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Systems biology analysis of human genomes points to key pathways conferring spina bifida risk

Authors :
Ekta Khurana
Gaurav Thareja
Alexander Martinez-Fundichely
Karsten Suhre
Gary M. Shaw
Vanessa Aguiar-Pulido
M. Elizabeth Ross
Nader Chalhoub
Tawny N. Cuykendall
Alice AbdelAleem
Olivier Elemento
Richard H. Finnell
Abdulla Al-Kaabi
James M. Musser
Jamel Al-Zamer
Christopher E. Mason
Paul Wolujewicz
Haitham O. El-Bashir
Eran Elhaik
Yunping Lei
Source :
Proceedings of the National Academy of Sciences of the United States of America
Publication Year :
2021
Publisher :
Proceedings of the National Academy of Sciences, 2021.

Abstract

Significance Genetic investigations of most structural birth defects, including spina bifida (SB), congenital heart disease, and craniofacial anomalies, have been underpowered for genome-wide association studies because of their rarity, genetic heterogeneity, incomplete penetrance, and environmental influences. Our systems biology strategy to investigate SB predisposition controls for population stratification and avoids much of the bias inherent in candidate gene searches that are pervasive in the field. We examine both protein coding and noncoding regions of whole genomes to analyze sequence variants, collapsed by gene or regulatory region, and apply machine learning, gene enrichment, and pathway analyses to elucidate molecular pathways and genes contributing to human SB.<br />Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases versus controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation, and essential transcriptional regulation consistent with their having impact on the pathogenesis of human SB. Additionally, an interrogation of conserved noncoding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to the interrogation of coding and noncoding sequence variant contributions to rare complex genetic disorders.

Details

ISSN :
10916490 and 00278424
Volume :
118
Database :
OpenAIRE
Journal :
Proceedings of the National Academy of Sciences
Accession number :
edsair.doi.dedup.....9bd51a183a18a26f240b9d16ffed112a
Full Text :
https://doi.org/10.1073/pnas.2106844118