in SRS and IQ scores via cross-validation, respectively. Predicted in-sample SRS and IQ showed significant positive and negative associations with ASD case-control status.

Limitations The ELGAN is a cohort of children born pre-term, andgeneralization may be affected by unmeasured confounders associated with low gestational age. We conducted external validation of predictive models, though the sample size of the out-sample dataset (N = 49) and the scope of the available placental datasets are limited. Further validation of the models is merited.

Conclusions Aggregating information from biomarkers within and between molecular data types improves prediction of complex traits like social and intellectual ability in children born extremely preterm, suggesting that traits influenced by the placenta-brain axis may be omnigenic.