ABSTRACT: UNDERSTANDING GENETIC COMPLEXITY IN SPINA BIFIDA Among neural tube defects (NTDs), myelomeningocele (spina bifida:SB), is a devastating but survivable, human structural malformation. Up to 70% of SB cases are attributed to genetic predisposition, with intrauterine environment precipitating SB manifestation in those at risk. Despite decades of research into genetic factors that underlie NTDs in mouse models, translation to human risk assessment and amelioration of SB cases remain elusive. This is largely attributable to limitations of the typical candidate gene approaches used in genetic studies of human NTDs. Here, our comprehensive systems biology approach to mutation burden in whole genome sequence (WGS) analyses is illuminating molecular pathways to human SB through interrogation of protein coding and non-coding regions, and introduces machine learning to select, in an untargeted fashion, genes with SB discriminatory potential based on gene enrichment by rare, likely deleterious protein coding variants. The project extends our comprehensive genomic effort, generating new WGS on 200 recently collected patient-parent trio (600 genomes) samples. Studies aim to identify specific gene drivers of human SB, illuminate gene-gene interactions leading to SB, and improve mechanistic understanding of this complex birth defect. Aim 1 uses family study and systems biology approaches to seek genes with transmitted or de novo rare variants suggesting SB-association. This begins assessment of parental vs de novo, “second hit”, contributions to SB. Analyses include protein-coding and noncoding sequence single nucleotide variants (SNVs), rare copy number variants (rCNVs), and state-of-the art computational probes leveraging genome 3D structure. Aim 2 tests the functional significance and interactions of these detected genes and variations to: (a) use CRISPR edited isogenic, double heterozygous human stem cells in a novel SOSRS, 3-D in vitro method to evaluate proliferation, self-organization, and differentiation, (b) test the transcriptional impact of these mutations using bulk and single cell RNAseq in mutagenized cells. Aim 3 examines double/multiple-heterozygous protein coding mutations using existing and CRISPR- edited mice to test the histological and gene expression impact of gene interactions on NT closure. Our computational approaches are highly innovative in the field, using machine learning to build network models of human SB risk. We then apply advanced technology for the functional testing of these genetic risk models, including gene editing of human stem cells, compared to isogenic controls, and mice for cellular and systems based hypothesis testing in vitro and in vivo, along with evaluation of the cell-type gene expression changes induced by these variants. Insights from our studies will pave the way for a precision medicine capability to individualize NTD prevention and care for families and the hundreds of thousands of patients l...