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Identification of novel candidate risk genes for myelomeningocele within the glucose homeostasis/oxidative stress and folate/one‐carbon metabolism networks
Author(s) -
Hillman Paul,
Baker Craig,
Hebert Luke,
Brown Michael,
Hixson James,
AshleyKoch Allison,
Morrison Alanna C.,
Northrup Hope,
Au Kit Sing
Publication year - 2020
Publication title -
molecular genetics and genomic medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 29
ISSN - 2324-9269
DOI - 10.1002/mgg3.1495
Subject(s) - exome sequencing , genetics , nonsynonymous substitution , biology , missense mutation , candidate gene , exome , gene , bioinformatics , disease gene identification , allele , computational biology , mutation , genome
Background Neural tube defects (NTDs) are the second most common complex birth defect, yet, our understanding of the genetic contribution to their development remains incomplete. Two environmental factors associated with NTDs are Folate and One Carbon Metabolism (FOCM) and Glucose Homeostasis and Oxidative Stress (GHOS). Utilizing next‐generation sequencing of a large patient cohort, we identify novel candidate genes in these two networks to provide insights into NTD mechanisms. Methods Exome sequencing (ES) was performed in 511 patients, born with myelomeningocele, divided between European American and Mexican American ethnicities. Healthy control data from the Genome Aggregation database were ethnically matched and used as controls. Rare, high fidelity, nonsynonymous predicted damaging missense, nonsense, or canonical splice site variants in independently generated candidate gene lists for FOCM and GHOS were identified. We used a gene‐based collapsing approach to quantify mutational burden in case and controls, with the control cohort estimated using cumulative allele frequencies assuming Hardy–Weinberg equilibrium. Results We identified 45 of 837 genes in the FOCM network and 22 of 568 genes in the GHOS network as possible NTD risk genes with p  < 0.05. No nominally significant risk genes were shared between ethnicities. Using a novel approach to mutational burden we identify 55 novel NTD risk associations. Conclusions We provide a means of utilizing large publicly available sequencing datasets as controls for sequencing projects examining rare disease. This approach confirmed existing risk genes for myelomeningocele and identified possible novel risk genes. Lastly, it suggests possible distinct genetic etiologies for this malformation between different ethnicities.

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